Cachexia is a multifactorial metabolic syndrome characterized by severe skeletal muscle loss that cannot be reversed by nutritional support. Common in cancer and other chronic diseases, it significantly increases morbidity and mortality. Muscle wasting is primarily driven by activation of the ubiquitin–proteasome pathway through the E3 ubiquitin ligases MuRF1 and Atrogin-1, which promote protein degradation and muscle atrophy. This study uses computational molecular modeling and docking approaches to identify small-molecule inhibitors targeting these ligases, including repurposed drugs with anti-cachectic potential. Top candidates will be validated in C2C12 myotube-based cachexia models to evaluate their therapeutic efficacy against cancer-associated muscle wasting.
","acknowledgements":"After writing the editorial, ChatGPT5.1 was used to check for grammatical errors and to improve the flow of the text. AI was not used in idea generation or content creation. It was only used for fixing grammar and flow.
","authors":[{"affiliations":["Lawrence Technological University"],"departments":["Natural Sciences"],"credit":["conceptualization","supervision","resources","writing_originalDraft"],"email":"imuqbil@ltu.edu","firstName":"Irfana","lastName":"Muqbil","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":"0000-0001-6889-7035"},{"affiliations":["Lawrence Technological University"],"departments":["Deaprtment of Biomedical Engineering"],"credit":["dataCuration","formalAnalysis","methodology","visualization"],"email":"jjohns14@ltu.edu","firstName":"Jordan D","lastName":"Johnson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":null},"extendedData":[],"funding":"none
","image":{"url":"https://portal.micropublication.org/uploads/6af318103a7b9a9e6490e0c17ef3de18.jpg"},"imageCaption":"(A) DiffDock docking analysis showing the distribution of predicted binding affinities (smina affinity scores) for screened compounds against Atrogin-1/FBXO32 and MuRF1/TRIM63. Highlighted regions indicate top-ranking candidate compounds. (B–C) ADME and drug-likeness radar plots generated using SwissADME for EM12-So2F (B) and thalidomide (C), demonstrating favorable physicochemical and pharmacokinetic properties. (D–E) Predicted binding poses of EM12-So2F within the ligand-binding regions of MuRF1 and Atrogin-1, respectively. (F–G) Predicted binding interactions of thalidomide with MuRF1 and Atrogin-1.
","imageTitle":"Computational identification of EM12-So2F and thalidomide as candidate inhibitors of muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin-1
","methods":"Protein and Ligand Preparation
Protein sequences and Structural information for the MuRF1 (TRIM63) coiled-coil domain was obtained from the Protein Data Bank (PDB ID: 4M3L), while sequence and structural information for Atrogin-1 (FBXO32; UniProt accession Q969P5) were retrieved from UniProt and corresponding structural models were utilized for molecular docking analyses. Candidate E3 ligase inhibitors were selected based on published literature and structural relevance to ubiquitin–proteasome pathway modulation. Ligand structures were retrieved in SMILES or SDF format from chemical databases and converted into optimized three-dimensional conformations using Open Babel for molecular modeling and docking analyses. Protein structures were prepared by removing water molecules and non-essential ligands, followed by optimization of structural geometry prior to docking simulations.
DiffDock Molecular Docking Analysis
Protein–ligand docking studies were performed using DiffDock, a generative artificial intelligence–based docking platform that utilizes diffusion modeling to predict ligand-binding poses and protein–ligand interactions. Similar to previously described DiffDock workflows, multiple ligand conformations and docking poses were generated to evaluate translational, rotational, and torsional flexibility during docking inference. Docking outputs included predicted binding poses, confidence score rankings, and smina affinity scores, which were used as indicators of docking stability and binding strength. More negative smina affinity scores were interpreted as stronger predicted binding interactions consistent with favorable binding free energy estimates.
SwissDock Validation and ADME Profiling
Additional docking analyses and interaction visualization were carried out using SwissDock to validate ligand-binding conformations and evaluate intermolecular interactions within predicted binding pockets. Drug likeness, molecular properties, and absorption, distribution, metabolism, and excretion (ADME) characteristics were further assessed to prioritize compounds with favorable pharmacokinetic profiles and therapeutic potential. Candidate compounds were ranked based on docking affinity, interaction stability, confidence scores, ligand orientation, and predicted pharmacological properties.
Among the screened compounds, EM12-So2F and thalidomide demonstrated favorable binding characteristics toward E3 ligase-associated targets, including stable docking conformations and strong predicted interaction profiles, and were therefore selected for further investigation as potential therapeutic candidates against cancer-associated cachexia.
","reagents":"","patternDescription":"Cachexia or muscle wasting is associated with poor quality of life and is one of the leading causes of morbidity in patients with advanced cancer. Therefore, the identification of novel therapeutic approaches to target cachexia is of critical importance in the management of advanced malignancies. Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle wasting, with or without adipose tissue loss, that cannot be fully reversed by nutritional supplementation alone (Fearon et al., 2011). It is frequently observed in patients with pancreatic, lung, gastrointestinal, and other advanced malignancies, where it contributes significantly to weakness, fatigue, reduced mobility, poor tolerance to anticancer therapies, and decreased overall survival (Argilés et al., 2014). Despite its major clinical impact, effective therapies for cancer-associated cachexia remain limited.
One of the major molecular mechanisms underlying cachexia is the activation of the ubiquitin–proteasome system (UPS), the primary intracellular pathway responsible for regulated protein degradation. In skeletal muscle, the UPS contributes to accelerated breakdown of structural and contractile proteins during catabolic conditions (Bodine et al., 2001; Gomes et al., 2001). Protein degradation through the UPS involves sequential ubiquitin activation by E1 enzymes, ubiquitin conjugation by E2 enzymes, and substrate-specific ubiquitination mediated by E3 ubiquitin ligases, ultimately directing proteins toward degradation by the 26S proteasome. Among these components, E3 ubiquitin ligases provide substrate specificity and therefore represent attractive therapeutic targets. Two muscle-specific E3 ubiquitin ligases, Muscle RING Finger-1 (MuRF1) and Atrogin-1/MAFbx, are consistently upregulated in experimental and clinical models of cachexia and are considered central mediators of skeletal muscle atrophy (Bodine et al., 2001; Gomes et al., 2001). MuRF1 primarily targets sarcomeric and contractile proteins including myosin heavy chain, whereas Atrogin-1 regulates proteins involved in muscle growth, differentiation, and protein synthesis. Persistent activation of these ligases results in excessive proteolysis, impaired muscle regeneration, and progressive muscle wasting.
Current therapeutic approaches for cachexia mainly focus on nutritional supplementation, exercise, appetite stimulants, and anti-inflammatory interventions; however, these strategies often provide only modest clinical benefit (Baracos et al., 2018). Consequently, there is growing interest in directly targeting the molecular pathways responsible for muscle protein degradation. Inhibition of muscle-specific E3 ligases represents a promising strategy to preserve skeletal muscle integrity and improve outcomes in cachectic patients.
Recent advances in computational biology, molecular modeling, and artificial intelligence-based drug discovery have accelerated the identification of candidate therapeutics against disease-associated targets (Jumper et al., 2021). Computational approaches enable rapid screening of compounds, prediction of protein–ligand interactions, and evaluation of drug-like properties prior to experimental validation. In the present study, we performed in silico screening of multiple E3 ubiquitin ligase inhibitors, including compounds currently in clinical use as well as investigational agents undergoing preclinical evaluation. Protein sequence and structural information were obtained through UniProt, which provides curated protein annotations, domain information, and structural datasets relevant for molecular modeling studies (UniProt Consortium, 2023). Ligand preparation and structural optimization were carried out using Open Babel, enabling conversion and refinement of molecular structures for downstream docking analyses (O’Boyle et al., 2011).
Docking studies were performed using DiffDock, an artificial intelligence–based platform that predicts protein–ligand binding poses and interaction confidence through diffusion generative modeling approaches (Corso et al., 2023). DiffDock enables flexible docking and provides binding confidence estimates that facilitate prioritization of candidate compounds. Additional analyses were conducted using SwissDock to evaluate docking conformations, binding interactions, drug likeness, and pharmacokinetic properties including absorption, distribution, metabolism, and excretion (ADME) characteristics (Grosdidier et al., 2011). Together, these integrated computational approaches provided a robust framework for identifying candidate inhibitors targeting muscle-associated E3 ligases (Figure 1A–C).
Among the compounds screened thus far, EM12-So2F and thalidomide emerged as promising candidates (Figure 1A). EM12-So2F is a potent cereblon (CRBN) inhibitor that interferes with substrate recruitment to the CRBN E3 ubiquitin ligase complex. Thalidomide, an immunomodulatory drug already approved for clinical use in multiple cancers including multiple myeloma, also functions through modulation and inhibition of cereblon-dependent E3 ligase activity (Ito et al., 2010). Both compounds demonstrated favorable predicted binding affinities and drug-likeness profiles (Figure 1A–C). Predicted binding poses revealed stable interactions of EM12-So2F with MuRF1 and Atrogin-1 (Figure 1D–E), while thalidomide exhibited favorable binding interactions with both ligases (Figure 1F–G).The identification of these compounds is particularly significant because cereblon is widely utilized as a recruiting component in proteolysis-targeting chimeras (PROTACs), an emerging therapeutic strategy that selectively induces degradation of target proteins through hijacking endogenous E3 ligases (Sakamoto et al., 2001; Békés et al., 2022).
The ability of cereblon-targeting compounds to modulate E3 ligase activity highlights the broader therapeutic potential of manipulating ubiquitin–proteasome system components through targeted protein degradation strategies. Recent advances in PROTAC technology have demonstrated that E3 ligases such as cereblon (CRBN) and von Hippel–Lindau (VHL) can be selectively recruited to induce degradation of disease-associated proteins or even other E3 ligases themselves (Girardini et al., 2019). These findings raise the possibility of extending PROTAC-based approaches toward muscle-specific E3 ligases implicated in cachexia, including MuRF1 and Atrogin-1. In addition to conventional small-molecule inhibition, future development of selective degraders targeting these ligases or their upstream catabolic regulators may provide a highly specific therapeutic strategy to suppress skeletal muscle proteolysis while minimizing off-target effects. Thus, our findings support the concept that targeting E3 ligase pathways through both small-molecule inhibitors and next-generation targeted protein degradation technologies may represent a promising avenue for the treatment of cancer-associated cachexia and related muscle-wasting disorders.
","references":[{"reference":"Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. 2014. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer 14(11): 754-62.
","pubmedId":"25291291","doi":""},{"reference":"Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. 2018. Cancer-associated cachexia. Nat Rev Dis Primers 4: 17105.
","pubmedId":"29345251","doi":""},{"reference":"Békés M, Langley DR, Crews CM. 2022. PROTAC targeted protein degraders: the past is prologue. Nat Rev Drug Discov 21(3): 181-200.
","pubmedId":"35042991","doi":""},{"reference":"Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, Clarke BA, et al., Glass DJ. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294(5547): 1704-8.
","pubmedId":"11679633","doi":""},{"reference":"Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2023;2210.01776.
","pubmedId":"","doi":""},{"reference":"Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al., Baracos VE. 2011. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12(5): 489-95.
","pubmedId":"21296615","doi":""},{"reference":"Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL, New Collective Author. 2001. Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98(25): 14440-5.
","pubmedId":"11717410","doi":""},{"reference":"Grosdidier A, Zoete V, Michielin O. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39(Web Server issue): W270-7.
","pubmedId":"21624888","doi":""},{"reference":"Ito T, Ando H, Suzuki T, Ogura T, Hotta K, Imamura Y, Yamaguchi Y, Handa H. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327(5971): 1345-50.
","pubmedId":"20223979","doi":""},{"reference":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873): 583-589.
","pubmedId":"34265844","doi":""},{"reference":"O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. 2011. Open Babel: An open chemical toolbox. J Cheminform 3: 33.
","pubmedId":"21982300","doi":""},{"reference":"Sakamoto KM, Kim KB, Kumagai A, Mercurio F, Crews CM, Deshaies RJ. 2001. Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation. Proc Natl Acad Sci U S A 98(15): 8554-9.
","pubmedId":"11438690","doi":""},{"reference":"UniProt Consortium. 2023. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1): D523-D531.
","pubmedId":"36408920","doi":""},{"reference":"Girardini M, Maniaci C, Hughes SJ, Testa A, Ciulli A. 2019. Cereblon versus VHL: Hijacking E3 ligases against each other using PROTACs. Bioorg Med Chem 27(12): 2466-2479.
","pubmedId":"30826187","doi":""}],"title":"Targeting Muscle-Specific E3 Ligases in Cancer Cachexia Through Structure-Based Virtual Screening of Small-Molecule Inhibitors\n\n\n\n","reviews":[],"curatorReviews":[]},{"id":"129aea33-0ac6-45f1-9fd2-538f5364505f","decision":"revise","abstract":"Cachexia is a multifactorial metabolic syndrome characterized by severe skeletal muscle loss that cannot be reversed by nutritional support. Common in cancer and other chronic diseases, it significantly increases morbidity and mortality. Muscle wasting is primarily driven by activation of the ubiquitin proteasome pathway through the E3 ubiquitin ligases MuRF1 and Atrogin-1, which promote protein degradation and muscle atrophy. This study uses computational molecular modeling and docking approaches to identify small-molecule inhibitors targeting these ligases, including repurposed drugs with anti-cachectic potential. Top candidates will be validated in C2C12 myotube-based cachexia models to evaluate their therapeutic efficacy against cancer-associated muscle wasting.
","acknowledgements":"After writing the editorial, ChatGPT5.1 was used to check for grammatical errors and to improve the flow of the text. AI was not used in idea generation or content creation. It was only used for fixing grammar and flow.
","authors":[{"affiliations":["Lawrence Technological University"],"departments":["Department of Natural Sciences"],"credit":["conceptualization","supervision","resources","writing_originalDraft"],"email":"imuqbil@ltu.edu","firstName":"Irfana","lastName":"Muqbil","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":"0000-0001-6889-7035"},{"affiliations":["Lawrence Technological University"],"departments":["Department of Biomedical Engineering"],"credit":["dataCuration","formalAnalysis","methodology","visualization"],"email":"jjohns14@ltu.edu","firstName":"Jordan D","lastName":"Johnson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":"https://portal.micropublication.org/uploads/d97ebda26f1f7e25ecbf12ec9b60d0eb.csv"},"extendedData":[],"funding":"none
","image":{"url":"https://portal.micropublication.org/uploads/46747d35328351f2c1840a0050d3daf0.jpg"},"imageCaption":"(A) DiffDock docking analysis showing the distribution of predicted binding affinities (smina affinity scores) for screened compounds against Atrogin-1/FBXO32 and MuRF1/TRIM63. Highlighted regions indicate top-ranking candidate compounds. (B–C) ADME and drug-likeness radar plots generated using SwissADME for EM12-So2F (B) and thalidomide (C), demonstrating favorable physicochemical and pharmacokinetic properties. (D–E) Predicted binding poses of EM12-So2F within the ligand-binding regions of MuRF1 and Atrogin-1, respectively. (F–G) Predicted binding interactions of thalidomide with MuRF1 and Atrogin-1.
Table: Predicted binding affinities of selected E3 ubiquitin ligase inhibitors against Atrogin-1 (FBXO32) and MuRF1 (TRIM63) determined by DiffDock analysis. Predicted binding interactions were evaluated using smina affinity scores (kcal/mol), where more negative values indicate stronger predicted binding affinity.
","imageTitle":"Computational identification of EM12-So2F and thalidomide as candidate inhibitors of muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin-1.
","methods":"Protein and Ligand Preparation
Protein sequences and Structural information for the MuRF1 (TRIM63) coiled-coil domain was obtained from the Protein Data Bank (PDB ID: 4M3L), while sequence and structural information for Atrogin-1 (FBXO32; UniProt accession Q969P5) were retrieved from UniProt and corresponding structural models were utilized for molecular docking analyses. Candidate E3 ligase inhibitors were selected based on published literature and structural relevance to ubiquitin–proteasome pathway modulation. Ligand structures were retrieved in SMILES or SDF format from chemical databases and converted into optimized three-dimensional conformations using Open Babel for molecular modeling and docking analyses. Protein structures were prepared by removing water molecules and non-essential ligands, followed by optimization of structural geometry prior to docking simulations.
DiffDock Molecular Docking Analysis
Protein–ligand docking studies were performed using DiffDock, a generative artificial intelligence–based docking platform that utilizes diffusion modeling to predict ligand-binding poses and protein–ligand interactions. Similar to previously described DiffDock workflows, multiple ligand conformations and docking poses were generated to evaluate translational, rotational, and torsional flexibility during docking inference. Docking outputs included predicted binding poses, confidence score rankings, and smina affinity scores, which were used as indicators of docking stability and binding strength. More negative smina affinity scores were interpreted as stronger predicted binding interactions consistent with favorable binding free energy estimates.
SwissDock Validation and ADME Profiling
Additional docking analyses and interaction visualization were carried out using SwissDock to validate ligand-binding conformations and evaluate intermolecular interactions within predicted binding pockets. Drug likeness, molecular properties, and absorption, distribution, metabolism, and excretion (ADME) characteristics were further assessed to prioritize compounds with favorable pharmacokinetic profiles and therapeutic potential. Candidate compounds were ranked based on docking affinity, interaction stability, confidence scores, ligand orientation, and predicted pharmacological properties.
Among the screened compounds, EM12-So2F and thalidomide demonstrated favorable binding characteristics toward E3 ligase-associated targets, including stable docking conformations and strong predicted interaction profiles, and were therefore selected for further investigation as potential therapeutic candidates against cancer-associated cachexia.
","reagents":"","patternDescription":"Cachexia or muscle wasting is associated with poor quality of life and is one of the leading causes of morbidity in patients with advanced cancer. Therefore, the identification of novel therapeutic approaches to target cachexia is of critical importance in the management of advanced malignancies. Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle wasting, with or without adipose tissue loss, that cannot be fully reversed by nutritional supplementation alone (Fearon et al., 2011). It is frequently observed in patients with pancreatic, lung, gastrointestinal, and other advanced malignancies, where it contributes significantly to weakness, fatigue, reduced mobility, poor tolerance to anticancer therapies, and decreased overall survival (Argilés et al., 2014). Despite its major clinical impact, effective therapies for cancer-associated cachexia remain limited.
One of the major molecular mechanisms underlying cachexia is the activation of the ubiquitin–proteasome system (UPS), the primary intracellular pathway responsible for regulated protein degradation. In skeletal muscle, the UPS contributes to accelerated breakdown of structural and contractile proteins during catabolic conditions (Bodine et al., 2001; Gomes et al., 2001). Protein degradation through the UPS involves sequential ubiquitin activation by E1 enzymes, ubiquitin conjugation by E2 enzymes, and substrate-specific ubiquitination mediated by E3 ubiquitin ligases, ultimately directing proteins toward degradation by the 26S proteasome. Among these components, E3 ubiquitin ligases provide substrate specificity and therefore represent attractive therapeutic targets. Two muscle-specific E3 ubiquitin ligases, Muscle RING Finger-1 (MuRF1) and Atrogin-1/FBXO32, are consistently upregulated in experimental and clinical models of cachexia and are considered central mediators of skeletal muscle atrophy (Bodine et al., 2001; Gomes et al., 2001). MuRF1 primarily targets sarcomeric and contractile proteins including myosin heavy chain, whereas Atrogin-1 regulates proteins involved in muscle growth, differentiation, and protein synthesis. Persistent activation of these ligases results in excessive proteolysis, impaired muscle regeneration, and progressive muscle wasting.
Current therapeutic approaches for cachexia mainly focus on nutritional supplementation, exercise, appetite stimulants, and anti-inflammatory interventions; however, these strategies often provide only modest clinical benefit (Baracos et al., 2018). Consequently, there is growing interest in directly targeting the molecular pathways responsible for muscle protein degradation. Inhibition of muscle-specific E3 ligases represents a promising strategy to preserve skeletal muscle integrity and improve outcomes in cachectic patients.
Recent advances in computational biology, molecular modeling, and artificial intelligence-based drug discovery have accelerated the identification of candidate therapeutics against disease-associated targets (Jumper et al., 2021). Computational approaches enable rapid screening of compounds, prediction of protein–ligand interactions, and evaluation of drug-like properties prior to experimental validation. In the present study, we performed in silico screening of multiple E3 ubiquitin ligase inhibitors, including compounds currently in clinical use as well as investigational agents undergoing preclinical evaluation (Table 1). Protein sequence and structural information were obtained through UniProt, which provides curated protein annotations, domain information, and structural datasets relevant for molecular modeling studies (UniProt Consortium, 2023). Ligand preparation and structural optimization were carried out using Open Babel, enabling conversion and refinement of molecular structures for downstream docking analyses (O’Boyle et al., 2011).
Docking studies were performed using DiffDock, an artificial intelligence–based platform that predicts protein–ligand binding poses and interaction confidence through diffusion generative modeling approaches (Corso et al., 2023). DiffDock enables flexible docking and provides binding confidence estimates that facilitate prioritization of candidate compounds. Additional analyses were conducted using SwissDock to evaluate docking conformations, binding interactions, drug likeness, and pharmacokinetic properties including absorption, distribution, metabolism, and excretion (ADME) characteristics (Grosdidier et al., 2011). Together, these integrated computational approaches provided a robust framework for identifying candidate inhibitors targeting muscle-associated E3 ligases (Figure 1A–C).
Among the compounds screened thus far, EM12-So2F and thalidomide emerged as promising candidates (Figure 1A). EM12-So2F is a potent cereblon (CRBN) inhibitor that interferes with substrate recruitment to the CRBN E3 ubiquitin ligase complex. Thalidomide, an immunomodulatory drug already approved for clinical use in multiple cancers including multiple myeloma, also functions through modulation and inhibition of cereblon-dependent E3 ligase activity (Ito et al., 2010). Both compounds demonstrated favorable predicted binding affinities and drug-likeness profiles (Figure 1A–C). Predicted binding poses revealed stable interactions of EM12-So2F with MuRF1 and Atrogin-1 (Figure 1D–E), while thalidomide exhibited favorable binding interactions with both ligases (Figure 1F–G).The identification of these compounds is particularly significant because cereblon is widely utilized as a recruiting component in proteolysis-targeting chimeras (PROTACs), an emerging therapeutic strategy that selectively induces degradation of target proteins through hijacking endogenous E3 ligases (Sakamoto et al., 2001; Békés et al., 2022).
The ability of cereblon-targeting compounds to modulate E3 ligase activity highlights the broader therapeutic potential of manipulating ubiquitin–proteasome system components through targeted protein degradation strategies. Recent advances in PROTAC technology have demonstrated that E3 ligases such as cereblon (CRBN) and von Hippel–Lindau (VHL) can be selectively recruited to induce degradation of disease-associated proteins or even other E3 ligases themselves (Girardini et al., 2019). These findings raise the possibility of extending PROTAC-based approaches toward muscle-specific E3 ligases implicated in cachexia, including MuRF1 and Atrogin-1. In addition to conventional small-molecule inhibition, future development of selective degraders targeting these ligases or their upstream catabolic regulators may provide a highly specific therapeutic strategy to suppress skeletal muscle proteolysis while minimizing off-target effects. Thus, our findings support the concept that targeting E3 ligase pathways through both small-molecule inhibitors and next-generation targeted protein degradation technologies may represent a promising avenue for the treatment of cancer-associated cachexia and related muscle-wasting disorders.
","references":[{"reference":"Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. 2014. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer 14(11): 754-62.
","pubmedId":"25291291","doi":""},{"reference":"Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. 2018. Cancer-associated cachexia. Nat Rev Dis Primers 4: 17105.
","pubmedId":"29345251","doi":""},{"reference":"Békés M, Langley DR, Crews CM. 2022. PROTAC targeted protein degraders: the past is prologue. Nat Rev Drug Discov 21(3): 181-200.
","pubmedId":"35042991","doi":""},{"reference":"Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, Clarke BA, et al., Glass DJ. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294(5547): 1704-8.
","pubmedId":"11679633","doi":""},{"reference":"Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2023;2210.01776.
","pubmedId":"","doi":""},{"reference":"Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al., Baracos VE. 2011. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12(5): 489-95.
","pubmedId":"21296615","doi":""},{"reference":"Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL, New Collective Author. 2001. Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98(25): 14440-5.
","pubmedId":"11717410","doi":""},{"reference":"Grosdidier A, Zoete V, Michielin O. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39(Web Server issue): W270-7.
","pubmedId":"21624888","doi":""},{"reference":"Ito T, Ando H, Suzuki T, Ogura T, Hotta K, Imamura Y, Yamaguchi Y, Handa H. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327(5971): 1345-50.
","pubmedId":"20223979","doi":""},{"reference":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873): 583-589.
","pubmedId":"34265844","doi":""},{"reference":"O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. 2011. Open Babel: An open chemical toolbox. J Cheminform 3: 33.
","pubmedId":"21982300","doi":""},{"reference":"Sakamoto KM, Kim KB, Kumagai A, Mercurio F, Crews CM, Deshaies RJ. 2001. Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation. Proc Natl Acad Sci U S A 98(15): 8554-9.
","pubmedId":"11438690","doi":""},{"reference":"UniProt Consortium. 2023. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1): D523-D531.
","pubmedId":"36408920","doi":""},{"reference":"Girardini M, Maniaci C, Hughes SJ, Testa A, Ciulli A. 2019. Cereblon versus VHL: Hijacking E3 ligases against each other using PROTACs. Bioorg Med Chem 27(12): 2466-2479.
","pubmedId":"30826187","doi":""}],"title":"Targeting Muscle-Specific E3 Ligases in Cancer Cachexia Through Structure-Based Virtual Screening of Small-Molecule Inhibitors\n\n\n\n","reviews":[{"reviewer":{"displayName":"Sajith Jayasinghe"},"openAcknowledgement":false,"status":{"submitted":true}}],"curatorReviews":[]},{"id":"326f1bbb-507d-4060-959b-bdb2166a4b53","decision":"revise","abstract":"Cachexia is a multifactorial metabolic syndrome characterized by severe skeletal muscle loss that cannot be reversed by nutritional support. Common in cancer and other chronic diseases, it significantly increases morbidity and mortality. Muscle wasting is primarily driven by activation of the ubiquitin–proteasome pathway through the E3 ubiquitin ligases MuRF1 and Atrogin-1, which promote protein degradation and muscle atrophy. This study used computational modeling and docking to identify small-molecule inhibitors targeting these ligases, including repurposed drugs with anti-cachectic potential. Thalidomide and EM12-So2F were prioritized based on integrated docking, binding affinity, and drug-likeness/ADME analyses for validation in C2C12 myotube-based cachexia models.
","acknowledgements":"After writing the editorial, ChatGPT5.1 was used to check for grammatical errors and to improve the flow of the text. AI was not used in idea generation or content creation. It was only used for fixing grammar and flow.
","authors":[{"affiliations":["Lawrence Technological University"],"departments":["Department of Natural Sciences"],"credit":["conceptualization","supervision","resources","writing_originalDraft"],"email":"imuqbil@ltu.edu","firstName":"Irfana","lastName":"Muqbil","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":"0000-0001-6889-7035"},{"affiliations":["Lawrence Technological University"],"departments":["Department of Biomedical Engineering"],"credit":["dataCuration","formalAnalysis","methodology","visualization"],"email":"jjohns14@ltu.edu","firstName":"Jordan D","lastName":"Johnson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":"https://portal.micropublication.org/uploads/af6f2f339f63fdb5751564369ec0670f.csv"},"extendedData":[{"description":"Supplementary Figure S1. Two-dimensional interaction maps of the prioritized compounds within the predicted binding regions of MuRF1 and Atrogin-1. (A–B) Predicted interactions of EM12-So2F with MuRF1 and Atrogin-1, respectively. (C–D) Predicted interactions of thalidomide with MuRF1 and Atrogin-1, respectively. Interacting residues and interaction types are shown, including conventional hydrogen bonds (green dashed lines), van der Waals contacts (light green), Pi–sigma interactions (purple), and Pi–alkyl interactions (pink). These interactions provide structural context for the predicted binding poses shown in Figure 1D–G.
","doi":null,"resourceType":"Image","name":"S1_MP.jpg","url":"https://portal.micropublication.org/uploads/9ac78b53c0e86d39ef2455c436a853cb.jpg"}],"funding":"none
","image":{"url":"https://portal.micropublication.org/uploads/46747d35328351f2c1840a0050d3daf0.jpg"},"imageCaption":"(A) DiffDock analysis showing the distribution of smina affinity scores across predicted binding poses for compounds screened against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). EM12-So2F and thalidomide are highlighted as compounds prioritized for further analysis based on integrated consideration of docking pose/confidence, predicted affinity, protein–ligand interactions, and predicted drug-likeness/ADME properties; highlighting does not indicate selection based solely on the most favorable affinity score. (B–C) SwissADME drug-likeness radar plots for EM12-So2F (B) and thalidomide (C). (D–E) Predicted binding poses of EM12-So2F with MuRF1 and Atrogin-1, respectively. (F–G) Predicted binding poses of thalidomide with MuRF1 and Atrogin-1, respectively. Detailed two-dimensional maps of predicted protein–ligand interactions are provided in Supplementary Figure S1.
Table 1 : Smina binding affinity scores (kcal/mol) associated with the highest-confidence DiffDock poses of screened compounds against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). More negative values indicate more favorable predicted binding affinity.
","imageTitle":"Computational screening and prioritization of EM12-So2F and thalidomide as candidate ligands of the muscle-specific E3 ubiquitin ligases Atrogin-1 and MuRF1
","methods":"Protein and Ligand Preparation
Protein sequences and structural information for the MuRF1 (TRIM63) coiled-coil domain were obtained from the Protein Data Bank (PDB ID: 4M3L) (Berman et al., 2000: Franke et al., 2014), while sequence and structural information for Atrogin-1 (FBXO32; UniProt accession Q969P5) were retrieved from UniProt, and corresponding structural models were utilized for molecular docking analyses. Candidate E3 ligase inhibitors were selected based on published literature and structural relevance to ubiquitin–proteasome pathway modulation. Ligand structures were retrieved in SMILES or SDF format from chemical databases and converted into optimized three-dimensional conformations using Open Babel for molecular modeling and docking analyses. Protein structures were prepared by removing water molecules and non-essential ligands, followed by optimization of structural geometry prior to docking simulations.
DiffDock Molecular Docking Analysis
Protein–ligand docking studies were performed using DiffDock, a generative artificial intelligence–based docking platform that utilizes diffusion modeling to predict ligand-binding poses and protein–ligand interactions. Similar to previously described DiffDock workflows, multiple ligand conformations and docking poses were generated to evaluate translational, rotational, and torsional flexibility during docking inference. Docking outputs included predicted binding poses, confidence score rankings, and smina affinity scores, allowing assessment of both the confidence of the predicted ligand pose and its predicted binding affinity. More negative smina affinity scores were interpreted as more favorable predicted binding affinities. Figure 1A shows the distribution of smina affinity scores across the generated docking poses, whereas Table 1 summarizes the smina affinity score associated with the highest-confidence DiffDock pose for each compound against Atrogin-1 and MuRF1.
SwissDock Validation and ADME Profiling
Additional docking analyses and interaction visualization were carried out using SwissDock to further evaluate ligand-binding conformations and intermolecular interactions within predicted binding pockets. Predicted protein–ligand interactions, including hydrogen bonds, van der Waals contacts, and hydrophobic interactions, were examined to characterize the binding environment of prioritized compounds. Drug-likeness, molecular properties, and absorption, distribution, metabolism, and excretion (ADME) characteristics were further assessed to prioritize compounds with favorable pharmacokinetic profiles and therapeutic potential.
Candidate Prioritization
Candidate prioritization was based on an integrated assessment of DiffDock confidence ranking, smina-predicted binding affinity, predicted binding poses and protein–ligand interactions, and drug-likeness/ADME properties rather than binding affinity alone. Among the compounds screened thus far, thalidomide and EM12-So2F were prioritized for further investigation based on their combined computational profiles. Thalidomide exhibited the most favorable smina affinity scores toward both Atrogin-1 and MuRF1, whereas EM12-So2F was retained based on the integrated assessment of its docking and predicted pharmacological characteristics despite not exhibiting the most favorable affinity score for Atrogin-1.
","reagents":"","patternDescription":"Cachexia or muscle wasting is associated with poor quality of life and is one of the leading causes of morbidity in patients with advanced cancer. Therefore, the identification of novel therapeutic approaches to target cachexia is of critical importance in the management of advanced malignancies. Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle wasting, with or without adipose tissue loss, that cannot be fully reversed by nutritional supplementation alone (Fearon et al., 2011). It is frequently observed in patients with pancreatic, lung, gastrointestinal, and other advanced malignancies, where it contributes significantly to weakness, fatigue, reduced mobility, poor tolerance to anticancer therapies, and decreased overall survival (Argilés et al., 2014). Despite its major clinical impact, effective therapies for cancer-associated cachexia remain limited.
One of the major molecular mechanisms underlying cachexia is the activation of the ubiquitin–proteasome system (UPS), the primary intracellular pathway responsible for regulated protein degradation. In skeletal muscle, the UPS contributes to accelerated breakdown of structural and contractile proteins during catabolic conditions (Bodine et al., 2001; Gomes et al., 2001). Protein degradation through the UPS involves sequential ubiquitin activation by E1 enzymes, ubiquitin conjugation by E2 enzymes, and substrate-specific ubiquitination mediated by E3 ubiquitin ligases, ultimately directing proteins toward degradation by the 26S proteasome. Among these components, E3 ubiquitin ligases provide substrate specificity and therefore represent attractive therapeutic targets. Two muscle-specific E3 ubiquitin ligases, Muscle RING Finger-1 (MuRF1/TRIM63) and Atrogin-1/FBXO32, are consistently upregulated in experimental and clinical models of cachexia and are considered central mediators of skeletal muscle atrophy (Bodine et al., 2001; Gomes et al., 2001). MuRF1 primarily targets sarcomeric and contractile proteins including myosin heavy chain, whereas Atrogin-1 regulates proteins involved in muscle growth, differentiation, and protein synthesis. Persistent activation of these ligases results in excessive proteolysis, impaired muscle regeneration, and progressive muscle wasting.
Current therapeutic approaches for cachexia mainly focus on nutritional supplementation, exercise, appetite stimulants, and anti-inflammatory interventions; however, these strategies often provide only modest clinical benefit (Baracos et al., 2018). Consequently, there is growing interest in directly targeting the molecular pathways responsible for muscle protein degradation. Inhibition of muscle-specific E3 ligases represents a promising strategy to preserve skeletal muscle integrity and improve outcomes in cachectic patients.
Recent advances in computational biology, molecular modeling, and artificial intelligence-based drug discovery have accelerated the identification of candidate therapeutics against disease-associated targets (Jumper et al., 2021). Computational approaches enable rapid screening of compounds, prediction of protein–ligand interactions, and evaluation of drug-like properties prior to experimental validation. In the present study, we performed in silico screening of multiple E3 ubiquitin ligase inhibitors, including compounds currently in clinical use as well as investigational agents undergoing preclinical evaluation. Protein sequence and structural information were obtained through UniProt, which provides curated protein annotations, domain information, and structural datasets relevant for molecular modeling studies (UniProt Consortium, 2023). Ligand preparation and structural optimization were carried out using Open Babel, enabling conversion and refinement of molecular structures for downstream docking analyses (O’Boyle et al., 2011).
Docking studies were performed using DiffDock, an artificial intelligence–based platform that predicts protein–ligand binding poses and interaction confidence through diffusion generative modeling approaches (Corso et al., 2023). DiffDock enables flexible docking and provides binding confidence estimates that facilitate prioritization of candidate compounds. Additional analyses were conducted using SwissDock to evaluate docking conformations, binding interactions, drug likeness, and pharmacokinetic properties including absorption, distribution, metabolism, and excretion (ADME) characteristics (Grosdidier et al., 2011). Together, these integrated computational approaches provided a robust framework for identifying candidate inhibitors targeting muscle-associated E3 ligases (Figure 1A–C).
Among the compounds screened thus far, EM12-So2F and thalidomide were prioritized for further investigation based on an integrated assessment of multiple computational parameters rather than predicted binding affinity alone (Figure 1A–G). Initial DiffDock analysis evaluated predicted binding poses, confidence rankings, and smina affinity scores for the screened compounds (Figure 1A; Table 1). Thalidomide showed the most favorable smina affinity scores for both Atrogin-1 and MuRF1, whereas EM12-So2F showed favorable predicted binding, particularly toward MuRF1, but was not the highest-affinity compound for Atrogin-1. Therefore, its prioritization was based on the combined assessment of docking characteristics and subsequent drug-likeness and ADME analyses. SwissADME profiling showed favorable physicochemical and pharmacokinetic characteristics for EM12-So2F and thalidomide (Figure 1B–C), further supporting their selection. Examination of the predicted binding poses showed that both compounds could be accommodated within predicted binding regions of MuRF1 and Atrogin-1 (Figure 1D–G), with interaction analysis identifying hydrogen bonds, van der Waals contacts, and hydrophobic interactions with residues surrounding the ligands. Together, these complementary analyses supported prioritization of EM12-So2F and thalidomide for subsequent experimental evaluation. Interestingly, both compounds are established cereblon (CRBN) binders; however, the present study does not establish a mechanistic role for CRBN or PROTAC-mediated activity in their predicted interactions with MuRF1 or Atrogin-1.
Together, these findings demonstrate the utility of integrating structure-based docking, binding affinity prediction, interaction analysis, and drug-likeness/ADME profiling to prioritize candidate compounds targeting muscle-specific E3 ubiquitin ligases. EM12-So2F and thalidomide represent promising candidates identified from the compounds screened thus far, and additional small molecules will be screened using the same computational workflow to expand the pool of potential candidates. Lead compounds emerging from this analysis will undergo target-validation studies to confirm their interaction with and specificity toward MuRF1 and Atrogin-1 before advancing to functional evaluation. Selected lead compounds will then be tested in C2C12 myotube-based models of muscle atrophy to determine whether target modulation translates into preservation of the muscle phenotype. These studies will provide experimental validation of the computational findings and establish whether direct targeting of MuRF1 and Atrogin-1 represents a viable therapeutic strategy for cancer-associated muscle wasting.
","references":[{"reference":"Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. 2014. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer 14(11): 754-62.
","pubmedId":"25291291","doi":""},{"reference":"Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. 2018. Cancer-associated cachexia. Nat Rev Dis Primers 4: 17105.
","pubmedId":"29345251","doi":""},{"reference":"Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, Clarke BA, et al., Glass DJ. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294(5547): 1704-8.
","pubmedId":"11679633","doi":""},{"reference":"Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2023;2210.01776.
","pubmedId":"","doi":""},{"reference":"Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al., Baracos VE. 2011. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12(5): 489-95.
","pubmedId":"21296615","doi":""},{"reference":"Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL, New Collective Author. 2001. Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98(25): 14440-5.
","pubmedId":"11717410","doi":""},{"reference":"Grosdidier A, Zoete V, Michielin O. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39(Web Server issue): W270-7.
","pubmedId":"21624888","doi":""},{"reference":"Ito T, Ando H, Suzuki T, Ogura T, Hotta K, Imamura Y, Yamaguchi Y, Handa H. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327(5971): 1345-50.
","pubmedId":"20223979","doi":""},{"reference":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873): 583-589.
","pubmedId":"34265844","doi":""},{"reference":"O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. 2011. Open Babel: An open chemical toolbox. J Cheminform 3: 33.
","pubmedId":"21982300","doi":""},{"reference":"UniProt Consortium. 2023. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1): D523-D531.
","pubmedId":"36408920","doi":""},{"reference":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. 2000. The Protein Data Bank. Nucleic Acids Res 28(1): 235-42.
","pubmedId":"10592235","doi":""},{"reference":"Franke B, Gasch A, Rodriguez D, Chami M, Khan MM, Rudolf R, et al., Mayans O. 2014. Molecular basis for the fold organization and sarcomeric targeting of the muscle atrogin MuRF1. Open Biol 4(3): 130172.
","pubmedId":"24671946","doi":""}],"title":"Targeting Muscle-Specific E3 Ligases in Cancer Cachexia Through Structure-Based Virtual Screening of Small-Molecule Inhibitors\n\n\n\n","reviews":[{"reviewer":{"displayName":"Sajith Jayasinghe"},"openAcknowledgement":false,"status":{"submitted":true}}],"curatorReviews":[]},{"id":"345e5d68-29d2-464a-8b0a-3db84ea5c7be","decision":"edit","abstract":"Cachexia is a multifactorial metabolic syndrome characterized by severe skeletal muscle loss that cannot be reversed by nutritional support. Common in cancer and other chronic diseases, it significantly increases morbidity and mortality. Muscle wasting is primarily driven by activation of the ubiquitin–proteasome pathway through the E3 ubiquitin ligases MuRF1 and Atrogin-1, which promote protein degradation and muscle atrophy. This study used computational modeling and docking to identify small-molecule inhibitors targeting these ligases, including repurposed drugs with anti-cachectic potential. Thalidomide and EM12-So2F were prioritized based on integrated docking, binding affinity, and drug-likeness/ADME analyses for validation in C2C12 myotube-based cachexia models.
","acknowledgements":"After writing the manuscript, ChatGPT5.1 was used to check for grammatical errors and to improve the flow of the text. AI was not used in idea generation or content creation. It was only used for fixing grammar and flow.
","authors":[{"affiliations":["Lawrence Technological University, 21000 W 10 Mile Rd, Southfield, MI 48075, USA"],"departments":["Department of Natural Sciences"],"credit":["conceptualization","supervision","resources","writing_originalDraft"],"email":"imuqbil@ltu.edu","firstName":"Irfana","lastName":"Muqbil","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":"0000-0001-6889-7035"},{"affiliations":["Lawrence Technological University, 21000 W 10 Mile Rd, Southfield, MI 48075, USA"],"departments":["Department of Biomedical Engineering"],"credit":["dataCuration","formalAnalysis","methodology","visualization"],"email":"jjohns14@ltu.edu","firstName":"Jordan D","lastName":"Johnson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":"https://portal.micropublication.org/uploads/af6f2f339f63fdb5751564369ec0670f.csv"},"extendedData":[{"description":"Extended Data Figure 1. Two-dimensional interaction maps of prioritized compounds in the predicted binding regions of MuRF1 and Atrogin-1. (A–B) Predicted interactions of EM12-So2F with MuRF1 and Atrogin-1, respectively. (C–D) Predicted interactions of thalidomide with MuRF1 and Atrogin-1, respectively. The maps show interacting residues and interaction types: conventional hydrogen bonds (green dashed lines), van der Waals contacts (light green), π–sigma interactions (purple), and π–alkyl interactions (pink). These maps provide structural context for the predicted binding poses in Figure 1D–G.
","doi":"10.22002/v1njh-67936","resourceType":"Image","name":"Extended data Figure 1.jpg","url":"https://portal.micropublication.org/uploads/699ba9b556d65dd5e5148cf16dc0420a.jpg"}],"funding":"none
","image":{"url":"https://portal.micropublication.org/uploads/46747d35328351f2c1840a0050d3daf0.jpg"},"imageCaption":"(A) DiffDock analysis showing the distribution of smina affinity scores across predicted binding poses for compounds screened against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). EM12-So2F and thalidomide are highlighted as compounds prioritized for further analysis based on integrated consideration of docking pose/confidence, predicted affinity, protein–ligand interactions, and predicted drug-likeness/ADME properties; highlighting does not indicate selection based solely on the most favorable affinity score. (B–C) SwissADME drug-likeness radar plots for EM12-So2F (B) and thalidomide (C). (D–E) Predicted binding poses of EM12-So2F with MuRF1 and Atrogin-1, respectively. (F–G) Predicted binding poses of thalidomide with MuRF1 and Atrogin-1, respectively. Detailed two-dimensional maps of predicted protein–ligand interactions are provided in Extended Data Figure 1.
Table 1: Smina binding affinity scores (kcal/mol) associated with the highest-confidence DiffDock poses of screened compounds against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). More negative values indicate more favorable predicted binding affinity.
","imageTitle":"Computational screening and prioritization of EM12-So2F and thalidomide as candidate ligands of the muscle-specific E3 ubiquitin ligases Atrogin-1 and MuRF1
","methods":"Protein and Ligand Preparation
Protein sequences and structural information for the MuRF1 (TRIM63) coiled-coil domain were obtained from the Protein Data Bank (PDB ID: 4M3L) (Berman et al., 2000; Franke et al., 2014), while sequence and structural information for Atrogin-1 (FBXO32; UniProt accession Q969P5) were retrieved from UniProt, and corresponding structural models were utilized for molecular docking analyses. Candidate E3 ligase inhibitors were selected based on published literature and structural relevance to ubiquitin–proteasome pathway modulation. Ligand structures were retrieved in SMILES or SDF format from chemical databases and converted into optimized three-dimensional conformations using Open Babel for molecular modeling and docking analyses. Protein structures were prepared by removing water molecules and non-essential ligands, followed by optimization of structural geometry prior to docking simulations.
DiffDock Molecular Docking Analysis
Protein–ligand docking studies were performed using DiffDock, a generative artificial intelligence–based docking platform that utilizes diffusion modeling to predict ligand-binding poses and protein–ligand interactions. Similar to previously described DiffDock workflows, multiple ligand conformations and docking poses were generated to evaluate translational, rotational, and torsional flexibility during docking inference. Docking outputs included predicted binding poses, confidence score rankings, and smina affinity scores, allowing assessment of both the confidence of the predicted ligand pose and its predicted binding affinity. More negative smina affinity scores were interpreted as more favorable predicted binding affinities. Figure 1A shows the distribution of smina affinity scores across the generated docking poses, whereas Table 1 summarizes the smina affinity score associated with the highest-confidence DiffDock pose for each compound against Atrogin-1 and MuRF1.
SwissDock Validation and ADME Profiling
Additional docking analyses and interaction visualization were carried out using SwissDock to further evaluate ligand-binding conformations and intermolecular interactions within predicted binding pockets. Predicted protein–ligand interactions, including hydrogen bonds, van der Waals contacts, and hydrophobic interactions, were examined to characterize the binding environment of prioritized compounds. Drug-likeness, molecular properties, and absorption, distribution, metabolism, and excretion (ADME) characteristics were further assessed to prioritize compounds with favorable pharmacokinetic profiles and therapeutic potential.
Candidate Prioritization
Candidate prioritization was based on an integrated assessment of DiffDock confidence ranking, smina-predicted binding affinity, predicted binding poses and protein–ligand interactions, and drug-likeness/ADME properties rather than binding affinity alone. Among the compounds screened thus far, thalidomide and EM12-So2F were prioritized for further investigation based on their combined computational profiles. Thalidomide exhibited the most favorable smina affinity scores toward both Atrogin-1 and MuRF1, whereas EM12-So2F was retained based on the integrated assessment of its docking and predicted pharmacological characteristics despite not exhibiting the most favorable affinity score for Atrogin-1.
","reagents":"","patternDescription":"Cachexia or muscle wasting is associated with poor quality of life and is one of the leading causes of morbidity in patients with advanced cancer. Therefore, the identification of novel therapeutic approaches to target cachexia is of critical importance in the management of advanced malignancies. Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle wasting, with or without adipose tissue loss, that cannot be fully reversed by nutritional supplementation alone (Fearon et al., 2011). It is frequently observed in patients with pancreatic, lung, gastrointestinal, and other advanced malignancies, where it contributes significantly to weakness, fatigue, reduced mobility, poor tolerance to anticancer therapies, and decreased overall survival (Argilés et al., 2014). Despite its major clinical impact, effective therapies for cancer-associated cachexia remain limited.
One of the major molecular mechanisms underlying cachexia is the activation of the ubiquitin–proteasome system (UPS), the primary intracellular pathway responsible for regulated protein degradation. In skeletal muscle, the UPS contributes to accelerated breakdown of structural and contractile proteins during catabolic conditions (Bodine et al., 2001; Gomes et al., 2001). Protein degradation through the UPS involves sequential ubiquitin activation by E1 enzymes, ubiquitin conjugation by E2 enzymes, and substrate-specific ubiquitination mediated by E3 ubiquitin ligases, ultimately directing proteins toward degradation by the 26S proteasome. Among these components, E3 ubiquitin ligases provide substrate specificity and therefore represent attractive therapeutic targets. Two muscle-specific E3 ubiquitin ligases, Muscle RING Finger-1 (MuRF1/TRIM63) and Atrogin-1/FBXO32, are consistently upregulated in experimental and clinical models of cachexia and are considered central mediators of skeletal muscle atrophy (Bodine et al., 2001; Gomes et al., 2001). MuRF1 primarily targets sarcomeric and contractile proteins including myosin heavy chain, whereas Atrogin-1 regulates proteins involved in muscle growth, differentiation, and protein synthesis. Persistent activation of these ligases results in excessive proteolysis, impaired muscle regeneration, and progressive muscle wasting.
Current therapeutic approaches for cachexia mainly focus on nutritional supplementation, exercise, appetite stimulants, and anti-inflammatory interventions; however, these strategies often provide only modest clinical benefit (Baracos et al., 2018). Consequently, there is growing interest in directly targeting the molecular pathways responsible for muscle protein degradation. Inhibition of muscle-specific E3 ligases represents a promising strategy to preserve skeletal muscle integrity and improve outcomes in cachectic patients.
Recent advances in computational biology, molecular modeling, and artificial intelligence-based drug discovery have accelerated the identification of candidate therapeutics against disease-associated targets (Jumper et al., 2021). Computational approaches enable rapid screening of compounds, prediction of protein–ligand interactions, and evaluation of drug-like properties prior to experimental validation. In the present study, we performed in silico screening of multiple E3 ubiquitin ligase inhibitors, including compounds currently in clinical use as well as investigational agents undergoing preclinical evaluation. Protein sequence and structural information were obtained through UniProt, which provides curated protein annotations, domain information, and structural datasets relevant for molecular modeling studies (UniProt Consortium, 2023). Ligand preparation and structural optimization were carried out using Open Babel, enabling conversion and refinement of molecular structures for downstream docking analyses (O’Boyle et al., 2011).
Docking studies were performed using DiffDock, an artificial intelligence–based platform that predicts protein–ligand binding poses and interaction confidence through diffusion generative modeling approaches (Corso et al., 2023). DiffDock enables flexible docking and provides binding confidence estimates that facilitate prioritization of candidate compounds. Additional analyses were conducted using SwissDock to evaluate docking conformations, binding interactions, drug likeness, and pharmacokinetic properties including absorption, distribution, metabolism, and excretion (ADME) characteristics (Grosdidier et al., 2011). Together, these integrated computational approaches provided a robust framework for identifying candidate inhibitors targeting muscle-associated E3 ligases (Figure 1A–C).
Among the compounds screened thus far, EM12-So2F and thalidomide were prioritized for further investigation based on an integrated assessment of multiple computational parameters rather than predicted binding affinity alone (Figure 1A–G). Initial DiffDock analysis evaluated predicted binding poses, confidence rankings, and smina affinity scores for the screened compounds (Figure 1A; Table 1). Thalidomide showed the most favorable smina affinity scores for both Atrogin-1 and MuRF1, whereas EM12-So2F showed favorable predicted binding, particularly toward MuRF1, but was not the highest-affinity compound for Atrogin-1. Therefore, its prioritization was based on the combined assessment of docking characteristics and subsequent drug-likeness and ADME analyses. SwissADME profiling showed favorable physicochemical and pharmacokinetic characteristics for EM12-So2F and thalidomide (Figure 1B–C), further supporting their selection. Examination of the predicted binding poses showed that both compounds could be accommodated within predicted binding regions of MuRF1 and Atrogin-1 (Figure 1D–G), with interaction analysis identifying hydrogen bonds, van der Waals contacts, and hydrophobic interactions with residues surrounding the ligands. Together, these complementary analyses supported prioritization of EM12-So2F and thalidomide for subsequent experimental evaluation. Interestingly, both compounds are established cereblon (CRBN) binders; however, the present study does not establish a mechanistic role for CRBN or PROTAC-mediated activity in their predicted interactions with MuRF1 or Atrogin-1.
Together, these findings demonstrate the utility of integrating structure-based docking, binding affinity prediction, interaction analysis, and drug-likeness/ADME profiling to prioritize candidate compounds targeting muscle-specific E3 ubiquitin ligases. EM12-So2F and thalidomide represent promising candidates identified from the compounds screened thus far, and additional small molecules will be screened using the same computational workflow to expand the pool of potential candidates. Lead compounds emerging from this analysis will undergo target-validation studies to confirm their interaction with and specificity toward MuRF1 and Atrogin-1 before advancing to functional evaluation. Selected lead compounds will then be tested in C2C12 myotube-based models of muscle atrophy to determine whether target modulation translates into preservation of the muscle phenotype. These studies will provide experimental validation of the computational findings and establish whether direct targeting of MuRF1 and Atrogin-1 represents a viable therapeutic strategy for cancer-associated muscle wasting.
","references":[{"reference":"Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. 2014. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer 14(11): 754-62.
","pubmedId":"25291291","doi":""},{"reference":"Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. 2018. Cancer-associated cachexia. Nat Rev Dis Primers 4: 17105.
","pubmedId":"29345251","doi":""},{"reference":"Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, Clarke BA, et al., Glass DJ. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294(5547): 1704-8.
","pubmedId":"11679633","doi":""},{"reference":"Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2023;2210.01776.
","pubmedId":"","doi":""},{"reference":"Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al., Baracos VE. 2011. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12(5): 489-95.
","pubmedId":"21296615","doi":""},{"reference":"Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL, New Collective Author. 2001. Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98(25): 14440-5.
","pubmedId":"11717410","doi":""},{"reference":"Grosdidier A, Zoete V, Michielin O. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39(Web Server issue): W270-7.
","pubmedId":"21624888","doi":""},{"reference":"Ito T, Ando H, Suzuki T, Ogura T, Hotta K, Imamura Y, Yamaguchi Y, Handa H. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327(5971): 1345-50.
","pubmedId":"20223979","doi":""},{"reference":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873): 583-589.
","pubmedId":"34265844","doi":""},{"reference":"O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. 2011. Open Babel: An open chemical toolbox. J Cheminform 3: 33.
","pubmedId":"21982300","doi":""},{"reference":"UniProt Consortium. 2023. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1): D523-D531.
","pubmedId":"36408920","doi":""},{"reference":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. 2000. The Protein Data Bank. Nucleic Acids Res 28(1): 235-42.
","pubmedId":"10592235","doi":""},{"reference":"Franke B, Gasch A, Rodriguez D, Chami M, Khan MM, Rudolf R, et al., Mayans O. 2014. Molecular basis for the fold organization and sarcomeric targeting of the muscle atrogin MuRF1. Open Biol 4(3): 130172.
","pubmedId":"24671946","doi":""}],"title":"Targeting Muscle-Specific E3 Ligases in Cancer Cachexia Through Structure-Based Virtual Screening of Small-Molecule Inhibitors\n\n\n\n","reviews":[],"curatorReviews":[]},{"id":"a35acba8-6cb4-4af9-a21d-a6455de92539","decision":"accept","abstract":"Cachexia is a multifactorial metabolic syndrome characterized by severe skeletal muscle loss that cannot be reversed by nutritional support. Common in cancer and other chronic diseases, it significantly increases morbidity and mortality. Muscle wasting is primarily driven by activation of the ubiquitin–proteasome pathway through the E3 ubiquitin ligases MuRF1 and Atrogin-1, which promote protein degradation and muscle atrophy. This study used computational modeling and docking to identify small-molecule inhibitors targeting these ligases, including repurposed drugs with anti-cachectic potential. Thalidomide and EM12-So2F were prioritized based on integrated docking, binding affinity, and drug-likeness/ADME analyses for validation in C2C12 myotube-based cachexia models.
","acknowledgements":"After writing the manuscript, ChatGPT5.1 was used to check for grammatical errors and to improve the flow of the text. AI was not used in idea generation or content creation. It was only used for fixing grammar and flow.
","authors":[{"affiliations":["Lawrence Technological University, Southfield, MI USA"],"departments":["Department of Natural Sciences"],"credit":["conceptualization","supervision","resources","writing_originalDraft"],"email":"imuqbil@ltu.edu","firstName":"Irfana","lastName":"Muqbil","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":"0000-0001-6889-7035"},{"affiliations":["Lawrence Technological University, Southfield, MI USA"],"departments":["Department of Biomedical Engineering"],"credit":["dataCuration","formalAnalysis","methodology","visualization"],"email":"jjohns14@ltu.edu","firstName":"Jordan D","lastName":"Johnson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":"https://portal.micropublication.org/uploads/af6f2f339f63fdb5751564369ec0670f.csv"},"extendedData":[{"description":"Extended Data Figure 1. Two-dimensional interaction maps of prioritized compounds in the predicted binding regions of MuRF1 and Atrogin-1. (A–B) Predicted interactions of EM12-So2F with MuRF1 and Atrogin-1, respectively. (C–D) Predicted interactions of thalidomide with MuRF1 and Atrogin-1, respectively. The maps show interacting residues and interaction types: conventional hydrogen bonds (green dashed lines), van der Waals contacts (light green), π–sigma interactions (purple), and π–alkyl interactions (pink). These maps provide structural context for the predicted binding poses in Figure 1D–G.
","doi":"10.22002/v1njh-67936","resourceType":"Image","name":"Extended data Figure 1.jpg","url":"https://portal.micropublication.org/uploads/699ba9b556d65dd5e5148cf16dc0420a.jpg"}],"funding":"none
","image":{"url":"https://portal.micropublication.org/uploads/46747d35328351f2c1840a0050d3daf0.jpg"},"imageCaption":"(A) DiffDock analysis showing the distribution of smina affinity scores across predicted binding poses for compounds screened against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). EM12-So2F and thalidomide are highlighted as compounds prioritized for further analysis based on integrated consideration of docking pose/confidence, predicted affinity, protein–ligand interactions, and predicted drug-likeness/ADME properties; highlighting does not indicate selection based solely on the most favorable affinity score. (B–C) SwissADME drug-likeness radar plots for EM12-So2F (B) and thalidomide (C). (D–E) Predicted binding poses of EM12-So2F with MuRF1 and Atrogin-1, respectively. (F–G) Predicted binding poses of thalidomide with MuRF1 and Atrogin-1, respectively. Detailed two-dimensional maps of predicted protein–ligand interactions are provided in Extended Data Figure 1.
Table 1: Smina binding affinity scores (kcal/mol) associated with the highest-confidence DiffDock poses of screened compounds against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). More negative values indicate more favorable predicted binding affinity.
","imageTitle":"Computational screening and prioritization of EM12-So2F and thalidomide as candidate ligands of the muscle-specific E3 ubiquitin ligases Atrogin-1 and MuRF1
","methods":"Protein and Ligand Preparation
Protein sequences and structural information for the MuRF1 (TRIM63) coiled-coil domain were obtained from the Protein Data Bank (PDB ID: 4M3L) (Berman et al., 2000; Franke et al., 2014), while sequence and structural information for Atrogin-1 (FBXO32; UniProt accession Q969P5) were retrieved from UniProt, and corresponding structural models were utilized for molecular docking analyses. Candidate E3 ligase inhibitors were selected based on published literature and structural relevance to ubiquitin–proteasome pathway modulation. Ligand structures were retrieved in SMILES or SDF format from chemical databases and converted into optimized three-dimensional conformations using Open Babel for molecular modeling and docking analyses. Protein structures were prepared by removing water molecules and non-essential ligands, followed by optimization of structural geometry prior to docking simulations.
DiffDock Molecular Docking Analysis
Protein–ligand docking studies were performed using DiffDock, a generative artificial intelligence–based docking platform that utilizes diffusion modeling to predict ligand-binding poses and protein–ligand interactions. Similar to previously described DiffDock workflows, multiple ligand conformations and docking poses were generated to evaluate translational, rotational, and torsional flexibility during docking inference. Docking outputs included predicted binding poses, confidence score rankings, and smina affinity scores, allowing assessment of both the confidence of the predicted ligand pose and its predicted binding affinity. More negative smina affinity scores were interpreted as more favorable predicted binding affinities. Figure 1A shows the distribution of smina affinity scores across the generated docking poses, whereas Table 1 summarizes the smina affinity score associated with the highest-confidence DiffDock pose for each compound against Atrogin-1 and MuRF1.
SwissDock Validation and ADME Profiling
Additional docking analyses and interaction visualization were carried out using SwissDock to further evaluate ligand-binding conformations and intermolecular interactions within predicted binding pockets. Predicted protein–ligand interactions, including hydrogen bonds, van der Waals contacts, and hydrophobic interactions, were examined to characterize the binding environment of prioritized compounds. Drug-likeness, molecular properties, and absorption, distribution, metabolism, and excretion (ADME) characteristics were further assessed to prioritize compounds with favorable pharmacokinetic profiles and therapeutic potential.
Candidate Prioritization
Candidate prioritization was based on an integrated assessment of DiffDock confidence ranking, smina-predicted binding affinity, predicted binding poses and protein–ligand interactions, and drug-likeness/ADME properties rather than binding affinity alone. Among the compounds screened thus far, thalidomide and EM12-So2F were prioritized for further investigation based on their combined computational profiles. Thalidomide exhibited the most favorable smina affinity scores toward both Atrogin-1 and MuRF1, whereas EM12-So2F was retained based on the integrated assessment of its docking and predicted pharmacological characteristics despite not exhibiting the most favorable affinity score for Atrogin-1.
","reagents":"","patternDescription":"Cachexia or muscle wasting is associated with poor quality of life and is one of the leading causes of morbidity in patients with advanced cancer. Therefore, the identification of novel therapeutic approaches to target cachexia is of critical importance in the management of advanced malignancies. Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle wasting, with or without adipose tissue loss, that cannot be fully reversed by nutritional supplementation alone (Fearon et al., 2011). It is frequently observed in patients with pancreatic, lung, gastrointestinal, and other advanced malignancies, where it contributes significantly to weakness, fatigue, reduced mobility, poor tolerance to anticancer therapies, and decreased overall survival (Argilés et al., 2014). Despite its major clinical impact, effective therapies for cancer-associated cachexia remain limited.
One of the major molecular mechanisms underlying cachexia is the activation of the ubiquitin–proteasome system (UPS), the primary intracellular pathway responsible for regulated protein degradation. In skeletal muscle, the UPS contributes to accelerated breakdown of structural and contractile proteins during catabolic conditions (Bodine et al., 2001; Gomes et al., 2001). Protein degradation through the UPS involves sequential ubiquitin activation by E1 enzymes, ubiquitin conjugation by E2 enzymes, and substrate-specific ubiquitination mediated by E3 ubiquitin ligases, ultimately directing proteins toward degradation by the 26S proteasome. Among these components, E3 ubiquitin ligases provide substrate specificity and therefore represent attractive therapeutic targets. Two muscle-specific E3 ubiquitin ligases, Muscle RING Finger-1 (MuRF1/TRIM63) and Atrogin-1/FBXO32, are consistently upregulated in experimental and clinical models of cachexia and are considered central mediators of skeletal muscle atrophy (Bodine et al., 2001; Gomes et al., 2001). MuRF1 primarily targets sarcomeric and contractile proteins including myosin heavy chain, whereas Atrogin-1 regulates proteins involved in muscle growth, differentiation, and protein synthesis. Persistent activation of these ligases results in excessive proteolysis, impaired muscle regeneration, and progressive muscle wasting.
Current therapeutic approaches for cachexia mainly focus on nutritional supplementation, exercise, appetite stimulants, and anti-inflammatory interventions; however, these strategies often provide only modest clinical benefit (Baracos et al., 2018). Consequently, there is growing interest in directly targeting the molecular pathways responsible for muscle protein degradation. Inhibition of muscle-specific E3 ligases represents a promising strategy to preserve skeletal muscle integrity and improve outcomes in cachectic patients.
Recent advances in computational biology, molecular modeling, and artificial intelligence-based drug discovery have accelerated the identification of candidate therapeutics against disease-associated targets (Jumper et al., 2021). Computational approaches enable rapid screening of compounds, prediction of protein–ligand interactions, and evaluation of drug-like properties prior to experimental validation. In the present study, we performed in silico screening of multiple E3 ubiquitin ligase inhibitors, including compounds currently in clinical use as well as investigational agents undergoing preclinical evaluation. Protein sequence and structural information were obtained through UniProt, which provides curated protein annotations, domain information, and structural datasets relevant for molecular modeling studies (UniProt Consortium, 2023). Ligand preparation and structural optimization were carried out using Open Babel, enabling conversion and refinement of molecular structures for downstream docking analyses (O’Boyle et al., 2011).
Docking studies were performed using DiffDock, an artificial intelligence–based platform that predicts protein–ligand binding poses and interaction confidence through diffusion generative modeling approaches (Corso et al., 2023). DiffDock enables flexible docking and provides binding confidence estimates that facilitate prioritization of candidate compounds. Additional analyses were conducted using SwissDock to evaluate docking conformations, binding interactions, drug likeness, and pharmacokinetic properties including absorption, distribution, metabolism, and excretion (ADME) characteristics (Grosdidier et al., 2011). Together, these integrated computational approaches provided a robust framework for identifying candidate inhibitors targeting muscle-associated E3 ligases (Figure 1A–C).
Among the compounds screened thus far, EM12-So2F and thalidomide were prioritized for further investigation based on an integrated assessment of multiple computational parameters rather than predicted binding affinity alone (Figure 1A–G). Initial DiffDock analysis evaluated predicted binding poses, confidence rankings, and smina affinity scores for the screened compounds (Figure 1A; Table 1). Thalidomide showed the most favorable smina affinity scores for both Atrogin-1 and MuRF1, whereas EM12-So2F showed favorable predicted binding, particularly toward MuRF1, but was not the highest-affinity compound for Atrogin-1. Therefore, its prioritization was based on the combined assessment of docking characteristics and subsequent drug-likeness and ADME analyses. SwissADME profiling showed favorable physicochemical and pharmacokinetic characteristics for EM12-So2F and thalidomide (Figure 1B–C), further supporting their selection. Examination of the predicted binding poses showed that both compounds could be accommodated within predicted binding regions of MuRF1 and Atrogin-1 (Figure 1D–G), with interaction analysis identifying hydrogen bonds, van der Waals contacts, and hydrophobic interactions with residues surrounding the ligands. Together, these complementary analyses supported prioritization of EM12-So2F and thalidomide for subsequent experimental evaluation. Interestingly, both compounds are established cereblon (CRBN) binders; however, the present study does not establish a mechanistic role for CRBN or PROTAC-mediated activity in their predicted interactions with MuRF1 or Atrogin-1.
Together, these findings demonstrate the utility of integrating structure-based docking, binding affinity prediction, interaction analysis, and drug-likeness/ADME profiling to prioritize candidate compounds targeting muscle-specific E3 ubiquitin ligases. EM12-So2F and thalidomide represent promising candidates identified from the compounds screened thus far, and additional small molecules will be screened using the same computational workflow to expand the pool of potential candidates. Lead compounds emerging from this analysis will undergo target-validation studies to confirm their interaction with and specificity toward MuRF1 and Atrogin-1 before advancing to functional evaluation. Selected lead compounds will then be tested in C2C12 myotube-based models of muscle atrophy to determine whether target modulation translates into preservation of the muscle phenotype. These studies will provide experimental validation of the computational findings and establish whether direct targeting of MuRF1 and Atrogin-1 represents a viable therapeutic strategy for cancer-associated muscle wasting.
","references":[{"reference":"Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. 2014. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer 14(11): 754-62.
","pubmedId":"25291291","doi":""},{"reference":"Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. 2018. Cancer-associated cachexia. Nat Rev Dis Primers 4: 17105.
","pubmedId":"29345251","doi":""},{"reference":"Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, Clarke BA, et al., Glass DJ. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294(5547): 1704-8.
","pubmedId":"11679633","doi":""},{"reference":"Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2023;2210.01776.
","pubmedId":"","doi":""},{"reference":"Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al., Baracos VE. 2011. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12(5): 489-95.
","pubmedId":"21296615","doi":""},{"reference":"Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL, New Collective Author. 2001. Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98(25): 14440-5.
","pubmedId":"11717410","doi":""},{"reference":"Grosdidier A, Zoete V, Michielin O. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39(Web Server issue): W270-7.
","pubmedId":"21624888","doi":""},{"reference":"Ito T, Ando H, Suzuki T, Ogura T, Hotta K, Imamura Y, Yamaguchi Y, Handa H. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327(5971): 1345-50.
","pubmedId":"20223979","doi":""},{"reference":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873): 583-589.
","pubmedId":"34265844","doi":""},{"reference":"O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. 2011. Open Babel: An open chemical toolbox. J Cheminform 3: 33.
","pubmedId":"21982300","doi":""},{"reference":"UniProt Consortium. 2023. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1): D523-D531.
","pubmedId":"36408920","doi":""},{"reference":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. 2000. The Protein Data Bank. Nucleic Acids Res 28(1): 235-42.
","pubmedId":"10592235","doi":""},{"reference":"Franke B, Gasch A, Rodriguez D, Chami M, Khan MM, Rudolf R, et al., Mayans O. 2014. Molecular basis for the fold organization and sarcomeric targeting of the muscle atrogin MuRF1. Open Biol 4(3): 130172.
","pubmedId":"24671946","doi":""}],"title":"Targeting Muscle-Specific E3 Ligases in Cancer Cachexia Through Structure-Based Virtual Screening of Small-Molecule Inhibitors\n\n\n\n","reviews":[],"curatorReviews":[]},{"id":"3134eb79-6e4c-4959-875b-e3c967add917","decision":"publish","abstract":"Cachexia is a multifactorial metabolic syndrome characterized by severe skeletal muscle loss that cannot be reversed by nutritional support. Common in cancer and other chronic diseases, it significantly increases morbidity and mortality. Muscle wasting is primarily driven by activation of the ubiquitin–proteasome pathway through the E3 ubiquitin ligases MuRF1 and Atrogin-1, which promote protein degradation and muscle atrophy. This study used computational modeling and docking to identify small-molecule inhibitors targeting these ligases, including repurposed drugs with anti-cachectic potential. Thalidomide and EM12-So2F were prioritized based on integrated docking, binding affinity, and drug-likeness/ADME analyses for validation in C2C12 myotube-based cachexia models.
","acknowledgements":"After writing the manuscript, ChatGPT5.1 was used to check for grammatical errors and to improve the flow of the text. AI was not used in idea generation or content creation. It was only used for fixing grammar and flow.
","authors":[{"affiliations":["Lawrence Technological University, Southfield, MI USA"],"departments":["Department of Natural Sciences"],"credit":["conceptualization","supervision","resources","writing_originalDraft"],"email":"imuqbil@ltu.edu","firstName":"Irfana","lastName":"Muqbil","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":"0000-0001-6889-7035"},{"affiliations":["Lawrence Technological University, Southfield, MI USA"],"departments":["Department of Biomedical Engineering"],"credit":["dataCuration","formalAnalysis","methodology","visualization"],"email":"jjohns14@ltu.edu","firstName":"Jordan D","lastName":"Johnson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":"https://portal.micropublication.org/uploads/af6f2f339f63fdb5751564369ec0670f.csv"},"extendedData":[{"description":"Extended Data Figure 1. Two-dimensional interaction maps of prioritized compounds in the predicted binding regions of MuRF1 and Atrogin-1. (A–B) Predicted interactions of EM12-So2F with MuRF1 and Atrogin-1, respectively. (C–D) Predicted interactions of thalidomide with MuRF1 and Atrogin-1, respectively. The maps show interacting residues and interaction types: conventional hydrogen bonds (green dashed lines), van der Waals contacts (light green), π–sigma interactions (purple), and π–alkyl interactions (pink). These maps provide structural context for the predicted binding poses in Figure 1D–G.
","doi":"10.22002/v1njh-67936","resourceType":"Image","name":"Extended data Figure 1.jpg","url":"https://portal.micropublication.org/uploads/699ba9b556d65dd5e5148cf16dc0420a.jpg"}],"funding":"none
","image":{"url":"https://portal.micropublication.org/uploads/46747d35328351f2c1840a0050d3daf0.jpg"},"imageCaption":"(A) DiffDock analysis showing the distribution of smina affinity scores across predicted binding poses for compounds screened against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). EM12-So2F and thalidomide are highlighted as compounds prioritized for further analysis based on integrated consideration of docking pose/confidence, predicted affinity, protein–ligand interactions, and predicted drug-likeness/ADME properties; highlighting does not indicate selection based solely on the most favorable affinity score. (B–C) SwissADME drug-likeness radar plots for EM12-So2F (B) and thalidomide (C). (D–E) Predicted binding poses of EM12-So2F with MuRF1 and Atrogin-1, respectively. (F–G) Predicted binding poses of thalidomide with MuRF1 and Atrogin-1, respectively. Detailed two-dimensional maps of predicted protein–ligand interactions are provided in Extended Data Figure 1.
Table 1. Smina binding affinity scores (kcal/mol) associated with the highest-confidence DiffDock poses of screened compounds against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). More negative values indicate more favorable predicted binding affinity.
","imageTitle":"Computational screening and prioritization of EM12-So2F and thalidomide as candidate ligands of the muscle-specific E3 ubiquitin ligases Atrogin-1 and MuRF1
","methods":"Protein and Ligand Preparation
Protein sequences and structural information for the MuRF1 (TRIM63) coiled-coil domain were obtained from the Protein Data Bank (PDB ID: 4M3L) (Berman et al., 2000; Franke et al., 2014), while sequence and structural information for Atrogin-1 (FBXO32; UniProt accession Q969P5) were retrieved from UniProt, and corresponding structural models were utilized for molecular docking analyses. Candidate E3 ligase inhibitors were selected based on published literature and structural relevance to ubiquitin–proteasome pathway modulation. Ligand structures were retrieved in SMILES or SDF format from chemical databases and converted into optimized three-dimensional conformations using Open Babel for molecular modeling and docking analyses. Protein structures were prepared by removing water molecules and non-essential ligands, followed by optimization of structural geometry prior to docking simulations.
DiffDock Molecular Docking Analysis
Protein–ligand docking studies were performed using DiffDock, a generative artificial intelligence–based docking platform that utilizes diffusion modeling to predict ligand-binding poses and protein–ligand interactions. Similar to previously described DiffDock workflows, multiple ligand conformations and docking poses were generated to evaluate translational, rotational, and torsional flexibility during docking inference. Docking outputs included predicted binding poses, confidence score rankings, and smina affinity scores, allowing assessment of both the confidence of the predicted ligand pose and its predicted binding affinity. More negative smina affinity scores were interpreted as more favorable predicted binding affinities. Figure 1A shows the distribution of smina affinity scores across the generated docking poses, whereas Table 1 summarizes the smina affinity score associated with the highest-confidence DiffDock pose for each compound against Atrogin-1 and MuRF1.
SwissDock Validation and ADME Profiling
Additional docking analyses and interaction visualization were carried out using SwissDock to further evaluate ligand-binding conformations and intermolecular interactions within predicted binding pockets. Predicted protein–ligand interactions, including hydrogen bonds, van der Waals contacts, and hydrophobic interactions, were examined to characterize the binding environment of prioritized compounds. Drug-likeness, molecular properties, and absorption, distribution, metabolism, and excretion (ADME) characteristics were further assessed to prioritize compounds with favorable pharmacokinetic profiles and therapeutic potential.
Candidate Prioritization
Candidate prioritization was based on an integrated assessment of DiffDock confidence ranking, smina-predicted binding affinity, predicted binding poses and protein–ligand interactions, and drug-likeness/ADME properties rather than binding affinity alone. Among the compounds screened thus far, thalidomide and EM12-So2F were prioritized for further investigation based on their combined computational profiles. Thalidomide exhibited the most favorable smina affinity scores toward both Atrogin-1 and MuRF1, whereas EM12-So2F was retained based on the integrated assessment of its docking and predicted pharmacological characteristics despite not exhibiting the most favorable affinity score for Atrogin-1.
","reagents":"","patternDescription":"Cachexia or muscle wasting is associated with poor quality of life and is one of the leading causes of morbidity in patients with advanced cancer. Therefore, the identification of novel therapeutic approaches to target cachexia is of critical importance in the management of advanced malignancies. Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle wasting, with or without adipose tissue loss, that cannot be fully reversed by nutritional supplementation alone (Fearon et al., 2011). It is frequently observed in patients with pancreatic, lung, gastrointestinal, and other advanced malignancies, where it contributes significantly to weakness, fatigue, reduced mobility, poor tolerance to anticancer therapies, and decreased overall survival (Argilés et al., 2014). Despite its major clinical impact, effective therapies for cancer-associated cachexia remain limited.
One of the major molecular mechanisms underlying cachexia is the activation of the ubiquitin–proteasome system (UPS), the primary intracellular pathway responsible for regulated protein degradation. In skeletal muscle, the UPS contributes to accelerated breakdown of structural and contractile proteins during catabolic conditions (Bodine et al., 2001; Gomes et al., 2001). Protein degradation through the UPS involves sequential ubiquitin activation by E1 enzymes, ubiquitin conjugation by E2 enzymes, and substrate-specific ubiquitination mediated by E3 ubiquitin ligases, ultimately directing proteins toward degradation by the 26S proteasome. Among these components, E3 ubiquitin ligases provide substrate specificity and therefore represent attractive therapeutic targets. Two muscle-specific E3 ubiquitin ligases, Muscle RING Finger-1 (MuRF1/TRIM63) and Atrogin-1/FBXO32, are consistently upregulated in experimental and clinical models of cachexia and are considered central mediators of skeletal muscle atrophy (Bodine et al., 2001; Gomes et al., 2001). MuRF1 primarily targets sarcomeric and contractile proteins including myosin heavy chain, whereas Atrogin-1 regulates proteins involved in muscle growth, differentiation, and protein synthesis. Persistent activation of these ligases results in excessive proteolysis, impaired muscle regeneration, and progressive muscle wasting.
Current therapeutic approaches for cachexia mainly focus on nutritional supplementation, exercise, appetite stimulants, and anti-inflammatory interventions; however, these strategies often provide only modest clinical benefit (Baracos et al., 2018). Consequently, there is growing interest in directly targeting the molecular pathways responsible for muscle protein degradation. Inhibition of muscle-specific E3 ligases represents a promising strategy to preserve skeletal muscle integrity and improve outcomes in cachectic patients.
Recent advances in computational biology, molecular modeling, and artificial intelligence-based drug discovery have accelerated the identification of candidate therapeutics against disease-associated targets (Jumper et al., 2021). Computational approaches enable rapid screening of compounds, prediction of protein–ligand interactions, and evaluation of drug-like properties prior to experimental validation. In the present study, we performed in silico screening of multiple E3 ubiquitin ligase inhibitors, including compounds currently in clinical use as well as investigational agents undergoing preclinical evaluation. Protein sequence and structural information were obtained through UniProt, which provides curated protein annotations, domain information, and structural datasets relevant for molecular modeling studies (UniProt Consortium, 2023). Ligand preparation and structural optimization were carried out using Open Babel, enabling conversion and refinement of molecular structures for downstream docking analyses (O’Boyle et al., 2011).
Docking studies were performed using DiffDock, an artificial intelligence–based platform that predicts protein–ligand binding poses and interaction confidence through diffusion generative modeling approaches (Corso et al., 2023). DiffDock enables flexible docking and provides binding confidence estimates that facilitate prioritization of candidate compounds. Additional analyses were conducted using SwissDock to evaluate docking conformations, binding interactions, drug likeness, and pharmacokinetic properties including absorption, distribution, metabolism, and excretion (ADME) characteristics (Grosdidier et al., 2011). Together, these integrated computational approaches provided a robust framework for identifying candidate inhibitors targeting muscle-associated E3 ligases (Figure 1A–C).
Among the compounds screened thus far, EM12-So2F and thalidomide were prioritized for further investigation based on an integrated assessment of multiple computational parameters rather than predicted binding affinity alone (Figure 1A–G). Initial DiffDock analysis evaluated predicted binding poses, confidence rankings, and smina affinity scores for the screened compounds (Figure 1A; Table 1). Thalidomide showed the most favorable smina affinity scores for both Atrogin-1 and MuRF1, whereas EM12-So2F showed favorable predicted binding, particularly toward MuRF1, but was not the highest-affinity compound for Atrogin-1. Therefore, its prioritization was based on the combined assessment of docking characteristics and subsequent drug-likeness and ADME analyses. SwissADME profiling showed favorable physicochemical and pharmacokinetic characteristics for EM12-So2F and thalidomide (Figure 1B–C), further supporting their selection. Examination of the predicted binding poses showed that both compounds could be accommodated within predicted binding regions of MuRF1 and Atrogin-1 (Figure 1D–G), with interaction analysis identifying hydrogen bonds, van der Waals contacts, and hydrophobic interactions with residues surrounding the ligands. Together, these complementary analyses supported prioritization of EM12-So2F and thalidomide for subsequent experimental evaluation. Interestingly, both compounds are established cereblon (CRBN) binders; however, the present study does not establish a mechanistic role for CRBN or PROTAC-mediated activity in their predicted interactions with MuRF1 or Atrogin-1.
Together, these findings demonstrate the utility of integrating structure-based docking, binding affinity prediction, interaction analysis, and drug-likeness/ADME profiling to prioritize candidate compounds targeting muscle-specific E3 ubiquitin ligases. EM12-So2F and thalidomide represent promising candidates identified from the compounds screened thus far, and additional small molecules will be screened using the same computational workflow to expand the pool of potential candidates. Lead compounds emerging from this analysis will undergo target-validation studies to confirm their interaction with and specificity toward MuRF1 and Atrogin-1 before advancing to functional evaluation. Selected lead compounds will then be tested in C2C12 myotube-based models of muscle atrophy to determine whether target modulation translates into preservation of the muscle phenotype. These studies will provide experimental validation of the computational findings and establish whether direct targeting of MuRF1 and Atrogin-1 represents a viable therapeutic strategy for cancer-associated muscle wasting.
","references":[{"reference":"Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. 2014. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer 14(11): 754-62.
","pubmedId":"25291291","doi":""},{"reference":"Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. 2018. Cancer-associated cachexia. Nat Rev Dis Primers 4: 17105.
","pubmedId":"29345251","doi":""},{"reference":"Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, Clarke BA, et al., Glass DJ. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294(5547): 1704-8.
","pubmedId":"11679633","doi":""},{"reference":"Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2023;2210.01776.
","pubmedId":"","doi":""},{"reference":"Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al., Baracos VE. 2011. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12(5): 489-95.
","pubmedId":"21296615","doi":""},{"reference":"Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL, New Collective Author. 2001. Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98(25): 14440-5.
","pubmedId":"11717410","doi":""},{"reference":"Grosdidier A, Zoete V, Michielin O. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39(Web Server issue): W270-7.
","pubmedId":"21624888","doi":""},{"reference":"Ito T, Ando H, Suzuki T, Ogura T, Hotta K, Imamura Y, Yamaguchi Y, Handa H. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327(5971): 1345-50.
","pubmedId":"20223979","doi":""},{"reference":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873): 583-589.
","pubmedId":"34265844","doi":""},{"reference":"O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. 2011. Open Babel: An open chemical toolbox. J Cheminform 3: 33.
","pubmedId":"21982300","doi":""},{"reference":"UniProt Consortium. 2023. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1): D523-D531.
","pubmedId":"36408920","doi":""},{"reference":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. 2000. The Protein Data Bank. Nucleic Acids Res 28(1): 235-42.
","pubmedId":"10592235","doi":""},{"reference":"Franke B, Gasch A, Rodriguez D, Chami M, Khan MM, Rudolf R, et al., Mayans O. 2014. Molecular basis for the fold organization and sarcomeric targeting of the muscle atrogin MuRF1. Open Biol 4(3): 130172.
","pubmedId":"24671946","doi":""}],"title":"Targeting Muscle-Specific E3 Ligases in Cancer Cachexia Through Structure-Based Virtual Screening of Small-Molecule Inhibitors\n\n\n\n","reviews":[],"curatorReviews":[]},{"id":"4bfb3f7d-4833-4f9d-a4d6-7c06be4fe755","decision":"publish","abstract":"Cachexia is a multifactorial metabolic syndrome characterized by severe skeletal muscle loss that cannot be reversed by nutritional support. Common in cancer and other chronic diseases, it significantly increases morbidity and mortality. Muscle wasting is primarily driven by activation of the ubiquitin–proteasome pathway through the E3 ubiquitin ligases MuRF1 and Atrogin-1, which promote protein degradation and muscle atrophy. This study used computational modeling and docking to identify small-molecule inhibitors targeting these ligases, including repurposed drugs with anti-cachectic potential. Thalidomide and EM12-So2F were prioritized based on integrated docking, binding affinity, and drug-likeness/ADME analyses for validation in C2C12 myotube-based cachexia models.","acknowledgements":"After writing the manuscript, ChatGPT5.1 was used to check for grammatical errors and to improve the flow of the text. AI was not used in idea generation or content creation. It was only used for fixing grammar and flow.
","authors":[{"affiliations":["Lawrence Technological University, Southfield, MI USA"],"departments":["Department of Natural Sciences"],"credit":["conceptualization","supervision","resources","writing_originalDraft"],"email":"imuqbil@ltu.edu","firstName":"Irfana","lastName":"Muqbil","submittingAuthor":true,"correspondingAuthor":true,"equalContribution":false,"WBId":null,"orcid":"0000-0001-6889-7035"},{"affiliations":["Lawrence Technological University, Southfield, MI USA"],"departments":["Department of Biomedical Engineering"],"credit":["dataCuration","formalAnalysis","methodology","visualization"],"email":"jjohns14@ltu.edu","firstName":"Jordan D","lastName":"Johnson","submittingAuthor":false,"correspondingAuthor":false,"equalContribution":false,"WBId":null,"orcid":null}],"awards":[],"conflictsOfInterest":"The authors declare that there are no conflicts of interest present.
","dataTable":{"url":"https://portal.micropublication.org/uploads/af6f2f339f63fdb5751564369ec0670f.csv"},"extendedData":[{"description":"Extended Data Figure 1. Two-dimensional interaction maps of prioritized compounds in the predicted binding regions of MuRF1 and Atrogin-1. (A–B) Predicted interactions of EM12-So2F with MuRF1 and Atrogin-1, respectively. (C–D) Predicted interactions of thalidomide with MuRF1 and Atrogin-1, respectively. The maps show interacting residues and interaction types: conventional hydrogen bonds (green dashed lines), van der Waals contacts (light green), π–sigma interactions (purple), and π–alkyl interactions (pink). These maps provide structural context for the predicted binding poses in Figure 1D–G.
","doi":"10.22002/v1njh-67936","resourceType":"Image","name":"Extended data Figure 1.jpg","url":"https://portal.micropublication.org/uploads/699ba9b556d65dd5e5148cf16dc0420a.jpg"}],"funding":"none
","image":{"url":"https://portal.micropublication.org/uploads/46747d35328351f2c1840a0050d3daf0.jpg"},"imageCaption":"(A) DiffDock analysis showing the distribution of smina affinity scores across predicted binding poses for compounds screened against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). EM12-So2F and thalidomide are highlighted as compounds prioritized for further analysis based on integrated consideration of docking pose/confidence, predicted affinity, protein–ligand interactions, and predicted drug-likeness/ADME properties; highlighting does not indicate selection based solely on the most favorable affinity score. (B–C) SwissADME drug-likeness radar plots for EM12-So2F (B) and thalidomide (C). (D–E) Predicted binding poses of EM12-So2F with MuRF1 and Atrogin-1, respectively. (F–G) Predicted binding poses of thalidomide with MuRF1 and Atrogin-1, respectively. Detailed two-dimensional maps of predicted protein–ligand interactions are provided in Extended Data Figure 1.
Table 1. Smina binding affinity scores (kcal/mol) associated with the highest-confidence DiffDock poses of screened compounds against Atrogin-1 (FBXO32) and MuRF1 (TRIM63). More negative values indicate more favorable predicted binding affinity.
","imageTitle":"Computational screening and prioritization of EM12-So2F and thalidomide as candidate ligands of the muscle-specific E3 ubiquitin ligases Atrogin-1 and MuRF1
","methods":"Protein and Ligand Preparation
Protein sequences and structural information for the MuRF1 (TRIM63) coiled-coil domain were obtained from the Protein Data Bank (PDB ID: 4M3L) (Berman et al., 2000; Franke et al., 2014), while sequence and structural information for Atrogin-1 (FBXO32; UniProt accession Q969P5) were retrieved from UniProt, and corresponding structural models were utilized for molecular docking analyses. Candidate E3 ligase inhibitors were selected based on published literature and structural relevance to ubiquitin–proteasome pathway modulation. Ligand structures were retrieved in SMILES or SDF format from chemical databases and converted into optimized three-dimensional conformations using Open Babel for molecular modeling and docking analyses. Protein structures were prepared by removing water molecules and non-essential ligands, followed by optimization of structural geometry prior to docking simulations.
DiffDock Molecular Docking Analysis
Protein–ligand docking studies were performed using DiffDock, a generative artificial intelligence–based docking platform that utilizes diffusion modeling to predict ligand-binding poses and protein–ligand interactions. Similar to previously described DiffDock workflows, multiple ligand conformations and docking poses were generated to evaluate translational, rotational, and torsional flexibility during docking inference. Docking outputs included predicted binding poses, confidence score rankings, and smina affinity scores, allowing assessment of both the confidence of the predicted ligand pose and its predicted binding affinity. More negative smina affinity scores were interpreted as more favorable predicted binding affinities. Figure 1A shows the distribution of smina affinity scores across the generated docking poses, whereas Table 1 summarizes the smina affinity score associated with the highest-confidence DiffDock pose for each compound against Atrogin-1 and MuRF1.
SwissDock Validation and ADME Profiling
Additional docking analyses and interaction visualization were carried out using SwissDock to further evaluate ligand-binding conformations and intermolecular interactions within predicted binding pockets. Predicted protein–ligand interactions, including hydrogen bonds, van der Waals contacts, and hydrophobic interactions, were examined to characterize the binding environment of prioritized compounds. Drug-likeness, molecular properties, and absorption, distribution, metabolism, and excretion (ADME) characteristics were further assessed to prioritize compounds with favorable pharmacokinetic profiles and therapeutic potential.
Candidate Prioritization
Candidate prioritization was based on an integrated assessment of DiffDock confidence ranking, smina-predicted binding affinity, predicted binding poses and protein–ligand interactions, and drug-likeness/ADME properties rather than binding affinity alone. Among the compounds screened thus far, thalidomide and EM12-So2F were prioritized for further investigation based on their combined computational profiles. Thalidomide exhibited the most favorable smina affinity scores toward both Atrogin-1 and MuRF1, whereas EM12-So2F was retained based on the integrated assessment of its docking and predicted pharmacological characteristics despite not exhibiting the most favorable affinity score for Atrogin-1.
","reagents":"","patternDescription":"Cachexia or muscle wasting is associated with poor quality of life and is one of the leading causes of morbidity in patients with advanced cancer. Therefore, the identification of novel therapeutic approaches to target cachexia is of critical importance in the management of advanced malignancies. Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive skeletal muscle wasting, with or without adipose tissue loss, that cannot be fully reversed by nutritional supplementation alone (Fearon et al., 2011). It is frequently observed in patients with pancreatic, lung, gastrointestinal, and other advanced malignancies, where it contributes significantly to weakness, fatigue, reduced mobility, poor tolerance to anticancer therapies, and decreased overall survival (Argilés et al., 2014). Despite its major clinical impact, effective therapies for cancer-associated cachexia remain limited.
One of the major molecular mechanisms underlying cachexia is the activation of the ubiquitin–proteasome system (UPS), the primary intracellular pathway responsible for regulated protein degradation. In skeletal muscle, the UPS contributes to accelerated breakdown of structural and contractile proteins during catabolic conditions (Bodine et al., 2001; Gomes et al., 2001). Protein degradation through the UPS involves sequential ubiquitin activation by E1 enzymes, ubiquitin conjugation by E2 enzymes, and substrate-specific ubiquitination mediated by E3 ubiquitin ligases, ultimately directing proteins toward degradation by the 26S proteasome. Among these components, E3 ubiquitin ligases provide substrate specificity and therefore represent attractive therapeutic targets. Two muscle-specific E3 ubiquitin ligases, Muscle RING Finger-1 (MuRF1/TRIM63) and Atrogin-1/FBXO32, are consistently upregulated in experimental and clinical models of cachexia and are considered central mediators of skeletal muscle atrophy (Bodine et al., 2001; Gomes et al., 2001). MuRF1 primarily targets sarcomeric and contractile proteins including myosin heavy chain, whereas Atrogin-1 regulates proteins involved in muscle growth, differentiation, and protein synthesis. Persistent activation of these ligases results in excessive proteolysis, impaired muscle regeneration, and progressive muscle wasting.
Current therapeutic approaches for cachexia mainly focus on nutritional supplementation, exercise, appetite stimulants, and anti-inflammatory interventions; however, these strategies often provide only modest clinical benefit (Baracos et al., 2018). Consequently, there is growing interest in directly targeting the molecular pathways responsible for muscle protein degradation. Inhibition of muscle-specific E3 ligases represents a promising strategy to preserve skeletal muscle integrity and improve outcomes in cachectic patients.
Recent advances in computational biology, molecular modeling, and artificial intelligence-based drug discovery have accelerated the identification of candidate therapeutics against disease-associated targets (Jumper et al., 2021). Computational approaches enable rapid screening of compounds, prediction of protein–ligand interactions, and evaluation of drug-like properties prior to experimental validation. In the present study, we performed in silico screening of multiple E3 ubiquitin ligase inhibitors, including compounds currently in clinical use as well as investigational agents undergoing preclinical evaluation. Protein sequence and structural information were obtained through UniProt, which provides curated protein annotations, domain information, and structural datasets relevant for molecular modeling studies (UniProt Consortium, 2023). Ligand preparation and structural optimization were carried out using Open Babel, enabling conversion and refinement of molecular structures for downstream docking analyses (O’Boyle et al., 2011).
Docking studies were performed using DiffDock, an artificial intelligence–based platform that predicts protein–ligand binding poses and interaction confidence through diffusion generative modeling approaches (Corso et al., 2023). DiffDock enables flexible docking and provides binding confidence estimates that facilitate prioritization of candidate compounds. Additional analyses were conducted using SwissDock to evaluate docking conformations, binding interactions, drug likeness, and pharmacokinetic properties including absorption, distribution, metabolism, and excretion (ADME) characteristics (Grosdidier et al., 2011). Together, these integrated computational approaches provided a robust framework for identifying candidate inhibitors targeting muscle-associated E3 ligases (Figure 1A–C).
Among the compounds screened thus far, EM12-So2F and thalidomide were prioritized for further investigation based on an integrated assessment of multiple computational parameters rather than predicted binding affinity alone (Figure 1A–G). Initial DiffDock analysis evaluated predicted binding poses, confidence rankings, and smina affinity scores for the screened compounds (Figure 1A; Table 1). Thalidomide showed the most favorable smina affinity scores for both Atrogin-1 and MuRF1, whereas EM12-So2F showed favorable predicted binding, particularly toward MuRF1, but was not the highest-affinity compound for Atrogin-1. Therefore, its prioritization was based on the combined assessment of docking characteristics and subsequent drug-likeness and ADME analyses. SwissADME profiling showed favorable physicochemical and pharmacokinetic characteristics for EM12-So2F and thalidomide (Figure 1B–C), further supporting their selection. Examination of the predicted binding poses showed that both compounds could be accommodated within predicted binding regions of MuRF1 and Atrogin-1 (Figure 1D–G), with interaction analysis identifying hydrogen bonds, van der Waals contacts, and hydrophobic interactions with residues surrounding the ligands. Together, these complementary analyses supported prioritization of EM12-So2F and thalidomide for subsequent experimental evaluation. Interestingly, both compounds are established cereblon (CRBN) binders; however, the present study does not establish a mechanistic role for CRBN or PROTAC-mediated activity in their predicted interactions with MuRF1 or Atrogin-1.
Together, these findings demonstrate the utility of integrating structure-based docking, binding affinity prediction, interaction analysis, and drug-likeness/ADME profiling to prioritize candidate compounds targeting muscle-specific E3 ubiquitin ligases. EM12-So2F and thalidomide represent promising candidates identified from the compounds screened thus far, and additional small molecules will be screened using the same computational workflow to expand the pool of potential candidates. Lead compounds emerging from this analysis will undergo target-validation studies to confirm their interaction with and specificity toward MuRF1 and Atrogin-1 before advancing to functional evaluation. Selected lead compounds will then be tested in C2C12 myotube-based models of muscle atrophy to determine whether target modulation translates into preservation of the muscle phenotype. These studies will provide experimental validation of the computational findings and establish whether direct targeting of MuRF1 and Atrogin-1 represents a viable therapeutic strategy for cancer-associated muscle wasting.
","references":[{"reference":"Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. 2014. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer 14(11): 754-62.
","pubmedId":"25291291","doi":""},{"reference":"Baracos VE, Martin L, Korc M, Guttridge DC, Fearon KCH. 2018. Cancer-associated cachexia. Nat Rev Dis Primers 4: 17105.
","pubmedId":"29345251","doi":""},{"reference":"Bodine SC, Latres E, Baumhueter S, Lai VK, Nunez L, Clarke BA, et al., Glass DJ. 2001. Identification of ubiquitin ligases required for skeletal muscle atrophy. Science 294(5547): 1704-8.
","pubmedId":"11679633","doi":""},{"reference":"Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv. 2023;2210.01776.
","pubmedId":"","doi":""},{"reference":"Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al., Baracos VE. 2011. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 12(5): 489-95.
","pubmedId":"21296615","doi":""},{"reference":"Gomes MD, Lecker SH, Jagoe RT, Navon A, Goldberg AL, New Collective Author. 2001. Atrogin-1, a muscle-specific F-box protein highly expressed during muscle atrophy. Proc Natl Acad Sci U S A 98(25): 14440-5.
","pubmedId":"11717410","doi":""},{"reference":"Grosdidier A, Zoete V, Michielin O. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39(Web Server issue): W270-7.
","pubmedId":"21624888","doi":""},{"reference":"Ito T, Ando H, Suzuki T, Ogura T, Hotta K, Imamura Y, Yamaguchi Y, Handa H. 2010. Identification of a primary target of thalidomide teratogenicity. Science 327(5971): 1345-50.
","pubmedId":"20223979","doi":""},{"reference":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al., Hassabis D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873): 583-589.
","pubmedId":"34265844","doi":""},{"reference":"O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. 2011. Open Babel: An open chemical toolbox. J Cheminform 3: 33.
","pubmedId":"21982300","doi":""},{"reference":"UniProt Consortium. 2023. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1): D523-D531.
","pubmedId":"36408920","doi":""},{"reference":"Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. 2000. The Protein Data Bank. Nucleic Acids Res 28(1): 235-42.
","pubmedId":"10592235","doi":""},{"reference":"Franke B, Gasch A, Rodriguez D, Chami M, Khan MM, Rudolf R, et al., Mayans O. 2014. Molecular basis for the fold organization and sarcomeric targeting of the muscle atrogin MuRF1. Open Biol 4(3): 130172.
","pubmedId":"24671946","doi":""}],"title":"Targeting Muscle-Specific E3 Ligases in Cancer Cachexia Through Structure-Based Virtual Screening of Small-Molecule Inhibitors","reviews":[],"curatorReviews":[]}]}},"species":{"species":[{"value":"acer saccharum","label":"Acer saccharum","imageSrc":"","imageAlt":"","mod":"TreeGenes","modLink":"https://treegenesdb.org","linkVariable":""},{"value":"achillea millefolium","label":"Achillea millefolium","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"acinetobacter baylyi","label":"Acinetobacter baylyi","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"actinobacteria bacterium","label":"Actinobacteria bacterium","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"adelges tsugae","label":"Adelges tsugae","imageSrc":"","imageAlt":"","mod":"","modLink":"","linkVariable":""},{"value":"adenocaulon chilense","label":"Adenocaulon 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