Accurate PROTAC-targeted degradation prediction with DegradeMaster

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dc.contributor.authorLiu, J.
dc.contributor.authorRoy, M.J.
dc.contributor.authorIsbel, L.
dc.contributor.authorLi, F.
dc.date.issued2025
dc.description.abstractMotivation Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules that can degrade “undruggable” protein of interest by recruiting E3 ligases and hijacking the ubiquitin-proteasome system. Some efforts have been made to develop deep learning-based approaches to predict the degradation ability of a given PROTAC. However, existing deep learning methods either simplify proteins and PROTACs as 2D graphs by disregarding crucial 3D spatial information or exclusively rely on limited labels for supervised learning without considering the abundant information from unlabeled data. Nevertheless, considering the potential to accelerate drug discovery, it is critical to develop more accurate computational methods for PROTAC-targeted protein degradation prediction. Results This study proposes DegradeMaster, a semisupervised E(3)-equivariant graph neural network-based predictor for targeted degradation prediction of PROTACs. DegradeMaster leverages an E(3)-equivariant graph encoder to incorporate 3D geometric constraints into the molecular representations and utilizes a memory-based pseudolabeling strategy to enrich annotated data during training. A mutual attention pooling module is also designed for interpretable graph representation. Experiments on both supervised and semisupervised PROTAC datasets demonstrate that DegradeMaster outperforms state-of-the-art baselines, with substantial improvement of AUROC by 10.5%. Case studies show DegradeMaster achieves 88.33% and 77.78% accuracy in predicting the degradability of VZ185 candidates on BRD9 and ACBI3 on KRAS mutants. Visualization of attention weights on 3D molecule graph demonstrates that DegradeMaster recognizes linking and binding regions of warhead and E3 ligands and emphasizes the importance of structural information in these areas for degradation prediction. Together, this shows the potential for cutting-edge tools to highlight functional PROTAC components, thereby accelerating novel compound generation.
dc.description.statementofresponsibilityJie Liu, Michael J Roy, Luke Isbel, Fuyi Li
dc.identifier.citationBioinformatics, 2025; 41(Supplement_1):i342-i351
dc.identifier.doi10.1093/bioinformatics/btaf191
dc.identifier.issn1367-4803
dc.identifier.issn1367-4811
dc.identifier.orcidLiu, J. [0000-0002-2800-608X]
dc.identifier.orcidRoy, M.J. [0000-0003-0198-9108]
dc.identifier.orcidIsbel, L. [0000-0002-5270-4347]
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/147764
dc.language.isoen
dc.publisherOxford University Press
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/2041439
dc.rights© The Author(s) 2025. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.source.urihttps://doi.org/10.1093/bioinformatics/btaf191
dc.subjectComputational Biology
dc.subjectDeep Learning
dc.subjectDrug Discovery
dc.subjectNeural Networks, Computer
dc.subjectProteins
dc.subjectProteolysis
dc.subjectUbiquitin-Protein Ligases
dc.subjectPROTACs; E(3)-equivariant modeling; degradation prediction
dc.titleAccurate PROTAC-targeted degradation prediction with DegradeMaster
dc.typeJournal article
pubs.publication-statusPublished

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