Prediction of Protein-protein Contacts with Structure-aware Single-sequence Protein Language Models
Keywords: Protein bioinformatics, Protein language models, Protein-protein contact prediction, Protein representations, Deep neural networks
Abstract: Accurate prediction of the interface residue-residue contacts between interacting proteins is valuable for determining the structure and function of protein complexes. Recent deep learning methods have drastically improved the accuracy of predicting the interface contacts of protein complexes. However, existing methods rely on Multiple Sequence Alignments (MSA) features which pose limitations on prediction accuracy, speed, and computational efficiency. Here, we propose a transformer-powered deep learning method to predict the inter-protein residue-residue contacts based on both single-sequence and structure-aware protein language models (PLM), called DeepSSInter. Utilizing the intra-protein distance and graph representations and the ESM2 and SaProt protein language models, we are able to generate the structure-aware features for the protein receptor, ligand, and complex. These structure-aware features are passed into the Resnet Inception module and the Triangle-aware module to effectively produce the predicted inter-protein contact map. Extensive experiments on both homo- and hetero-dimeric complexes show that our DeepSSInter model significantly improves the performance compared to previous state-of-the-art methods.
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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 10812
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