ArticleJournal of chemical information and modeling2023
Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- PpF: a density functional fine-tuned for noncovalent interactions of protein and peptide residues.Chemical science · 2026Article
- Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights.Briefings in bioinformatics · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions.Acta pharmacologica Sinica · 2026Article
- Topological deep learning for enhancing peptide-protein complex prediction.Communications chemistry · 2025Article
- DiffPepDock: Efficient protein-peptide docking and binder screening via SE(3)-equivariant diffusion.Protein science : a publication of the Protein Society · 2025Article
- Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions.Nature communications · 2025Article
- Protein-Peptide Docking with ESMFold Language Model.Journal of chemical theory and computation · 2025Article
- Neutrophil elastase binds at the central domain of extracellular Toll-like receptor 4: AI prediction, docking, and validation in disease model.Scientific reports · 2025Article
- Leveraging coevolutionary insights and AI-based structural modeling to unravel receptor-peptide ligand-binding mechanisms.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Insight into the Binding Interaction between PEDCs and hERRγ Utilizing Molecular Docking and Molecular Dynamics Simulations.Molecules (Basel, Switzerland) · 2024Article
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
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- From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2.Nature communications · 2024Article
- Current Computational Methods for Protein-peptide Complex Structure Prediction.Current medicinal chemistry · 2024Review
- Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era.Wiley interdisciplinary reviews. Computational molecular scienceArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.