ArticleBioinformatics (Oxford, England)2020
InterPep2: global peptide-protein docking using interaction surface templates.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.Journal of chemical information and modeling · 2026Article
- A Review of Current Computational Tools for Peptide-Protein Docking.Journal of computational chemistry · 2026Review
- Harnessing deep statistical potential for biophysical scoring of protein-peptide interactions.Acta pharmacologica Sinica · 2026Article
- IntelligentFrontiers in cellular and infection microbiology · 2026Article
- DiffPepDock: Efficient protein-peptide docking and binder screening via SE(3)-equivariant diffusion.Protein science : a publication of the Protein Society · 2025Article
- Systematic Search for Blood-Brain Barrier Modulating Peptides Based on Exhaustive E-Cadherin Domain-Domain Docking.Journal of chemical information and modeling · 2025Article
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
- Protein-Peptide Docking with ESMFold Language Model.Journal of chemical theory and computation · 2025Article
- TPepPro: a deep learning model for predicting peptide-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- PepCA: Unveiling protein-peptide interaction sites with a multi-input neural network model.iScience · 2024Article
- Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives.Amino acids · 2024Review
- Peptriever: a Bi-Encoder approach for large-scale protein-peptide binding search.Bioinformatics (Oxford, England) · 2024Article
- Current Computational Methods for Protein-peptide Complex Structure Prediction.Current medicinal chemistry · 2024Review
- A Random Forest Model for Peptide Classification Based on Virtual Docking Data.International journal of molecular sciences · 2023Article
- InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.Bioinformatics (Oxford, England) · 2022Article
- Matching protein surface structural patches for high-resolution blind peptide docking.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment.Journal of chemical information and modeling · 2022Article
- Harnessing protein folding neural networks for peptide-protein docking.Nature communications · 2022Article
- Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Improving peptide-protein docking with AlphaFold-Multimer using forced sampling.Frontiers in bioinformatics · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationInteractions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability.
resultsInterPep2 is a freely available method for predicting the structure of peptide-protein interactions. Improved performance is obtained by using templates from both peptide-protein and regular protein-protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide-protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide-protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). AVAILABILITY AND IMPLEMENTATION: The program is available from: http://wallnerlab.org/InterPep2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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