ArticleProteins2014
PAIRpred: partner-specific prediction of interacting residues from sequence and structure.
Article in Proteins, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 130 citations in OpenAlex.
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- HVIface: sequence-based deep learning for decoding human-virus protein-protein interfaces.Frontiers in bioinformatics · 2026Article
- A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.Nature biotechnology · 2025Article
- Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures.International journal of molecular sciences · 2025Article
- Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.Bioinformatics (Oxford, England) · 2024Article
- EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information.NPJ systems biology and applications · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.bioRxiv : the preprint server for biology · 2024Article
- Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.BMC bioinformatics · 2023Article
- DeepDrug: A general graph-based deep learning framework for drug-drug interactions and drug-target interactions prediction.Quantitative biology (Beijing, China) · 2023Article
- DIPS-Plus: The enhanced database of interacting protein structures for interface prediction.Scientific data · 2023Article
- E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.PLoS computational biology · 2023Article
- Machine learning approaches to predict drug efficacy and toxicity in oncology.Cell reports methods · 2023Review
- MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations.Biomolecules · 2023Article
- Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions.Frontiers in oncology · 2023Review
- Prediction of the tetramer protein complex interaction based on CNN and SVM.Frontiers in genetics · 2023Article
- Protein-Protein Docking: Past, Present, and Future.The protein journal · 2022Review
- Residue-Residue Interaction Prediction via Stacked Meta-Learning.International journal of molecular sciences · 2021Article
- Simplified geometric representations of protein structures identify complementary interaction interfaces.Proteins · 2021Article
- A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers.Molecules (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/.
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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.