ArticleNucleic acids research2006
Protein binding site prediction using an empirical scoring function.
Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.
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Who cites it
110 citing papers in PubMed.
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- Computational prediction of multiple antigen epitopes.Bioinformatics (Oxford, England) · 2024Article
- Leveraging machine learning models for peptide-protein interaction prediction.RSC chemical biology · 2024Review
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- ANN based prediction of ligand binding sites outside deep cavities to facilitate drug designing.Current research in structural biology · 2024Article
- E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.PLoS computational biology · 2023Article
- Two point mutations in protocadherin-1 disrupt hantavirus recognition and afford protection against lethal infection.Nature communications · 2023Article
- Interplay between C1-inhibitor and group IIA secreted phospholipase AImmunologic research · 2023Article
- B-Cell Epitope Predictions Using Computational Methods.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction.Biology · 2022Article
- Integrated structure-based protein interface prediction.BMC bioinformatics · 2022Article
- Article
- Dissecting and predicting different types of binding sites in nucleic acids based on structural information.Briefings in bioinformatics · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Mechanisms of allosteric and mixed mode aromatase inhibitors.RSC chemical biology · 2021Article
- DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.International journal of molecular sciences · 2021Article
- CATH functional families predict functional sites in proteins.Bioinformatics (Oxford, England) · 2021Article
- A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.Frontiers in genetics · 2021Article
- Review
- CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information.Biomolecules · 2020Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Most biological processes are mediated by interactions between proteins and their interacting partners including proteins, nucleic acids and small molecules. This work establishes a method called PINUP for binding site prediction of monomeric proteins. With only two weight parameters to optimize, PINUP produces not only 42.2% coverage of actual interfaces (percentage of correctly predicted interface residues in actual interface residues) but also 44.5% accuracy in predicted interfaces (percentage of correctly predicted interface residues in the predicted interface residues) in a cross validation using a 57-protein dataset. By comparison, the expected accuracy via random prediction (percentage of actual interface residues in surface residues) is only 15%. The binding sites of the 57-protein set are found to be easier to predict than that of an independent test set of 68 proteins. The average coverage and accuracy for this independent test set are 30.5 and 29.4%, respectively. The significant gain of PINUP over expected random prediction is attributed to (i) effective residue-energy score and accessible-surface-area-dependent interface-propensity, (ii) isolation of functional constraints contained in the conservation score from the structural constraints through the combination of residue-energy score (for structural constraints) and conservation score and (iii) a consensus region built on top-ranked initial patches.
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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.