ArticleGenome biology2007
InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale.
Article in Genome biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- PPILS: Protein-protein interaction prediction with language of biological coding.Computers in biology and medicine · 2025Article
- PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions.PLoS computational biology · 2021Article
- BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.Bioinformatics (Oxford, England) · 2019Article
- Pattern to Knowledge: Deep Knowledge-Directed Machine Learning for Residue-Residue Interaction Prediction.Scientific reports · 2018Article
- Inference of domain-disease associations from domain-protein, protein-disease and disease-disease relationships.BMC systems biology · 2016Article
- PAIRpred: partner-specific prediction of interacting residues from sequence and structure.Proteins · 2014Article
- DockRank: ranking docked conformations using partner-specific sequence homology-based protein interface prediction.Proteins · 2014Article
- High-resolution network biology: connecting sequence with function.Nature reviews. Genetics · 2013Review
- Embryonic stem cell interactomics: the beginning of a long road to biological function.Stem cell reviews and reports · 2012Review
- Ranking Docked Models of Protein-Protein Complexes Using Predicted Partner-Specific Protein-Protein Interfaces: A Preliminary Study.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2011Article
- Binding site prediction for protein-protein interactions and novel motif discovery using re-occurring polypeptide sequences.BMC bioinformatics · 2011Article
- DOMINE: a comprehensive collection of known and predicted domain-domain interactions.Nucleic acids research · 2011Article
- Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction.PLoS computational biology · 2010Article
- Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins.Bioinformatics (Oxford, England) · 2010Article
- Struct2Net: a web service to predict protein-protein interactions using a structure-based approach.Nucleic acids research · 2010Article
- Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences.BMC bioinformatics · 2009Article
- Knowledge-guided inference of domain-domain interactions from incomplete protein-protein interaction networks.Bioinformatics (Oxford, England) · 2009Article
- Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels.BMC bioinformatics · 2009Article
- ModLink+: improving fold recognition by using protein-protein interactions.Bioinformatics (Oxford, England) · 2009Article
- A complex-based reconstruction of the Saccharomyces cerevisiae interactome.Molecular & cellular proteomics : MCP · 2009Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose InSite, a computational method that integrates high-throughput protein and sequence data to infer the specific binding regions of interacting protein pairs. We compared our predictions with binding sites in Protein Data Bank and found significantly more binding events occur at sites we predicted. Several regions containing disease-causing mutations or cancer polymorphisms in human are predicted to be binding for protein pairs related to the disease, which suggests novel mechanistic hypotheses for several diseases.
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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.