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Predicting peptide-mediated interactions on a genome-wide scale
- Source :
- PLoS Computational Biology, Vol 11, Iss 5, p e1004248 (2015), PLoS Computational Biology
- Publication Year :
- 2015
- Publisher :
- Public Library of Science (PLoS), 2015.
-
Abstract
- We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome.<br />Author Summary Complexes formed between a structured domain on one protein and an unstructured peptide on another are ubiquitous. However, they are often quite difficult to detect experimentally. The development of computational approaches to predict domain-motif interactions is therefore an important goal. We report a method to predict domain-motif interactions using a Bayesian approach to integrate evidence from a variety of sources, including three-dimensional structural and non-structural information. The method was applied to the entire human proteome and showed significant improvement over existing methods. The method was incorporated into PrePPI, a computational pipeline for the prediction of protein-protein interactions that relies heavily on structural information. Approximately 80,000 new interactions were detected. The new PrePPI database provides easy access to about 400,000 human protein-protein interactions and should thus constitute a valuable resource in a variety of biological applications including the characterization of molecular interaction networks and, more generally, in the study of interactions mediated by proteins in families that may not be extensively studied experimentally.
- Subjects :
- Proteomics
Support Vector Machine
Bayesian probability
Protein domain
Computational biology
Biology
computer.software_genre
Models, Biological
Interactome
Protein–protein interaction
03 medical and health sciences
Cellular and Molecular Neuroscience
Bayes' theorem
Naive Bayes classifier
Protein structure
Protein Interaction Mapping
Genetics
Humans
Protein Interaction Domains and Motifs
Databases, Protein
Molecular Biology
lcsh:QH301-705.5
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Likelihood Functions
0303 health sciences
Ecology
Genome, Human
030302 biochemistry & molecular biology
Computational Biology
Bayes Theorem
Computational Theory and Mathematics
lcsh:Biology (General)
Modeling and Simulation
Data mining
computer
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 11
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....723c375a6ff1bbebd7482c3db6de70c5