Prediction of Protein-Protein Interactions by Evidence Combining Methods.
Journal:
International journal of molecular sciences
Published Date:
Nov 22, 2016
Abstract
Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.
Authors
Keywords
Animals
Arabidopsis
Computational Biology
Computer Simulation
Data Mining
Datasets as Topic
Drosophila melanogaster
Escherichia coli
Gene Ontology
High-Throughput Nucleotide Sequencing
Humans
Mice
Models, Molecular
Molecular Sequence Annotation
Protein Interaction Domains and Motifs
Protein Interaction Mapping
Proteins
Saccharomyces cerevisiae
Support Vector Machine