Prediction of Protein-Protein Interactions by Evidence Combining Methods

• Published in: International Journal of Molecular Sciences Vol. 17; no. 11; p. 1946
• Main Authors: Chang, Ji-Wei, Zhou, Yan-Qing, Ul Qamar, Muhammad Tahir, Chen, Ling-Ling, Ding, Yu-Duan
• Format: Journal Article Book Review
• Language: English
• Published: Switzerland MDPI AG 22.11.2016; MDPI
• Subjects: Animals; Arabidopsis - metabolism; Computational Biology - methods; Computer Simulation; Data Mining - statistics & numerical data; Datasets as Topic; Drosophila melanogaster - metabolism; Escherichia coli - metabolism; Gene Ontology; High-Throughput Nucleotide Sequencing; Humans; interaction prediction; Mice; Models, Molecular; Molecular Sequence Annotation; Network topologies; physical interactions; PPIs; Protein Interaction Domains and Motifs; Protein Interaction Mapping - methods; Protein Interaction Mapping - statistics & numerical data; Proteins; Proteins - chemistry; Proteins - metabolism; Review; Saccharomyces cerevisiae - metabolism; Support Vector Machine; Vectors (Biology); support vector machine; interaction prediction; physical interactions; PPIs
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms17111946

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.