Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners

By Benjamin A. Shoemaker, Anna R. Panchenko. PLoS Comput. Biol., 2007

364 citations recorded by the source corpus.

Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e7fc757ddc8e8edb3e5123e1579dc65ddaa4ab1a

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Citations in the corpus

Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases (2007, background): “The low coverage together with the small overlap between different experimental methods calls for the development of theoretical approaches for interaction data verification and prediction, the topic we address in our companion review [33].”
Global Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins (2009, background): “performed large-scale proteomic analysis to determine orphan participation as components of multimeric protein complexes, and inferred functional relationships based on genomic context inference, which exploits the patterns of gene conservation across bacterial genomes [31].”
iRefIndex: A consolidated protein interaction database with provenance (2008, methodology): “These data are generated by a multitude of methods including both high-throughput and more traditional low-throughput proteomics studies [1] as well as in silico predictions based on known interactions [2].”
Structure-based prediction of protein-protein interactions on a genome-wide scale (2012, methodology): “To assess quantitatively the performance of structural modelling (SM), we compared it with a number of non-structural clues previously used to infer PPIs: (1) essentiality of the proteins in the interacting pair; (2) co-expression level; (3) gene ontology (GO) functional similarity; (4) Munich Infor”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, background): “The more general problem of predicting if two proteins interact with each other has been studied extensively using a wide variety of approaches ( de Juan et al., 2013 ; Hosur et al., 2012; Zhang et al., 2012 ; Shoemaker and Panchenko, 2007 , Valencia and Pazos, 2002 , Ochoa and Pazos, 2010 ).”
Predicting protein function from sequence and structure (2007, methodology): “Various computational approaches exist for predicting protein interactions using protein sequence”
Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins (2010, methodology): “We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins.”
Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method (2008, methodology): “(1) Here n is the total number receivers of class c that have an amino acid α in column i of the alignment (gaps are treated as a 21st amino acid) and λ is the pseudo-count resulting from the Dirichlet prior (we used the Jeﬀreys’ prior λ = 1 / 2). w is thus the estimated probability of seeing amino…”