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InterPred: A pipeline to identify and model protein-protein interactions
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-3772-8279
2017 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 85, no 6, p. 1159-1170Article in journal (Refereed) Published
Abstract [en]

Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modeling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modeling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment. InterPred source code can be downloaded from http://wallnerlab.org/InterPred (C) 2017 Wiley Periodicals, Inc.

Place, publisher, year, edition, pages
WILEY , 2017. Vol. 85, no 6, p. 1159-1170
Keyword [en]
protein structure prediction; protein modeling; machine learning; random forest; docking
National Category
Biophysics
Identifiers
URN: urn:nbn:se:liu:diva-139294DOI: 10.1002/prot.25280ISI: 000403690200013PubMedID: 28263438OAI: oai:DiVA.org:liu-139294DiVA, id: diva2:1120963
Note

Funding Agencies|Swedish Research Council [2012-5270, 2016-05369]; Swedish e-Science Research Center; Foundation Blanceflor Boncompagni Ludovisi, nee Bildt

Available from: 2017-07-07 Created: 2017-07-07 Last updated: 2018-01-26

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