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Predicting protein-peptide interaction sites using distant protein complexes as structural templates
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.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-3772-8279
2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 4267Article in journal (Refereed) Published
Abstract [en]

Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2019. Vol. 9, article id 4267
National Category
Biophysics
Identifiers
URN: urn:nbn:se:liu:diva-155896DOI: 10.1038/s41598-019-38498-7ISI: 000460924100007PubMedID: 30862810OAI: oai:DiVA.org:liu-155896DiVA, id: diva2:1301919
Note

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

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03

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Johansson-Åkhe, IsakMirabello, ClaudioWallner, Björn
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