Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions

Nathalie Lagarde, View ORCID ProfileAlessandra Carbone, View ORCID ProfileSophie Sacquin-Mora
doi: https://doi.org/10.1101/244913
Nathalie Lagarde
Molecules Therapeutiques in silico, Universite Paris Diderot - Inserm UMR-S 973;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alessandra Carbone
Universite Pierre et Marie Curie;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Alessandra Carbone
Sophie Sacquin-Mora
Laboratoire de Biochimie Theorique, CNRS UPR9080
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Sophie Sacquin-Mora
  • For correspondence: sacquin@ibpc.fr
  • Abstract
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Protein-protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein-protein interactions. Cross-docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross-docking simulations of 358 proteins with two different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting area under the specificity-sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross-docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, i.e. partners not included in the original cross-docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted January 08, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions
Nathalie Lagarde, Alessandra Carbone, Sophie Sacquin-Mora
bioRxiv 244913; doi: https://doi.org/10.1101/244913
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions
Nathalie Lagarde, Alessandra Carbone, Sophie Sacquin-Mora
bioRxiv 244913; doi: https://doi.org/10.1101/244913

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (1003)
  • Biochemistry (1502)
  • Bioengineering (954)
  • Bioinformatics (6860)
  • Biophysics (2451)
  • Cancer Biology (1808)
  • Cell Biology (2547)
  • Clinical Trials (108)
  • Developmental Biology (1710)
  • Ecology (2589)
  • Epidemiology (1508)
  • Evolutionary Biology (5048)
  • Genetics (3632)
  • Genomics (4651)
  • Immunology (1186)
  • Microbiology (4275)
  • Molecular Biology (1637)
  • Neuroscience (10865)
  • Paleontology (83)
  • Pathology (243)
  • Pharmacology and Toxicology (411)
  • Physiology (560)
  • Plant Biology (1469)
  • Scientific Communication and Education (414)
  • Synthetic Biology (546)
  • Systems Biology (1891)
  • Zoology (261)