1. Structural Biology and Molecular Biophysics
  2. Computational and Systems Biology
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Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis

  1. Etai Jacob
  2. Ron Unger
  3. Amnon Horovitz  Is a corresponding author
  1. Bar-Ilan University, Israel
  2. Weizmann Institute of Science, Israel
Research Article
  • Cited 6
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Cite this article as: eLife 2015;4:e08932 doi: 10.7554/eLife.08932


Methods for analysing correlated mutations in proteins are becoming an increasingly powerful tool for predicting contacts within and between proteins. Nevertheless, limitations remain due to the requirement for large multiple sequence alignments (MSA) and the fact that, in general, only the relatively small number of top-ranking predictions are reliable. To date, methods for analysing correlated mutations have relied exclusively on amino acid MSAs as inputs. Here, we describe a new approach for analysing correlated mutations that is based on combined analysis of amino acid and codon MSAs. We show that a direct contact is more likely to be present when the correlation between the positions is strong at the amino acid level but weak at the codon level. The performance of different methods for analysing correlated mutations in predicting contacts is shown to be enhanced significantly when amino acid and codon data are combined.

Article and author information

Author details

  1. Etai Jacob

    The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
    Competing interests
    The authors declare that no competing interests exist.
  2. Ron Unger

    The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
    Competing interests
    The authors declare that no competing interests exist.
  3. Amnon Horovitz

    Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Michael Levitt, Stanford University, United States

Publication history

  1. Received: May 22, 2015
  2. Accepted: September 13, 2015
  3. Accepted Manuscript published: September 15, 2015 (version 1)
  4. Accepted Manuscript updated: September 25, 2015 (version 2)
  5. Version of Record published: October 13, 2015 (version 3)


© 2015, Jacob et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.


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