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Predicting accurate contacts in thousands of Pfam domain families using PconsC3
KTH, School of Computer Science and Communication (CSC).
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2017 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 18, 2859-2866 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: A few years ago it was shown that by using a maximum entropy approach to describe couplings between columns in a multiple sequence alignment it is possible to significantly increase the accuracy of residue contact predictions. For very large protein families with more than 1000 effective sequences the accuracy is sufficient to produce accurate models of proteins as well as complexes. Today, for about half of all Pfam domain families no structure is known, but unfortunately most of these families have at most a few hundred members, i.e. are too small for such contact prediction methods. Results: To extend accurate contact predictions to the thousands of smaller protein families we present PconsC3, a fast and improved method for protein contact predictions that can be used for families with even 100 effective sequence members. PconsC3 outperforms direct coupling analysis (DCA) methods significantly independent on family size, secondary structure content, contact range, or the number of selected contacts. Availability and implementation: PconsC3 is available as a web server and downloadable version at http://c3.pcons.net. The downloadable version is free for all to use and licensed under the GNU General Public License, version 2. At this site contact predictions for most Pfam families are also available. We do estimate that more than 4000 contact maps for Pfam families of unknown structure have more than 50% of the top-ranked contacts predicted correctly. Contact: arne@bioinfo.se Supplementary information: Supplementary data are available at Bioinformatics online.

Place, publisher, year, edition, pages
Oxford University Press, 2017. Vol. 33, no 18, 2859-2866 p.
National Category
Composite Science and Engineering
Identifiers
URN: urn:nbn:se:kth:diva-214872DOI: 10.1093/bioinformatics/btx332ISI: 000409541400009Scopus ID: 2-s2.0-85029813783OAI: oai:DiVA.org:kth-214872DiVA: diva2:1152319
Funder
Swedish Research Council, VR-NT 2012-5046Swedish eā€Science Research Center
Note

QC 20171024

Available from: 2017-10-24 Created: 2017-10-24 Last updated: 2017-10-30Bibliographically approved

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Ekeberg, Magnus
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CiteExportLink to record
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Citation style
  • apa
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