Portal de Periódicos da CAPES

Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction

2015; Wiley; Volume: 83; Issue: 5 Linguagem: Inglês

10.1002/prot.24782

1097-0134

Myong‐Ho Chae, Florian Krull, Ernst‐Walter Knapp,

Machine Learning in Materials Science

Proteins: Structure, Function, and BioinformaticsVolume 83, Issue 5 p. 881-890 Article Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction Myong-Ho Chae, Corresponding Author Myong-Ho Chae Department of Biology, University of Science, Unjong-District, Pyongyang, DPR KoreaCorrespondence to: Ernst-Walter Knapp, Fachbereich Biologie, Chemie, Pharmazie/Institute of Chemistry and Biochemistry, Fabeckstrasse 36a, Berlin 14195, Germany. E-mail: knapp@chemie.fu-berlin.de or Myong-Ho Chae, Department of Biology, University of Science, Unjong-District, Pyongyang, DPR KoreaSearch for more papers by this authorFlorian Krull, Florian Krull Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, Berlin, 14195 GermanySearch for more papers by this authorErnst-Walter Knapp, Corresponding Author Ernst-Walter Knapp Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, Berlin, 14195 GermanyCorrespondence to: Ernst-Walter Knapp, Fachbereich Biologie, Chemie, Pharmazie/Institute of Chemistry and Biochemistry, Fabeckstrasse 36a, Berlin 14195, Germany. E-mail: knapp@chemie.fu-berlin.de or Myong-Ho Chae, Department of Biology, University of Science, Unjong-District, Pyongyang, DPR KoreaSearch for more papers by this author Myong-Ho Chae, Corresponding Author Myong-Ho Chae Department of Biology, University of Science, Unjong-District, Pyongyang, DPR KoreaCorrespondence to: Ernst-Walter Knapp, Fachbereich Biologie, Chemie, Pharmazie/Institute of Chemistry and Biochemistry, Fabeckstrasse 36a, Berlin 14195, Germany. E-mail: knapp@chemie.fu-berlin.de or Myong-Ho Chae, Department of Biology, University of Science, Unjong-District, Pyongyang, DPR KoreaSearch for more papers by this authorFlorian Krull, Florian Krull Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, Berlin, 14195 GermanySearch for more papers by this authorErnst-Walter Knapp, Corresponding Author Ernst-Walter Knapp Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36a, Berlin, 14195 GermanyCorrespondence to: Ernst-Walter Knapp, Fachbereich Biologie, Chemie, Pharmazie/Institute of Chemistry and Biochemistry, Fabeckstrasse 36a, Berlin 14195, Germany. E-mail: knapp@chemie.fu-berlin.de or Myong-Ho Chae, Department of Biology, University of Science, Unjong-District, Pyongyang, DPR KoreaSearch for more papers by this author First published: 18 February 2015 https://doi.org/10.1002/prot.24782Citations: 8Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat ABSTRACT The DOcking decoy-based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance-dependent atom-pair interactions. To optimize the atom-pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root-mean-square-deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network-based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel-like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel-like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom-pair-based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation-dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu-berlin.de/doop/. Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc. Citing Literature Supporting Information Additional Supporting Information may be found in the online version of this article. Filename Description prot24782-sup-0001-suppinfo1.pdf153.8 KB Supplementary Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume83, Issue5May 2015Pages 881-890 RelatedInformation