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Title: Automatic Classification of Protein Structure Using the Maximum Contact Map Overlap Metric

In this paper, we propose a new distance measure for comparing two protein structures based on their contact map representations. We show that our novel measure, which we refer to as the maximum contact map overlap (max-CMO) metric, satisfies all properties of a metric on the space of protein representations. Having a metric in that space allows one to avoid pairwise comparisons on the entire database and, thus, to significantly accelerate exploring the protein space compared to no-metric spaces. We show on a gold standard superfamily classification benchmark set of 6759 proteins that our exact k-nearest neighbor (k-NN) scheme classifies up to 224 out of 236 queries correctly and on a larger, extended version of the benchmark with 60; 850 additional structures, up to 1361 out of 1369 queries. Finally, our k-NN classification thus provides a promising approach for the automatic classification of protein structures based on flexible contact map overlap alignments.
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [5]
  1. Univ. of Rennes 1 (France); National Inst. of Research in Computer Science and Automation (INRIA), Rennes (France)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. National Research Inst. for Mathematics and Computer Science (CWI), Amsterdam (Netherlands). Life Sciences
  4. Univ. of Rennes 1 (France); National Inst. of Research in Computer Science and Automation (INRIA), Rennes (France)
  5. Univ. of Duisburg-Essen, Essen (Germany). Genome Informatics; Univ. of Lubeck (Germany). Inst. of Neurogenetics and for Integrative and Experimental Genomics. Platform for Genome Analytics
Publication Date:
Report Number(s):
LA-UR-15-24867
Journal ID: ISSN 1999-4893
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Algorithms
Additional Journal Information:
Journal Volume: 8; Journal Issue: 4; Journal ID: ISSN 1999-4893
Publisher:
MDPI
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Contributing Orgs:
Univ. of Rennes 1 (France); National Inst. of Research in Computer Science and Automation (INRIA); National Research Inst. for Mathematics and Computer Science (CWI); Univ. of Duisburg-Essen, Essen (Germany); Univ. of Lubeck (Germany)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; biological science; computer science; mathematics; maximum contact map overlap; protein space metric; k-nearest neighbor classification; superfamily classification; SCOP
OSTI Identifier:
1329875