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A simple topological representation of protein structure: Implications for new, fast, and robust structural classification
Author(s) -
Bostick David L.,
Shen Min,
Vaisman Iosif I.
Publication year - 2004
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20146
Subject(s) - topology (electrical circuits) , representation (politics) , structural classification of proteins database , structural alignment , delaunay triangulation , pattern recognition (psychology) , similarity (geometry) , metric (unit) , euclidean distance , computer science , sequence (biology) , protein structure , mathematics , artificial intelligence , algorithm , sequence alignment , biology , combinatorics , image (mathematics) , peptide sequence , biochemistry , operations management , genetics , politics , political science , gene , economics , law
A topological representation of proteins is developed that makes use of two metrics: the Euclidean metric for identifying natural nearest neighboring residues via the Delaunay tessellation in Cartesian space and the distance between residues in sequence space. Using this representation, we introduce a quantitative and computationally inexpensive method for the comparison of protein structural topology. The method ultimately results in a numerical score quantifying the distance between proteins in a heuristically defined topological space. The properties of this scoring scheme are investigated and correlated with the standard C α distance root‐mean‐square deviation measure of protein similarity calculated by rigid body structural alignment. The topological comparison method is shown to have a characteristic dependence on protein conformational differences and secondary structure. This distinctive behavior is also observed in the comparison of proteins within families of structural relatives. The ability of the comparison method to successfully classify proteins into classes, superfamilies, folds, and families that are consistent with standard classification methods, both automated and human‐driven, is demonstrated. Furthermore, it is shown that the scoring method allows for a fine‐grained classification on the family, protein, and species level that agrees very well with currently established phylogenetic hierarchies. This fine classification is achieved without requiring visual inspection of proteins, sequence analysis, or the use of structural superimposition methods. Implications of the method for a fast, automated, topological hierarchical classification of proteins are discussed. Proteins 2004. © 2004 Wiley‐Liss, Inc.