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Comparative modeling in CASP5: Progress is evident, but alignment errors remain a significant hindrance
Author(s) -
Venclovas Česlovas
Publication year - 2003
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.10591
Subject(s) - computer science , template , homology modeling , computational biology , selection (genetic algorithm) , homology (biology) , multiple sequence alignment , sequence alignment , reliability (semiconductor) , protein structure prediction , data mining , protein structure , artificial intelligence , biology , genetics , peptide sequence , biochemistry , enzyme , gene , programming language , power (physics) , physics , quantum mechanics
Models for 20 comparative modeling targets were submitted for the fifth round of the "blind" test of protein structure prediction methods (CASP5; http://predictioncenter.llnl.gov/casp5). The modeling approach used in CASP5 was similar to that used 2 years ago in CASP4 (Venclovas, Proteins 2001; Suppl 5:47-54). The main features of this approach include use of multiple templates, initial assessment of alignment reliability in a region-specific manner, and structure-based selection of alignment variants in unreliable regions. The CASP5 modeling results presented here show significant improvement in comparison to CASP4, especially in the area of distant homology. The improvements include more effective use of multiple templates and better alignments. However, a number of structurally conserved regions in submitted distant homology models were misaligned. Analysis of these errors indicates that the absolute majority of them occurred in regions deemed unreliable in the course of model building. Most of these error-prone regions can be characterized by their peripheral location and a lack of conserved sequence patterns. For a few of the error-prone regions, all methods evaluated during CASP5 proved ineffective, pointing to the need for more sensitive energy-based methods. Despite these remaining issues, the applicability of comparative modeling continues to expand into more distant evolutionary relationships, providing a means to structurally characterize a significant number of currently available protein sequences.