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Effect of using suboptimal alignments in template‐based protein structure prediction
Author(s) -
Chen Hao,
Kihara Daisuke
Publication year - 2011
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.22885
Subject(s) - probabilistic logic , protein structure prediction , computer science , modeller , threading (protein sequence) , template , sequence alignment , structural genomics , casp , ranking (information retrieval) , data mining , machine learning , protein structure , structural alignment , artificial intelligence , homology modeling , peptide sequence , biology , biochemistry , gene , programming language , enzyme
Computational protein structure prediction remains a challenging task in protein bioinformatics. In the recent years, the importance of template‐based structure prediction is increasing because of the growing number of protein structures solved by the structural genomics projects. To capitalize the significant efforts and investments paid on the structural genomics projects, it is urgent to establish effective ways to use the solved structures as templates by developing methods for exploiting remotely related proteins that cannot be simply identified by homology. In this work, we examine the effect of using suboptimal alignments in template‐based protein structure prediction. We showed that suboptimal alignments are often more accurate than the optimal one, and such accurate suboptimal alignments can occur even at a very low rank of the alignment score. Suboptimal alignments contain a significant number of correct amino acid residue contacts. Moreover, suboptimal alignments can improve template‐based models when used as input to Modeller. Finally, we use suboptimal alignments for handling a contact potential in a probabilistic way in a threading program, SUPRB. The probabilistic contacts strategy outperforms the partly thawed approach, which only uses the optimal alignment in defining residue contacts, and also the re‐ranking strategy, which uses the contact potential in re‐ranking alignments. The comparison with existing methods in the template‐recognition test shows that SUPRB is very competitive and outperforms existing methods. Proteins 2010. © 2010 Wiley‐Liss, Inc.