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Multiple mapping method: A novel approach to the sequence‐to‐structure alignment problem in comparative protein structure modeling
Author(s) -
Rai Brajesh K.,
Fiser András
Publication year - 2006
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.20835
Subject(s) - multiple sequence alignment , structural alignment , computer science , bottleneck , sequence alignment , alignment free sequence analysis , set (abstract data type) , sequence (biology) , algorithm , protein structure prediction , data mining , range (aeronautics) , pattern recognition (psychology) , artificial intelligence , protein structure , biology , peptide sequence , gene , programming language , embedded system , materials science , composite material , biochemistry , genetics
A major bottleneck in comparative protein structure modeling is the quality of input alignment between the target sequence and the template structure. A number of alignment methods are available, but none of these techniques produce consistently good solutions for all cases. Alignments produced by alternative methods may be superior in certain segments but inferior in others when compared to each other; therefore, an accurate solution often requires an optimal combination of them. To address this problem, we have developed a new approach, Multiple Mapping Method (MMM). The algorithm first identifies the alternatively aligned regions from a set of input alignments. These alternatively aligned segments are scored using a composite scoring function, which determines their fitness within the structural environment of the template. The best scoring regions from a set of alternative segments are combined with the core part of the alignments to produce the final MMM alignment. The algorithm was tested on a dataset of 1400 protein pairs using 11 combinations of two to four alignment methods. In all cases MMM showed statistically significant improvement by reducing alignment errors in the range of 3 to 17%. MMM also compared favorably over two alignment meta‐servers. The algorithm is computationally efficient; therefore, it is a suitable tool for genome scale modeling studies. Proteins 2006. © 2006 Wiley‐Liss, Inc.