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Improved pairwise alignments of proteins in the Twilight Zone using local structure predictions
Author(s) -
Yao-Ming Huang,
Christopher Bystroff
Publication year - 2005
Publication title -
bioinformatics
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
ISBN - 0-7695-2442-7
DOI - 10.1093/bioinformatics/bti828
Subject(s) - structural alignment , sequence alignment , protein superfamily , computer science , pairwise comparison , context (archaeology) , sequence (biology) , multiple sequence alignment , hidden markov model , substitution (logic) , computational biology , protein structure , homology modeling , threading (protein sequence) , similarity (geometry) , markov chain , alignment free sequence analysis , smith–waterman algorithm , structural similarity , set (abstract data type) , peptide sequence , artificial intelligence , biology , genetics , machine learning , gene , paleontology , biochemistry , enzyme , image (mathematics) , programming language
In recent years, advances have been made in the ability of computational methods to discriminate between homologous and non-homologous proteins in the 'twilight zone' of sequence similarity, where the percent sequence identity is a poor indicator of homology. To make these predictions more valuable to the protein modeler, they must be accompanied by accurate alignments. Pairwise sequence alignments are inferences of orthologous relationships between sequence positions. Evolutionary distance is traditionally modeled using global amino acid substitution matrices. But real differences in the likelihood of substitutions may exist for different structural contexts within proteins, since structural context contributes to the selective pressure.

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