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Scoring profile‐to‐profile sequence alignments
Author(s) -
Wang Guoli,
Dunbrack Roland L.
Publication year - 2004
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.03601504
Subject(s) - multiple sequence alignment , weighting , sequence (biology) , sequence alignment , structural alignment , benchmark (surveying) , alignment free sequence analysis , computer science , column (typography) , sensitivity (control systems) , position (finance) , algorithm , pattern recognition (psychology) , data mining , computational biology , artificial intelligence , peptide sequence , biology , genetics , physics , engineering , telecommunications , geodesy , finance , frame (networking) , electronic engineering , gene , acoustics , economics , geography
Sequence alignment profiles have been shown to be very powerful in creating accurate sequence alignments. Profiles are often used to search a sequence database with a local alignment algorithm. More accurate and longer alignments have been obtained with profile‐to‐profile comparison. There are several steps that must be performed in creating profile–profile alignments, and each involves choices in parameters and algorithms. These steps include (1) what sequences to include in a multiple alignment used to build each profile, (2) how to weight similar sequences in the multiple alignment and how to determine amino acid frequencies from the weighted alignment, (3) how to score a column from one profile aligned to a column of the other profile, (4) how to score gaps in the profile–profile alignment, and (5) how to include structural information. Large‐scale benchmarks consisting of pairs of homologous proteins with structurally determined sequence alignments are necessary for evaluating the efficacy of each scoring scheme. With such a benchmark, we have investigated the properties of profile–profile alignments and found that (1) with optimized gap penalties, most column–column scoring functions behave similarly to one another in alignment accuracy; (2) some functions, however, have much higher search sensitivity and specificity; (3) position‐specific weighting schemes in determining amino acid counts in columns of multiple sequence alignments are better than sequence‐specific schemes; (4) removing positions in the profile with gaps in the query sequence results in better alignments; and (5) adding predicted and known secondary structure information improves alignments.