Protein database searches using compositionally adjusted substitution matrices.
Almost all protein database search methods use amino acid substitution matrices for scoring, optimizing, and assessing the statistical significance of sequence alignments. Much care and effort has therefore gone into constructing substitution matrices, and the quality of search results can depend strongly upon the choice of the proper matrix. A long-standing problem has been the comparison of sequences with biased amino acid compositions, for which standard substitution matrices are not optimal. To address this problem, we have recently developed a general procedure for transforming a standard matrix into one appropriate for the comparison of two sequences with arbitrary, and possibly differing compositions. Such adjusted matrices yield, on average, improved alignments and alignment scores when applied to the comparison of proteins with markedly biased compositions. Here we review the application of compositionally adjusted matrices and consider whether they may also be applied fruitfully to general purpose protein sequence database searches, in which related sequence pairs do not necessarily have strong compositional biases. Although it is not advisable to apply compositional adjustment indiscriminately, we describe several simple criteria under which invoking such adjustment is on average beneficial. In a typical database search, at least one of these criteria is satisfied by over half the related sequence pairs. Compositional substitution matrix adjustment is now available in NCBI's protein-protein version of blast.
Altschul
Stephen F
SF
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. altschul@ncbi.nlm.nih.gov
Wootton
John C
JC
Gertz
E Michael
EM
Agarwala
Richa
R
Morgulis
Aleksandr
A
Schäffer
Alejandro A
AA
Yu
Yi-Kuo
YK
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D020407:Internet; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D012372:ROC Curve; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins