Methods for alignment of protein sequences typicaily measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evoIutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 50Q groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups. Among the most useful computer-based tools in modern biology are those that involve sequence alignments of proteins, since these alignments often provide important insights into gene and protein function. There are several different types of alignments: global alignments of pairs of proteins related by common ancestry throughout their lengths, local alignments invohing related segments of proteins, multiple alignments of members of protein families, and alignments m d e during data base searches to detect homology. In each case, competing alignments are evaluated by using a scoring scheme for estimating similarity. Although several different scoring schemes have been proposed (1-61, the mutation data matrices of DayhofS (1, 7-9) are generally considered the standard and are often the default in alignment and searching programs. In the Dayhoff model, substitution rates are derived from alignments of protein sequences that are at least 85% identical. However, the most common task involving substitution matrices is the detection of much more distant relationships, which are only inferred from substitution rates in the Dayhoff model. Therefore, we wondered whether a better approach might be to use alignments in which these relationships are explicitly represented. An incentive for investigating this possibility is that implementation of an improved matrix in numerous important applications requires only trivial effort. Local alignments can be represented as ungapped blocks with each row a different protein segment and each column an aligned residue position. Previously, we described an automated system, PROTOMAT, for obtaining a set of blocks given a group of related proteins (10). This system was applied to a catalog of several hundred protein groups, yielding a data base of >2OOO blocks. Consider a single block representing a conserved region of a protein family. For a new member of this family, we seek a set of scores for matches and mismatches that best favors a correct alignment with each of the other segments in the block relative to an incorrect align- The expected probability of occurrence ec for each i, j pair is then p;pj for i = j and pipj + pipi = Zpipj for i # j . In the example, the expected probability of AA is 0.9 x 0.9 = 0.81, that of AS + SA is 2 X (0.9 X 0.1) = 0.18, and that of SS is 0.1 x 0.1 = 0.01. An odds ratio matrix is calculated where each entry is qu/eu. A lod ratio is then calculated in bit units as sc = log2(qQ/eu). If the observed frequencies are as expected. si = 0; if less than expected, sii C 0; if more than expected, sij > 0. Lod ratios are multiplied by a scaling factor of 2 and then rounded to the nearest integer value to produce Abbreviation: lod, logarithm of odds. METHODS Deriving a Frequency
For some applications, such matrices may be available, like BLOSUM in the context of protein sequence alignment REF .
Amino acid substitution matrices from protein blocks.
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Biology Medicine