The effect of genetic mutation on phenotype is of significant interest in genetics. The type of genetic mutation that causes a single amino acid substitution (AAS) in a protein sequence is called a non-synonymous single nucleotide polymorphism (nsSNP). An nsSNP could potentially affect the function of the protein, subsequently altering the carrier's phenotype. This protocol describes the use of the 'Sorting Tolerant From Intolerant' (SIFT) algorithm in predicting whether an AAS affects protein function. To assess the effect of a substitution, SIFT assumes that important positions in a protein sequence have been conserved throughout evolution and therefore substitutions at these positions may affect protein function. Thus, by using sequence homology, SIFT predicts the effects of all possible substitutions at each position in the protein sequence. The protocol typically takes 5-20 min, depending on the input. SIFT is available as an online tool (http://sift.jcvi.org).

Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each substitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study. We have shown that SIFT can distinguish between functionally neutral and deleterious amino acid changes in mutagenesis studies and on human polymorphisms. SIFT is available at http://blocks.fhcrc.org/sift/SIFT.html.

Methods for alignment of protein sequences typically measure similait 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 evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 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 modem 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 involving related segments of proteins, multiple alignments of members of protein families, and alignments made 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 (1-6), the mutation data matrices of Dayhoff (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. METHODSDeriving a Frequency Table from a Data Base of Blocks. 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 >2000 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 alignment. For each column of the block, we first count the number of matches and mismatches of each type between the new sequence and every other sequence in the block. For example, if the residue of the new sequence that aligns with the first column of the first block is A and the column has 9 A residues and 1 S residue, then there are 9 AA matches and 1 AS mismatch. This proce...

Many missense substitutions are identified in single nucleotide polymorphism (SNP) data and large-scale random mutagenesis projects. Each amino acid substitution potentially affects protein function. We have constructed a tool that uses sequence homology to predict whether a substitution affects protein function. SIFT, which sorts intolerant from tolerant substitutions, classifies substitutions as tolerated or deleterious. A higher proportion of substitutions predicted to be deleterious by SIFT gives an affected phenotype than substitutions predicted to be deleterious by substitution scoring matrices in three test cases. Using SIFT before mutagenesis studies could reduce the number of functional assays required and yield a higher proportion of affected phenotypes. SIFT may be used to identify plausible disease candidates among the SNPs that cause missense substitutions.