Predicting Deleterious Amino Acid Substitutions

TLDR

Missense substitutions are common in SNP data and large‑scale mutagenesis, and each can alter protein function. The study develops SIFT, a homology‑based tool, to predict the functional impact of missense substitutions and to identify disease‑associated SNPs. SIFT classifies substitutions as tolerated or deleterious by sorting them based on sequence homology. SIFT predictions correlate better with phenotypic effects than scoring matrices, and using SIFT can reduce functional assays while increasing detection of affected phenotypes.

Abstract

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 s orts i ntolerant f rom t olerant 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.