Ng P C, Henikoff S
Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
Genome Res. 2001 May;11(5):863-74. doi: 10.1101/gr.176601.
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. may be used to identify plausible disease candidates among the SNPs that cause missense substitutions.
在单核苷酸多态性（SNP）数据和大规模随机诱变项目中发现了许多错义替换。每个氨基酸替换都可能影响蛋白质功能。我们构建了一个利用序列同源性来预测替换是否影响蛋白质功能的工具。SIFT（将不耐受替换与耐受替换区分开来）将替换分类为耐受或有害。在三个测试案例中，与通过替换评分矩阵预测为有害的替换相比，被SIFT预测为有害的替换产生受影响表型的比例更高。在诱变研究之前使用SIFT可以减少所需功能测定的数量，并产生更高比例的受影响表型。可用于在导致错义替换的SNP中识别可能的疾病候选基因。
