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Predicting Deleterious Amino Acid Substitutions
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.
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Functional prediction algorithms (8 of 10), including SIFT and PolyPhen, considered p.Gln55Arg benign and tolerated, with only Mutation taster and LRT scoring this change as deleterious.
“Functional prediction algorithms (8 of 10), including SIFT33 and PolyPhen34, considered this change to be benign and tolerated, with only Mutation taster35 and LRT36 scoring this change as deleterious.”
From Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans (2023) · cites this paper as [33]
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Metadata
- Authors
- Ng, P. C., & Henikoff, S.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2001-05-01
- Volume
- 11
- Issue
- 5
- Pages
- 863-874
- ISSN
- 1088-9051
- 1549-5469
- Cited by
- 2,368
- References
- 2
- Language
- en
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