Predicting deleterious amino acid substitutions.

By Pauline Crystal Ng, Steven Henikoff. 2001

2,706 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Quantitative missense variant effect prediction using large-scale mutagenesis data (2017, neutral): “…(annotations and predictions) Adzhubei et al. (2010) http://genetics.bwh.harvard.edu/pph2/dokuwiki/downloads SIFT predictions Ng and Henikoff (2001) http://sift.jcvi.org/ EVmutation predictions Hopf et al. (2017) https://marks.hms.harvard.edu/evmutation/ SNAP2 predictions…”
SIFT web server: predicting effects of amino acid substitutions on proteins (2012, methodology): “SIFT was originally trained and tested on LacI, lysozyme and HIV protease substitutions (21,22).”
F-SNP: computationally predicted functional SNPs for disease association studies (2007, methodology): “The tools, PolyPhen (as of 15 August 2007) (6), SIFT (as of 15 August 2007) (7), SNPeffect (version 2.”
Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods (2006, methodology): “Different approaches have been explored, including sequence-based methods (6,7), structure-based algorithms (8–12) and a combination of both (13–15).”
PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels (2015, methodology): “For this purpose, computational tools such as PolyPhen (Adzhubei et al. , 2010; Sunyaev et al. , 1999) and SIFT (Kumar et al. , 2009; Ng and Henikoff, 2001) were developed to provide functional predictions for coding variants.”
Large-scale discovery of induced point mutations with high-throughput TILLING. (2003, background): “For many model organisms this goal has already been approached through development of a variety of gene knockout strategies (e.g., see Zambrowicz et al. 1998; Liu et al. 1999; RossMacdonald et al. 1999; Winzeler et al. 1999; Fraser et al. 2000; Piano et al. 2000; Giaever et al. 2002).”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “…of rank scores (upper triangle) and agreement ratio of binary predictions (lower triangle) between pairs of deleteriousness prediction scores or conservation scores LIST-S2, MPC, MutationAssessor [45], MVP, Polyphen2 HDIV and Polyphen2 HVAR [46], PROVEAN [47], SIFT [48], SIFT4G, VEST4 [49]).”
Disease-related mutations predicted to impact protein function (2012, methodology): “For all mutations in the above four data sets (disease-causing, disease-relation unknown, observed function-changing, and random), we predicted their effects on function with SNAP [19] and SIFT [20].”
Prediction of Missense Mutation Functionality Depends on both the Algorithm and Sequence Alignment Employed (2011, methodology): “…SIFT (http://sift.jcvi.org/): the method ‘‘Sorts Intolerant From Tolerant’’ is a sequence homology-based tool that predicts variants in the query sequence as ‘‘neutral’’ or ‘‘deleterious’’ using normalized probabilities calculated from the input multiple sequence alignment [Ng and Henikoff, 2001].”
The amino-acid mutational spectrum of human genetic disease (2003, background): “Application of other scores, for example those based on the BLOSUM matrices, gave qualitatively similar results [3].”
SVScore: an impact prediction tool for structural variation (2016, background): “In recent years, there have been many efforts to predict the effects of single nucleotide polymorphisms (SNPs) in silico, including SIFT (Ng and Henikoff, 2001), PROVEAN (Choi et al., 2012), PolyPhen (Adzhubei et al., 2012), and VEP (McLaren et al., 2010).”
Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation (2016, background): “The earliest computational methods available for broad use considered evolutionary conservation (Ng and Henikoff 2001; Thomas et al. 2003), structural effects (Wang and Moult 2001), or a combination of the two (Ramensky et al. 2002).”
Using bioinformatics to predict the functional impact of SNVs (2011, methodology): “Ng and co-workers suggested that the importance of this distance depends on the position where an amino acid substitution occurs (Ng and Henikoff, 2001).”