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      SIFT web server: predicting effects of amino acid substitutions on proteins

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          Abstract

          The Sorting Intolerant from Tolerant (SIFT) algorithm predicts the effect of coding variants on protein function. It was first introduced in 2001, with a corresponding website that provides users with predictions on their variants. Since its release, SIFT has become one of the standard tools for characterizing missense variation. We have updated SIFT’s genome-wide prediction tool since our last publication in 2009, and added new features to the insertion/deletion (indel) tool. We also show accuracy metrics on independent data sets. The original developers have hosted the SIFT web server at FHCRC, JCVI and the web server is currently located at BII. The URL is http://sift-dna.org (24 May 2012, date last accessed).

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          Automated inference of molecular mechanisms of disease from amino acid substitutions.

          Biao Li, Vidhya G Krishnan, Matthew Mort … (2009)
          Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism. We have developed a new computational model, MutPred, that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences. These changes, expressed as probabilities of gain or loss of structure and function, can provide insight into the specific molecular mechanism responsible for the disease state. MutPred also builds on the established SIFT method but offers improved classification accuracy with respect to human disease mutations. Given conservative thresholds on the predicted disruption of molecular function, we propose that MutPred can generate accurate and reliable hypotheses on the molecular basis of disease for approximately 11% of known inherited disease-causing mutations. We also note that the proportion of changes of functionally relevant residues in the sets of cancer-associated somatic mutations is higher than for the inherited lesions in the Human Gene Mutation Database which are instead predicted to be characterized by disruptions of protein structure. http://mutdb.org/mutpred predrag@indiana.edu; smooney@buckinstitute.org.
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            Accounting for human polymorphisms predicted to affect protein function.

            Pauline C. Ng, Steven Henikoff (2002)
            A major interest in human genetics is to determine whether a nonsynonymous single-base nucleotide polymorphism (nsSNP) in a gene affects its protein product and, consequently, impacts the carrier's health. We used the SIFT (Sorting Intolerant From Tolerant) program to predict that 25% of 3084 nsSNPs from dbSNP, a public SNP database, would affect protein function. Some of the nsSNPs predicted to affect function were variants known to be associated with disease. Others were artifacts of SNP discovery. Two reports have indicated that there are thousands of damaging nsSNPs in an individual's human genome; we find the number is likely to be much lower.
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              Discovery of chemically induced mutations in rice by TILLING

              Bradley Till, Jennifer Cooper, Thomas H Tai … (2007)
              Background Rice is both a food source for a majority of the world's population and an important model system. Available functional genomics resources include targeted insertion mutagenesis and transgenic tools. While these can be powerful, a non-transgenic, unbiased targeted mutagenesis method that can generate a range of allele types would add considerably to the analysis of the rice genome. TILLING (Targeting Induced Local Lesions in Genomes), a general reverse genetic technique that combines traditional mutagenesis with high throughput methods for mutation discovery, is such a method. Results To apply TILLING to rice, we developed two mutagenized rice populations. One population was developed by treatment with the chemical mutagen ethyl methanesulphonate (EMS), and the other with a combination of sodium azide plus methyl-nitrosourea (Az-MNU). To find induced mutations, target regions of 0.7–1.5 kilobases were PCR amplified using gene specific primers labeled with fluorescent dyes. Heteroduplexes were formed through denaturation and annealing of PCR products, mismatches digested with a crude preparation of CEL I nuclease and cleaved fragments visualized using denaturing polyacrylamide gel electrophoresis. In 10 target genes screened, we identified 27 nucleotide changes in the EMS-treated population and 30 in the Az-MNU population. Conclusion We estimate that the density of induced mutations is two- to threefold higher than previously reported rice populations (about 1/300 kb). By comparison to other plants used in public TILLING services, we conclude that the populations described here would be suitable for use in a large scale TILLING project.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Journal ID (hwp): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: July 2012
                Publication date (Print): July 2012
                Publication date (Electronic): 9 June 2012
                Publication date PMC-release: 9 June 2012
                Volume: 40
                Issue: Web Server issue
                Pages: W452-W457
                Affiliations
                1Computational and Systems Biology, Genome Institute of Singapore, 2Genomic Medicine, J. Craig Venter Institute, 3Department of Mathematics and Computer Science, Franklin & Marshall College, 4Howard Hughes Medical Institute & Basic Sciences Division, Fred Hutchinson Cancer Research Center and 5Biomolecular Function Discovery Division, Bioinformatics Institute of Singapore, Singapore
                Author notes
                *To whom correspondence should be addressed. Tel: +65 6808 8310; Fax: +65 6808 8292; Email: ngpc4@ 123456gis.a-star.edu.sg

                Present addresses: Prateek Kumar, Dana-Farber Cancer Institute, 44 Binney St., Boston, MA 02115, USA. Pauline C. Ng, Genome Institute of Singapore, 60 Biopolis Street, Singapore 138672, Singapore.

                Article
                Publisher ID: gks539
                DOI: 10.1093/nar/gks539
                PMC ID: 3394338
                PubMed ID: 22689647
                SO-VID: cf5c5405-5325-4ea0-ab52-c7ad947a831f
                Copyright © © The Author(s) 2012. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 30 January 2012
                Date revision received : 11 May 2012
                Date accepted : 12 May 2012
                Page count
                Pages: 6
                Categories
                Subject: Articles

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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