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      A method and server for predicting damaging missense mutations

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          Most cited references 8

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          SIFT: Predicting amino acid changes that affect protein function.

           P C Ng (2003)
          Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each substitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study. We have shown that SIFT can distinguish between functionally neutral and deleterious amino acid changes in mutagenesis studies and on human polymorphisms. SIFT is available at
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            Human non-synonymous SNPs: server and survey.

             V. Ramensky (2002)
            Human single nucleotide polymorphisms (SNPs) represent the most frequent type of human population DNA variation. One of the main goals of SNP research is to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. Non-synonymous coding SNPs (nsSNPs) comprise a group of SNPs that, together with SNPs in regulatory regions, are believed to have the highest impact on phenotype. Here we present a World Wide Web server to predict the effect of an nsSNP on protein structure and function. The prediction method enabled analysis of the publicly available SNP database HGVbase, which gave rise to a dataset of nsSNPs with predicted functionality. The dataset was further used to compare the effect of various structural and functional characteristics of amino acid substitutions responsible for phenotypic display of nsSNPs. We also studied the dependence of selective pressure on the structural and functional properties of proteins. We found that in our dataset the selection pressure against deleterious SNPs depends on the molecular function of the protein, although it is insensitive to several other protein features considered. The strongest selective pressure was detected for proteins involved in transcription regulation.
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              Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.

              Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence. In this paper, we develop a method based on support vector machines (SVMs) that starting from the protein sequence information can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. Using a dataset of 21 185 single point mutations, 61% of which are disease-related, out of 3587 proteins, we show that our predictor can reach more than 74% accuracy in the specific task of predicting whether a single point mutation can be disease related or not. Our method, although based on less information, outperforms other web-available predictors implementing different approaches. A beta version of the web tool is available at

                Author and article information

                [1 ]Division of Genetics, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
                [2 ]Department of Biochemistry, Max Planck Institute for Developmental Biology, Tübingen, Germany
                [3 ]Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
                [4 ]Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
                [5 ]Life Sciences Institute and Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, USA
                [6 ]European Molecular Biology Laboratory, Heidelberg, Germany
                Author notes
                Correspondence to: Shamil R. Sunyaev 1 ssunyaev@

                These authors contributed equally to this work

                Nat Methods
                Nature methods
                24 March 2010
                April 2010
                1 October 2010
                : 7
                : 4
                : 248-249

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                Funded by: National Institute of Mental Health : NIMH
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 MH084676-02 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 GM078598-03 ||GM

                Life sciences


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