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      SDM—a server for predicting effects of mutations on protein stability and malfunction

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          Abstract

          The sheer volume of non-synonymous single nucleotide polymorphisms that have been generated in recent years from projects such as the Human Genome Project, the HapMap Project and Genome-Wide Association Studies means that it is not possible to characterize all mutations experimentally on the gene products, i.e. elucidate the effects of mutations on protein structure and function. However, automatic methods that can predict the effects of mutations will allow a reduced set of mutations to be studied. Site Directed Mutator (SDM) is a statistical potential energy function that uses environment-specific amino-acid substitution frequencies within homologous protein families to calculate a stability score, which is analogous to the free energy difference between the wild-type and mutant protein. Here, we present a web server for SDM ( http://www-cryst.bioc.cam.ac.uk/~sdm/sdm.php), which has obtained more than 10 000 submissions since being online in April 2008. To run SDM, users must upload a wild-type structure and the position and amino acid type of the mutation. The results returned include information about the local structural environment of the wild-type and mutant residues, a stability score prediction and prediction of disease association. Additionally, the wild-type and mutant structures are displayed in a Jmol applet with the relevant residues highlighted.

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          The complete genome of an individual by massively parallel DNA sequencing.

          The association of genetic variation with disease and drug response, and improvements in nucleic acid technologies, have given great optimism for the impact of 'genomic medicine'. However, the formidable size of the diploid human genome, approximately 6 gigabases, has prevented the routine application of sequencing methods to deciphering complete individual human genomes. To realize the full potential of genomics for human health, this limitation must be overcome. Here we report the DNA sequence of a diploid genome of a single individual, James D. Watson, sequenced to 7.4-fold redundancy in two months using massively parallel sequencing in picolitre-size reaction vessels. This sequence was completed in two months at approximately one-hundredth of the cost of traditional capillary electrophoresis methods. Comparison of the sequence to the reference genome led to the identification of 3.3 million single nucleotide polymorphisms, of which 10,654 cause amino-acid substitution within the coding sequence. In addition, we accurately identified small-scale (2-40,000 base pair (bp)) insertion and deletion polymorphism as well as copy number variation resulting in the large-scale gain and loss of chromosomal segments ranging from 26,000 to 1.5 million base pairs. Overall, these results agree well with recent results of sequencing of a single individual by traditional methods. However, in addition to being faster and significantly less expensive, this sequencing technology avoids the arbitrary loss of genomic sequences inherent in random shotgun sequencing by bacterial cloning because it amplifies DNA in a cell-free system. As a result, we further demonstrate the acquisition of novel human sequence, including novel genes not previously identified by traditional genomic sequencing. This is the first genome sequenced by next-generation technologies. Therefore it is a pilot for the future challenges of 'personalized genome sequencing'.
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            Very fast empirical prediction and rationalization of protein pKa values.

            A very fast empirical method is presented for structure-based protein pKa prediction and rationalization. The desolvation effects and intra-protein interactions, which cause variations in pKa values of protein ionizable groups, are empirically related to the positions and chemical nature of the groups proximate to the pKa sites. A computer program is written to automatically predict pKa values based on these empirical relationships within a couple of seconds. Unusual pKa values at buried active sites, which are among the most interesting protein pKa values, are predicted very well with the empirical method. A test on 233 carboxyl, 12 cysteine, 45 histidine, and 24 lysine pKa values in various proteins shows a root-mean-square deviation (RMSD) of 0.89 from experimental values. Removal of the 29 pKa values that are upper or lower limits results in an RMSD = 0.79 for the remaining 285 pKa values. Proteins 2005. 2005 Wiley-Liss, Inc.
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              The interpretation of protein structures: estimation of static accessibility.

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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                1 July 2011
                1 July 2011
                18 May 2011
                18 May 2011
                : 39
                : Web Server issue , Web Server issue
                : W215-W222
                Affiliations
                1Biochemistry Department, University of Cambridge, Cambridge CB2 1GA, UK and 2Institute for Physiology, Charité Universitätsmedizin, Lindenberger Weg 80, 13125 Berlin, Germany
                Author notes
                *To whom correspondence should be addressed. Tel: +441223 333628; Fax: +441223 766002; Email: tom@ 123456cryst.bioc.cam.ac.uk
                Article
                gkr363
                10.1093/nar/gkr363
                3125769
                21593128
                2f35aa47-4dd0-4a54-982f-11d41d8ac539
                © The Author(s) 2011. Published by Oxford University Press.

                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
                : 18 February 2011
                : 20 April 2011
                : 27 April 2011
                Page count
                Pages: 8
                Categories
                Articles

                Genetics
                Genetics

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