Abstract
The elucidation of the complex relationships linking genotypic and phenotypic variations to protein structure is a major challenge in the post-genomic era. We present MSV3d (Database of human MisSense Variants mapped to 3D protein structure), a new database that contains detailed annotation of missense variants of all human proteins (20 199 proteins). The multi-level characterization includes details of the physico-chemical changes induced by amino acid modification, as well as information related to the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model. Major releases of the database are automatically generated and updated regularly in line with the dbSNP (database of Single Nucleotide Polymorphism) and SwissVar releases, by exploiting the extensive Décrypthon computational grid resources. The database (http://decrypthon.igbmc.fr/msv3d) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in XML or flat file formats.Database URL: http://decrypthon.igbmc.fr/msv3d
Highlights
Single nucleotide polymorphisms (SNPs) refer to a genetic change in which one nucleotide is replaced by another one and represent one of the most common forms of human genomic variation
The non-synonymous SNPs, called missense variants, are important since they result in an alteration of the amino acid sequence of the encoded protein
The large missense variant database mapped to 3D structures with regular updates is available for our scientific partners and for the wider community
Summary
Single nucleotide polymorphisms (SNPs) refer to a genetic change in which one nucleotide is replaced by another one and represent one of the most common forms of human genomic variation. With the huge amount of protein information available in various biological databases, including sequences, structures, functions, interactions, pathways, together with the development of in silico analysis tools, it is possible to better understand the correlation between a missense mutation and the associated molecular phenotypes.
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