Dataset Information

SNPeffect: a database mapping molecular phenotypic effects of human non-synonymous coding SNPs.

ABSTRACT: Single nucleotide polymorphisms (SNPs) are an increasingly important tool for genetic and biomedical research. However, the accumulated sequence information on allelic variation is not matched by an understanding of the effect of SNPs on the functional attributes or 'molecular phenotype' of a protein. Towards this aim we developed SNPeffect, an online resource of human non-synonymous coding SNPs (nsSNPs) mapping phenotypic effects of allelic variation in human genes. SNPeffect contains 31 659 nsSNPs from 12 480 human proteins. The current release of SNPeffect incorporates data on protein stability, integrity of functional sites, protein phosphorylation and glycosylation, subcellular localization, protein turnover rates, protein aggregation, amyloidosis and chaperone interaction. The SNP entries are accessible through both a search and browse interface and are linked to most major biological databases. The data can be displayed as detailed descriptions of individual SNPs or as an overview of all SNPs for a given protein. SNPeffect will be regularly updated and can be accessed at http://snpeffect.vib.be/.

SUBMITTER: Reumers J

PROVIDER: S-EPMC540040 | biostudies-literature | 2005 Jan

REPOSITORIES: biostudies-literature

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SNPeffect: a database mapping molecular phenotypic effects of human non-synonymous coding SNPs.

Reumers Joke J Schymkowitz Joost J Ferkinghoff-Borg Jesper J Stricher Francois F Serrano Luis L Rousseau Frederic F

Nucleic acids research 20050101 Database issue

Single nucleotide polymorphisms (SNPs) are an increasingly important tool for genetic and biomedical research. However, the accumulated sequence information on allelic variation is not matched by an understanding of the effect of SNPs on the functional attributes or 'molecular phenotype' of a protein. Towards this aim we developed SNPeffect, an online resource of human non-synonymous coding SNPs (nsSNPs) mapping phenotypic effects of allelic variation in human genes. SNPeffect contains 31 659 ns ...[more]

PMID: 15608254

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Project description:BackgroundTuftelin 1 (TUFT1) gene is important in the development and mineralization of dental enamel. The study aimed to identify potential functionally deleterious non-synonymous SNPs (nsSNPs) in the TUFT1 gene by using different in silico tools. The deleterious missense SNPs were identified from SIFT, PolyPhen-2, PROVEAN, SNPs & GO, PANTHER, and SNAP2. The stabilization, conservation, and three-dimensional modeling of mutant proteins were analyzed by I-Mutant 3.0, Consurf, and Project HOPE, respectively. The protein-protein interaction using STRING, GeneMANIA for gene-gene interaction, and DynaMut for evaluating the impact of the mutation on protein stability, conformation, and flexibility.ResultsEight deleterious nsSNPs (E242A, R303W, K182N, K123N, R117W, H289Q, R203W, and Q107R) out of 304 were found to have high-risk damaging effects using six in silico tools. Among them, K182N and K123N alone had increased stability, whereas E242A, R303W, R117W, H289Q, Q107R, and R203W exhibited a decrease in protein stability, based on DDG values. Meanwhile, all the eight deleterious nsSNPs altered the size, charge, hydrophobicity, and spatial organization of the amino acids and predominantly had alpha helix domains. These deleterious variants were located in highly conserved regions except R203W. Protein-protein interaction predicted that TUFT1 interacted with ten proteins that are involved in enamel mineralization and odontogenesis. Gene-gene interaction network showed that TUFT1 is involved in physical interactions, gene co-localization, and pathway interactions. DynaMut ΔΔG values predicted that five nsSNPs were destabilizing the protein, ΔΔG ENCoM values showed a destabilizing effect for all mutants, and seven nsSNPs increased the molecular flexibility of TUFT1.ConclusionOur study predicted eight functional SNPs that had detrimental effects on the structure and function of the TUFT1 gene. This will aid in the development of candidate deleterious markers as a potential target for disease diagnosis and therapeutic interventions.

Project description:Mannose binding lectin (MBL) is a liver derived protein which plays an important role in innate immunity. Mannose binding lectin gene 2 (MBL2) polymorphisms are reported to be associated with various diseases. In spite of being exhaustively studied molecule, no attempt has been made till date to comprehensively and systematically analyze the SNPs of MBL2 gene. The present study was carried out to identify and prioritize the SNPs of MBL2 gene for further genotyping and functional studies. To predict the possible impact of SNPs on MBL structure and function SNP data obtained from dbSNP database were analyzed using various bioinformatics tools. Out of total 661 SNPs, only 37 validated SNPs having minor allele frequency ≥0.10 were considered for the present study. These 37 SNPs includes one in 3' near gene, nine in 3' UTR, one non-synonymous SNP (nsSNP), thirteen intronic SNPs and thirteen in 5' near gene. From these 37 SNPs, 11 non-coding SNPs were identified to be of functional significance and evolutionary conserved. Out of these, 4 SNPs from 3' UTR were found to play role in miRNA binding, 7 SNPs from 5' near and intronic region were predicted to involve in transcription factor binding and expression of MBL2 gene. One nsSNP Gly54Asp (rs1800450) was found to be deleterious and damaging by both SIFT and Polyphen-2 servers and thus affecting MBL2 protein stability and expression. Protein structural analysis with this amino acid variant was performed by using I-TASSER, RAMPAGE, Swiss-PdbViewer, Chimera and I-mutant. Information regarding solvent accessibility, molecular dynamics and energy minimization calculations showed that this variant causes clashes with neighboring amino acids residues that must interfere in the normal triple helix formation of trimeric subunit and further with the normal assembly of MBL oligomeric form, hence decrease in stability. Thus, findings of the present study indicated 12 SNPs of MBL2 gene to be functionally important. Exploration of these variants may provide novel remedial markers for various diseases.

Project description:Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/.

Dataset Information

SNPeffect: a database mapping molecular phenotypic effects of human non-synonymous coding SNPs.

Publications

SNPeffect: a database mapping molecular phenotypic effects of human non-synonymous coding SNPs.

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