VarMod
Variant Modeller

Modelling functional effects of non-synonymous variants


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About VarMod

VarMod is a method for investigating the functional effects of non-synonymous single nucleotide variants (nsSNVs) in proteins. We have recently demonstrated that disease associated nsSNVs are frequently located at protein-protein interfaces [1] and others have shown them to occur frequently at small molecule binding sites [2]. We have also observed that nsSNVs in Genome wide association studies (GWAS) that increase disease risk can be located in or close to protein-small molecule binding sites [3, 4]. VarMod uses both of these functional features in addition to other structural and sequence features to analyse nsSNVs and predict those that are likely to have a functional effect upon the protein.

Users submit a protein sequence or a UniProt accession (future versions will accept other forms of accession). VarMod first generates a model of the protein structure to model small molecule binding sites in the protein using 3DLigandSite [5]. Protein-Protein interfaces are then identified using Interactome3D [6]. VarMod combines these features with standard features including residue conservation (Jensen-Shannon divergence [7]), location within the protein structure and predictions from SIFT [8] and PolyPhen [9]. The data are combined using a machine learning approach (a Support Vector Machine, SVM) resulting in a prediction of the nsSNVs that are likely to have an effect on the protein function.

In addition to the machine learning approach, VarMod provides extensive features to analyse visually the protein model and location of the nsSNVs relative to ligand binding and protein-protein interface sites. These features are provided using Jmol (www.jmol.org), which requires java to be installed and enabled in your web browser. Any additional details required for Jmol can found in the Jmol documentation at www.jmol.org.


References

  • 1. David,A., Razali,R., Wass,M.N. and Sternberg,M.J.E. (2012) Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs. Hum. Mutat., 33, 359-363.
  • 2. Bordner,A.J. and Zorman,B. (2013) Predicting non-neutral missense mutations and their biochemical consequences using genome-scale homology modeling of human protein complexes. arXiv.
  • 3. Chambers,J.C., Zhang,W., Lord,G.M., Van der Harst,P., Lawlor,D.A., Sehmi,J.S., Gale,D.P., Wass,M.N., Ahmadi,K.R., Bakker,S.J.L., et al. (2010) Genetic loci influencing kidney function and chronic kidney disease. Nat Genet, 42, 373-375.
  • 4. Chambers,J.C., Zhang,W., Sehmi,J., Li,X., Wass,M.N., Van der Harst,P., Holm,H., Sanna,S., Kavousi,M., Baumeister,S.E., et al. (2011) Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat Genet, 43, 1131-1138.
  • 5. Wass,M.N., Kelley,L.A. and Sternberg,M.J.E. (2010) 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res., 38, W469-73.
  • 6. Mosca,R., Ceol,A. and Aloy,P. (2012) Interactome3D: adding structural details to protein networks. Nat. Methods, 10, 47-53.
  • 7. Capra J and Singh,M. (2008) Characterization and prediction of residues determining protein functional specificity. Bioinformatics, 24, 1473-1480.
  • 8. Ng,P.C. and Henikoff,S. (2003) SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res., 31, 3812-3814.
  • 9. Adzhubei,I.A., Schmidt,S., Peshkin,L., Ramensky,V.E., Gerasimova,A., Bork,P., Kondrashov,A.S. and Sunyaev,S.R. (2010) A method and server for predicting damaging missense mutations. Nat. Methods, 7, 248-249.
 
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Computational Biology Group, University of Kent, UK
Mark Wass