Amino acid mutations in human proteins are often associated with inherited predispositions to specific diseases. Yet most observed missense polymorphisms, those involving a single nucleotide change leading to a changed amino acid, have not been characterized in terms of their effects on protein structure and function. We hypothesize that many of the most deleterious missense mutations affect protein function in one of two ways: 1) by altering interaction of proteins with other molecules, including other proteins, DMA, and small ligands; or 2) by altering stability of the protein. Both of these mechanisms depend primarily on the location of the mutation and its physical properties: changes in protein interactions are usually caused by mutations in or very near to a binding site; changes in stability are usually caused by mutations of buried hydrophobic residues.
The aim of this proposal is to develop a computational system for predicting the functional effects of missense mutations through homology modeling of protein complexes. New functional data on 1000 random mutations in two dimeric enzyme systems will be obtained to train and test the model. The primary application of this computational system will be to genes associated with the development of cancer. Cancer is usually linked to a number of genetic changes, some inherited and others somatic. These include loss of DNA-damage repair, breakdown of cell-cycle checkpoints, and resistance to apoptosis. Each of these processes requires many protein interactions, often in large protein complexes. These interactions may be compromised by missense mutations that alter individual interactions between molecules or mutations that lower protein stability.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM073784-03
Application #
7369808
Study Section
Special Emphasis Panel (ZRG1-BCMB-Q (02))
Program Officer
Wehrle, Janna P
Project Start
2006-03-01
Project End
2010-02-28
Budget Start
2008-03-01
Budget End
2009-02-28
Support Year
3
Fiscal Year
2008
Total Cost
$307,176
Indirect Cost
Name
Research Institute of Fox Chase Cancer Center
Department
Type
DUNS #
064367329
City
Philadelphia
State
PA
Country
United States
Zip Code
19111
Modi, Vivek; Dunbrack Jr, Roland L (2016) Assessment of refinement of template-based models in CASP11. Proteins 84 Suppl 1:260-81
Wei, Qiong; Dunbrack Jr, Roland L (2013) The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One 8:e67863
Wei, Qiong; Xu, Qifang; Dunbrack Jr, Roland L (2013) Prediction of phenotypes of missense mutations in human proteins from biological assemblies. Proteins 81:199-213
Xu, Qifang; Dunbrack Jr, Roland L (2011) The protein common interface database (ProtCID)--a comprehensive database of interactions of homologous proteins in multiple crystal forms. Nucleic Acids Res 39:D761-70
Wei, Qiong; Wang, Liqun; Wang, Qiang et al. (2010) Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase. Proteins 78:2058-74
Xue, Bin; Dunbrack, Roland L; Williams, Robert W et al. (2010) PONDR-FIT: a meta-predictor of intrinsically disordered amino acids. Biochim Biophys Acta 1804:996-1010
Weitzner, Brian; Meehan, Thomas; Xu, Qifang et al. (2009) An unusually small dimer interface is observed in all available crystal structures of cytosolic sulfotransferases. Proteins 75:289-95
Wang, Qiang; Canutescu, Adrian A; Dunbrack Jr, Roland L (2008) SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat Protoc 3:1832-47
Pietsch, E Christine; Perchiniak, Erin; Canutescu, Adrian A et al. (2008) Oligomerization of BAK by p53 utilizes conserved residues of the p53 DNA binding domain. J Biol Chem 283:21294-304
Xu, Qifang; Canutescu, Adrian A; Wang, Guoli et al. (2008) Statistical analysis of interface similarity in crystals of homologous proteins. J Mol Biol 381:487-507

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