Models Predicting Effects of Missense Mutations in Oncogenesis
Date
2014-09-23
Authors
Addepalli, KanakaDurga
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Abstract
The recent avalanche in high-throughput genotyping, next generation sequencing technologies and re-sequencing of cancer genomes has revolutionized the field of cancer genomics. It has generated a humungous amount of mutational data and changed the way the cancer is being studied. Identification and characterization of these mutations and their mutational effect has become one of the major goals of cancer research. We present here a computational geometry approach based on the application of Delaunay tessellation derived four-body statistical potential function where the potentials are directly derived from the high-resolution protein x-ray crystallographic structures utilizing their atomic coordinates. Proteins and their mutants are characterized by potential topological scores and profiles, which measure the relative change in the overall sequence-structure compatibility. Residual scores and profiles are generated which quantify environmental perturbations from wild-type amino acids at every mutational position. We also present here an integrated database of human cancer missense mutations linked to their 3D structures, which has been created with the whole motivation of building a one stop shop of human missense mutations data sets huge and versatile enough to be used for training and testing of machine learning methodologies. With protein data from this database, we illustrate the use of potential topological cores and residual profiles in the prediction of mutational effects on protein structure and function and generating predictive models using machine-learning algorithms. We successfully apply supervised learning to training sets of protein mutants and generate models, which make statistically meaningful predictions of effects of missense mutations on cancer proteins.
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Cancer, Database, Machine learning, Missense mutations