Improving the prediction of disease-related variants using protein three-dimensional structure.

ABSTRACT:

Background

Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.

Results

In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphi

SUBMITTER: Capriotti E 

PROVIDER: S-EPMC3194195 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature