Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms.

ABSTRACT: Recently, predicting proteins three-dimensional (3D) structure from its sequence information has made a significant progress due to the advances in computational techniques and the growth of experimental structures. However, selecting good models from a structural model pool is an important and challenging task in protein structure prediction. In this study, we present the first application of random forest based model quality assessment (RFMQA) to rank protein models using its structural features and knowledge-based potential energy terms. The method predicts a relative score of a model by using its secondary structure, solvent accessibility and knowledge-based potential energy terms. We trained and tested the RFMQA method on CASP8 and CASP9 targets using 5-fold cross-validation. The corr

SUBMITTER: Manavalan B 

PROVIDER: S-EPMC4164442 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature