SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations.

ABSTRACT: Accurate protein side-chain conformation prediction is crucial for protein modeling and existing methods for the task are widely used; however, faster and more accurate methods are still required. Here we present a new machine learning approach to the problem where an energy function for each rotamer in a structure is computed additively over pairs of contacting atoms. A family of 156 neural networks indexed by amino acid and contacting atom types is used to compute these rotamer energies as a function of atomic contact distances. Although direct energy targets are not available for training, the neural networks can still be optimized by converting the energies to probabilities and optimizing these probabilities using Markov Chain Monte Carlo methods. The resulting predictor SIDEpro makes

SUBMITTER: Nagata K 

PROVIDER: S-EPMC3240718 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature