Minimizing and Learning Energy Functions for Side-Chain Prediction

Abstract. Side-chain prediction is an important subproblem of the general protein folding problem. Despite much progress in side-chain prediction, performance is far from satisfactory. As an example, the ROSETTA program that uses simulated annealing to select the minimum energy conformations, correctly predicts the first two side-chain angles for approximately 72% of the buried residues in a standard data set. Is further improvement more likely to come from better search methods, or from better energy functions? Given that exact minimization of the energy is NP hard, it is difficult to get a systematic answer to this question. In this paper, we present a novel search method and a novel method for learning energy functions from training data that are both based on Tree Reweighted Belief Propagation (TRBP). We find that TRBP can find the global optimum of the ROSETTA energy function in a few minutes of computation for approximately 85% of the proteins in a standard benchmark set. TRBP ca...

• Type: Conference
• Year: 2007
• Where: RECOMB
• Authors: Chen Yanover, Ora Schueler-Furman, Yair Weiss
• Keywords: Computational Biology, Conditional Random Fields, RECOMB 2007, ROSETTA Energy Function, Tree Reweighted Belief