An evolutionary framework to sample near-native protein conformations

Abstract

Structural characterization of the protein native state is an important problem in computational biology. Thermodynamically, the native state is that of lowest free energy in the protein conformational space [1]. Predicting it ab initio from the amino-acid sequence can be posed as an optimization problem that has proven to be NP-hard [2]. Due to imperfect modeling of interatomic interactions, the native state often does not correspond to the global minimum. As a result, the goal in ab-initio protein structure prediction is to first arrive at a diverse ensemble of low-energy (decoy) conformations potentially relevant for the native state. Decoys are often computed using a coarse-grained energy function that expedites sampling of low-energy conformations. Select decoys are then refined with heavy-duty protocols using fine-grained energy functions to allow prediction of the native state.