A global optimization strategy for predicting α-helical protein tertiary structure

Abstract

We present a global optimization strategy that incorporates predicted restraints in both a local optimization context and as directives for global optimization approaches, to predict protein tertiary structure for α-helical proteins. Specifically, neural networks are used to predict the secondary structure of a protein, restraints are defined as manifestations of the network with a predicted secondary structure and the secondary structure is formed using local minimizations on a protein energy surface, in the presence of the restraints. Those residues predicted to be coil, by the network, define a conformational sub-space that is subject to optimization using a global approach known as stochastic perturbation that has been found to be effective for Lennard–Jones clusters and homo-polypeptides. Our energy surface is an all-atom ‘gas phase’ molecular mechanics force field, that is combined with a new solvation energy function that penalizes hydrophobic group exposure. This energy function gives the crystal structure of four different α-helical proteins as the lowest energy structure relative to other conformations, with correct secondary structure but incorrect tertiary structure. We demonstrate this global optimization strategy by determining the tertiary structure of the A-chain of the α-helical protein, uteroglobin and of a four-helix bundle, DNA binding protein.