Protein Structure Prediction Using Basin-Hopping

TL;DR

This paper demonstrates that basin-hopping global optimization effectively finds low-energy conformations in protein structure prediction, outperforming molecular dynamics in small systems and guiding improvements in energy functions.

Contribution

It introduces basin-hopping as a novel, transferable optimization method for protein structure prediction and enhances it with bioinformatics techniques and umbrella sampling.

Findings

Basin-hopping locates lower minima than simulated annealing.

Efficiency decreases for larger systems with initial implementation.

Guided improvements lead to better structural predictions.

Abstract

Associative memory Hamiltonian structure prediction potentials are not overly rugged, thereby suggesting their landscapes are like those of actual proteins. In the present contribution we show how basin-hopping global optimization can identify low-lying minima for the corresponding mildly frustrated energy landscapes. For small systems the basin-hopping algorithm succeeds in locating both lower minima and conformations closer to the experimental structure than does molecular dynamics with simulated annealing. For large systems the efficiency of basin-hopping decreases for our initial implementation, where the steps consist of random perturbations to the Cartesian coordinates. We implemented umbrella sampling using basin-hopping to further confirm when the global minima are reached. We have also improved the energy surface by employing bioinformatic techniques for reducing the roughness or variance of the energy surface. Finally, the basin-hopping calculations have guided improvements in the excluded volume of the Hamiltonian, producing better structures. These results suggest a novel and transferable optimization scheme for future energy function development.