Protein folding using basin-hopping and energy landscapes

PHYS 96

Michael C. Prentiss, mcprentiss@gmail.com, Department of Chemistry, University of California, San Diego, 9500 Gilman Dr. MC-0371, La Jolla, CA 92093-0371, David J. Wales, dw34@cam.ac.uk, Department of Chemistry, University of Cambridge, University Chemical Laboratories, Lensfield Road, Cambridge CM23 5NS, United Kingdom, and Peter G. Wolynes, pwolynes@ucsd.edu, Deapartment of Chemistry and Biochemistry, Center for Theoretical Biological Physics, University of California,San Diego, La Jolla, CA 92093.
Associative memory Hamiltonian potentials have previously been used to predict protein structure from sequence, suggesting that their landscapes like the actual protein energy landscapes are not overly rugged. In the present contribution we show how basin-hopping global optimisation 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. Umbrella sampling using basin-hopping can also show when the global minima are reached for a selected order parameter. Previously employed bioinformatic techniques for reducing the roughness or variance of the energy surface also improve the energy surface. Using basin-hopping specifically improved in the excluded volume and the long range interactions of the Hamiltonian, producing better structures. Furthermore, these results suggest a novel and transferable optimisation scheme for future energy function development.