Zlatibor, Serbia, 23-27 April 2017
Structure optimisation by thermal cycling for the hydrophobic-polar lattice model of protein folding
1 Helmholtz-Zentrum Dresden-Rossendorf, Institut für Ionenstrahlphysik und Material forschung, Center for Advancing Electronics Dresden (cfaed), 01328 Dresden, Germany
2 Technische Universität Dresden, Institut für Physikalische Chemie und Elektrochemie, 01062 Dresden, Germany
3 Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden (IFW), Institut für Theoretische Festkörperphysik, 01069 Dresden, Germany
4 Technische Universität Chemnitz, Institut für Physik, 09107 Chemnitz, Germany
Received: 17 October 2016
Revised: 19 November 2016
Published online: 5 April 2017
The function of a protein depends strongly on its spatial structure. Therefore the transition from an unfolded stage to the functional fold is one of the most important problems in computational molecular biology. Since the corresponding free energy landscapes exhibit huge numbers of local minima, the search for the lowest-energy configurations is very demanding. Because of that, efficient heuristic algorithms are of high value. In the present work, we investigate whether and how the thermal cycling (TC) approach can be applied to the hydrophobic-polar (HP) lattice model of protein folding. Evaluating the efficiency of TC for a set of two- and three-dimensional examples, we compare the performance of this strategy with that of multi-start local search (MSLS) procedures and that of simulated annealing (SA). For this aim, we incorporated several simple but rather efficient modifications into the standard procedures: in particular, a strong improvement was achieved by also allowing energy conserving state modifications. Furthermore, the consideration of ensembles instead of single samples was found to greatly improve the efficiency of TC. In the framework of different benchmarks, for all considered HP sequences, we found TC to be far superior to SA, and to be faster than Wang-Landau sampling.
© The Author(s) 2017
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