Protein Folding Optimization using Differential Evolution Extended with Local Search and Component Reinitialization

TL;DR

This paper introduces an enhanced Differential Evolution algorithm with local search and component reinitialization for protein folding, achieving superior results on standard sequences and finding new best solutions.

Contribution

It proposes novel mechanisms to improve convergence and solution quality in protein folding optimization using Differential Evolution.

Findings

Achieved 100% hit ratio for sequences up to 18 monomers.

Obtained new best-known solutions for most tested sequences.

Demonstrated the existence of symmetric best solutions.

Abstract

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of $10^{11}$ solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.