Protein folding optimization using differential evolution extended with local search and component reinitialization

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.

Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model

“…In the described reinitialization method we have three different best vectors. The best population vector is the best vector in the current population, the local best vector is the best vector among all similar vectors, and the global best vector is the best vector obtained within the evolutionary process [4]. How long the current population best and local best vector stayed unchanged within the optimization process and the value of control parameters H c , L b , and P b , determine the reinitialization and optimization level.…”

“…On the other hand, random reinitialization guides the search process to unexplored search space regions. For a detailed description of all mechanisms of our previous work and its influence to the algorithm's efficiency, we refer readers to [2] and [4].…”

“…Two-level optimization was designed to increase the quality of the hydrophobic cores and, consequently, improve the efficiency of the algorithm. In order to demonstrate these advantages, the algorithm with two-level optimization DE 2L was compared with the algorithm DE lscr [4] that does not have two-level optimization. Within this comparison, the algorithms were compared by using two scenarios.…”

“…In the first comparison, the stopping condition was NSE lmt , which was set according to the literature [4,19]. The obtained results are shown in Tables 7 and S5 Table 6).…”

“…It has been shown that PFO has a highly rugged landscape structure, containing many local optima and needle-like funnels [14]. In order to explore this search space effectively, we already have proposed a Differential Evolution (DE) algorithm [2,4] that, in contrast to all previous methods, follows only one attractor. The DE algorithm was selected because of its simplicity and efficiency, and because it was successfully used in various optimization problems [6], such as an animated trees reconstruction [36], an post hoc analysis of sport performance [8], and parametric design and optimization of magnetic gears [33].…”