An effective evolutionary algorithm for protein folding on 3D FCC HP model by lattice rotation and generalized move sets

Abstract

BackgroundProteins are essential biological molecules which play vital roles in nearly all biological processes. It is the tertiary structure of a protein that determines its functions. Therefore the prediction of a protein's tertiary structure based on its primary amino acid sequence has long been the most important and challenging subject in biochemistry, molecular biology and biophysics. In the past, the HP lattice model was one of the ab initio methods that many researchers used to forecast the protein structure. Although these kinds of simplified methods could not achieve high resolution, they provided a macrocosm-optimized protein structure. The model has been employed to investigate general principles of protein folding, and plays an important role in the prediction of protein structures.MethodsIn this paper, we present an improved evolutionary algorithm for the protein folding problem. We study the problem on the 3D FCC lattice HP model which has been widely used in previous research. Our focus is to develop evolutionary algorithms (EA) which are robust, easy to implement and can handle various energy functions. We propose to combine three different local search methods, including lattice rotation for crossover, K-site move for mutation, and generalized pull move; these form our key components to improve previous EA-based approaches.ResultsWe have carried out experiments over several data sets which were used in previous research. The results of the experiments show that our approach is able to find optimal conformations which were not found by previous EA-based approaches.ConclusionsWe have investigated the geometric properties of the 3D FCC lattice and developed several local search techniques to improve traditional EA-based approaches to the protein folding problem. It is known that EA-based approaches are robust and can handle arbitrary energy functions. Our results further show that by extensive development of local searches, EA can also be very effective for finding optimal conformations on the 3D FCC HP model. Furthermore, the local searches developed in this paper can be integrated with other approaches such as the Monte Carlo and Tabu searches to improve their performance.

A Multi-Objective Approach for Protein Structure Prediction Based on an Energy Model and Backbone Angle Preferences

“…The objective function with another objective function formed a multiobjective optimization problem. The objective functions are defined in Equations (1) and (2); Stage 2: this study proposed two improved multiobjective optimisation methods ( and ) incorporating rotation-based crossovers, local search of generalized pull move and K -site mutation methods [ 14 ]. Multiobjective optimisation experiments were conducted on all benchmarks.…”

“…To increase the crossover success rate, rotation-based crossovers [ 14 ] were adopted in this study. For this method, the protein backbone structure in the FCC lattice was regarded as a rigid structure, in which each carbon atom in an amino acid represents a coordinate point in the FCC lattice, and the distance between the coordinate points is fixed.…”

“…For the structural complexity issue, although the interaction between individual atoms can be calculated to model the folding of a protein in a search of the tertiary structure at the lowest free energy, the massive degree of computational complexity makes this approach infeasible. Therefore, researchers have proposed to develop simplified lattice models to reduce the computational complexity in modelling protein tertiary structure, such as a 2-D square [ 3 , 4 , 5 ], 2-D triangular [ 6 ], 3-D cubic [ 7 , 8 , 9 ], and 3-D face-centered cubic (FCC) [ 10 , 11 , 12 , 13 , 14 ] lattice models. Studies on these simplified models have typically used C α atoms, which are centers of amino acids, as the backbone of the protein structure [ 15 ], and the research results have elucidated the relationship between protein sequences and structures.…”

Exact methods for lattice protein models

“…As exhaustive structure enumeration approaches [20] are restricted to very short sequence lengths, usually heuristic methods are applied. They apply various techniques, e.g., simulated annealing [21,22], quantum annealing [23], ant colonization optimization [24], evolutionary algorithms [25], energy landscape paving [26], large neighborhood search [27,28], constraint programming [29][30][31], or dedicated search heuristics [32][33][34][35]. They enable performance-guaranteed approximations close to the optimum for both backbone-only [36,37] and sidechain models [38,39].…”

Comparando algoritmos de otimização computacional aplicados ao problema de predição de estruturas proteicas com modelo HP-2D

“…Portanto, o emprego de algoritmos de otimização para solucionar o problema PSP apresenta-se como uma boa alternativa ante aos métodos tradicionais. Podem-se citar diversas técnicas de otimização computacional aplicadas ao PSP, como Algoritmos Evolutivos [1,2,6,15,20,23,24], Otimização por Colônia de Formiga [10,14,18,21,25], Otimização por Enxame de Abelhas [3,19], entre outros [26,27]. Vale ressaltar que, em geral, usando esses métodos para problemas extremamente complexos não é obtida uma única solução (mas um conjunto de soluções); inclusive a solução ótima pode nunca ser alcançada, e nestes casos, pode ser difícil definir qual a melhor solução no conjunto das possíveis soluções.…”