Protein Folding in Simplified Models With Estimation of Distribution Algorithms

Abstract

Abstract-Simplified lattice models have played an important role in protein structure prediction and protein folding problems. These models can be useful for an initial approximation of the protein structure, and for the investigation of the dynamics that govern the protein folding process. Estimation of distribution algorithms (EDAs) are efficient evolutionary algorithms that can learn and exploit the search space regularities in the form of probabilistic dependencies. This paper introduces the application of different variants of EDAs to the solution of the protein structure prediction problem in simplified models, and proposes their use as a simulation tool for the analysis of the protein folding process. We develop new ideas for the application of EDAs to the bidimensional and tridimensional (2-d and 3-d) simplified protein folding problems. This paper analyzes the rationale behind the application of EDAs to these problems, and elucidates the relationship between our proposal and other population-based approaches proposed for the protein folding problem. We argue that EDAs are an efficient alternative for many instances of the protein structure prediction problem and are indeed appropriate for a theoretical analysis of search procedures in lattice models. All the algorithms introduced are tested on a set of difficult 2-d and 3-d instances from lattice models. Some of the results obtained with EDAs are superior to the ones obtained with other well-known population-based optimization algorithms.Index Terms-Estimation of distribution algorithm (EDAs), hydrophobic-polar (HP) model, protein folding, protein structure prediction.

A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

“…Many works in the literature confirm the good performance of EDAs in the solution of problems from diverse fields. Protein Folding [45], Capacitated Vehicle Routing Problems [52], Calibration of Chemical Applications [35], Finding the Optimal Path in 3D Spaces [53], Software Testing [44], Chemotherapy Treatment Optimization for Cancer [6] or Nuclear Reactor Fuel Management Parameter Optimization [21] are some examples of many real-world problems where EDA-based approaches were applied to find optimal solutions.…”

Algoritmos evolutivos e modelo HP para predição de estruturas de proteínas

“…Implementação de técnicas de busca local, cujo impacto positivo tem sido demonstrado em trabalhos relacionados (Krasnogor, 2002;Bazzoli and Tettamanzi, 2004); 4. Pesquisa do impacto de técnicas de aumento de desempenho de AEs desenvolvidas recentemente, como os chamados algoritmos de estimação de distribuição (Santana et al, 2008;Melo, 2009). …”

Mateda-2.0: AMATLABPackage for the Implementation and Analysis of Estimation of Distribution Algorithms

“…In Santana et al (2008b), it was shown that EDAs can achieve state-of-the-art results in the optimization of the HP protein model. Here we show how Mateda-2.0 can be used to implement an EDA approach to this problem.…”

“…Figure 1 shows one of the optimal solutions found by this algorithm in generation 144. This solution is very hard to find for state-of-the-art algorithms (Santana et al 2008b). Figure 2 illustrates the steady increase in the average fitness of the solutions with the generations.…”