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Multi-objective evolutionary strategy approaches for protein structure prediction

宋 双宝 富山大学

2020.03.24

概要

The problem of predicting the three-dimensional structure of a protein from its one- dimensional sequence has been called the “holy grail of molecular biology”, and it has become an important part of structural genomics projects. Despite half-century’s unremitting efforts, the prediction of protein structure from its amino acid sequence remains a grand challenge in computational biology and bioinformatics. Two key factors are crucial to solving the protein structure prediction (PSP) problem: an effective energy function and an efficient conformation search strategy.

In my research of defending PhD, I focus on modeling the PSP problem as a multi-objective optimization problem, and use an evolutionary strategy to solve the problem. A method MO3 and its improved version AIMOES, were proposed during my research of defending PhD. They are illustrated as follows:

(1) Firstly, in MO3, we propose a multi-objective evolutionary algorithm. We decompose the protein energy function Chemistry at HARvard Macromolecular Me- chanics force fields into bond and non-bond energies as the first and second objectives. Considering the effect of solvent, we innovatively adopt a solvent-accessible surface area as the third objective. We use 66 benchmark proteins to verify the proposed method and obtain better or competitive results in comparison with the existing methods. The results suggest the necessity to incorporate the effect of solvent into a multi-objective evolutionary algorithm to improve protein structure prediction in terms of accuracy and efficiency.

(2) Secondly, in AIMOES, we model the PSP as a multi-objective optimiza- tion problem. A three-objective evolution algorithm called AIMOES is proposed. AIMOES adopts three physical energy terms: bond energy, non-bond energy, and solvent accessible surface area. In AIMOES, an evolution scheme which flexibly reuse past search experiences is incorporated to enhance the efficiency of conformation search. A decision maker based on the hierarchical clustering is carried out to select representative solutions. A set of benchmark proteins with 30 ∼ 91 residues is tested to verify the performance of the proposed method. Experimental results show the effectiveness of AIMOES in terms of the root mean square deviation (RMSD) metric, the distribution diversity of the obtained Pareto front and the success rate of mu- tation operators. The superiority of AIMOES is demonstrated by the performance comparison with other five state-of-the-art PSP methods.

This thesis is organized as follows: Chapter 1 gives a brief introduction about the PSP problem and multi-objective optimization. Chapter 2 presents some important concepts. Chapter 3 presents the energy function used in these two methods. In Chapter 4, we shows the multi-objective evolutionary strategy where solvent effect are incorporated into, i.e. MO3, for solving the PSP problem. The experimental results of MO3 are also shown in this chapter. Then, in Chapter 5, the archive information assisted multi-objective evolutionary strategy, i.e. AIMOES, for solving the PSP problem is described. Finally, we draw the conclusions of this thesis in Chapter 6.

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