Multiobjective evolutionary algorithm with many tables to ab initio protein structure prediction

Author(s)

Christiane Regina Soares Brasil

• Document type: Doctoral Thesis
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2012-05-10

Examining board members

Alexandre Cláudio Botazzo Delbem; Hélio José Corrêa Barbosa; Fernando Luis Barroso da Silva; Ivan Nunes da Silva; Ricardo Hiroshi Caldeira Takahashi

• Advisor: Alexandre Cláudio Botazzo Delbem

Abstract

This work focuses on the development of optimization algorithms for the purely ab initio Protein Structure Prediction (PSP) problem. Algorithms that better explore the space of potential solutions can in general find better solutions. Such algorithms can benefit both ab initio and template-based PSP, that uses priori knowledge. Researches have shown that Multiobjective evolutionary algorithms can contribute significantly in the context of purely ab initio PSP. In this context, this research investigates the Multiobjective Evolutionary Algorithm based on Tables applied to purely ab initio PSP, which has shown interesting results for relatively simple proteins. For example, one challenge for purely ab initio PSP is the prediction of structures with -sheets. To work with such proteins, this research has developed computationally efficient procedures to estimate hydrogen bond and solvation energies. In general, they are not considered by PSP approaches combining optimization methods with priori knowledge. Only by considering van der Waals and electrostatic, the two interaction energies that mostly contribute to defining a protein structure, and the hydrogen bond and solvation energies, the PSP problem has four objectives. Combinatorial problems (such as the PSP) with more than three objective usually require specific methods capable of dealing with many goals. To address this limitation, we propose a new method for many objective optimization, called Multiobjective Evolutionary Algorithm with Many Tables (MEAMT). This method performs a more adequate sampling of the space of objective functions and, therefore, can better map the promising regions of this space. The ability of dealing with many objectives enables the MEANT to better use information generated by solvation and hydrogen bond energies, and then predict structures with -sheets and some relatively complex proteins. From the computational point of view, the MEAMT is a new method for dealing with many objectives (more than ten) finding relevant solutions (AU)