Some Modeling Issues for Protein Structure Prediction Using Evolutionary Algorithms

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Abstract

It is noteworthy that the modelling, sampling and convergence properties might be a critical issue in the PSP. From a computation perspective based on EAS, different modeling issues for PSP were revised in this Chapter. Although lattice models are relatively simple, they are very appealing for EAS approaches where the computational efficiency can be highly improved, enabling the prediction of better protein structures. In fact, the data structure based on AR + CM (Section 2) simplifies the objective function of lattice models since there is no need for an additional function penalizing amino acid collisions. As a consequence, the objective function uses only one criterion, i.e., the evaluation of the number of interactions between hydrophobic amino acids. The hydrophobicity of protein is a measure of the interplay of the protein and solvent interactions. The objective function of the lattice models based on AR + CM estimates this interaction. Thus, the EA using such model may also lead to a computationally efficient process in order to find protein conformations with more plausible solvent interaction. The solvent effect on PSP is an important issue: for most cases, the solvation energy basically drives the process. Different alternatives on how to model the solvent have been pointed out on Section 3. Despite the fact that some protein structure were successfully obtained with models based on potential energy functions with no hydration free energy contributions, this is not a general rule. For a general protein case, the solvation free energy and interaction

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