Computational Methods for Protein Fold Prediction: an Ab-initio Topological Approach

Abstract

Summary. The prediction of protein native conformations is still a big challenge in science, although a strong research activity has been carried out on this topic in the last decades. In this chapter we focus on ab-initio computational methods for protein fold predictions that do not rely heavily on comparisons with known protein structures and hence appear to be the most promising methods for determining conformations not yet been observed experimentally. To identify main trends in the research concerning protein fold predictions, we briefly review several ab-initio methods, including a recent topological approach that models the protein conformation as a tube having maximum thickness without any self-contacts. This representation leads to a constrained global optimization problem. We introduce a modification in the tube model to increase the compactness of the computed conformations, and present results of computational experiments devoted to simulating α-helices and all-α proteins. A Metropolis Monte Carlo Simulated Annealing algorithm is used to search the protein conformational space.

Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods

“…The model considered in this section is proposed in [3,19] and modified in [8]. The model is named the tube model because a protein conformation is modeled as a tube with a certain diameter such that the tube does not cross itself.…”

“…In [5], the properties of the acceptance probability have been analyzed and an algorithm for computing a temperature that is compatible with a given acceptance ratio is presented. SA is used for solving the problem discussed in Section 4.1, and it has also been used in [3,8] in the very first studies on the problem discussed in Section 4.3.…”

“…The specific problem on which we want to focus is based on protein geometric features only. These features are used for analyzing geometric aspects of the protein molecules [3,8]. The objective function is not complex like the ones modeling the energy in the molecule, yet it is not easy to find the optimum solution.…”

Monkey search: a novel metaheuristic search for global optimization

“…The tube model is a geometric model for protein folding proposed in [1,9] and recently modified in [3]. Protein folding is one of the major challenges in biology and medicine: nobody knows how a chain of amino acids folds forming the typical compact and functional structures the proteins have inside the cells.…”

“…We also consider an optimization problem arising from a model developed for studying the protein folding problem, which has a growing need of fast and efficient optimization methods. This model is called the "tube model" due to its protein representation, and it simulates protein conformations based on geometric features of proteins [I, 3,9].…”