Abstract. The prediction of a protein's structure from its amino-acid sequence is one of the most important problems in computational biology. In the current work, we focus on a widely studied abstraction of this problem, the 2-dimensional hydrophobic-polar (2D HP) protein folding problem. We present an improved version of our recently proposed Ant Colony Optimisation (ACO) algorithm for this £ ¥ ¤ -hard combinatorial problem and demonstrate its ability to solve standard benchmark instances substantially better than the original algorithm; the performance of our new algorithm is comparable with state-of-the-art Evolutionary and Monte Carlo algorithms for this problem. The improvements over our previous ACO algorithm include long range moves that allows us to perform modification of the protein at high densities, the use of improving ants, and selective local search. Overall, the results presented here establish our new ACO algorithm for 2D HP protein folding as a state-of-the-art method for this highly relevant problem from bioinformatics.
Background: The ab initio protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing an energy function; it is one of the most important and challenging problems in biochemistry, molecular biology and biophysics. The ab initio protein folding problem is computationally challenging and has been shown to be -hard even when conformations are restricted to a lattice. In this work, we implement and evaluate the replica exchange Monte Carlo (REMC) method, which has already been applied very successfully to more complex protein models and other optimization problems with complex energy landscapes, in combination with the highly effective pull move neighbourhood in two widely studied Hydrophobic Polar (HP) lattice models.
Background: The protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model -the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model.
Current methods for predicting protein structure depend on two interrelated components: (i) an energy function that should have a low value near the correct structure and (ii) a method for searching through different conformations of the polypeptide chain. Identification of the most efficient search methods is essential if we are to be able to apply such methods broadly and with confidence. In addition, efficient search methods provide a rigorous test of existing energy functions, which are generally knowl- By using a set of nonnative low-energy structures found by our extensive sampling, we discovered that the long-range and short-range backbone hydrogen-bonding energy terms of the Rosetta energy discriminate between the nonnative and native-like structures significantly better than the low-resolution score used in Rosetta.conformational search ͉ protein folding ͉ Rosetta force field P redicting the functional 3-dimensional structure (the native state) of a protein from its amino acid sequences is of central importance to structural and functional biology and has enormous applications in alleviating human disease. Even if the structures of all proteins were known, we would still not be able to answer questions related to diseases directly caused by protein misfolding, such as certain types of cancer and Alzheimer's and Parkinson disease. For this we would need to understand the physical basis of the energy terms that make the native state so special. Such understanding of the energetics of the system would also lead to more efficient and comprehensive drug design. Structure prediction depends on solving two problems: (i) describing the energy function with sufficient accuracy and (ii) searching the conformational space sufficiently well. These problems are particularly severe for proteins of biologically relevant lengths (Ͼ150 aa).In this work we focus on conformational sampling, which has been recognized as the critical step in high-resolution structure prediction (1-3). Most widely used standard methods for de novo structure prediction are based on the variants of the Monte Carlo method (4-6) and are unable to explore low-energy regions efficiently because of the ruggedness of the potential energy surface. To overcome these problems, a number of generalized ensemble Monte Carlo methods have been developed (7-10). These methods strive to search energy space better by computing the density of states, sampling expanded ranges of temperatures, or computing other physical quantities affecting transitions between the states during the search. In particular, advanced methods such as Temperature Replica Exchange Monte Carlo (TREM) (8) and Hamiltonian Replica Exchange Monte Carlo (HREM) (10), have been shown to outperform standard Monte Carlo in terms of sampling for both simplified and all-atom force fields of small proteins (8,10,11).For longer proteins, the computational cost and ruggedness of the all-atom energy function makes solving this problem particularly challenging as evidenced by the modest success of full...
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