Differential Evolution to Multi-Objective Protein Structure Prediction (original) (raw)
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A multi-objective evolutionary approach to the protein structure prediction problem
2005
The protein structure prediction (PSP) problem is concerned with the prediction of the folded, native, tertiary structure of a protein given its sequence of amino acids. It is a challenging and computationally open problem, as proven by the numerous methodological attempts and the research effort applied to it in the last few years. The potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms). In this paper, we show experimentally that those types of interactions are in conflict, and do so by using the potential energy function Chemistry at HARvard Macromolecular Mechanics. A multi-objective formulation of the PSP problem is introduced and its applicability studied. We use a multi-objective evolutionary algorithm as a search procedure for exploring the conformational space of the PSP problem.
MULTI-OBJECTIVE EVOLUTIONARY APPROACH TO AB INITIO PROTEIN TERTIARY STRUCTURE PREDICTION
… on Mathematical and …, 2007
The Protein Structure Prediction (PSP) problem aims at determining protein tertiary structure from its amino acids sequence. PSP is a computationally open problem. Several methodologies have been investigated to solve it. Two main strategies have been employed to work with PSP: homology and Ab initio prediction. This paper presents a Multi-Objective Evolutionary Algorithm (MOEA) to PSP problem using an ab initio approach. The proposed MOEA uses dihedral angles and main angles of the lateral chains to model a protein structure. This article investigates advantages of multi-objective evolutionary approach and discusses about methods and other approaches to the PSP problem.
The Protein Structure Prediction (PSP) problem is concerned about the prediction of the native tertiary structure of a protein in respect to its amino acids sequence. PSP is a challenging and computationally open problem. Therefore, several researches and methodologies have been developed for it. In this way, developers are working to integrate frameworks in order to improve their capabilities and make their use more straightforward. This paper presents the application of NSGA-II algorithm using structural and energetic properties of protein. The implementation of this algorithm is based on ProtPred-GROMACS (2PG), an evolutionary framework for PSP. This framework is the integration between ProtPred and GROMACS. Six proteins were used to measure the capacity of ab initio predictions. The results were interesting since in all cases the native-like topology was obtained.
Speed up differential evolution for computationally expensive protein structure prediction problems
Swarm and Evolutionary Computation, 2019
Protein structure prediction (PSP) plays an important role in the field of computational molecular biology. Although powerful optimization algorithms have been proven effective to tackle the PSP, researchers are faced with the challenge of time consuming simulations. This paper introduces a new modification of differential evolution (DE) which makes use of the computationally cheap surrogate models and gene expression programming (GEP) in order to address the aforementioned issue. The incorporated GEP is used to generate a diversified set of configurations, while radial basis function (RBF) surrogate model helps DE to find the best set of configurations. In addition to this, covariance matrix adaptation evolution strategy (CMAES) is also adopted to explore the search space more efficiently. The introduced algorithm, called SGDE, is tested on real-world proteins from the Protein data bank (PDB) using both a simplified and an all-atom model. The experiments show that SGDE performs better than the state-of-the-art algorithms on the PSP problems in both terms of the convergence rate and accuracy. In the case of run time complexity, SGDE significantly outperforms the other competitive algorithms for the adopted all-atom model.
Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey
Revista de Informática Teórica e Aplicada, 2021
Protein Structure Prediction (PSP) problem is to determine the three-dimensional structure of a protein only from its primary structure. Misfolding of a protein causes human diseases. Thus, the knowledge of the structure and functionality of proteins, combined with the prediction of their structure is a complex problem and a challenge for the area of computational biology. The metaheuristic optimization algorithms are naturally applicable to support in solving NP-hard problems.These algorithms are bio-inspired, since they were designed based on procedures found in nature, such as the successful evolutionary behavior of natural systems. In this paper, we present a survey on methods to approach the \textit{ab initio} protein structure prediction based on evolutionary computing algorithms, considering both single and multi-objective optimization. An overview of the works is presented, with some details about which characteristics of the problem are considered, as well as specific point...
Protein Structure Prediction with Evolutionary Algorithms
1999
Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the conformational representation, the energy formulation and the way in which infeasible conformations are penalized. Further we empirically evaluate the impact of these factors on a small set of polymer sequences. Our analysis leads to speci c recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.
A multi-objective genetic algorithm for the Protein Structure Prediction
2011 11th International Conference on Intelligent Systems Design and Applications, 2011
The Protein Structure Prediction (PSP) problem consists of predicting the structure of a protein from its amino acids sequence, and have received much attention lately. In fact, being able to predict the structure of a protein, would allow to know the function of the protein. In this paper, we propose a multi-objective evolutionary algorithm for the PSP problem. The prediction model consists of a set of rules that determine possible contacts between amino acids. Such rules are based on four specific amino acid properties, which are involved in the folding process: hydrophobicity, polarity, net charge and residue size. In order to increase the interpretability of the results, rules are organized in a 20x20 matrix where each cell contains the specific rules for a possible pair of residues. The high accuracy values obtained confirm the validity of our proposal.
A multiple minima genetic algorithm for protein structure prediction
Applied Soft Computing, 2014
Protein structure prediction (PSP) has a large potential for valuable biotechnological applications. However the prediction itself encompasses a difficult optimization problem with thousands of degrees of freedom and is associated with extremely complex energy landscapes. In this work a simplified threedimensional protein model (hydrophobic-polar model, HP in a cubic lattice) was used in order to allow for the fast development of a robust and efficient genetic algorithm based methodology. The new methodology employs a phenotype based crowding mechanism for the maintenance of useful diversity within the populations, which resulted in increased performance and granted the algorithm multiple solutions capabilities. Tests against several benchmark HP sequences and comparative results showed that the proposed genetic algorithm is superior to other evolutionary algorithms. The proposed algorithm was then successfully adapted to an all-atom protein model and tested on poly-alanines. The native structure, an alpha helix, was found in all test cases as a local or a global minimum, in addition to other conformations with similar energies. The results showed that optimization strategies with multiple solutions capability present two advantages for PSP applications. The first one is a more efficient investigation of complex energy landscapes; the second one is an increase in the probability of finding native structures, even when they are not at the global optimum.