Protein Structure Prediction by Evolutionary Multi-objective Optimization: Search Space Reduction by Using Rotamers

2009 ◽  
pp. 861-868
Author(s):  
J. C. Calvo ◽  
J. Ortega ◽  
M. Anguita ◽  
J. M. Urquiza ◽  
J. P. Florido
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Search Space ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Search Space Reduction ◽  
Optimization Search

scholarly journals Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

2019 ◽  
Vol 4 (2) ◽  
pp. 35 ◽  
Author(s):  
Ulrich A. Ngamalieu-Nengoue ◽  
Pedro L. Iglesias-Rey ◽  
F. Javier Martínez-Solano
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Urban Drainage ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Decision Variables ◽  
Drainage Networks ◽  
Search Space Reduction ◽  
Pareto Fronts

The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.

AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction

2018 ◽  
Vol 146 ◽  
pp. 58-72 ◽  
Author(s):  
Shuangbao Song ◽  
Shangce Gao ◽  
Xingqian Chen ◽  
Dongbao Jia ◽  
Xiaoxiao Qian ◽  
...  
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Evolutionary Strategy ◽  
Multi Objective

scholarly journals A Novel Framework for Ab Initio Coarse Protein Structure Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Sandhya Parasnath Dubey ◽  
S. Balaji ◽  
N. Gopalakrishna Kini ◽  
M. Sathish Kumar
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Search Algorithm ◽  
Search Space ◽  
Population Based ◽  
Hill Climbing ◽  
Superior Performance ◽  
Protein Database ◽  
Fitness Value

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

scholarly journals A multi-objective evolutionary approach to the protein structure prediction problem

2005 ◽  
Vol 3 (6) ◽  
pp. 139-151 ◽  
Author(s):  
Vincenzo Cutello ◽  
Giuseppe Narzisi ◽  
Giuseppe Nicosia
Keyword(s):  
Protein Structure ◽  
Potential Energy ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Research Effort ◽  
Potential Energy Function ◽  
Conformational Space ◽  
Multi Objective ◽  
Protein Structure Prediction Problem

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.

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