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A Memetic algorithm for 3D Protein Structure Prediction Problem

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第3期15卷 690-704页

作者： Correa, Leonardo Borguesan, Bruno Farfan, Camilo Inostroza-Ponta, Mario Dorn, Marcio Univ Fed Rio Grande do Sul Inst Informat BR-90040060 Porto Alegre RS Brazil Univ Santiago Dept Ingn Informat Ctr Biotechnol & Bioengn Santiago Chile

Memetic algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic algorithms. In this paper, we present a Memetic algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem. It takes advantage of structural knowledge stored in the Protein Data Bank, by using an Angle Probability List that helps to reduce the search space and to guide the search strategy. The proposed algorithm was tested on 19 protein sequences of amino acid residues, and the results show the ability of the algorithm to find native-like protein structures. Experimental results have revealed that the proposed algorithm can find good solutions regarding root-mean-square deviation and global distance total score test in comparison with the experimental protein structures. We also show that our results are comparable in terms of folding organization with state-of-the-art prediction methods, corroborating the effectiveness of our proposal.

关键词： Optimization metaheuristics evolutionary algorithms knowledge based algorithm structural bioinformatics

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Uma proposta de algoritmo memético baseado em conhecimento para o problema de predição de estruturas 3-D de proteínas

Uma proposta de algoritmo memético baseado em conhecimento ...

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作者： Correa, Leonardo de Lima

Memetic algorithms are evolutionary metaheuristics intrinsically concerned with the exploiting and incorporation of all available knowledge about the problem under study. In this dissertation, we present a knowledge-based memetic algorithm to tackle the threedimensional protein structure prediction problem without the explicit use of template experimentally determined structures. The algorithm was divided into two main steps of processing: (i) sampling and initialization of the algorithm solutions; and (ii) optimization of the structural models from the previous stage. The first step aims to generate and classify several structural models for a determined target protein, by the use of the strategy Angle Probability List, aiming the definition of different structural groups and the creation of better structures to initialize the initial individuals of the memetic algorithm. The Angle Probability List takes advantage of structural knowledge stored in the Protein Data Bank in order to reduce the complexity of the conformational search space. The second step of the method consists in the optimization process of the structures generated in the first stage, through the applying of the proposed memetic algorithm, which uses a tree-structured population, where each node can be seen as an independent subpopulation that interacts with others, over global search operations, aiming at information sharing, population diversity, and better exploration of the multimodal search space of the problem The method also encompasses ad-hoc global search operators, whose objective is to increase the exploration capacity of the method turning to the characteristics of the protein structure prediction problem, combined with the Artificial Bee Colony algorithm to be used as a local search technique applied to each node of the tree. The proposed algorithm was tested on a set of 24 amino acid sequences, as well as compared with two reference methods in the protein structure prediction area, Ros

关键词： Bioinformática Optimization Metaheuristics Algorítmo Evolutionary algorithms knowledge based algorithm Structural bioinformatics Memetic algorithms Dissertação

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Color image detection by biomolecular photoreceptor using bacteriorhodopsin-based complex LB films

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BIOSENSORS & BIOELECTRONICS 2001年第9-12期16卷 925-935页

作者： Choi, HG Jung, WC Min, JH Lee, WH Choi, JW Sogang Univ Dept Chem Engn Seoul 100611 South Korea

A biomolecular photoreceptor consisting of bacteriorhodopsin (bR)-based complex Langmuir-Blodgett (LB) films was developed for color image detection. By mimicking the functions of the pigments in retina of human visual system, biomolecules with photoelectric conversion function were chosen and used as constituents for an artificial photoreceptor. bR and flavin were deposited onto the patterned (9-pixelized) ITO glass by LB technique. A 9-pixel biomolecular photoreceptor was fabricated with a sandwich-type structure of ITO/LB films/electrolyte gel/Pt. Since each functional molecule shows its own response characteristic according to the light illumination in the visible region, the simplified knowledge-based algorithm for interpretation of the incident light wavelength (color) was proposed based on the basic rule describing the relationship between the photoelectric response characteristics and the incident light wavelength. When simple color images were projected onto the photoreceptor, the primary colors in visible light region, red, green, and blue were clearly recognized, and the projected color images were fairly well reproduced onto the color monitor by the proposed photoreceptor with the knowledge-based algorithm. It is concluded that the proposed device has a capability of recognizing the color images and can be used as a model system to simulate the information processing function of the human visual system. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： color image detection biomolecular photoreceptor knowledge based algorithm bacteriorhodopsin complex Langmuir-Blodgett films

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Color image detection by biomolecular photoreceptor using bacteriorhodopsin-based complex LB films

Color image detection by biomolecular photoreceptor using ba...

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6th World Congress on Biosensors (Biosensors 2000)

作者： Choi, HG Jung, WC Min, JH Lee, WH Choi, JW Sogang Univ Dept Chem Engn Seoul 100611 South Korea

关键词： color image detection biomolecular photoreceptor knowledge based algorithm bacteriorhodopsin complex Langmuir-Blodgett films

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