平行粒子群算法在蛋白质折叠问题中的应用

Luis Germán Pérez-Hernández, K. Rodríguez-Vázquez, R. Garduño-Juárez
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引用次数: 8

摘要

针对考虑结构限制的真实构象的蛋白质折叠问题,提出了一种以最小化构象能ECEPP/3函数为目标的仿生粒子群优化算法(PSO)。在这种情况下,使用骨架和侧链的扭转角表示,应用肽leu-enkephalin的氨基酸序列来预测最小能量的3D结构。结果的质量与文献中报道的其他技术进行了比较。随后,PSO被用于预测未知蛋白质的结构。
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Parallel particle swarm optimization applied to the protein folding problem
This article presents the implementation of a bio-inspired algorithm, which is the algorithm of particle swarm optimization (PSO) in with the objective of minimizing the function of conformational energy ECEPP/3 for the protein folding problem (PFP) for real conformations considering structural restrictions. In this case, using a representation of torsion angles of the skeleton and the side chains, applying the sequence of amino acid of the peptide leu-enkephalin for the prediction of 3D structure of minimum energy. The quality of the results is compared with other techniques reported in literature. Subsequently, the PSO is used to predict the structure of unknown proteins.
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