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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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使用GRSA和生物灵感算法预测的三级结构的增强方法.

Diego A Soto-Monterrubio1,2, Hernán Peraza-Vázquez1, Adrián F Peña-Delgado2

  • 1Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Km.14.5 Carretera Tampico-Puerto Industrial Altamira, Altamira 89600, Tamaulipas, Mexico.

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一种新的GRSABio-FCNN方法通过结合生物灵感算法和神经网络来改善蛋白质结构预测. 这种方法为高达50个氨基酸的提供了竞争力的结果,在更短的序列中表现优于其他.

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生物启发的算法金色比率模拟化模拟化这是一种超听证学 (metaheuristics).质结构预测和预测

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 生物物理学的生物物理.

背景情况:

  • 蛋白质折叠问题 (PFP) 对于理解蛋白质功能至关重要,并且在计算上具有挑战性 (NP-hard).
  • 尽量减少能量功能是预测稳定,生物相关蛋白质结构的关键.
  • 生物启发的算法为PFP等复杂的计算问题提供了有效的解决方案.

研究的目的:

  • 开发一种新的混合算法,用于精确预测蛋白质结构.
  • 通过使用集成的计算框架来增强蛋白质结构的精细化.
  • 评估拟议的算法的性能与现有的最先进的方法相比.

主要方法:

  • 介绍GRSABio算法,集成跳跃蜘蛛算法 (JSOA) 和金比例模拟化 (GRSA).
  • 整合一个卷积神经网络用于碎片预测 (FCNN),以创建GRSABio-FCNN方法.
  • 将GRSABio-FCNN方法应用于60个的数据集,并使用Wilcoxon和弗里德曼测试进行统计比较.

主要成果:

  • 该GRSABio-FCNN方法展示了与最先进的方法对抗的竞争性性能,用于高达50个氨基酸的.
  • 改进的方法超越了领先的PFP算法,用于预测多达30个氨基酸的的结构.
  • 综合框架显示了蛋白质预测的基于能源的结构改进.

结论:

  • GRSABio-FCNN在蛋白质结构预测方面取得了重大进展,特别是对于较短的序列.
  • 混合方法有效地利用生物启发的策略和深度学习来提高预测准确度.
  • 这项研究证实了将GRSABio和FCNN结合起来,应对复杂的PFP挑战的潜力.