基于人工神经网络的扩展力场蛋白质结构识别方法

T. M. Fawcett, S. Irausquin, Mikhail Simin, H. Valafar
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引用次数: 2

摘要

目前在CHARMM或explore - nih中看到的蛋白质力场有许多术语,包括键合和非键合术语。然而,这些力场并没有考虑到氢键的使用,而氢键对于蛋白质的二级结构的形成和稳定是很重要的。SCOPE是一个从旋转体空间生成蛋白质的开源程序。然后,它创建一个力场,仅使用非键和氢键能量项来创建给定蛋白质的轮廓。然后,这些轮廓可以用于人工神经网络,以创建一个线性模型,该模型汇集到真实的蛋白质构象中。
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An Artificial Neural Network Based Approach for Identification of Native Protein Structures Using an Extended Forcefield
Current protein force fields like the ones seen in CHARMM or Xplor-NIH have many terms that include bonded and non-bonded terms. Yet the force fields do not take into account the use of hydrogen bonds which are important for secondary structure creation and stabilization of proteins. SCOPE is an open-source program that generates proteins from rotamer space. It then creates a force field that uses only non-bonded and hydrogen bond energy terms to create a profile for a given protein. The profiles can then be used in an artificial neural network to create a linear model which is funneled to the true protein conformation.
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