论文标题
一种基于人工神经网络的方法，用于使用扩展的力场来识别天然蛋白质结构
An Artificial Neural Network Based Approach for Identification of Native Protein Structures using an Extended ForceField
论文作者
论文摘要
当前的蛋白质力场（如Charmm或Xplor-NIH中看到的）具有许多术语，其中包括键合和非键入项。然而，力场并未考虑使用氢键的使用，这对于二级结构创造和稳定蛋白质很重要。范围是一个开源程序，可从旋转空间生成蛋白质。然后，它创建了一个仅使用非键和氢键键项来创建给定蛋白质的轮廓的力场。然后可以在人工神经网络中使用该轮廓，以创建一个线性模型，该模型被插入真正的蛋白质构象。
Current protein forcefields like the ones seen in CHARMM or Xplor-NIH have many terms that include bonded and non-bonded terms. Yet the forcefields 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 forcefield 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 that is funneled to the true protein conformation.
