Computer Science & Engineering, University of South Carolina, Columbia, SC 29208, USA.
Proteomics. 2013 Jan;13(2):230-8. doi: 10.1002/pmic.201200330. Epub 2012 Dec 23.
Computational approaches to modeling protein structures have made significant advances over the past decade. However, the current limitation in modeling protein structures is to produce protein structures consistently below the limit of 6 Å compared to their native structure. Therefore, improvement of protein structures consistently below the 6 Å limit using simulation of biophysical forces is of significant interest. Current protein force fields such as those implemented in CHARMM, AMBER, and NAMD have been deemed complete, yet their use in ab initio approaches to protein structure determination has been unsuccessful. Here, we introduce a new approach in evaluation of protein structures based on analysis of energy profiles produced by the SCOPE software package. The latest version of SCOPE produces a hydrogen bond profile that is substantially more informative than a single hydrogen bond energy value. We demonstrate how analysis of SCOPE's energy profile by an artificial neural network shows a significant improvement compared to the traditional force-based approaches to evaluation of structures. The artificial neural network based analysis of SCOPE's energy profile showed identification of structures to within the range of 1.5-3.0 Å of the native structure. These results have been obtained by testing structures in the same Homology, Topology, Architecture, or Class of the CATH family.
在过去的十年中，用于模拟蛋白质结构的计算方法取得了重大进展。然而，目前建模蛋白质结构的限制是，与天然结构相比，只能一致地生成低于 6Å 的蛋白质结构。因此，使用生物物理力模拟来一致地改进低于 6Å 限制的蛋白质结构具有重要意义。目前的蛋白质力场，如 CHARMM、AMBER 和 NAMD 中实现的力场，被认为是完整的，但它们在从头开始的蛋白质结构测定方法中的应用并不成功。在这里，我们介绍了一种基于 SCOPE 软件包生成的能量分布分析的蛋白质结构评估新方法。最新版本的 SCOPE 生成的氢键分布比单个氢键能量值更具信息量。我们展示了如何通过人工神经网络对 SCOPE 的能量分布进行分析，与传统的基于力的结构评估方法相比，这显示出了显著的改进。基于人工神经网络的 SCOPE 能量分布分析表明，可以识别出与天然结构在 1.5-3.0Å 范围内的结构。这些结果是通过在同源、拓扑、结构或 CATH 家族的同一类中测试结构获得的。
