Bioinformatics and Computational Biology Interdepartmental Graduate Program, Iowa State University, Ames, Iowa 50011, USA.
J Phys Chem B. 2012 Jun 14;116(23):6725-31. doi: 10.1021/jp2120143. Epub 2012 Apr 23.
Protein structure prediction and protein-protein docking are important and widely used tools, but methods to confidently evaluate the quality of a predicted structure or binding pose have had limited success. Typically, either knowledge-based or physics-based energy functions are employed to evaluate a set of predicted structures (termed "decoys" in structure prediction and "poses" in docking), with the lowest energy structure being assumed to be the one closest to the native state. While successful for many cases, failures are still common. Thus, improvements to structure evaluation methods are essential for future improvements. In this work, we combine multibody statistical potentials with dynamics models, evaluating fluctuation-based entropies that include contributions from the entire structure. This leads to enhanced selection of native-like structures for CASP9 decoys, refined ClusPro docking poses, as well as large sets of docking poses from the Benchmark 3.0 and Dockground data sets. The data used include both bound and unbound docking, and positive results are found for each type. Not only does this method yield improved average results, but for high quality docking poses, we often pick the best pose.
蛋白质结构预测和蛋白质-蛋白质对接是重要且广泛使用的工具，但有信心评估预测结构或结合构象质量的方法一直没有取得很大成功。通常，使用基于知识或基于物理的能量函数来评估一组预测结构（在结构预测中称为“诱饵”，在对接中称为“构象”），假设能量最低的结构最接近天然状态。虽然在许多情况下都取得了成功，但失败仍然很常见。因此，改进结构评估方法对于未来的改进至关重要。在这项工作中，我们将多体统计势与动力学模型相结合，评估基于波动的熵，其中包括整个结构的贡献。这导致对 CASP9 诱饵的天然样结构进行了更好的选择，对 ClusPro 对接构象进行了细化，以及对来自 Benchmark 3.0 和 Dockground 数据集的大量对接构象进行了选择。所使用的数据包括结合和未结合的对接，并且每种类型都得到了阳性结果。这种方法不仅提高了平均结果，而且对于高质量的对接构象，我们经常选择最佳构象。
