Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, 65211, USA.
Department of Physics and Astronomy, University of Missouri, Columbia, MO, 65211, USA.
J Comput Aided Mol Des. 2018 Jan;32(1):103-111. doi: 10.1007/s10822-017-0082-x. Epub 2017 Nov 10.
D3R 2016 Grand Challenge 2 focused on predictions of binding modes and affinities for 102 compounds against the farnesoid X receptor (FXR). In this challenge, two distinct methods, a docking-based method and a template-based method, were employed by our team for the binding mode prediction. For the new template-based method, 3D ligand similarities were calculated for each query compound against the ligands in the co-crystal structures of FXR available in Protein Data Bank. The binding mode was predicted based on the co-crystal protein structure containing the ligand with the best ligand similarity score against the query compound. For the FXR dataset, the template-based method achieved a better performance than the docking-based method on the binding mode prediction. For the binding affinity prediction, an in-house knowledge-based scoring function ITScore2 and MM/PBSA approach were employed. Good performance was achieved for MM/PBSA, whereas the performance of ITScore2 was sensitive to ligand composition, e.g. the percentage of carbon atoms in the compounds. The sensitivity to ligand composition could be a clue for the further improvement of our knowledge-based scoring function.
D3R 2016 年的第二项挑战赛主要集中于预测 102 种化合物与法尼醇 X 受体（FXR）的结合模式和亲和力。在本次挑战赛中，我们团队采用了两种截然不同的方法，即对接法和基于模板的方法，用于进行结合模式预测。对于新的基于模板的方法，针对每个查询化合物，根据 3D 配体相似性，计算了其与蛋白质数据库中 FXR 共晶结构中配体的相似度。基于与查询化合物具有最佳配体相似度的配体的共晶蛋白结构，预测了结合模式。对于 FXR 数据集，基于模板的方法在结合模式预测方面优于对接法。对于结合亲和力预测，采用了内部基于知识的评分函数 ITScore2 和 MM/PBSA 方法。MM/PBSA 方法的性能良好，而 ITScore2 的性能对配体组成敏感，例如化合物中碳原子的百分比。这种对配体组成的敏感性可能是进一步改进我们基于知识的评分函数的线索。
