Novartis Institutes for Biomedical Research, Novartis Pharma AG, Forum 1, Novartis Campus, CH 4056 Basel, Basel-Stadt, Switzerland.
J Chem Inf Model. 2014 Oct 27;54(10):2697-717. doi: 10.1021/ci5003735. Epub 2014 Sep 30.
There is a tendency in the literature to be critical of scoring functions when docking programs perform poorly. The assumption is that existing scoring functions need to be enhanced or new ones developed in order to improve the performance of docking programs for tasks such as pose prediction and virtual screening. However, failures can result from either sampling or scoring (or a combination of the two), although less emphasis tends to be given to the former. In this work, we use the programs GOLD and Glide on a high-quality data set to explore whether failures in pose prediction and binding affinity estimation can be attributable more to sampling or scoring. We show that identification of the correct pose (docking power) can be improved by incorporating ligand strain into the scoring function or rescoring an ensemble of diverse docking poses with MM-GBSA in a postprocessing step. We explore the use of nondefault docking settings and find that enhancing ligand sampling also improves docking power, again suggesting that sampling is more limiting than scoring for the docking programs investigated in this work. In cross-docking calculations (docking a ligand to a noncognate receptor structure) we observe a significant reduction in the accuracy of pose ranking, as expected and has been reported by others; however, we demonstrate that these alternate poses may in fact be more complementary between the ligand and the rigid receptor conformation, emphasizing that treating the receptor rigidly is an artificial constraint on the docking problem. We simulate protein flexibility by the use of multiple crystallographic conformations of a protein and demonstrate that docking results can be improved with this level of protein sampling. This work indicates the need for better sampling in docking programs, especially for the receptor. This study also highlights the variable descriptive value of RMSD as the sole arbiter of pose replication quality. It is shown that ligand poses within 2 Å of the crystallographic one can show dramatic differences in calculated relative protein-ligand energies. MM-GBSA rescoring of distinct poses overcomes some of the sensitivities of pose ranking experienced by the docking scoring functions due to protein preparation and binding site definition.
文献中存在一种倾向，即在对接程序表现不佳时对打分函数持批评态度。这种假设是，为了提高对接程序在构象预测和虚拟筛选等任务中的性能，需要增强现有的打分函数或开发新的打分函数。然而，失败可能是由于采样或打分（或两者的组合）造成的，尽管前者往往受到的关注较少。在这项工作中，我们使用 GOLD 和 Glide 程序在高质量数据集上进行研究，以探讨构象预测和结合亲和力估计中的失败是否更多归因于采样或打分。我们表明，通过将配体应变纳入打分函数，或者在后续处理步骤中使用 MM-GBSA 对多样化的对接构象进行重新打分，可以提高正确构象（对接能力）的识别能力。我们探索了非默认对接设置的使用，并发现增强配体采样也可以提高对接能力，这再次表明，对于本工作中研究的对接程序来说，采样比打分更为受限。在交叉对接计算（将配体对接至非同源受体结构）中，我们观察到构象排序的准确性显著降低，这与预期的情况一致，并且其他研究人员也有报道；然而，我们证明这些替代构象实际上在配体和刚性受体构象之间可能更加互补，这强调了将受体视为刚性是对接问题的人为约束。我们通过使用蛋白质的多个晶体构象来模拟蛋白质的柔性，并证明这种蛋白质采样水平可以提高对接结果。这项工作表明对接程序需要更好的采样，特别是对受体。本研究还突出了 RMSD 作为构象复制质量唯一判断标准的可变描述性价值。结果表明，与晶体结构相比，距离在 2 Å 内的配体构象在计算相对蛋白-配体能量时可能存在显著差异。通过对不同构象进行 MM-GBSA 重新打分，可以克服对接打分函数由于蛋白质准备和结合位点定义而导致的构象排序敏感性问题。
