Schrödinger, Inc., 120 West 45th street, New York, New York 10036, USA.
J Chem Inf Model. 2013 Jul 22;53(7):1689-99. doi: 10.1021/ci400128m. Epub 2013 Jul 10.
Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein-protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.
预测柔性多肽与蛋白质的结合模式是一项重要的任务，超出了大多数小分子和蛋白质-蛋白质对接工具的应用范围。在这里，我们测试了小分子柔性配体对接程序 Glide 在一组 19 条非α-螺旋肽上的应用，并通过增强 Glide 对柔性多肽的采样来系统地提高构象预测的准确性。此外，通过基于物理的隐式溶剂 MM-GBSA 计算的后处理来改进构象的打分。使用前 10 个打分构象中最佳 RMSD 作为度量标准，对于重新对接至天然蛋白质结构的情况，通过默认 Glide SP 设置的成功率（界面骨架原子的 RMSD≤2.0Å）从 21%提高到了增强多肽采样和打分方案的 58%。这与最近开发的 Rosetta FlexPepDock 方法的准确性（对于这 19 个肽，成功率为 63%）相当，但速度要快 100 多倍。对于存在未结合受体结构的子集案例进行了交叉对接，在这种情况下，有 40%的肽成功对接。我们分析了结果，发现优化的多肽方案对于大小和形式电荷数量有限的伸展肽最为准确，为该方法定义了一个适用范围。
