Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.
Computational Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States.
J Chem Inf Model. 2021 Jan 25;61(1):535-546. doi: 10.1021/acs.jcim.0c01354. Epub 2020 Dec 18.
The first important step in a structure-based virtual screening is the judicious selection of a receptor protein. In cases where the holo protein receptor structure is unavailable, significant reduction in virtual screening performance has been reported. In this work, we present a robust method to generate reliable holo protein structure conformations from apo structures using molecular dynamics (MD) simulation with restraints derived from holo structure binding-site templates. We perform benchmark tests on two different datasets: 40 structures from a directory of useful decoy-enhanced (DUD-E) and 84 structures from the Gunasekaran dataset. Our results show successful refinement of apo binding-site structures toward holo conformations in 82% of the test cases. In addition, virtual screening performance of 40 DUD-E structures is significantly improved using our MD-refined structures as receptors with an average enrichment factor (EF), an EF value of 6.2 compared to apo structures with 3.5. Docking of native ligands to the refined structures shows an average ligand root mean square deviation (RMSD) of 1.97 Å (DUD-E dataset and Gunasekaran dataset) relative to ligands in the holo crystal structures, which is comparable to the self-docking (i.e., docking of the native ligand back to its crystal structure receptor) average, 1.34 Å (DUD-E dataset) and 1.36 Å (Gunasekaran dataset). On the other hand, docking to the apo structures yields an average ligand RMSD of 3.65 Å (DUD-E) and 2.90 Å (Gunasekaran). These results indicate that our method is robust and can be useful to improve virtual screening performance of apo structures.
基于结构的虚拟筛选的第一步是明智地选择受体蛋白。在没有完整蛋白受体结构的情况下,虚拟筛选性能会显著降低。在这项工作中,我们提出了一种使用分子动力学(MD)模拟结合来自完整结构结合位点模板的约束来从apo 结构生成可靠的完整蛋白结构构象的稳健方法。我们在两个不同的数据集上进行了基准测试:来自有用诱饵增强(DUD-E)目录的 40 个结构和 Gunasekaran 数据集的 84 个结构。我们的结果表明,在 82%的测试案例中,apo 结合位点结构成功地向完整构象进行了细化。此外,使用我们的 MD 细化结构作为受体进行 40 个 DUD-E 结构的虚拟筛选性能得到了显著提高,平均富集因子(EF)为 6.2,而 apo 结构的 EF 值为 3.5。将天然配体对接至细化结构,配体的平均配体均方根偏差(RMSD)为 1.97 Å(DUD-E 数据集和 Gunasekaran 数据集),与完整晶体结构中的配体相当,与自对接(即,将天然配体对接回其晶体结构受体)的平均 RMSD 1.34 Å(DUD-E 数据集)和 1.36 Å(Gunasekaran 数据集)相当。另一方面,对接至 apo 结构的平均配体 RMSD 为 3.65 Å(DUD-E)和 2.90 Å(Gunasekaran)。这些结果表明,我们的方法是稳健的,可以提高 apo 结构的虚拟筛选性能。