High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.
High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.
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DOI：
10.1371/journal.pcbi.1000528
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发表时间：
2009-10
影响因子：
4.3
通讯作者：
Taiji M
中科院分区：
文献类型：
--
作者：
Okimoto N;Futatsugi N;Fuji H;Suenaga A;Morimoto G;Yanai R;Ohno Y;Narumi T;Taiji M
利用分子对接进行虚拟化合物筛选被广泛应用于发现新的药物设计先导化合物。然而，这种方法并不完全可靠，因此不能令人满意。在这项研究中，我们使用大量的分子动力学模拟分子对接得到的蛋白质-配体构象，以提高分子对接的富集性能。我们的筛选方法采用分子力学/Poisson-Boltzmann和表面积方法来估计结合自由能。对于通过与靶蛋白对接获得的排名最高的1，000种化合物，在大约一周内使用多个对接姿势进行了大约6，000次分子动力学模拟。结果表明，我们的方法对前100种化合物的富集性能比分子对接的富集性能提高了1.6 - 4.0倍。这一结果表明，分子动力学模拟应用于虚拟筛选先导发现是有效和实用的。然而，需要进一步优化的计算方案，以筛选各种靶蛋白。先导化合物发现是药物合理设计的重要环节之一。为了提高先导化合物的检出率，各种技术如高通量筛选和组合化学已被引入制药工业。然而，由于这些技术本身可能无法提高铅生产率，计算筛选变得重要。计算筛选的中心方法是分子对接。这种方法通常将许多柔性配体对接到刚性蛋白质上，并在实际时间内预测每个配体的结合亲和力。然而，它检测铅化合物的能力不太可靠。相比之下，分子动力学模拟可以灵活地处理蛋白质和配体，直接估计明确的水分子的影响，并提供更准确的结合亲和力，虽然它们的计算成本和时间显着大于分子对接。为此，我们研制了一台用于分子动力学模拟的专用计算机“MDGRAPE-3”，并将其应用于计算筛选。在本文中，我们报告了一种有效的方法进行计算筛选，这种方法是分子对接和双尺度分子动力学模拟相结合。与单独使用的分子对接方法相比，所提出的方法表现出更高和更稳定的富集性能。
Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins. Lead discovery is one of the most important processes in rational drug design. To improve the rate of the detection of lead compounds, various technologies such as high-throughput screening and combinatorial chemistry have been introduced into the pharmaceutical industry. However, since these technologies alone may not improve lead productivity, computational screening has become important. A central method for computational screening is molecular docking. This method generally docks many flexible ligands to a rigid protein and predicts the binding affinity for each ligand in a practical time. However, its ability to detect lead compounds is less reliable. In contrast, molecular dynamics simulations can treat both proteins and ligands in a flexible manner, directly estimate the effect of explicit water molecules, and provide more accurate binding affinity, although their computational costs and times are significantly greater than those of molecular docking. Therefore, we developed a special purpose computer “MDGRAPE-3” for molecular dynamics simulations and applied it to computational screening. In this paper, we report an effective method for computational screening; this method is a combination of molecular docking and massive-scale molecular dynamics simulations. The proposed method showed a higher and more stable enrichment performance than the molecular docking method used alone.
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影响因子：
3
作者：
Gohlke, H;Case, DA
通讯作者：
Case, DA
影响因子：
--
作者：
Dunn, D;Orlowski, M;Burbaum, J
通讯作者：
Burbaum, J
影响因子：
3
作者：
Duan, Y;Wu, C;Kollman, P
通讯作者：
Kollman, P
影响因子：
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作者：
Davis, ST;Benson, BG;Kuyper, LF
通讯作者：
Kuyper, LF
DOI：
10.1023/b:jcam.0000017496.76572.6f
发表时间：
2003-11-01
影响因子：
3.5
作者：
Bursulaya, BD;Totrov, M;Brooks, CL
通讯作者：
Brooks, CL