Yunhui Ge, Vineet Pande, Mark J. Seierstad and Kelly L. Damm-Ganamet*,
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引用次数: 0
Abstract
The characterization of cryptic pockets has been elusive, despite substantial efforts. Computational modeling approaches, such as molecular dynamics (MD) simulations, can provide atomic-level details of binding site motions and binding pathways. However, the time scale that MD can achieve at a reasonable cost often limits its application for cryptic pocket identification. Enhanced sampling techniques can improve the efficiency of MD simulations by focused sampling of important regions of the protein, but prior knowledge of the simulated system is required to define the appropriate coordinates. In the case of a novel, unknown cryptic pocket, such information is not available, limiting the application of enhanced sampling techniques for cryptic pocket identification. In this work, we explore the ability of SiteMap and Site Finder, widely used commercial packages for pocket identification, to detect focus points on the protein and further apply other advanced computational methods. The information gained from this analysis enables the use of computational modeling, including enhanced MD sampling techniques, to explore potential cryptic binding pockets suggested by SiteMap and Site Finder. Here, we examined SiteMap and Site Finder results on 136 known cryptic pockets from a combination of the PocketMiner dataset (a recently curated set of cryptic pockets), the Cryptosite Set (a classic set of cryptic pockets), and Natural killer group 2D (NKG2D, a protein target where a cryptic pocket is confirmed). Our findings demonstrate the application of existing, well-studied tools in efficiently mapping potential regions harboring cryptic pockets.
尽管做出了大量努力,但隐匿口袋的特征描述一直难以确定。分子动力学(MD)模拟等计算建模方法可以提供结合位点运动和结合途径的原子级细节。然而,分子动力学模拟能以合理的成本实现的时间尺度往往限制了其在隐蔽口袋鉴定中的应用。增强采样技术可以通过对蛋白质的重要区域进行集中采样来提高 MD 模拟的效率,但要确定适当的坐标,需要事先了解模拟系统。对于未知的新型隐秘口袋,这种信息是不可用的,从而限制了增强采样技术在隐秘口袋识别中的应用。在这项工作中,我们探索了 SiteMap 和 Site Finder(广泛应用于口袋识别的商业软件包)检测蛋白质焦点的能力,并进一步应用了其他先进的计算方法。从这一分析中获得的信息可用于计算建模,包括增强的 MD 采样技术,以探索 SiteMap 和 Site Finder 提出的潜在隐蔽结合口袋。在这里,我们研究了SiteMap和Site Finder在136个已知隐性口袋上的结果,这些口袋来自PocketMiner数据集(最近策划的隐性口袋集)、Cryptosite Set(经典的隐性口袋集)和Natural killer group 2D (NKG2D,一个被证实存在隐性口袋的蛋白质靶标)。我们的研究结果表明,现有的、经过充分研究的工具可用于高效绘制潜在的隐口袋区域图。
期刊介绍:
An essential criterion for acceptance of research articles in the journal is that they provide new physical insight. Please refer to the New Physical Insights virtual issue on what constitutes new physical insight. Manuscripts that are essentially reporting data or applications of data are, in general, not suitable for publication in JPC B.