PDF(Pubmed)
Abstract：
Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.
摘要：
基于结构的虚拟筛选（SBVS）是一种广泛用于计算机药物发现的方法。它需要受体结构或结合位点来预测配体的结合状态和适合性。因此,SBVS的性能受蛋白质构象的影响。SBVS中最常用的方法是蛋白质-配体对接程序，它利用基于原子距离的评分函数。因此，它们很容易对受体结构的变化敏感，据报道，构象变化显着降低了对接程序的性能。为了解决这个问题，我们介绍了一个新颖的SBVS程序，名为PL-PatchSurfer。该程序利用分子表面补丁和Zernike描述符。口袋和配体的表面被程序分割成几个补丁。然后，在将这些补丁转换为Zernike描述符之前，将其映射为物理化学特性，例如形状和静电势，它是旋转不变的。通过比较分子中片的描述符和几何分布来评估蛋白质和配体之间的互补性。一项基准研究表明，PL-PatchSurfer2能够快速筛选活性分子，而与受体结构变化无关。然而,该程序无法实现对氢键特征重要的目标，如核激素受体的高性能。在本文中,我们介绍了新版本的PL-PatchSurfer，PL-PatchSurfer3，其中包含两个新功能：氢键互补性定义的变化和考虑包含补丁曲率信息的可见性。我们的评估表明，新程序优于其前身和其他SBVS方法，同时保留了其对受体结构变化的特征性耐受性。感兴趣的个人可以在kiharalab.org/plps3访问该程序。
