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使用内切几丁质酶模型系统评估片段对接和评分。

Assessment of fragment docking and scoring with the endothiapepsin model system.

机构信息

Institute of Pharmacy and Food Chemistry, Julius-Maximilians-Universität, Würzburg, Germany.

出版信息

Arch Pharm (Weinheim). 2024 Jun;357(6):e2400061. doi: 10.1002/ardp.202400061. Epub 2024 Apr 17.

Abstract

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSX, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

摘要

片段筛选在药物发现中已不可或缺。然而,这些小分子的弱结合亲和力仍然是可靠检测片段命中的一个挑战。在文献中,天冬氨酸蛋白酶内切酶 endothiapepsin 的情况说明了这个问题:当七种生化和生物物理的体外筛选方法应用于筛选 361 个片段文库时,各个方法鉴定的命中片段之间的重叠非常差,导致根据相互比较的方法出现高水平的假阳性和/或假阴性结果。在这里,报告的体外发现与来自计算对接和评分方法的结果并列。对接程序 GOLD 和 Glide 与评分函数 ASP、ChemScore、ChemPLP、GoldScore、DSX 和 GlideScore 一起考虑。首先,评估了命名评分函数的排名能力和评分能力。其次,在基于结构的重对接方法中测试了重现结晶片段结合模式的能力。重对接的成功显著取决于所考虑片段的配体效率。第三,采用盲虚拟筛选方法来评估计算筛选是否能够在片段数据库的富集方面与体外方法竞争。

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