Abstract

Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-X_L, an oncological protein target involved in the regulation of apoptosis through protein–protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-X_L binders. This represents, to date, the largest computational fragments screening entirely based on MD.

摘要

基于片段的先导化合物发现（FBLD）是开发新药最有效的方法之一。我们在这里提出一种称为高通量监督分子动力学（HT-SuMD）的新计算协议，它可以自动筛选多达数千个片段，因此代表了一种新的宝贵资源，可以在 FBLD 活动中优先考虑片段。该方案适用于 Bcl-XL _，这是一种通过蛋白质-蛋白质相互作用参与细胞凋亡调节的肿瘤蛋白质靶点。最初，使用同一组 100 个片段，根据基于 NMR 的强大筛选来验证 HT-SuMD 性能。这些独立的结果表明两种方法之间具有显着的一致性。然后，对包含另外 300 个片段的更大文库进行虚拟筛选，并通过 NMR 验证最佳命中。值得注意的是，所有计算机选择的片段均被确认为 Bcl-XL_结合物。这代表了迄今为止最大的完全基于 MD 的计算片段筛选。