Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 115 27, Athens, Greece.
Greek Research and Technology Network, S.A., 7 Kifissias Ave, 115 23, Athens, Greece.
J Comput Aided Mol Des. 2018 Jan;32(1):21-44. doi: 10.1007/s10822-017-0075-9. Epub 2017 Nov 8.
Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.
计算机辅助药物设计已成为制药和生物技术行业药物发现和开发不可或缺的一部分，目前广泛用于先导化合物鉴定和优化阶段。药物设计数据资源（D3R）组织针对盲测实验数据的挑战，以有前瞻性地测试计算方法，为改进方法和算法提供机会。我们参加了 Grand Challenge 2，以预测 36 个法尼醇 X 受体（FXR）结合配体的晶体构象和两个指定的 18 个和 15 个 FXR 结合配体子集的相对结合亲和力。在此，我们介绍了我们的构象和亲和力预测方法及其在实验数据发布后的评估。对于预测晶体构象，我们使用了对接和基于物理的构象预测方法，并以天然配体的结合构象为指导。对于 PDB 中具有已知化学类型的 FXR 配体，我们准确地预测了它们的结合模式，而对于那些具有未知化学类型的配体，预测则更具挑战性。我们小组在 46 个提交完整结合构象预测挑战的小组中排名第 1 位（基于中位数 RMSD）。对于相对结合亲和力预测挑战，我们进行了自由能微扰（FEP）计算与分子动力学（MD）模拟的耦合。FEP/MD 计算在识别具有比参考（母体）化合物更好或更差结合亲和力的化合物方面具有很高的成功率。我们的研究表明，当文献中有具有化学先例的配体时，使用对接和基于物理的方法进行结合构象预测是可靠的；然而，对于完全未知化学类型的配体，预测则具有挑战性。我们还表明，FEP/MD 计算具有预测价值，并且在当今，如果有足够高质量的晶体结构可用，它可以在高通量模式下用于先导化合物优化项目。
