In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 M^Pro, confirming the robustness of PharmaCore.

中文翻译：

---

PharmaCore：从蛋白质/配体复合物自动生成基于 3D 结构的药效团模型


在这项工作中，我们介绍了 PharmaCore：一种全新的全自动工作流程，旨在针对任何感兴趣的目标生成基于三维 (3D) 结构的药效团模型。所提出的方法依赖于使用共结晶配体来创建用于生成药效团假设的输入文件，不仅整合了配体的三维结构信息，还整合了有关放入蛋白质腔中的这些分子的结合模式的数据。我们开发了一个 Python 库，从正在研究的蛋白质的特定 UniProt ID 作为唯一需要用户干预的元素开始，随后以全自动方式收集和对齐带有已知配体的相应结构，将它们全部放入相同的结构中。坐标系。该协议包括最后一个阶段，其中使用对齐的小分子使用特定软件（例如 Phase (Schrödinger LLC)）直接在蛋白质结构上产生药效团假设。为了验证整个过程并强调在药物发现和重新定位领域的可能应用，我们首先生成了可溶性环氧化物水解酶（sEH）的药效团，并与已经发表的药效团进行了比较。然后，我们重现了已报告的 ATAD2 溴结构域 (AM879) 选择性结合物的结合特征，针对与 16 个表观遗传蛋白相关并由 PharmaCore 自动生成的 1741 个药效团进行测试，最终揭示了假定的前所未有的脱靶情况。计算预测已通过 AlphaScreen 检测成功验证，突出了所提出的工作流程在药物发现和重新定位中的适用性。 最后，该过程还在端锚聚合酶 2 和 SARS-CoV-2 M ^Pro 上进行了验证，证实了 PharmaCore 的稳健性。