The development of deep learning tools such as AlphaFold2 (AF2), which enable structural predictions of proteins with high confidence, provides a potential alternative or substitute for experimentally derived structures. AF2 was also envisioned to help identify new ligands through virtual screening. However, retrospective docking analysis comparing AF2-generated versus experimental structures revealed mixed results in predicting ligand-binding sites. Lyu et al. performed a prospective analysis to test the performance of AF2 with the σ₂ receptor and 5-HT2A receptor as candidates. The AF2 models for these receptors were predicted before the experimental structures were reported. The two datasets showed good concordance, potentially removing biases that may have occurred in the retrospective analysis. The team performed large-scale docking screening using chemically diverse libraries ranging from 490 million to 1.6 billion molecules against the AF2-generated receptor model and the experimental structure. For both receptors, the hit rate, affinities and K_i values between AF2 and experimental screening campaigns were similar, despite the lack of similarities in chemical structures between the screens. Cryo-electron microscopy (cryo-EM) analysis of an AF2-specific hit for 5-HT2A confirmed the predicted docking interaction, showing that AF2 models could be used to identify new ligands. In addition, the 5-HT2A screen revealed that the AF2 model produced compounds with higher potency and selectivity relative to the cryo-EM structure. Although the generality of these findings to other proteins remains unclear, the findings from Lyu et al. support the potential utility of docking with AF2-derived structures for ligand discovery.

Original reference: Science 384, eadn6354 (2024)

AlphaFold2 (AF2) 等深度学习工具的开发能够以高置信度对蛋白质进行结构预测，为实验衍生结构提供了潜在的替代方案或替代品。 AF2 还有望通过虚拟筛选帮助识别新配体。然而，比较 AF2 生成的结构与实验结构的回顾性对接分析揭示了预测配体结合位点的混合结果。吕等人。进行了前瞻性分析，以测试 AF2 以 σ ₂受体和 5-HT2A 受体作为候选者的性能。这些受体的 AF2 模型是在实验结构报告之前预测的。这两个数据集显示出良好的一致性，有可能消除回顾性分析中可能出现的偏差。该团队使用 4.9 亿至 16 亿个分子的化学多样性文库针对 AF2 生成的受体模型和实验结构进行了大规模对接筛选。对于这两种受体，AF2 和实验筛选活动之间的命中率、亲和力和K值相似，尽管筛选之间的化学结构缺乏相似性。对 5-HT2A 的 AF2 特异性命中的冷冻电子显微镜 (cryo-EM) 分析证实了预测的对接相互作用，表明 AF2 模型可用于识别新配体。此外，5-HT2A 筛选显示，AF2 模型产生的化合物相对于冷冻电镜结构具有更高的效力和选择性。尽管这些发现对其他蛋白质的普遍性尚不清楚，但 Lyu 等人的发现。支持与 AF2 衍生结构对接用于配体发现的潜在用途。

原文参考： Science 384 ，eadn6354 (2024)