Eguida Merveille, Rognan Didier
Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400, Illkirch, France.
J Cheminform. 2021 Nov 23;13(1):90. doi: 10.1186/s13321-021-00567-3.
Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.
在整个可成药蛋白质腔库中合理化隐藏相似性的识别，仍然是基于真正的全蛋白质组结构发现新型药物候选物的主要障碍。我们最近描述了一种受数字图像处理启发的新计算方法（ProCare），用于识别基于片段的子口袋中的局部相似性。在该方法的验证过程中，我们意外地发现了两个不相关靶点——人类肿瘤坏死因子α（TNF-α）和HIV-1逆转录酶（HIV-1 RT）——的结合口袋中可能存在的相似性。微量热泳实验证实了ProCare的预测，因为三种经过测试且已获FDA批准的HIV-1 RT抑制剂中的两种确实与可溶性人类TNF-α三聚体结合。有趣的是，本文所揭示的相似性既无法通过最先进的结合位点比较方法揭示，也无法通过基于配体的成对相似性搜索揭示，这表明ProCare中实施的点云配准方法特别适合识别完全不相关靶点之间的局部和不明显的相似性。
