Anighoro Andrew, Bajorath Jürgen
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany.
J Comput Aided Mol Des. 2016 Jun;30(6):447-56. doi: 10.1007/s10822-016-9918-z. Epub 2016 Jun 22.
We report an investigation designed to explore alternative approaches for ranking of docking poses in the search for antagonists of the adenosine A2A receptor, an attractive target for structure-based virtual screening. Calculation of 3D similarity of docking poses to crystallographic ligand(s) as well as similarity of receptor-ligand interaction patterns was consistently superior to conventional scoring functions for prioritizing antagonists over decoys. Moreover, the use of crystallographic antagonists and agonists, a core fragment of an antagonist, and a model of an agonist placed into the binding site of an antagonist-bound form of the receptor resulted in a significant early enrichment of antagonists in compound rankings. Taken together, these findings showed that the use of binding modes of agonists and/or antagonists, even if they were only approximate, for similarity assessment of docking poses or comparison of interaction patterns increased the odds of identifying new active compounds over conventional scoring.
我们报告了一项研究，旨在探索在寻找腺苷A2A受体拮抗剂（基于结构的虚拟筛选的一个有吸引力的靶点）时对接姿势排序的替代方法。对接姿势与晶体学配体的三维相似性计算以及受体-配体相互作用模式的相似性计算，在区分拮抗剂和诱饵方面始终优于传统评分函数。此外，使用晶体学拮抗剂和激动剂、拮抗剂的核心片段以及置于受体拮抗剂结合形式结合位点的激动剂模型，导致在化合物排名中拮抗剂显著早期富集。综上所述，这些发现表明，使用激动剂和/或拮抗剂的结合模式（即使只是近似的）进行对接姿势的相似性评估或相互作用模式的比较，相比于传统评分，增加了识别新活性化合物的几率。
