Department of Biomedicine, University of Bergen, Jonas Lies Vei, 5020 Bergen, Norway.
J Chem Inf Model. 2021 Aug 23;61(8):4068-4081. doi: 10.1021/acs.jcim.1c00155. Epub 2021 Jul 21.
RNA is an emerging target for drug discovery. However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predictor DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered predictions for selected RNA binding sites that agreed with manual assignment. In addition, most drug-like ligands contained in an RNA test set were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational and sequence changes in the binding sites and can contribute to direct drug discovery efforts for RNA targets.
RNA 是药物发现的新兴靶标。然而,与蛋白质一样,并非所有的 RNA 结合位点都适合用传统的类药配体来解决。为此,我们开发了基于结构的可成药性预测器 DrugPred_RNA,以识别可成药性的 RNA 结合位点。由于注释的 RNA 结合位点稀缺,该预测器是在蛋白质口袋上进行训练的,尽管仅使用可用于 RNA 和蛋白质结合位点计算的描述符。在区分蛋白质集上的可成药性和较不可成药性的结合位点方面,DrugPred_RNA 表现良好,并对选定的 RNA 结合位点进行了预测,这些预测与手动分配一致。此外,在预测为可成药性的口袋中发现了 RNA 测试集中的大多数类药配体,这进一步增加了对 DrugPred_RNA 性能的信心。该方法对结合位点的构象和序列变化具有鲁棒性,可以为 RNA 靶标的直接药物发现工作做出贡献。