The identification of molecules that can modulate RNA or protein function and the subsequent chemical and structural optimization to refine such molecules into drugs is a key activity in drug discovery. Here, we explored the extent to which chemical and structural differences in antisense oligonucleotides, designed as gapmers and capable of recruiting RNase H for target RNA cleavage, can affect their functional properties. To facilitate structure-activity learning, we analyzed two sets of iso-sequential locked nucleic acid (LNA)-modified gapmers, where we systematically varied the number and positions of LNA modifications in the flanks. In total, we evaluated 768 different and architecturally diverse gapmers in HeLa cells for target knockdown activity and cytotoxic potential and found widespread differences in both of these properties. Binding affinity between gapmer and RNA target, as well as the presence of certain short sequence motifs in the gap region, can explain these differences, and we propose statistical and machine-learning models that can be used to predict region-specific, optimal LNA-modification architectures. Once accessible regions in the target of interest have been identified, our results show how to refine and optimize LNA gapmers with improved pharmacological profiles targeting such regions.

鉴定可调节RNA或蛋白质功能的分子以及随后的化学和结构优化，以将此类分子精炼成药物，这是药物发现中的关键活动。在这里，我们探讨了反义寡核苷酸的化学和结构差异（被设计为gapmers并能够募集RNase H进行靶RNA切割）在多大程度上影响其功能特性。为促进结构活性学习，我们分析了两组等序锁定核酸（LNA）修饰的间隔体，在其中我们系统地改变了侧面LNA修饰的数量和位置。总体而言，我们评估了HeLa细胞中768种不同的和结构上不同的缺口聚体，其靶标敲除活性和细胞毒性潜力均存在，并且发现这两种特性存在广泛差异。gapmer与RNA靶标之间的结合亲和力以及间隙区域中某些短序列基序的存在可以解释这些差异，我们提出了统计和机器学习模型，可用于预测区域特异性的最佳LNA-修改架构。一旦确定了目标靶标中的可及区域，我们的结果就会显示出如何以针对此类区域的改进药理学特征来优化和优化LNA缺口聚体。