Yu Haixiang, Canoura Juan, Byrd Caleb, Alkhamis Obtin, Bacon Adara, Yan Amy, Sullenger Bruce A, Xiao Yi
Department of Surgery, Duke University Medical Center, 2 Genome Ct., Durham, North Carolina 27710, United States.
Department of Chemistry, North Carolina State University, 2620 Yarbrough Dr., Raleigh, North Carolina 27695, United States.
J Am Chem Soc. 2025 Mar 19;147(11):9472-9486. doi: 10.1021/jacs.4c17041. Epub 2025 Mar 7.
The affinity of nucleic acid aptamers isolated via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) is often limited because the entire potential sequence space cannot be screened. In this study, we introduce Motif-SELEX, a novel method that enables the optimization of existing underperforming aptamers by generating libraries that broadly represent both the sequence and length variations of the parent sequence. This approach enables the isolation of sequences with improved affinity without the biases and limitations of traditional mutagenesis methods like doped SELEX and error-prone PCR. As a demonstration, we applied Motif-SELEX to a DNA-based morphine aptamer and a 2' fluoro- and methoxy-RNA-based apixaban aptamer, discovering new, better-performing sequences with differing random domain lengths from their parents and up to 10-fold improvements in affinity. These new sequences would be inaccessible to traditional post-SELEX methods. Critically, our analysis of Motif-SELEX pools also enabled us to identify sequence and structural elements crucial for target binding and to predict secondary and tertiary structures for a given aptamer family─even when those structures involve noncanonical nucleotide interactions. We believe that Motif-SELEX offers an effective and generalizable solution for optimizing the structure and binding properties of functional nucleic acid molecules for diverse applications.
通过指数富集配体系统进化技术(SELEX)分离得到的核酸适配体的亲和力往往有限,因为无法筛选整个潜在的序列空间。在本研究中,我们引入了基序SELEX,这是一种新方法,通过生成能够广泛代表亲本序列的序列和长度变异的文库,来优化现有的性能不佳的适配体。这种方法能够分离出亲和力提高的序列,而没有掺杂SELEX和易错PCR等传统诱变方法的偏差和局限性。作为一个示范,我们将基序SELEX应用于基于DNA的吗啡适配体和基于2'-氟和甲氧基RNA的阿哌沙班适配体,发现了与其亲本具有不同随机结构域长度且亲和力提高了10倍的新的、性能更好的序列。这些新序列是传统的SELEX后方法无法获得的。至关重要的是,我们对基序SELEX文库的分析还使我们能够识别对靶标结合至关重要的序列和结构元件,并预测给定适配体家族的二级和三级结构——即使这些结构涉及非经典核苷酸相互作用。我们相信,基序SELEX为优化功能性核酸分子的结构和结合特性以用于各种应用提供了一种有效且可推广的解决方案。