Chen Zihao, Hu Long, Zhang Bao-Ting, Lu Aiping, Wang Yaofeng, Yu Yuanyuan, Zhang Ge
School of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong, China.
Law Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China.
Int J Mol Sci. 2021 Mar 30;22(7):3605. doi: 10.3390/ijms22073605.
Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer-target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer-target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.
适体是短的单链DNA、RNA或合成的异种核酸（XNA）分子，能够与相应靶标进行高亲和力相互作用。由于其独特的特性，包括生产成本低、易于化学修饰、热稳定性高、可重复性以及低免疫原性和毒性，适体可作为诊断和治疗中抗体的替代品。指数富集配体系统进化技术（SELEX）是一种用于适体筛选的实验方法，能够选择和鉴定具有高亲和力和特异性的体外适体。然而，SELEX过程耗时，且从SELEX中表征具有代表性的适体候选物相当费力。人工智能（AI）有助于从大量序列中快速识别潜在的适体候选物。本综述讨论了AI流程/方法的进展，包括基于结构的方法以及基于机器学习/深度学习的方法，用于预测适体与靶标的结合能力。基于结构的方法在计算机辅助药物设计中使用最为广泛。对于这部分内容，我们综述了适体的二级和三级结构预测方法、分子对接以及适体-靶标结合的分子动力学模拟方法。我们还进行了分析，以比较不同适体二级和三级结构预测方法的准确性。另一方面，先进的机器学习/深度学习模型在药物发现中预测靶标与配体之间的结合能力方面取得了成功，因此有可能提供一种强大而准确的方法来预测适体与靶标之间的结合。目前利用机器学习/深度学习技术预测适体-靶标结合的研究有限。因此，本文讨论了基于机器学习/深度学习方法的模型、算法和实施策略的前景。本综述有助于高通量且省力的计算机模拟方法在适体选择和表征中的开发与应用。
