Artificial Intelligence in Aptamer-Target Binding Prediction.
Artificial Intelligence in Aptamer-Target Binding Prediction.
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DOI：
10.3390/ijms22073605
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发表时间：
2021-03-30
影响因子：
5.6
通讯作者：
Zhang G
中科院分区：
文献类型：
--
作者：
Chen Z;Hu L;Zhang BT;Lu A;Wang Y;Yu Y;Zhang G
适体是短的单链DNA、RNA或合成的异种核酸（XNA）分子，其可以以高亲和力与相应的靶标相互作用。由于其独特的特征，包括生产成本低、易于化学修饰、高热稳定性、可重复性以及低水平的免疫原性和毒性，适体可在诊断和治疗中用作抗体的替代品。通过指数富集的配体系统进化（SELEX），用于适体筛选的实验方法，允许在体外选择和鉴定具有高亲和力和特异性的适体。然而，SELEX过程是耗时的，并且来自SELEX的代表性适体候选物的表征相当费力。人工智能（AI）可以帮助从大量序列中快速识别潜在的适体候选者。本文讨论了人工智能管道/方法的进展，包括基于结构和基于机器/深度学习的方法，用于预测适体与靶标的结合能力。基于结构的方法是计算机辅助药物设计中最常用的方法。在这一部分中，我们综述了适体的二级和三级结构预测方法、分子对接以及适体与靶标结合的分子动力学模拟方法。我们还进行了分析，以比较不同的二级和三级结构预测方法的适配体的准确性。另一方面，先进的机器/深度学习模型在预测药物发现中靶点和配体之间的结合能力方面取得了成功，因此可能提供一种强大而准确的方法来预测适体和靶点之间的结合。目前，利用机器/深度学习技术预测适体-靶标结合的研究有限。因此，讨论了基于机器/深度学习的方法的模型、算法和实现策略的前景。这一综述有助于高通量、低劳动强度的计算机模拟方法在适体筛选和表征中的发展和应用。
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.
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DOI：
10.1590/s1516-89132007000600008
发表时间：
2007-09-01
影响因子：
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作者：
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通讯作者：
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