Department of Bacteriology and Virology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Microbiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
J Mater Chem B. 2024 Sep 18;12(36):8825-8842. doi: 10.1039/d4tb00909f.
Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods.
适体是能够与特定靶分子结合的寡核苷酸序列，类似于单克隆抗体。它们可以通过指数富集的配体系统进化（SELEX）来选择，并且具有可修饰性和可合成性。即使 SELEX 方法已经得到了很大的改进，但是在实验中鉴定适体仍然是具有挑战性和耗时的。特别是，基于结构的方法是在适体的计算机辅助设计和开发中最常用的方法。为此，已经提出了许多基于网络的平台，用于预测 RNA 和 DNA 的二级结构和 3D 构象。此外，分子对接和分子动力学（MD）常用于基于结构信息的蛋白质化合物选择，也适用于适体选择。另一方面，人工智能（AI）可能能够从大量序列中快速发现可能的适体候选物。相反，复杂的机器和深度学习（DL）模型在药物发现过程中预测配体和靶标之间的结合特性方面表现出了有效性；因此，它们可能为预测适体与靶标的结合提供一种可靠而精确的方法。本研究探讨了适体结合能力预测的人工智能管道和策略的进展，如机器学习和深度学习，以及基于结构的方法、分子动力学和分子对接模拟方法。
