Arezoo Fallah, Seyed Asghar Havaei, Hamid Sedighian, Reza Kachuei and Abbas Ali Imani Fooladi
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引用次数: 0
摘要
适配体是能与特定目标分子连接的寡核苷酸序列,类似于单克隆抗体。它们可以通过指数富集配体的系统进化(SELEX)来选择,并且可以修改和合成。尽管 SELEX 方法已经有了很大改进,但要通过实验来识别适配体,往往仍具有挑战性且耗时较长。特别是,基于结构的方法是计算机辅助设计和开发适配体的最常用方法。为此,人们提出了许多基于网络的平台,用于预测 RNA 和 DNA 的二级结构和三维构型。此外,分子对接和分子动力学(MD)常用于通过结构信息选择蛋白质化合物,也适用于选择适配体。另一方面,人工智能(AI)可以从大量序列中快速发现可能的候选适配体。相反,复杂的机器和深度学习(DL)模型在预测药物发现过程中配体和靶标之间的结合特性方面已显示出功效;因此,它们可以为预测适配体与靶标的结合提供可靠而精确的方法。本研究探讨了机器学习和深度学习等人工智能管道和策略在预测适配体结合能力方面的进展,以及基于结构的方法、分子动力学和分子对接模拟方法。
Prediction of aptamer affinity using an artificial intelligence approach
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.
期刊介绍:
Journal of Materials Chemistry A, B & C cover high quality studies across all fields of materials chemistry. The journals focus on those theoretical or experimental studies that report new understanding, applications, properties and synthesis of materials. Journal of Materials Chemistry A, B & C are separated by the intended application of the material studied. Broadly, applications in energy and sustainability are of interest to Journal of Materials Chemistry A, applications in biology and medicine are of interest to Journal of Materials Chemistry B, and applications in optical, magnetic and electronic devices are of interest to Journal of Materials Chemistry C.Journal of Materials Chemistry B is a Transformative Journal and Plan S compliant. Example topic areas within the scope of Journal of Materials Chemistry B are listed below. This list is neither exhaustive nor exclusive:
Antifouling coatings
Biocompatible materials
Bioelectronics
Bioimaging
Biomimetics
Biomineralisation
Bionics
Biosensors
Diagnostics
Drug delivery
Gene delivery
Immunobiology
Nanomedicine
Regenerative medicine & Tissue engineering
Scaffolds
Soft robotics
Stem cells
Therapeutic devices