Lee Su Jin, Cho Junmin, Lee Byung-Hoon, Hwang Donghwan, Park Jee-Woong
Drug Manufacturing Center, Daegu-Gyeongbuk Medical Innovation Foundation (K-MEDI Hub), Daegu 41061, Republic of Korea.
Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (K-MEDI Hub), Daegu 41061, Republic of Korea.
Biomedicines. 2023 Jan 26;11(2):356. doi: 10.3390/biomedicines11020356.
An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.
适体是一种单链DNA或RNA,它能以高结合亲和力与特定靶标结合。适体是通过指数富集配体系统进化(SELEX)过程开发的,该过程会重复进行以提高结合能力和特异性。然而,SELEX过程耗时,并且对通过该过程筛选出的适体候选物进行表征需要额外的努力。在这里,我们描述了计算机模拟方法,以提出开发适体的最有效方法,并将筛选和优化适体所需的费力工作降至最低。我们通过构象结构预测、分子对接和分子动力学模拟研究了几种估计适体与靶分子结合的方法。此外,还介绍了用于预测新药开发中靶标与配体结合的机器学习和深度学习技术的实例。本综述将有助于计算机模拟适体筛选和表征的开发与应用。