Computational tools for aptamer identification and optimization - Sun, Di; Sun, Miao; Zhang, Jialu; Lin, Xin; Zhang, Yinkun; Lin, Fanghe; Zhang, Peng; Yang, Chaoyong; Song, Jia (TrAC, Trends in analytical chemistry (Regular ed.), 2022)
Aptamers are single-stranded DNA or RNA oligonucleotides that can selectively bind to a specific target. They are generally obtained by SELEX, but the procedure is challenging and time-consuming. Moreover, the identified aptamers tend to be insufficient in stability, specificity, and affinity. Thus, only a handful of aptamers have entered the practical use stage. Recently, computational approaches have demonstrated a significant capacity to assist in the discovery of high-performance aptamers. This review discusses the advances achieved in several aspects of computational tools in this field, as well as the new progress in machine learning and deep learning, which are used in aptamer identification and optimization. To illustrate these computationally aided processes, aptamer selections against SARS-CoV-2 are discussed in detail as a case study. We hope that this review will aid and motivate researchers to develop and utilize more computational techniques to discover ideal aptamers effectively. •A comprehensive summary was provided on computational approaches for obtaining high-performance aptamers.•The supervised machine learning-based algorithms for identification and optimization of aptamers are well detailed.•The practical studies of structure modeling and binding surface inference for aptamers against SARS-CoV-2 were presented.