Abstract
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.
Highlights
Aptamers are single-stranded nucleic acids with a high affinity toward target molecules [1,2]
Admachine-/deep-learning models have witnessed successes in predicting the binding abilvanced machine-/deep-learning models have witnessed successes in predicting the bindities between targets and ligands in drug discovery and potentially offer a robust ing abilities between targets and ligands in drug discovery and potentially offer a and accurate approach to predict the binding between aptamers and targets
More models robust and accurate approach to predict the binding between aptamers and targets
Summary
Aptamers are single-stranded nucleic acids (both DNAs and RNAs) with a high affinity toward target molecules [1,2]. SELEX has the ability to select aptamers bounding to target molecules with high selectivity and binding affinity [9]. The amplification and new selection cycle of target-binding oligonucleotides are conducted This whole process of high-affinity aptamers selection normally contains 6–15 rounds. Some researchers used computational methods to select aptamer candidates because of their convenience and low cost [10,11] These methods aim at predicting the aptamer affinity toward targets through structural information [12]. Artificial intelligence (AI) including machine/deep-learning algorithms has inspired novel computational methods for selection of aptamer candidates with high affinity and specificity to target molecules in drug discovery [15]. Machine/deep-learning methods do not require the structural information of aptamers and are able to effectively explore much larger amounts of experimental data. With these advantages, perspectives of employing machine/deep-learning algorithms in aptamer affinity prediction are discussed in this review
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