A Sequential Multidimensional Analysis Algorithm for Aptamer Identification based on Structure Analysis and Machine Learning

Abstract

Molecular recognition ligands are of great significance in many fields, but our ability to develop new recognition molecules remains to be expanded. Here, we developed a Sequential Multidimensional Analysis algoRiThm for aptamer discovery (SMART-Aptamer) from high-throughput sequencing (HTS) data of SELEX libraries based on multilevel structure analysis and unsupervised machine learning to discover nucleic acid recognition ligands with high accuracy and efficiency. We validated SMART-Aptamer with three sets of HTS data from screening pools against hESCs, EpCAM, and CSV. High affinity aptamers for all three targets were successfully obtained, and the results revealed that SMART-Aptamer is able to pick out high affinity aptamers with low false positive and negative rates. With the advantages of accuracy, efficiency, and robustness, SMART-Aptamer represents a paradigm-shift strategy for the discovery of binding ligands for a variety of biomedical applications.