Recent focus in non-SELEX-computational approach for de novo aptamer design: A mini review

Abstract

Aptamers, single-stranded nucleic acids that bind to specific targets with high affinity and specificity, hold significant promise in various biomedical and biotechnological applications. The traditional method of aptamer selection, SELEX (Systematic Evolution of Ligands by EXponential Enrichment) takes a lot of work and time. Recent advancements in computational methods have revolutionized aptamer design, offering efficient and effective alternatives. This review examines recent advances in non-SELEX and de novo aptamer design methods, such as Making Aptamers without SELEX (MAWS), AptaLoop, AptaDiff, RNAGEN, RaptGen, Apta-MCTS, UltraSelex, and Torkamanian-Afshar model. These computer methods utilize bioinformatics, machine learning, and molecular modeling to generate high-affinity aptamers, eliminating the need for multiple selection steps in vitro or in vivo. We provide a comprehensive analysis of each method's performance, including binding affinity, specificity, and stability, and discuss their practical applications in diagnostics, therapeutics, and environmental monitoring. Furthermore, we highlight the strengths and limitations of computational methods against the traditional one. The potential challenges, future directions, and emerging.technologies were also presented. This review underscores the transformative impact of computational aptamer design on research and industry, paving the way for rapid and cost-effective development of aptamer-based technologies.

Keywords: Apta-MCTS; AptaDiff; AptaLoop; Aptamers; Generative methods; MAWS; RNAGEN; RaptGen; UltraSelex.