Systematic Selection of High‐Affinity ssDNA Sequences to Carbon Nanotubes

Abstract

Abstract Single‐walled carbon nanotubes (SWCNTs) have gained significant interest for their potential in biomedicine and nanoelectronics. The functionalization of SWCNTs with single‐stranded DNA (ssDNA) enables the precise control of SWCNT alignment and the development of optical and electronic biosensors. This study addresses the current gaps in the field by employing high‐throughput systematic selection, enriching high‐affinity ssDNA sequences from a vast random library. Specific base compositions and patterns are identified that govern the binding affinity between ssDNA and SWCNTs. Molecular dynamics simulations validate the stability of ssDNA conformations on SWCNTs and reveal the pivotal role of hydrogen bonds in this interaction. Additionally, it is demonstrated that machine learning could accurately distinguish high‐affinity ssDNA sequences, providing an accessible model on a dedicated webpage (http://service.k‐medai.com/ssdna4cnt). These findings open new avenues for high‐affinity ssDNA‐SWCNT constructs for stable and sensitive molecular detection across diverse scientific disciplines.

Keywords

• binding affinity
• machine learning
• molecular dynamics
• selection
• single‐walled carbon nanotube
• ssDNA