DECIPHERING AIRBORNE MICROBIAL COMMUNITIES: LEVERAGING RNA APTAMERS VIA COMPUTATIONAL SLEUTHING (Applied Biological Research, 2025)
Airborne pathogens are a substantial threat to human health; hence, an efficient means to identify these contaminants is necessary to control their spread. The present work focused on developing innovative concepts for employing RNA aptamers to detect biological pollutants in indoor air environments. RNA aptamers are a promising tool for detecting indoor air microbes, thereby enhancing air quality and human health. This study utilized computational methods to identify and characterize RNA aptamer–target interactions. In silico techniques were used to design and predict aptamer sequences capable of binding to various microbial cells present in the air. Based on computational validation, lead aptamer candidates were identified by optimizing and refining sequences to demonstrate strong interactions with multiple microbial targets. RNA aptamer sequences were analyzed for various properties using an oligo-sequence analyzer. Secondary structures were predicted using RNAfold, followed by 3D structure prediction with RNA Composer and docking using the H-Dock server. Interaction modalities were further explored using the PHLIP server. RNA aptamers 1, 2, and 3 showed strong binding affinity scores of –344.95, –331.99, and –395.58, respectively, with target proteins. All aptamers demonstrated robust hydrophobic and hydrogen bond interactions through guanidine phosphate backbone-mediated contacts with Val, Lys, Arg, and Asp residues of target proteins. The in vitro synthesis and in situ environmental validation of these interactions will pave the way for implementing aptamer-based detection in real-time monitoring systems such as healthcare environments, air quality assessment frameworks, and environmental analysis platforms.
Keywords: Airborne microbial communities; Indoor air quality; public health; RNA aptamers; computational modelling; molecular docking