High-Throughput Virtual Screening of Protein-Catalyzed Capture Agents for Novel Hydrogel-Nanoparticle Fentanyl Sensors

Abstract

Fast, portable, and reliable detection of chemical and biological compounds is an important challenge in many safety and healthcare applications. Chemical sensors based on photonic crystals that use protein-catalyzed capture (PCC) agents as molecular sensors are promising tools for the portable detection of chemical and biological compounds. We present the development and deployment of a high-throughput virtual screening protocol to computationally identify PCC candidates that maximize the binding sensitivity and selectivity for fentanyl. The approach integrates enhanced sampling molecular dynamics free-energy calculations, Gaussian process regression surrogate models, and Bayesian optimization to efficiently navigate the design space of over 1 million PCC candidates, resolve the sensitivity–selectivity Pareto frontier, and identify the top-performing PCC candidates. We analyze the molecular interactions between our top candidates and fentanyl target to propose design rules for high-performance PCC agents to be used for experimental testing and, ultimately, incorporation into next-generation hydrogel–nanoparticle-based chemical sensing devices.