Bioinformatics-Driven Molecular Docking and Molecular Dynamics Simulation of Gold Nanoparticle Ligand Complexes for Targeted Antimicrobial Applications

Abstract

Inorganic nanoparticles (NPs), such as gold nanoparticles (AuNPs), are one such candidate nanoplatform that has been investigated extensively for its potential to combat multidrug-resistant (MDR) pathogens, due to their tunable size, shape-dependent surface chemistry, and their ability to multivalently conjugate with ligands. Simultaneously, the development of structural bioinformatics, such as molecular docking, molecular dynamics (MD) simulation, quantitative structure activity relationship (QSAR) modelling and machine-learning (ML) toxicity prediction, has revolutionised the rationalization of interactions of nanoparticles with biomolecules before the experiments in wet laboratories. The present review critically synthesises the literature available from 2021 to 2026 dealing with the docking and MD based characterisation of the AuNP–ligand system for targeted antimicrobial therapy. The physicochemical basis of AuNP bioactivity is examined, as well as the computational tool chain developed to model AuNP protein and AuNP membrane interactions, and the translational evidence showing the correlation between in silico predictions of protein binding and in vivo antibacterial, antifungal and antibiofilm activity. Special attention is given to the diversity in the use of different methods and different force fields in docking studies, to the lack of an AuNP-specific force field to guide MD, and thus the uncertainty of transferring docking methods derived from small molecules to a polydisperse AuNP system of many valence states. Based on this, we believe the field has yielded promising binding-affinity and mechanistic information, but there is still a significant disconnect between computational prediction and reproducible clinical translation, and we highlight the areas that need to be addressed to bridge this gap, such as standardisation of the representation of the nanoparticles, treatment of the gold core by hybrid quantum mechanics/molecular mechanics (QM/MM), and integrated ML-driven toxicity screening.