Development of a coarse-grained model for surface-functionalized gold nanoparticles: towards an accurate description of their aggregation behavior

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Molecular docking, molecular dynamics simulation, and MM/PBSA analysis of ginger phytocompounds as a potential inhibitor of AcrB for treating multidrug-resistant Klebsiella pneumoniae infections

The emergence of Multidrug resistance (MDR) in human pathogens has defected the existing antibiotics and compelled us to understand more about the basic science behind alternate anti-infective drug discovery. Soon, proteome analysis identified AcrB efflux pump protein as a promising drug target using plant-driven phytocompounds used in traditional medicine systems with lesser side effects. Thus, the present study aims to explore the novel, less toxic, and natural inhibitors of Klebsiella pneumoniae AcrB pump protein from 69 Zingiber officinale phyto-molecules available in the SpiceRx database through computational-biology approaches. AcrB protein's homology-modelling was carried out to get a 3D structure. The multistep-docking (HTVS, SP, and XP) were employed to eliminate less-suitable compounds in each step based on the docking score. The chosen hit-compounds underwent induced-fit docking (IFD). Based on the XP GScore, the top three compounds, epicatechin (-10.78), 6-gingerol (-9.71), and quercetin (-9.09) kcal/mol, were selected for further calculation of binding free energy (MM/GBSA). Furthermore, the short-listed compounds were assessed for their drug-like properties based on in silico ADMET properties and Pa, Pi values. In addition, the molecular dynamics simulation (MDS) studies for 250 ns elucidated the binding mechanism of epicatechin, 6-gingerol, and quercetin to AcrB. From the dynamic binding free energy calculations using MM/PBSA, 6-gingerol exhibited a strong binding affinity towards AcrB. Further, the 6-gingerol complex's energy fluctuation was observed from the free energy landscape. In conclusion, 6-gingerol has a promising inhibiting potential against the AcrB efflux pump and thus necessitates further validation through in vitro and in vivo experiments.Communicated by Ramaswamy H. Sarma.