Antiviral nanoparticle ligands identified with datamining and high-throughput virtual screening

Abstract

To help contain the spread of the COVID-19 pandemic and to protect front-line workers, new antiviral measures are required. Antiviral nanoparticles are one such possible measure. Metal nanoparticles made from a variety of metals including gold, silver, and copper can kill or disable viruses that cause significant health problems in humans (such as SARS-CoV-2, HIV, or influenza). To promote interaction between nanoparticles and viruses the stabilizing ligands on the nanoparticle surface should be optimized for docking with proteins. The enormous chemical space of possible nanoparticle ligands makes this optimization experimentally and computationally intractable. Here we present a datamining-based study that searched for nanoparticle ligands that have previously been used, and computationally tested these for their ability to dock with the SARS-CoV-2 spike glycoprotein. These ligands will coat future antiviral nanoparticles to be used outside of the body, not as drugs. The best of these ligands identified were: nitric acid (score: 0.95), phosphoroselenoic acid (score: 0.88), hydroxyammonium (score: 0.83), pyrophosphoric acid (score: 0.81). Inspection of the best of these ligands has suggested design principles for future antiviral nanoparticle ligands, and we suggest further ligands based on these principles. These results will be used to inspire further in vitro and in silico experimentation to accelerate the development of antiviral nanoparticles.

Fig. 1. (a) Cartoon of how the coronavirus interacts with host cells reproducing the virus. The spike glycoprotein (keys) of the virus interacts with the ACE2 enzymes (locks) to gain access to the cell (b) cartoon of antiviral nanoparticles using their ligands (locks) to stop the virus from accessing the cell. (c) Illustration of the datamining pipeline to discover novel nanoparticle ligands for use in antiviral nanoparticles. The database of a scientific publisher is searched for articles pertaining to our topic, nanoparticles. These articles are saved in a computer-readable format and consequently mined for relevant chemical names (relating to ligands). These chemical names are computationally docked with the spike glycoprotein on the SARS-CoV-2 virus. These docking poses are compared and ranked to find the best candidate nanoparticle ligands.

Fig. 2. Molecular structure drawings of the four best small molecules and ions found in the datamining study to identify antiviral nanoparticle ligands. (a) nitric acid (score: 0.95) (b) phosphoroselenoic acid (score: 0.88) (c) hydroxyammonium (score: 0.83) (d) pyrophosphoric acid (score: 0.81).

Fig. 3. The four best larger ligand molecules found in the datamining study to identify antiviral nanoparticle ligands. (a) (Kdo)2-lipid A 1-diphosphate (score: 0.99) (b) decaprenyl diphosphate (score: 0.98) (c) tetra-tert-butyl(1,4,8,11-tetraazacyclotetradecane-1,4,8,11-tetrayltetrapentane-5,1-diyl)tetrakis(benzylcarbamate) (score: 0.96) (d) 4-({15-[carboxy(hydroxy)methyl]-24,28,30-trihydroxy-18-[2-hydroxy-1-({3,7,11,13-tetrahydroxy-12-[(7-hydroxy-9-methoxy-4,4,6,8,8-pentamethyl-5,9-dioxo-2-nonenoyl)amino]-2,6,8,10,14-pentamethyl-8-pentadecenoyl}amino)propyl]-19,23,25,27,29,34-hexamethyl-13,16-dioxo-17,36-dioxa-14-azabicyclo[30.3.1]hexatriaconta-3,5,9,11,22,26-hexaen-20-yl}oxy)-2-hydroxy-4-oxobutanoic acid (score: 0.95).

Fig. 4. Molecular diagrams and associated scores of the molecules inspired by the results from the datamining campaign. (a) 2,2-Dichlorovinyl dihydrogen phosphate (score: 0.21) (b) Hamburg esterquat (score: 0.21) (c) malathion α-monoacid (score: 0.13) (d) malathion diacid (score: 0.09).