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

Area of Science:

• Nanotechnology
• Virology
• Computational Chemistry

Background:

• The COVID-19 pandemic necessitates novel antiviral strategies.
• Metal nanoparticles show potential for disabling viruses like SARS-CoV-2.
• Optimizing nanoparticle ligands is crucial for effective virus interaction but is computationally challenging.

Purpose of the Study:

• To identify effective nanoparticle ligands for antiviral applications.
• To computationally screen existing ligands for docking with the SARS-CoV-2 spike glycoprotein.
• To inform the design of future antiviral nanoparticles.

Main Methods:

• Datamining to identify previously used nanoparticle ligands.
• Computational testing of identified ligands for docking with the SARS-CoV-2 spike glycoprotein.
• Analysis of top-scoring ligands to establish design principles.

Main Results:

• Nitric acid (0.95), phosphoroselenoic acid (0.88), hydroxyammonium (0.83), and pyrophosphoric acid (0.81) were the top-scoring ligands.
• These ligands demonstrated potential for docking with the SARS-CoV-2 spike glycoprotein.
• Design principles for future antiviral nanoparticle ligands were suggested.

Conclusions:

• The identified ligands can be used to coat external antiviral nanoparticles.
• Computational screening is an effective method for accelerating antiviral nanoparticle development.
• Further in vitro and in silico experiments are recommended based on these findings.