Targeting RdRp of SARS-CoV-2 with De Novo Molecule Generation.

Area of Science:

• Virology
• Drug Discovery
• Computational Chemistry

Background:

• Viruses, particularly RNA viruses, exhibit high mutation rates, enabling immune evasion and resistance to conventional therapies.
• The RNA-dependent RNA polymerase (RdRp) is a conserved target crucial for viral replication, making it a prime candidate for therapeutic intervention.
• The COVID-19 pandemic necessitates the development of novel antiviral therapeutics targeting essential viral enzymes like SARS-CoV-2 RdRp.

Purpose of the Study:

• To develop and apply a de novo drug design algorithm for generating novel ligands targeting the SARS-CoV-2 RdRp.
• To identify and evaluate the binding affinity of computationally designed molecules against SARS-CoV-2 RdRp.
• To compare the binding efficacy of novel ligands with the FDA-approved drug remdesivir triphosphate (RTP).

Main Methods:

• Utilized a de novo drug design algorithm based on the target protein's receptor structure.
• Employed all-atom explicit-water free energy calculations and well-tempered metadynamics simulations (near-microsecond timescale).
• Analyzed binding mechanisms and compared the binding affinities of designed ligands and RTP.

Main Results:

• Generated several de novo molecules with strong binding potential to SARS-CoV-2 RdRp.
• Demonstrated that some computationally designed ligands exhibit superior binding affinity compared to remdesivir triphosphate (RTP).
• Elucidated binding mechanisms for top-performing ligands, offering insights into RdRp-ligand interactions.

Conclusions:

• The de novo drug design approach successfully identified potent inhibitors of SARS-CoV-2 RdRp.
• The designed ligands represent promising lead structures for developing new antiviral therapies against SARS-CoV-2.
• This study provides valuable insights into RdRp binding sites, facilitating future targeted drug development efforts.