Targeting SARS-CoV-2 Receptor Binding Domain and Main Protease with D-Peptides.

Area of Science:

• Biochemistry
• Computational Biology
• Drug Design

Background:

• D-peptide binders show promise as drug candidates due to enhanced binding specificity and metabolic stability compared to L-peptides.
• Efficient computational methods for *de novo* design of D-peptide binders targeting specific protein structures are lacking.

Purpose of the Study:

• To develop a general computational framework for the *de novo* design of D-helical peptide binders.
• To apply this framework to design D-peptide binders against key SARS-CoV-2 targets: the spike protein's receptor binding domain (RBD) and the main protease (3CLpro).

Main Methods:

• A computational framework integrating scaffold generation, docking, sequence design (using Rosetta), and *in silico* selection was developed.
• The framework utilizes a mirrored protein approach for conformational sampling during sequence design.
• The workflow was applied to design D-helical peptides targeting SARS-CoV-2 RBD and 3CLpro.

Main Results:

• The study identified favorable binding sites on both RBD and 3CLpro for D-helical peptides.
• Four out of eight designed D-peptides targeting RBD showed binding, with the best exhibiting submicromolar affinity and blocking spike protein-ACE2 interaction.
• Three out of twelve designed D-peptides targeting 3CLpro inhibited its activity, with the lead peptide LY09 showing submicromolar affinity and disrupting 3CLpro dimerization.

Conclusions:

• The developed computational framework is effective for *de novo* design of D-helical peptide binders.
• Designed D-peptides show significant potential as therapeutic agents against SARS-CoV-2 by targeting critical viral proteins.
• The computational tools are publicly available for further research and development.