DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

Area of Science:

• Computational Biology
• Drug Discovery
• Structural Biology

Background:

• SARS-CoV-2 protein inhibitors computational identification often neglects receptor flexibility.
• Key SARS-CoV-2 proteins targeted include main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp).

Approach:

• Implemented a computational tool for ensemble docking against SARS-CoV-2 proteins.
• Utilized DINC, a parallelized meta-docking tool, to generate binding modes considering ligand and receptor flexibility.
• Scored binding modes using three scoring functions.

Key Points:

• Ensemble docking accounts for receptor flexibility, a crucial factor in inhibitor identification.
• DINC provides a user-friendly interface for docking against ensembles of SARS-CoV-2 proteins.
• Plausible binding modes are generated and scored, facilitating drug design.

Conclusions:

• The developed ensemble docking approach enhances computational drug discovery for SARS-CoV-2.
• The DINC tool and protein ensembles are available at dinc-covid.kavrakilab.org.
• This method offers a more accurate way to identify potential inhibitors by including protein flexibility.