AI-Guided Binding Mechanisms and Molecular Dynamics for MERS-CoV

Area of Science:

• Virology and Computational Biology
• Drug Discovery and Development

Background:

• Middle East respiratory syndrome coronavirus (MERS-CoV) poses a significant public health threat due to its high mortality rate and limited treatment options.
• Viral entry mechanisms, specifically the interaction between MERS-CoV surface proteins and the human Dipeptidyl Peptidase-4 (DPP4) receptor, are crucial for understanding and combating infection.

Purpose of the Study:

• To identify critical interface residues involved in the MERS-CoV and DPP4 receptor interaction using computational methods.
• To provide a molecular basis for developing targeted antiviral therapies against MERS-CoV.

Main Methods:

• Utilized Molecular Dynamics (MD) simulations on a high-performance computing (HPC) platform to analyze MERS-CoV-DPP4 interactions.
• Identified key residue pairs by analyzing salt bridge and hydrogen bond occupancy.
• Validated residue stability through independent MD simulations at human body temperature and pressure.
• Calculated binding affinities to quantify interaction strength.

Main Results:

• Identified seven key interaction pairs between MERS-CoV and the DPP4 receptor.
• Confirmed two previously reported interaction pairs (Asp510-Arg317 and Arg511-Asp393).
• Proposed five novel interaction pairs for future experimental validation in antiviral drug discovery.

Conclusions:

• The study provides crucial molecular insights into the MERS-CoV-DPP4 binding mechanism.
• Findings support the rational design of structure-based inhibitors to disrupt viral entry.
• This research facilitates the translation of computational discoveries into potential MERS-CoV antiviral therapeutics.