Optimization of an elastic network augmented coarse grained model to study CCMV capsid deformation

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Viral capsids are protein shells crucial for virus structure and assembly.
• Current modeling approaches (atomistic and coarse-grained) have limitations in scale and detail.
• Multi-scale modeling offers a way to bridge these resolution gaps.

Purpose of the Study:

• To develop a coarse-grained (CG) model for Cowpea Chlorotic Mottle Virus (CCMV) capsids.
• To parameterize the CG model using atomistic simulations of protein dimers.
• To validate the model's ability to predict capsid mechanical properties.

Main Methods:

• Developed a CG model for CCMV by combining the MARTINI model with an elastic network.
• Derived model parameters from atomistic simulations of capsid protein dimers.
• Optimized network connectivity and strength based on structural fluctuations.

Main Results:

• The elastic-network optimized CG model accurately predicts inter-protein flexibility in capsid fragments.
• The model successfully reproduces experimental Atomic Force Microscopy indentation data for the entire CCMV capsid.
• Demonstrated the prediction of larger protein complex properties from smaller unit elastic properties.

Conclusions:

• Hierarchical modeling can effectively predict mechanical properties of large protein complexes.
• This approach allows for accurate simulation of viral capsid mechanics using limited atomistic data.
• The developed CG model provides a valuable tool for studying viral structure and mechanics.