An optimized contact map for GōMartini 3 enabling conformational changes in protein assemblies.

Area of Science:

• Structural Biology
• Computational Biophysics
• Protein Dynamics

Background:

• Cryo-electron microscopy enables high-resolution studies of protein assemblies.
• Computational methods are needed to simulate long-timescale protein conformational changes.
• GōMartini 3 offers efficient coarse-grained simulations but can be refined.

Purpose of the Study:

• To enhance GōMartini 3 by integrating all-atom molecular dynamics (AA-MD) data.
• To improve the representation of protein conformational states and dynamics.
• To develop a more efficient and accurate coarse-grained simulation approach.

Main Methods:

• Integrated dynamic contact information from AA-MD simulations into GōMartini.
• Defined high-frequency contacts to refine the GōMartini contact map, reducing contact numbers by 20-30%.
• Benchmarked contact selection criteria on single-chain proteins and the SARS-CoV-2 spike protein.

Main Results:

• The optimized contact map improved sampling efficiency and expanded the accessible conformational landscape.
• Inclusion of high-frequency intra- and interchain contacts captured protein assembly flexibility and domain dynamics.
• The refined method demonstrated improved representation of conformational states compared to standard Martini 3.

Conclusions:

• The optimized GōMartini approach enhances the simulation of protein conformational changes.
• This method provides a more accurate and efficient tool for studying large protein assemblies.
• The framework is available as an open-source tool for large-scale simulations.