A hybrid, bottom-up, structurally accurate, Go¯-like coarse-grained protein model.

Area of Science:

• Computational Biology
• Structural Biology
• Biophysics

Background:

• Coarse-grained (CG) protein models have evolved from simple tools to detailed structure predictors.
• Systematic development of CG models without bioinformatic data is an ongoing challenge.

Purpose of the Study:

• To develop a hybrid CG peptide model using bottom-up coarse-graining methods without bioinformatic data.
• To investigate the role of backbone and sidechain interactions in protein folding.
• To assess the accuracy of the developed CG model for predicting protein structures.

Main Methods:

• Utilized relative entropy coarse-graining to develop a hybrid CG peptide model.
• Employed an extended ensemble relative entropy method with multiple atomistic simulations.
• Combined backbone forcefield with sidechain interactions based on native contacts.

Main Results:

• The developed CG model achieved high accuracy (∼2 Å RMSD) in folding a range of proteins.
• Demonstrated the significant role of backbone conformational preferences in defining protein fold landscapes.
• Showcased the model's ability to capture varied secondary structures.

Conclusions:

• The systematic development of CG models from bottom-up methods is feasible without bioinformatic data.
• Backbone interactions are crucial for encoding the protein fold landscape.
• The developed model provides a foundation for extensions to non-natural amino acids and polymers.