Generating a conformational landscape of ubiquitin chains at atomistic resolution by back-mapping based sampling

Area of Science:

• Biochemistry and structural biology
• Computational biophysics
• Molecular dynamics simulations

Background:

• Ubiquitin chains are crucial for cellular signaling, but their conformational flexibility poses challenges for computational study.
• All-atom simulations are computationally expensive for large ubiquitin systems.
• Coarse-graining (CG) simplifies simulations but sacrifices atomic resolution.

Purpose of the Study:

• To apply and validate the back-mapping based sampling (BMBS) approach for K48-linked tri-ubiquitin.
• To assess the scalability and performance of BMBS with larger systems.
• To analyze the atomistic conformational landscape derived from CG simulations.

Main Methods:

• Application of the BMBS approach to K48-linked tri-ubiquitin.
• Testing of three different seeding strategies for atomistic trajectory initiation.
• Utilizing a conformational clustering algorithm on the back-mapped atomistic ensemble.
• Comparison of atomistic and CG conformational landscapes.

Main Results:

• BMBS successfully applied to K48-linked tri-ubiquitin, demonstrating scalability.
• Seeding strategies influenced the sampling of conformational states.
• Conformational clustering revealed distinct regions in the 2D projection map.
• The atomistic landscape derived from BMBS showed slight but significant differences from the CG map, correcting CG model flaws.

Conclusions:

• BMBS is a robust method for generating atomistic ensembles from CG simulations of polyubiquitin chains.
• The approach provides valuable insights into the structural composition and dynamics of ubiquitin chains.
• BMBS offers a way to systematically refine CG models by incorporating atomistic details.