Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Cataract disease is linked to the aggregation of γD-crystallin, a key structural protein in the eye lens.
• Network Hamiltonian models (NHMs) offer a topological coarse-graining framework for protein-protein interactions.

Purpose of the Study:

• To apply NHMs to model the aggregation of wild-type and W42R variant γD-crystallin.
• To assess the scalability and accuracy of NHMs for large protein systems and varying concentrations.

Main Methods:

• Generation of NHMs from atomistic simulations of γD-crystallin equilibrium distributions.
• Scaling system size from 375 to 10,000 monomers to analyze aggregate size distribution.
• Extrapolation to higher and lower protein concentrations.

Main Results:

• NHMs accurately reproduced aggregate size and structure from atomistic simulations.
• A reduction in the upper tail of the aggregate size distribution was observed for the W42R variant at larger scales.
• Transient protein-protein interactions within aggregates were elucidated.

Conclusions:

• NHMs are effective for coarse-grained simulation of protein aggregation, retaining crucial topological information.
• The NHM framework demonstrates scalability to large system sizes and high concentrations, reducing computational cost.
• This approach provides valuable insights into the molecular mechanisms of cataract formation related to γD-crystallin aggregation.