Back

Aggregation-Prone Region Mapping in Olfactomedin Domain of Myocilin through Classical and Enhanced Sampling Molecular Dynamics Simulations

Sardag, I.; Duvenci, Z. S.; Timucin, E.

2025-08-02 bioinformatics
10.1101/2025.08.02.668245 bioRxiv
Show abstract

The aggregation of the myocilin olfactomedin (OLF) domain, generally driven by genetic mutations, is the leading cause of primary open-angle glaucoma (POAG). Developing therapeutic strategies requires a detailed understanding its initial unfolding events that expose aggregation-prone regions (APRs). However, it has been a challenge, as the slow conformational dynamics of OLF hinders classical molecular dynamics (MD) simulations from capturing aggregation-prone OLF intermediates. To overcome this, we employed a multi-pronged computational strategy, integrating over 15 {micro}s of simulation time across diverse conditions, including high-temperature, enhanced sampling, chemical denaturation, and simulations of the pathogenic I499F mutant. Our results reveal that OLF unfolding is not random but initiates at specific structural regions pertinent to the terminal blade A and E. Specifically, the blade interfaces between A-B and A-E showed unique regions rich in aromatic/hydrophobic residues as aggregation hotspots. Overall, our simulations proved effective to generate a detailed map of seven distinct APRs. The accuracy of these APRs is partially validated by the close localization of these predicted regions with both previously identified amyloid peptides and the sites of known disease-causing mutations. By scrutinizing the OLF structure and dynamics under different MD settings, our study provides potential molecular targets for developing new therapeutic interventions against POAG.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.