A two-bead-per-aminoacid coarse-grained MD model with hydrogen bonding (2BPA-HB) to probe DNAJB6b-mediated suppression of polyglutamine aggregation in Huntingtons disease
ADUPA, V.; Polet, J. D.; Dekker, M.; Onck, P. R.
Show abstract
Polyglutamine (polyQ) aggregation plays a central role in several neurodegenerative diseases, including Huntington's disease. DNAJB6b, a molecular chaperone involved in protein quality control, is known to efficiently suppress polyQ aggregation, but its anti-aggregation mechanism remains unclear. In this work we investigate the interaction between DNAJB6b and the polyQ region (Q48) of mutant Huntingtin Exon 1 (mHttEx1) using a custom-built coarse-grained molecular dynamics model. The model incorporates a two-bead-per-amino-acid representation with hydrogen bonding (termed 2BPA-HB), and is calibrated against all-atom molecular dynamics data in terms of geometry, hydrophobicity, and hydrogen bonding. The model reproduces the tertiary structure of DNAJB6b and its interactions with Q48, and reveals an inverse correlation between DNAJB6b concentration and Q48 aggregation propensity. Our simulations show that DNAJB6b co-condensates with polyQ molecules, thereby shielding the polyQ from forming the intermolecular hydrogen bonds necessary for amyloid formation. The 2BPA-HB CGMD model en- ables efficient exploration of DNAJB6b conformations, supporting future studies of chaperone-mediated aggregation suppression and therapeutic development.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- How good is Generative Diffusion Model for Enhanced Sampling of Protein Conformations Across Scales and in All-atom Resolution? 95%
- SARS-COV-2 Spike Protein Fragment eases Amyloidogenesis of α-Synuclein 94%
- Self-induced Dimensional Reduction and Scaling Transition of mRNA in Polysomes: A Multiscale Simulation Study 94%
Similar papers in this journal
"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.