Multiscale simulations of folded and intrinsically disordered region-containing protein condensates
Bibliographic record
Abstract
We present a multiscale simulation framework that integrates all-atom (AA) structure modeling, coarse-grained (CG) Martini 3 simulations, and AA backmapping to investigate supramolecular assembly behaviors of proteins implicated in misfolding diseases. As representative systems, we modeled the C-terminal domain of human TDP-43 (residues 274–414) and a broad region of white-tailed deer PrP (residues 24–233), both associated with misfolding-related pathologies and exhibiting distinct structural features. This AA–CG–AA framework enables the construction and analysis of large multi-unit assemblies using two Martini 3–based CG parameter sets that differ in their treatment of nonbonded interactions, including electrostatic cutoffs and the scaling of interactions involving Na + and Cl - ions present in solution. The framework successfully captured key mesoscale features of early supramolecular condensation and revealed pronounced condition-dependent differences in both condensate morphology and chain dynamics. Parameter sets with longer cutoffs and rescaled ion interactions promoted more interconnected, network-like assemblies – particularly for PrP – whereas standard cutoffs yielded more dispersed systems with higher translational mobility. Mean-square displacement (MSD) analysis and diffusivity estimates reflected clear differences in translational mobility consistent with varying degrees of local association. Further analysis of low-MSD chains revealed the emergence of condensate-like subpopulations, especially in PrP, where percolation-like networks formed. Solvent-accessible surface area (SASA) analyses confirmed that protein chains remained solvent-exposed throughout, consistent with known fluid-like phase-condensation behavior. Follow-up all-atom analysis revealed persistent inter-chain dynamical coupling even in the absence of direct chain contacts, suggesting solvent-mediated interactions. Notably, analyses revealed an inverse relationship between protein translational mobility and Na + and Cl - ions contact counts, suggesting that ion association contributes to early-stage condensation. These findings demonstrate the potential of integrated CG–AA simulation strategies to probe supramolecular condensation in structurally heterogeneous protein systems and support their continued development for studying misfolding-driven assembly processes at mesoscopic scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".