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Record W4415111558 · doi:10.1016/j.bpj.2025.10.018

Multiscale simulations of folded and intrinsically disordered region-containing protein condensates

2025· article· en· W4415111558 on OpenAlexafffund
Lyudmyla Dorosh, Holger Wille, Maria Stepanova

Bibliographic record

VenueBiophysical Journal · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Alberta
FundersUniversity of California, San FranciscoAlberta InnovatesGovernment of AlbertaFondation Brain CanadaALS Society of Canada
KeywordsScalingElectrostaticsStatic electricityCondensationIonCoupling (piping)Chain (unit)Protein structureMolecular dynamics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.006
GPT teacher head0.247
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractno

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