Theories for Sequence-Dependent Phase Behaviors of Biomolecular Condensates
Bibliographic record
Abstract
Liquid-liquid phase separation and related condensation processes of intrinsically disordered proteins (IDPs), proteins with intrinsically disordered regions, and nucleic acids underpin various condensed-liquid droplets or gel-like assemblies in the cellular environment. Collectively referred to as condensates, these bodies provide spatial/temporal compartmentalization, often serving as hubs for regulated biomolecular interactions. Examples include certain extracellular materials, transcription complexes, and membraneless organelles such as germ and stress granules and the nucleolus. They are critically important to cellular function; thus misregulation of their assembly is implicated in many diseases. Biomolecular condensates are complex entities. Our understanding of their inner workings is only in its infancy. Nonetheless, insights into basic biophysical principles of their assembly can be gained by applying analytical theories to elucidate how IDP phase behaviors are governed by the properties of the multivalent, solvent-mediated interactions entailed by the proteins' amino acid sequences. Here we briefly review the background of the pertinent polymer theories and outline the approximations that enable a tractable theoretical account of the dependence of IDP phase behaviors on the charge pattern of the IDP sequence. Of relevance to the homeostatic assembly of compositionally and functionally distinct condensates in the cellular context, theory indicates that the propensity for populations of different IDP sequences to mix or demix upon phase separation is affected by the similarity or dissimilarity of the sequence charge patterns. We also explore prospects of extending analytical theories to account for dynamic aspects of biomolecular condensates and to incorporate effects of cation-π, π-π, and temperature-dependent hydrophobic interactions on IDP phase properties.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".