Understanding Biases in Liquid–Liquid Phase Separation: Investigating Amino Acid Enrichments in Phase-Separating Proteins toward Peptide Design
Bibliographic record
Abstract
Liquid-liquid phase separation (LLPS) facilitates the formation of membraneless organelles, enhancing biochemical processes. The stickers-and-spacers model explains LLPS but is mainly validated in prion-like RNA-binding proteins. To broaden our understanding, we investigated peptide motifs associated with LLPS across diverse protein contexts using a computational approach on the droplet-promoting regions (DPRs) of 178 phase-separating proteins. The study identified 129 enriched peptide motifs (3-6 residues), characterized by Gly-rich sequences interspersed with aromatic, charged, and polar residues, as well as homopeptide repeats (e.g., GGDR, SRGG, QQQQ). Analysis of motif presence and frequency revealed a widespread distribution across DPRs and significant repetitive patterns. Motif trios with a higher likelihood of co-occurrence were utilized in a data-driven approach to design peptides with LLPS propensity. The designed peptides exhibited liquid-like behavior with different dynamics upon experimental validation. This work provides insights into sequence determinants of phase separation and offers the potential for designing synthetic condensates with tailored 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".