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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".