Faculty Opinions recommendation of LLPSDB: a database of proteins undergoing liquid–liquid phase separation in vitro.
Bibliographic record
Abstract
Liquid-liquid phase separation (LLPS) leads to a conversion of homogeneous solution into a dense phase that often resembles liquid droplets, and a dilute phase.An increasing number of investigations have shown that biomolecular condensates formed by LLPS play important roles in both physiology and pathology.It has been suggested the phase behavior of proteins would be not only determined by sequences, but controlled by micro-environmental conditions.Here, we introduce LLPSDB (http://bio-comp.ucas.ac.cn/llpsdb or http://bio-comp.org.cn/llpsdb), a web-accessible database providing comprehensive, carefully curated collection of proteins involved in LLPS as well as corresponding experimental conditions in vitro from published literatures.The current release of LLPSDB incorporates 1182 entries with 273 independent proteins and 2394 specific conditions.The database provides a variety of data including biomolecular information (protein sequence, protein modification, nucleic acid, etc.), specific phase separation information (experimental conditions, phase behavior description, etc.) and comprehensive annotations.To our knowledge, LLPSDB is the first available database designed for LLPS related proteins specifically.It offers plenty of valuable resources for exploring the relationship between protein sequence and phase behavior, and will enhance the development of phase separation prediction methods, which may further provide more insights into a comprehensive understanding of LLPS in cellular function and related diseases.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.349 | 0.377 |
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".