The blobulator: a toolkit for identification and visual exploration of hydrophobic modularity in protein sequences
Bibliographic record
Abstract
ABSTRACT While contiguous subsequences of hydrophobic residues are essential to protein structure and function, as in the hydrophobic core and transmembrane regions, there are no current bioinformatics tools for module identification focused on hydrophobicity. To fill this gap, we created the blobulator toolkit for detecting, visualizing, and characterizing hydrophobic modules in protein sequences. This toolkit uses our previously developed algorithm, blobulation, which was critical in both interpreting intra-protein contacts in a series of intrinsically disordered protein simulations [1] and defining the “local context” around disease-associated mutations across the human proteome [2]. The blobulator toolkit provides accessible, interactive, and scalable implementations of blobulation. These are available via a webtool, a VMD plugin, and a command line interface. We highlight use cases for visualization, interaction analysis, and modular annotation through three example applications: a globular protein, two orthologous membrane proteins, and an IDP. The blobulator webtool can be found at www.blobulator.branniganlab.org , and the source code with pip installable command line tool, as well as the VMD plugin with installation instructions, can be found on GitHub at www.GitHub.com/BranniganLab/blobulator .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.019 |
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".