“From Protein Toxins to Applied Toxicological Testing” virtual workshop identifies the need for a bioinformatic framework to assess novel food protein safety
Bibliographic record
Abstract
On October 21-22, 2020 the HESI (Health and Environmental Sciences Institute) Protein Allergens, Toxins, and Bioinformatics Committee, and the Society of Toxicology Food Safety Specialty Section co-hosted a virtual workshop titled "From Protein Toxins to Applied Toxicological Testing". The workshop focused on the safety assessment of novel proteins contained in foods and feeds, was globally represented by over 200 stakeholder attendees, and featured contributions from experts in academia, government and non-government organizations, and agricultural biotechnology developers from the private sector. A range of topics relevant to novel protein safety were discussed, including: the state of protein toxin biology, modes and mechanisms of action, structures and activity, use of bioinformatic analyses to assess the safety of a protein, and ways to leverage computational biology with in silico approaches for protein toxin identification/characterization. Key outcomes of the workshop included the appreciation of the complexity of developing a definition for a protein toxin when viewed from the perspective of food and feed safety, confirming the need for a case-by-case hypothesis-driven interpretation of bioinformatic results that leverages additional metadata rather than an alignment threshold-driven interpretation, and agreement that a "toxin protein database" is not necessary, as the bioinformatic needs for toxin detection may be accomplished by existing databases such as Pfam and UniProtKB/Swiss-Prot. In this paper, a path forward is proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".