Functional domain annotation by structural similarity
Bibliographic record
Abstract
Abstract Traditional automated in silico functional annotation uses tools like Pfam that rely on sequence similarities for domain annotation. However, structural conservation often exceeds sequence conservation, suggesting an untapped potential for improved annotation through structural similarity. This approach was previously overlooked before the AlphaFold2 introduction due to the need for more high-quality protein structures. Leveraging structural information especially holds significant promise to enhance accurate annotation in diverse proteins across phylogenetic distances. In our study, we evaluated the feasibility of annotating Pfam domains based on structural similarity. To this end, we created a database from segmented full-length protein structures at their domain boundaries, representing the structure of Pfam seeds. We used Trypanosomabrucei , a phylogenetically distant protozoan parasite as our model organism. Its structome was aligned with our database using Foldseek, the ultra-fast structural alignment tool, and the top non-overlapping hits were annotated as domains. Our method identified over 400 new domains in the T. brucei proteome, surpassing the benchmark set by sequence-based tools, Pfam and Pfam-N, with some predictions validated manually. We have also addressed limitations and suggested avenues for further enhancing structure-based domain annotation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".