Compression of protein secondary structures enables ultra-fast and accurate structure searching
Bibliographic record
Abstract
Abstract Protein structure prediction has undergone a revolution with the advent of AI-based algorithms, such as AlphaFold and RoseTTAFold. As a result, over 200 million predicted protein structures have been published. This wealth of structural data has created a need for rapid structure comparison algorithms, such as Foldseek, which enable efficient searches across this vast space of protein structures. Here we introduce a new ultra-compact representation of protein structure in the form of Secondary Structure Elements (SSEs). These are short sequences around 8% of the length and with 10% of the information content of full amino acid sequences and 3Di sequences. We show that, despite this compression factor, SSEs can be used as a highly effective tertiary structure comparison tool, with accuracy that approaches that of Foldseek, while offering a 200-fold speedup. In addition SSEs offer comparable performance to Foldseek in domain boundary retrieval. Furthermore we show that the particular way in which SSEs encode structure can also be used to specifically detect proteins that differ due to conformational change. These findings demonstrate that SSEs offer a valuable complementary approach for protein structure characterisation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".