Exploring Protein Architecture using 3D Shape-based Signatures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Consider the scenario where, for a prescription drug designed to treat a terminal illness, a particular protein has been successfully identified as a crucial, beneficial component in the drug compound. However, this protein has contra-indications and causes severe adverse effects in a certain subset of the population. If another protein from the same family, with similar structure and functionality, but without these adverse effects, can be found, the subsequent modification of the harmful drug has obvious benefits. This paper describes a new indexing and similarity search system to retrieve such protein structure family members, based on their 3D shape. Our approach is translation, scale and rotation invariant, which eliminates the need for prior structure alignment. Our experimental evaluation against seven (7) diverse protein families indicate that our system accurately and precisely locate all members of a family. We further illustrate this by showing that our system precisely retrieves the Homo Sapiens Hemoglobin family members, against a database containing 26,000 protein structures.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it