Bibliographic record
Abstract
With the advent of high throughput systems to experimentally determine the three-dimensional (3-D) structure of proteins, molecular biologists are in urgent need of systems to automatically store, maintain and explore the vast structural databases that are thus being created. We have designed and implemented the Capri/MR system which makes it possible to identify families of protein structures, as contained in such very large 3-D protein structure databases. Our system is able to automatically index and search a database of proteins by three-dimensional shape, structural and/or physicochemical properties. For each of these diverse protein structure representations, we create a compact rotation and translation invariant index (or signature) which is placed in a database for future querying. A similarity search algorithm performs an exhaustive search against the entire database. Our search algorithm takes advantage of the compact signatures to rapidly find protein structures that are similar in 3-D shape and/or two-dimensional (2-D) properties. As a result, queries in our Capri/MR system run within a fraction of a second, and we are able to accurately group protein structures into the correct families, with very high precision and recall. In addition, our system dynamically processes new protein structures as they become available. We demonstrate the power of Capri/MR against the Protein Data Bank, which contains all known, experimentally determined, 3-D protein structures (48.000 as of January 2008). The main applications of our Capri/MR system lie in structural proteomics, protein evolution and mutation, as well as in drug design, in particular for studying the docking problem and the computer aided design of non-toxic drugs.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.116 |
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".