Targeting protein‐protein interaction networks: Structural genomics of FeS cluster assembly and human cancer biology
Bibliographic record
Abstract
The NESG is one of four Large‐Scale Centers of the NIGMS Protein Structure Initiative (PSI). A key goal of the PSI is to generate 3D structures for some 5,000 proteins selected using broad biological, genomic, and bioinformatics criteria, together with targets selected from specific biological theme projects, so as to provide significant structural coverage of a large number of protein sequences in nature. The PSI also develops new methods for protein structure analysis, and proactively disseminates these technologies to the broader biological community. Our efforts span three classes of proteins: (i) selected to provide course‐grained coverage of large protein domain families; (ii) nominated in collaborations with the biomedical research community; and (iii) NESG Biomedical Theme of ‘Networks of Proteins Associated with Human Cancer and Developmental Biology’. NESG technology development emphasizes: (i) new technologies for production of proteins, (ii) new methods for protein NMR; (iii) hybrid approaches exploiting synergies of crystallography, SAX, NMR, and computational prediction, and (iv) improved methods for crystallizing proteins. This technology development is driven by our Biomedical Theme focus on human proteins, including protein complexes associated with cancer biology. The many structures, reagents, methods and technologies developed in this project provide starting points for traditional hypothesis‐driven biological research, and provide powerful, broad‐impact infrastructure for biological science and engineering. Supported by Natl. Inst. of General Medical Sciences
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".