Biophysical characterization of recombinant proteins: A key to higher structural genomics success
Bibliographic record
Abstract
Hundreds of genomes have been successfully sequenced to date, and the data are publicly available. At the same time, the advances in large-scale expression and purification of recombinant proteins have paved the way for structural genomics efforts. Frequently, however, little is known about newly expressed proteins calling for large-scale protein characterization to better understand their biochemical roles and to enable structure-function relationship studies. In the Structural Genomics Consortium (SGC), we have established a platform to characterize large numbers of purified proteins. This includes screening for ligands, enzyme assays, peptide arrays and peptide displacement in a 384-well format. In this review, we describe this platform in more detail and report on how our approach significantly increases the success rate for structure determination. Coupled with high-resolution X-ray crystallography and structure-guided methods, this platform can also be used toward the development of chemical probes through screening families of proteins against a variety of chemical series and focused chemical libraries.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".