Cryo-EM structure determination of small therapeutic protein targets at 3 Å-resolution using a rigid imaging scaffold
Bibliographic record
Abstract
Abstract Numerous technical advances have made cryo-EM an attractive method for atomic structure determination. Cryo-EM is ideally suited for large macromolecular structures, while problems of low signal-to-noise prevent routine structure determination of proteins smaller than about 50 kDa. This size limitation excludes large numbers of important cellular proteins from structural characterization by this powerful technique, including many cell-signaling proteins of high therapeutic interest. In the present work, we use molecular engineering techniques to rigidify an imaging scaffold, based on a designed protein cage, to the point where 3 Å resolution can be achieved, even for very small proteins. After optimizing the design of the rigidified scaffold on test proteins, we apply this imaging system to the key oncogenic signaling protein KRAS, which represents an outstanding challenge in the area of structure-based drug design. Despite its 19 kDa size, we show that the structure of KRAS, in multiple mutant forms, and bound to its GDP ligand, can be readily interpreted at a resolution slightly better than 3.0 Å. This advance further expands the capability of cryo-EM to become an essentially universal method for protein structure determination, including for applications to small therapeutic protein targets.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".