Structure and dynamics of the Ovarian Tumour Domain Protease from the Crimean-Congo Hemorrhagic Fever Virus by Nuclear Magnetic Resonance spectroscopy
Bibliographic record
Abstract
Crimean-Congo Hemorrhagic Fever Virus (CCHFV) is endemic to more than 30 countries and shows fatality rates among humans ranging from 30-50%. The CCHFV 169-residue L-segment deubiquitinase (DUB) is a member of the superfamily of Ovarian Tumour (OTU) ubiquitin thiolesterases that interfere with innate immune responses and hence is an attractive antiviral target. I report here the application of Nuclear Magnetic Resonance spectroscopy to probe the role of CCHFV OTU enzyme dynamics in the catalytic mechanism of the enzyme. 13C/15N triple-resonance experiments and an amino acid “unlabelling” scheme were used for backbone resonances assignments. NMR chemical shift analysis, NMR spin-relaxation experiments at two magnetic fields, Lipari-Szabo Model-free formalism, reduced spectral density mapping and Carr-Purcell-Meiboom-Gill Relaxation Dispersion experiments were done to obtain structure and dynamics data. The dynamics data suggested an unfolded C-terminus and a well-packed protein core. Relaxation dispersion measurements show that a significant number of residues undergo conformational exchange on the millisecond timescale. Some of these are near the active site and neighbouring segments and may represent a rate-limiting event along the proteolytic kinetic pathway. They may also play a role in determining the enzyme’s broad substrate specificity.
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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 | 0.000 |
| 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".