A novel inhibitor-binding site on the HIV-1 capsid N-terminal domain leads to improved crystallization<i>via</i>compound-mediated dimerization
Bibliographic record
Abstract
Despite truly impressive achievements in the global battle against HIV there remains a need for new drugs directed against novel targets, and the viral capsid protein (CA) may represent one such target. Intense structural characterization of CA over the last two decades has provided unprecedented insight into the structure and assembly of this key viral protein. Furthermore, several inhibitor-binding sites that elicit antiviral activity have been reported on CA, two of which are located on its N-terminal domain (CANTD). In this work, the binding of a novel capsid-assembly inhibitor that targets a unique inhibitory site on CANTD is reported. Moreover, whereas cocrystallization of CANTD in complex with ligands has proven to be challenging in the past, the use of this inhibitor as a tool compound is shown to vastly facilitate ternary cocrystallizations with CANTD. This improvement in crystallization is likely to be achieved through the formation of a compound-mediated homodimer, the intrinsic symmetry of which greatly increases the prospect of generating a crystal lattice. While protein engineering has been used in the literature to support a link between the inherent symmetry of a macromolecule and its propensity to crystallize, to our knowledge this work represents the first use of a synthetic ligand for this purpose.
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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 | 0.001 | 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".