Gluebodies Offer a Route To Improve Crystal Reliability and Diversity through Transferable Nanobody Mutations That Introduce Constitutive Close Contacts
Bibliographic record
Abstract
Design of modular, transferable protein assemblies has broad applicability and in structural biology could help with the ever-troublesome crystallization bottleneck, including finding robustly behaved protein crystals for rapidly characterizing ligands or drug candidates or generating multiple polymorphs to illuminate diverse conformations. Nanobodies as crystallization chaperones are well-established but still unreliable, as we show here. Instead, we show an exemplar of how robust crystallization behavior can be engineered by exploring many combinations (>200) of nanobody surface mutations over several iterations. Critically, what needed testing was crystallization and diffraction quality, since target-nanobody binding affinity is decoupled from crystallizability enhancement. Our study yielded multiple polymorphs, all mediated by the same interface, with dramatically improved resolution and diffraction reliability for some mutants; we thus name them 'Gluebodies' (Gbs). We further demonstrate that these Gb mutations do transfer to some other targets, both for achieving robust crystallization in alternative packing forms and for establishing the ability to crystallize a key early stage readout. Since the Gb interface is evidently a favored interaction, it may be broadly applicable for modular assembly; more specifically, this work suggests that Gbs should be routinely attempted for crystallization whenever nanobodies are available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".