Peptidisc-assisted hydrophobic clustering towards the production of multimeric and multispecific nanobody proteins
Bibliographic record
Abstract
Protein multimerization is a powerful engineering strategy for enhancing structural stability, diversity and functional performance. Typical methods to cluster proteins include tandem linking, fusion to self-assembly domains and cross-linking. We present here an approach that leverages the peptidisc membrane mimetic to stabilize hydrophobic-driven protein associations. We apply the method to nanobodies (Nbs), effective substitutes to antibodies due to their production efficiency, cost effectiveness, and lower immunogenicity, and we demonstrate the formation of multimeric assemblies termed "polybodies" (Pbs). Starting with Nbs directed against the green fluorescent protein (GFP), we produce Pbs that display increased affinity for GFP due to the avidity effect. The benefit of avidity in affinity-based assays is also demonstrated using moderate-affinity Nbs against human serum albumin. With the same auto-assembly principle, we produce bispecific and auto-fluorescent Pbs, validating our method as a versatile and general engineering strategy to generate multispecific and multifunctional protein entities. Peptidisc-assisted hydrophobic clustering thus expands the protein engineering toolbox to broaden the scope of protein multimerization in life sciences.
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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 | 0.001 |
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".