Rationally designed antibodies selective for pathogenic tau aggregates
Bibliographic record
Abstract
Abstract Background Tauopathies are neurodegenerative disorders characterized by the abnormal aggregation of tau protein in the brain. Soluble toxic tau aggregates have the ability to transfer from cell‐to‐cell and to induce newly formed aggregates in recipient cells, thereby propagating tau pathology across the brain in a prion‐like manner. We used computational modeling to identify conformational “disease‐specific” epitopes predicted to become exposed on misfolded tau aggregates. Selectivity of antibodies for tau pathogenic species, as opposed to pan‐tau reactivity, is needed both in order to preserve normal tau function and to minimize the diversion of active antibody from the target through unproductive binding to more abundant non‐toxic forms of the protein. Method The ability of predicted disease‐associated conformational peptide epitopes to induce tau aggregation on their own was tested in a Thioflavin‐T fluorescence(ThT)‐based seeding assay. Mouse monoclonal antibodies raised against selected epitopes were screened by surface plasmon resonance (SPR) for binding to synthetic tau monomers, toxic oligomers, and soluble pre‐formed fibrils (PFFs). Recognition of naturally occurring tau species in Alzheimer’s disease (AD) brain was also confirmed by SPR. The ability of selected antibodies to inhibit induction of tau aggregation by AD brain seeds was assessed in a seeding assay using TauRD P301S FRET Biosensor Cells expressing tau constructs with different fluorescent labels producing a FRET signal when aggregated. Result Predicted conformational peptide epitopes capable of seeding activity in the ThT assay were used to immunize mice. SPR screening of hybridoma supernatants was used to identify antibody clones with the desired profile of preferential binding to a preparation of toxic tau oligomers vs non‐toxic tau monomers. A majority of these antibodies also bound soluble tau PFFs, a species believed to play a role in propagation of disease. Antibodies selected for further testing were found to recognize tau from AD brain extract by SPR and to inhibit seeding activity in a FRET assay. Conclusion Computational modeling for identification of predicted disease‐associated epitopes together with in vitro screening methods allowed for the generation of monoclonal antibodies with selectivity and activity against pathogenic, aggregated species of tau.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".