Computational Stabilization of the Human VH Germline Repertoire to Enable Conditional Multi-Specific Therapeutic Development
Bibliographic record
Abstract
Abstract Protein-based biologic therapies, particularly antibody-like therapeutics, have emerged as a major modality to treat nearly all chronic and infectious diseases. Antagonists have dominated the first wave of antibody and antibody-like biologics, whereas agonism has generally been challenging due to systemic activation leading to issues with therapeutic index and problems with pleiotropic activity. Additionally, agonists that use native proteins such as cytokines can be challenging to produce at scale given these proteins evolved to act locally and are not designed for large scale manufacturing. To address these challenges, we generated a biologics platform comprised of stabilized human VH domains (VH-Select™) which encompass the entire human germline repertoire with the goal of building multispecific biologics denoted Tentacles™ that use avidity-based binding to achieve conditional activity directed to specific cell types or tissues. Stabilizing disulfides and point mutations were identified computationally with Rosetta, evaluated in vitro , and combined into designs with 3-5 amino acid substitutions for each of the seven germline families (VH1-VH7). Computational design was also employed to reduce dimerization from both VL and homotypic VH-VH interactions. Optimization of specific sequences improved expression by greater than 600-fold and thermostability by more than 20°C. Each of the germline variants were screened for low HLA class II binding and incorporated into a library which demonstrated significant improvements in cellular protein production, thereby increasing sequence diversity for screening campaigns. These VH-Select™ scaffolds are useful for the discovery of novel binders used to build multispecific Tentacles™ designed for cis-interactions that achieve cell- and tissue-specific activation. As an example of the utility of the platform, we generated a set of Tentacles™ that use VH-Select™ binders to conditionally agonize IL2Rγβ and 41BB on PD1+, LAG3+, or CD8+ T cells. These Tentacles™ demonstrate promising manufacturability, antibody-like exposure in vivo , and strong anti-tumor activity in a humanized tumor model. Overall, we believe the incorporation of VH-Select™ binders into multispecific Tentacles™ has the potential to create a host of conditionally active biologics to treat various chronic and acute diseases.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".