AlbuCORE: an albumin-based molecular scaffold for multivalent biologics design
Bibliographic record
Abstract
As biologics have become a mainstay in the development of novel therapies, protein engineering tools to expand on their structural advantages, namely specificity, affinity, and valency are of interest. Antibodies have dominated this field as the preferred scaffold for biologics development while there has been limited exploration into the use of albumin with its unique physiological characteristics as a platform for biologics design. There has been a great deal of interest to create bispecific and more complex multivalent molecules to build on the advantages offered by protein-based therapeutics relative to small molecules. Here, we explore the use of human serum albumin (HSA) as a scaffold for the design of multispecific biologics. In particular, we describe a structure-guided approach to the design of split HSA molecules we refer to as AlbuCORE, that effectively and spontaneously forms a native albumin-like molecule, but in a heterodimeric state upon co-expression. We show that the split AlbuCORE designs allow the creation of novel fusion entities with unique alternate geometries. We also show that, apart from these AlbuCORE fusion entities, there is an opportunity to explore their albumin-like small hydrophobic molecule carrying capacity as a drug conjugate in these designs.
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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 | 0.000 |
| 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".