Bibliographic record
Abstract
If the isolation, production, and clinical use of insulin marked the inception of the age of biologics as therapeutics, the convergence of molecular biology and combinatorial engineering techniques marked its coming of age. The first wave of recombinant protein-based drugs in the 1980s demonstrated emphatically that proteins could be engineered, formulated, and employed for clinical advantage. Yet despite the successes of protein-based drugs such as antibodies, enzymes, and cytokines, the druggable target space for biologics is currently restricted to targets outside the cell. Insofar as estimates place the number of proteins either secreted or with extracellular domains in the range of 8000 to 9000, this represents only one-third of the proteome and circumscribes the pathways that can be targeted for therapeutic intervention. Clearly, a major objective for this field to reach maturity is to access, interrogate, and modulate the majority of proteins found inside the cell. However, owing to the large size, complex architecture, and general cellular impermeability of existing protein-based drugs, this poses a daunting challenge. In recent years, though, advances on the two related fronts of protein engineering and drug delivery are beginning to bring this goal within reach. First, prompted by the restrictions that limit the applicability of antibodies, intense efforts have been applied to identifying and engineering smaller alternative protein scaffolds for the modulation of intracellular targets. In parallel, innovative solutions for delivering proteins to the intracellular space while maintaining their stability and functional activity have begun to yield successes. This review provides an overview of bioactive intrabodies and alternative protein scaffolds amenable to engineering for intracellular targeting and also outlines advances in protein engineering and formulation for delivery of functional proteins to the interior of the cell to achieve therapeutic action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".