Physico-chemical determinants of soluble intrabody expression in mammalian cell cytoplasm
Bibliographic record
Abstract
Soluble antibody fragments are desirable not only as potential therapeutic and diagnostic agents for extracellular targets but also as 'intrabodies' for functional genomics, proteomics and gene therapy inside cells. However, antibody fragments are notoriously aggregation-prone when expressed intracellularly, due in part to unfavorable redox potential and macromolecular crowding in cell cytoplasm. Only a small proportion of intrabodies are soluble in cytoplasm and little is known about the sequence determinants that confer such stability. By comparing the cytoplasmic expression of several related human single-chain variable fragments and camelid V(HH)s in mammalian cells, we report that intrabody solubility is highly influenced by CDR content and is improved by an overall negative charge at cytoplasmic pH and reduced hydrophilicity. We hypothesize that ionic repulsion and weak hydrophobic interactions compensate, to different extents, for impaired disulfide bond formation in cytoplasm, thereby decreasing the risk for intrabody aggregation. As proof of principle, we demonstrate that the soluble expression of an aggregation-prone positively charged intrabody is modestly enhanced via cis or trans acidification using highly charged peptide tags (3XFLAG tag, SV40 NLS). These findings suggest that simple sequence analysis and electrostatic manipulation may aid in predicting and engineering solubility-enhanced intrabodies from antibody libraries for intracellular use.
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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".