Computational and nonglycosylated systems: a simpler approach for development of nanosized PEGylated proteins
Bibliographic record
Abstract
Cysteine PEGylation includes several steps, and is difficult to manage in practice. In the current investigation, the cysteine PEGylation of erythropoietin analogs was examined using computational and nonglycosylated systems to define a simpler approach for specific PEGylation. Two model analogs (E31C and E89C) were selected for PEGylation based on lowest structural deviation from the native form, accessibility, and nucleophilicity of the free thiol group. The selected analogs were cloned and the expression was assessed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western blot using Coomassie blue staining and anti-His monoclonal antibody, respectively. PEGylation with 20 kDa mPEG-maleimide resulted in 79% and 82% conjugation yield for E31C and E89C nonglycosylated erythropoietin (ngEPO) analogs, respectively. The size distribution and charge analysis showed an increase in size and negative charge of the PEGylated forms compared with nonconjugated ones. Biological assay revealed that E31C and E89C mutations and subsequent PEGylation of ngEPO analogs have no deleterious effects on in vitro biological activity when compared to CHO-derived recombinant human erythropoietin. In addition, PEG-conjugated ngEPOs showed a significant increase in plasma half-lives after injection into rats when compared to nonconjugated ones. The development of the cysteine-PEGylated proteins using nonglycosylated expression system and in silico technique can be considered an efficient approach in terms of optimization of PEGylation parameters, time, and cost.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".