PEGylation of a Peptide-Based Amphiphilic Delivery Agent and Influence on Protein Delivery to Cells
Bibliographic record
Abstract
The cell membrane is a restrictive biological barrier, especially for large, charged molecules, such as proteins. The use of cell-penetrating peptides (CPPs) can facilitate the delivery of proteins, protein complexes, and peptides across the membrane by a variety of mechanisms that are all limited by endosomal sequestration. To improve CPP-mediated delivery, we previously reported the rapid and effective cytosolic delivery of proteins in vitro and in vivo by their coadministration with the peptide S10, which combines a CPP and an endosomal leakage domain. Amphiphilic peptides with hydrophobic properties, such as S10, can interact with lipids to destabilize the cell membrane, thus promoting cargo internalization or escape from endosomal entrapment. However, acute membrane destabilization can result in a dose-limiting cytotoxicity. In this context, the partial or transient deactivation of S10 by modification with methoxy poly(ethylene glycol) (mPEG; i.e., PEGylation) may provide the means to alter membrane destabilization kinetics, thereby attenuating the impact of acute permeabilization on cell viability. This study investigates the influence of PEGylation parameters (molecular weight, architecture, and conjugation chemistry) on the delivery efficiency of a green fluorescent protein tagged with a nuclear localization signal (GFP-NLS) and cytotoxicity on cells in vitro. Results suggest that PEGylation mostly interferes with adsorption and secondary structure formation of S10 at the cell membrane, and this effect is exacerbated by the mPEG molecular weight. This effect can be compensated for by increasing the concentration of conjugates prepared with lower molecular weight mPEG (5 to ∼20 kDa) but not for conjugates prepared with higher molecular weight mPEG (40 kDa). For conjugates prepared with moderate-to-high molecular weight mPEG (10 to 20 kDa), partial compensation of inactivation could be achieved by the inclusion of a reducible disulfide bond, which provides a mechanism to liberate the S10 from the polymer. Grafting multiple copies of S10 to a high-molecular-weight multiarmed PEG (40 kDa) improved GFP-NLS delivery efficiency. However, these constructs were more cytotoxic than the native peptide. Considering that PEGylation could be harnessed for altering the pharmacokinetics and biodistribution profiles of peptide-based delivery agents in vivo, the trends observed herein provide new perspectives on how to manipulate the membrane permeabilization process, which is an important variable for achieving delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".