Peptides, polypeptides and peptide–polymer hybrids as nucleic acid carriers
Bibliographic record
Abstract
Cell penetrating peptides (CPPs), and protein transduction domains (PTDs) of viruses and other natural proteins serve as a template for the development of efficient peptide based gene delivery vectors. PTDs are sequences of acidic or basic amphipathic amino acids, with superior membrane trespassing efficacies. Gene delivery vectors derived from these natural, cationic and cationic amphipathic peptides, however, offer little flexibility in tailoring the physicochemical properties of single chain peptide based systems. Owing to significant advances in the field of peptide chemistry, synthetic mimics of natural peptides are often prepared and have been evaluated for their gene expression, as a function of amino acid functionalities, architecture and net cationic content of peptide chains. Moreover, chimeric single polypeptide chains are prepared by a combination of multiple small natural or synthetic peptides, which imparts distinct physiological properties to peptide based gene delivery therapeutics. In order to obtain multivalency and improve the gene delivery efficacies of low molecular weight cationic peptides, bioactive peptides are often incorporated into a polymeric architecture to obtain novel 'polymer-peptide hybrids' with improved gene delivery efficacies. Peptide modified polymers prepared by physical or chemical modifications exhibit enhanced endosomal escape, stimuli responsive degradation and targeting efficacies, as a function of physicochemical and biological activities of peptides attached onto a polymeric scaffold. The focus of this review is to provide comprehensive and step-wise progress in major natural and synthetic peptides, chimeric polypeptides, and peptide-polymer hybrids for nucleic acid delivery applications.
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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) | 0.001 | 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 | 0.001 |
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".