Helical Peptides Derived from Lactoferrin Bind Hepatitis C Virus Envelope Protein E2
Bibliographic record
Abstract
Hepatitis C virus is a major cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma infecting more than 170 million people. Hepatitis C virus envelope 2 glycoprotein (E2) binds several cell-surface molecules that act as receptor candidates mediating hepatitis C virus entry into hepatocytes. Peptides derived from human lactoferrin have been shown to bind hepatitis C virus-E2 protein thereby preventing hepatitis C virus entry in cultured hepatocytes. In this study, starting from a 33-residue human lactoferrin-derived peptide, a number of biotin-linked alpha-peptides were synthesized and investigated for their E2 protein binding activity. E2 protein from hepatitis C virus genotype 1b was expressed in 293 human embryonic kidney cells and purified using affinity chromatography. A biotin-streptavidin based binding assay was developed to determine the binding affinity of the synthetic peptides for E2 protein. Two of the peptides bound E2 specifically with submicromolar to low micromolar affinity [equilibrium dissociation constant (K(d)) of 0.569 and 28.8 microM]. Further, these two peptides had the highest helical content in solution as observed by circular dichroism spectroscopy, suggesting that binding affinity increases with increase in helicity. These results have provided new lead peptides for future investigations of hepatitis C virus entry inhibitors that may provide an interesting approach to prevent hepatitis C virus infectivity.
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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".