Design of Improved Anti-Influenza Peptide Mimetics Using In Silico Molecular Modeling
Bibliographic record
Abstract
Influenza virus is a major respiratory virus infection responsible for seasonal outbreaks, global pandemics, and an estimated 500,000 deaths annually. The RNA polymerase of influenza virus is a heterotrimeric enzyme complex made up of three subunits that interact to form an active holoenzyme. In this study, in silico molecular modeling was used to predict the hypothetical free energy of binding between the PB1 and PA subunits for various single amino acid substitutions within the PB1 subunit. Two significant substitutions for threonine at position six were identified: glutamic acid (T6E) and arginine (T6R). We engineered native and modified PB1 peptide mimetics with a carrier protein and a cell penetrating peptide (HIV Tat NLS) to deliver the peptides to cells. These peptide mimetics inhibited Influenza a virus transcription and translation in MDCK cells in a dose-dependent manner with 98% inhibition at 50 M. The inhibitory activity of peptide mimetics containing T6E and T6R substitutions, were three-to four-fold higher than the wild type peptide consistent with the in silico molecular modeling prediction. These results demonstrate that molecular modeling of protein-protein interactions can be used to design peptide mimetics as protein therapeutics which have increased anti-viral activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".