Computer-aided Discovery of Peptides that Specifically Attack Bacterial Biofilms
Bibliographic record
Abstract
Abstract Biofilms represent a multicellular growth state of bacteria that are intrinsically resistant to conventional antibiotics. It was recently shown that a synthetic immunomodulatory cationic peptide, 1018 (VRLIVAVRIWRR-NH 2 ), exhibits broad-spectrum antibiofilm activity but the sequence determinants of antibiofilm peptides have not been systematically studied. In the present work, a peptide library consisting of 96 single amino acid substituted variants of 1018 was SPOT-synthesized on cellulose arrays and evaluated against methicillin resistant Staphylococcus aureus (MRSA) biofilms. This dataset was used to establish quantitative structure-activity relationship (QSAR) models relating the antibiofilm activity of these peptides to hundreds of molecular descriptors derived from their sequences. The developed 3D QSAR models then predicted the probability that a peptide would possess antibiofilm activity from a library of 100,000 virtual peptide sequences in silico . A subset of these variants were SPOT-synthesized and their activity assessed, revealing that the QSAR models resulted in ~85% prediction accuracy. Notably, peptide 3002 (ILVRWIRWRIQW-NH 2 ) was identified that exhibited an 8-fold increased antibiofilm potency in vitro compared to 1018 and proved effective in vivo , significantly reducing abscess size in a chronic MRSA mouse infection model. This study demonstrates that QSAR modeling can successfully be used to identify antibiofilm specific peptides with therapeutic potential.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".