Gallocin-derived Engineered Peptides Targeting EGFR and VEGFR inColorectal Cancer: A Bioinformatic Approach
Bibliographic record
Abstract
Background: Colorectal cancer (CRC) treatment using time-saving and cost-effective targeted therapies with high selectivity and low toxicity drugs, is a great challenge. In primary investigations on Gallocin, as the most proposed factor in CRC pathogenesis caused by Streptococcus gallolyticus, it was surprisingly found that this bacteriocin has four α-helix structures and some anti-cancer sequences. Objective: The aim of this study was to determine the ability of Gallocin-based anticancer peptides (ACPs) against epidermal growth factor receptor (EGFR) and vascular epidermal growth factor receptor (VEGFR) and the evaluation of their pharmacokinetic properties using bioinformatic approaches. Methods: Support vector machine algorithm web-based tools were used for predicting ACPs. The physicochemical characteristics and the potential of anti-cancer activity of Gallocin-derived ACPs were determined by in silico tools. The 3D structure of predicted ACPs was modeled using modeling tools. The interactions between predicted ACPs and targets were investigated by molecular docking exercises. Then, the stability of ligand-receptor interactions was determined by molecular dynamic simulation. Finally, ADMET analysis was carried out to check the pharmacokinetic properties and toxicity of ACPs Results: Four amino acid sequences with anti-cancer potential were selected. Through molecular docking, Pep2, and Pep3 gained the best scores, more binding affinity, and strong attachments by the formation of reasonable H-bonds with both EGFR and VEGFR. Molecular simulation confirmed the stability of Pep3- EGFR. According to pharmacokinetic analysis, the ACPs were safe and truthful. Conclusion: Designed peptides can be nominated as drugs for CRC treatment. However, different in-vitro and in-vivo assessments are required to approve this claim.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".