Rational Design and Machine Learning-Guided Screening of Short α-Helical Antimicrobial Peptides Derived from Dendrocin-ZM1
Novelty in Biomedicine,
Vol. 14 No. 4 (2026),
13 October 2026
,
Page 189-197
https://doi.org/10.22037/nbm.v14i4.52578
Abstract
Background: The increasing prevalence of multidrug-resistant bacterial pathogens has created an urgent need for new antimicrobial strategies with improved efficacy and safety profiles. Antimicrobial peptides (AMPs) are promising therapeutic candidates due to their membrane-targeting mechanisms and broad antimicrobial potential. This study aimed to computationally design and screen shortened analogs of Dendrocin-ZM1 to identify potential antimicrobial peptide candidates against Staphylococcus aureus.
Materials and Methods: Dendrocin-ZM1, a 33-amino-acid α-helical antimicrobial peptide, was selected as the parental template for rational peptide redesign. A series of shortened and sequence-engineered analogs was generated by retaining selected cationic and hydrophobic residues associated with antimicrobial peptide activity. The designed peptides were evaluated based on physicochemical properties, including peptide length, net charge, hydrophobicity, and predicted α-helical structural characteristics. Antimicrobial potential was assessed using the CAMPR4 platform with Support Vector Machine, Random Forest, and Artificial Neural Network classifiers. Additional screening was performed using the DBAASP prediction platform, focusing on predicted activity against S. aureus. Potential toxicity and hemolytic effects were evaluated using ToxinPred and HemoPI2, respectively, and selected peptides were subjected to structural modeling to assess predicted α-helical conformations.
Results: The designed peptide analogs exhibited favorable predicted physicochemical characteristics, including reduced length, retained cationic character, and modified hydrophobicity. CAMPR4 analysis indicated high antimicrobial prediction scores across multiple machine learning classifiers. DBAASP screening identified P5, P6, and P7 as computationally predicted active candidates against S. aureus. Structural modeling suggested predominantly α-helical conformations among these peptides, consistent with structural features commonly observed in membrane-active AMPs. Toxicity prediction indicated low toxicity, while hemolysis analysis suggested favorable hemocompatibility profiles. Among the evaluated candidates, P7 was prioritized based on its integrated computational profile, combining predicted anti-S. aureus activity with favorable toxicity, hemolysis, and structural predictions.
Conclusion: This study presents a computational workflow that integrates rational peptide shortening and sequence engineering with machine learning-assisted screening to prioritize potential antimicrobial peptide candidates derived from Dendrocin-ZM1. P7 was identified as a promising computationally prioritized candidate; however, experimental validation, including antimicrobial activity and safety evaluation, is required to confirm its therapeutic potential.
- Antimicrobial peptide
- Dendrocin-ZM1
- rational design
- machine learning
- Staphylococcus aureus
- Computational screening
- Toxicity prediction
- Hemolysis
- α-helical peptide
How to Cite
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