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Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides
Diptomit Biswas1,2,3, Sara Benson1, Aidan Matunis2,4
1Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
Artificial intelligence accelerates host defense peptide discovery for drug-resistant infections. A new AI workflow (LIMITS) uses limited data to find potent, safe peptides against Mycobacterium tuberculosis.
Area of Science:
- Biochemistry
- Computational Biology
- Infectious Diseases
Background:
- Drug-resistant infections pose a significant global health threat.
- Host defense peptides (HDPs) show promise but discovering effective ones is difficult due to vast sequence space.
- Traditional artificial intelligence (AI) methods for HDP discovery require large datasets.
Purpose of the Study:
- To develop and validate an AI-driven workflow for discovering potent, selective, and safe HDPs using limited data.
- To apply this workflow against *Mycobacterium tuberculosis*.
- To identify key factors influencing antitubercular HDP activity.
Main Methods:
- Developed a novel AI workflow, Lead Informed Machine Interrogation of Therapeutic Sequences (LIMITS).
- Utilized limited datasets (approx. 100 peptides) combined with lead candidate mutational scanning.
- Applied the LIMITS approach to identify HDPs targeting *Mycobacterium tuberculosis*.
Main Results:
- Experimental validation demonstrated an order of magnitude improvement in HDP potency, selectivity, and safety.
- The LIMITS approach successfully identified effective HDPs under data-limited conditions.
- Sequence length was identified as a potentially significant, underappreciated factor in antitubercular HDP activity.
Conclusions:
- The LIMITS AI workflow enables efficient discovery of therapeutic HDPs even with limited data.
- This approach can uncover complex structure-function-performance relationships in peptide design.
- LIMITS offers a promising strategy for combating drug-resistant pathogens like *Mycobacterium tuberculosis*.
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