Lead Informed Artificial Intelligence Mining of Antitubercular Host Defense Peptides.

Journal: Biomacromolecules
PMID: 40310992

Abstract

Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artificial intelligence (AI)-guided design can accelerate HDP discovery, but it traditionally requires large data sets to operationalize. We report an AI workflow that utilizes limited data sets (∼100 peptides) to uncover potent, selective, and safe HDPs by informing selection through lead candidate mutational scanning. This approach, referred to as Lead Informed Machine Interrogation of Therapeutic Sequences (LIMITS), is applied against the exemplary pathogen . Experimental validation of predicted sequences shows nearly an order of magnitude improvement in potency, selectivity, and safety, relative to the initial template. Post hoc analysis suggests sequence length may be a unique and underappreciated driver of antitubercular HDP activity. These results demonstrate that, with continued development, the LIMITS approach can identify selective HDPs under data-limited conditions and elucidate structure-function-performance relationships previously hidden in biologic complexity.

Authors

  • Diptomit Biswas
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
  • Sara Benson
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
  • Aidan Matunis
    Huck Institutes of the Life Sciences, Penn State University, University Park, Pennsylvania 16802, United States.
  • Gebremichal Gebretsadik
    Department of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota 55455, United States.
  • Adam Wertz
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
  • Ben J StPierre
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
  • Nathan Schacht
    Department of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota 55455, United States.
  • Yue Yan
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
  • Hanna Y Gebremichael
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
  • Pak Kin Wong
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.
  • Anthony D Baughn
    Department of Microbiology and Immunology, University of Minnesota, Minneapolis, Minnesota 55455, United States.
  • Scott H Medina
    Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States.

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