Multi-label classification and features investigation of antimicrobial peptides with various functional classes

Chia Ru Chung, Jhen Ting Liou, Li Ching Wu, Jorng Tzong Horng, Tzong Yi Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The challenge of drug-resistant bacteria to global public health has led to increased attention on antimicrobial peptides (AMPs) as a targeted therapeutic alternative with a lower risk of resistance. However, high production costs and limitations in functional class prediction have hindered progress in this field. In this study, we used multi-label classifiers with binary relevance and algorithm adaptation techniques to predict different functions of AMPs across a wide range of pathogen categories, including bacteria, mammalian cells, fungi, viruses, and cancer cells. Our classifiers attained promising AUC scores varying from 0.8492 to 0.9126 on independent testing data. Forward feature selection identified sequence order and charge as critical, with specific amino acids (C and E) as discriminative. These findings provide valuable insights for the design of antimicrobial peptides (AMPs) with multiple functionalities, thus contributing to the broader effort to combat drug-resistant pathogens.

Original languageEnglish
Article number108250
JournaliScience
Volume26
Issue number12
DOIs
StatePublished - 15 Dec 2023

Keywords

  • Biochemistry
  • Microbiology
  • Pharmacology
  • Properties of biomolecules

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