AUTHOR=Lee Byungjo , Shin Min Kyoung , Yoo Jung Sun , Jang Wonhee , Sung Jung-Suk 

TITLE=Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning

JOURNAL=Frontiers in Microbiology

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.971503

DOI=10.3389/fmicb.2022.971503

ISSN=1664-302X

ABSTRACT=<p>Antimicrobial peptides (AMPs) show promises as valuable compounds for developing therapeutic agents to control the worldwide health threat posed by the increasing prevalence of antibiotic-resistant bacteria. Animal venom can be a useful source for screening AMPs due to its various bioactive components. Here, the deep learning model was developed to predict species-specific antimicrobial activity. To overcome the data deficiency, a multi-task learning method was implemented, achieving <italic>F</italic>1 scores of 0.818, 0.696, 0.814, 0.787, and 0.719 for <italic>Bacillus subtilis</italic>, <italic>Escherichia coli</italic>, <italic>Pseudomonas aeruginosa</italic>, <italic>Staphylococcus aureus</italic>, and <italic>Staphylococcus epidermidis</italic>, respectively. Peptides PA-Full and PA-Win were identified from the model using different inputs of full and partial sequences, broadening the application of transcriptome data of the spider <italic>Pardosa astrigera</italic>. Two peptides exhibited strong antimicrobial activity against all five strains along with cytocompatibility. Our approach enables excavating AMPs with high potency, which can be expanded into the fields of biology to address data insufficiency.</p>