TY - JOUR
T1 - Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning
AU - Lee, Byungjo
AU - Shin, Min Kyoung
AU - Yoo, Jung Sun
AU - Jang, Wonhee
AU - Sung, Jung Suk
N1 - Publisher Copyright:
Copyright © 2022 Lee, Shin, Yoo, Jang and Sung.
PY - 2022/8/24
Y1 - 2022/8/24
N2 - 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 F1 scores of 0.818, 0.696, 0.814, 0.787, and 0.719 for Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis, 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 Pardosa astrigera. 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.
AB - 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 F1 scores of 0.818, 0.696, 0.814, 0.787, and 0.719 for Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis, 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 Pardosa astrigera. 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.
KW - antimicrobial peptide
KW - deep learning
KW - multi-task learning
KW - species-specific prediction
KW - spider venom gland transcriptome
UR - http://www.scopus.com/inward/record.url?scp=85138063860&partnerID=8YFLogxK
U2 - 10.3389/fmicb.2022.971503
DO - 10.3389/fmicb.2022.971503
M3 - Article
AN - SCOPUS:85138063860
SN - 1664-302X
VL - 13
JO - Frontiers in Microbiology
JF - Frontiers in Microbiology
M1 - 971503
ER -