Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides

Introduction: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the structure-activity relationships of AMPs can direct th...

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Main Authors: Heqian Zhang, Yihan Wang, Yanran Zhu, Pengtao Huang, Qiandi Gao, Xiaojie Li, Zhaoying Chen, Yu Liu, Jiakun Jiang, Yuan Gao, Jiaquan Huang, Zhiwei Qin
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Journal of Advanced Research
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Online Access:http://www.sciencedirect.com/science/article/pii/S209012322400078X
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author Heqian Zhang
Yihan Wang
Yanran Zhu
Pengtao Huang
Qiandi Gao
Xiaojie Li
Zhaoying Chen
Yu Liu
Jiakun Jiang
Yuan Gao
Jiaquan Huang
Zhiwei Qin
author_facet Heqian Zhang
Yihan Wang
Yanran Zhu
Pengtao Huang
Qiandi Gao
Xiaojie Li
Zhaoying Chen
Yu Liu
Jiakun Jiang
Yuan Gao
Jiaquan Huang
Zhiwei Qin
author_sort Heqian Zhang
collection DOAJ
description Introduction: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the structure-activity relationships of AMPs can direct the synthesis of desirable peptide therapeutics. Objectives: In this study, the lipopolysaccharide-binding domain (LBD) was identified through machine learning-guided directed evolution, which acts as a functional domain of the anti-lipopolysaccharide factor family of AMPs identified from Marsupenaeus japonicus. Methods: LBDA-D was identified as an output of this algorithm, in which the original LBDMj sequence was the input, and the three-dimensional solution structure of LBDB was determined using nuclear magnetic resonance. Furthermore, our study involved a comprehensive series of experiments, including morphological studies and in vitro and in vivo antibacterial tests. Results: The NMR solution structure showed that LBDB possesses a circular extended structure with a disulfide crosslink at the terminus and two 310-helices and exhibits a broad antimicrobial spectrum. In addition, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that LBDB induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. Finally, coinjection of LBDB, Vibrio alginolyticus and Staphylococcus aureus in vivo improved the survival of M. japonicus, demonstrating the promising therapeutic role of LBDB for treating infectious disease. Conclusions: The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.
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spelling doaj-art-1285232a15f847fab006ff21a96b712e2025-01-18T05:04:19ZengElsevierJournal of Advanced Research2090-12322025-02-0168415428Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptidesHeqian Zhang0Yihan Wang1Yanran Zhu2Pengtao Huang3Qiandi Gao4Xiaojie Li5Zhaoying Chen6Yu Liu7Jiakun Jiang8Yuan Gao9Jiaquan Huang10Zhiwei Qin11Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaInternational Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Statistics and Data Science, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaInstrumentation and Service Center for Science and Technology, Beijing Normal University, Zhuhai, Guangdong 519087, ChinaCenter for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China; Corresponding authors.Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China; Corresponding authors.Introduction: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the structure-activity relationships of AMPs can direct the synthesis of desirable peptide therapeutics. Objectives: In this study, the lipopolysaccharide-binding domain (LBD) was identified through machine learning-guided directed evolution, which acts as a functional domain of the anti-lipopolysaccharide factor family of AMPs identified from Marsupenaeus japonicus. Methods: LBDA-D was identified as an output of this algorithm, in which the original LBDMj sequence was the input, and the three-dimensional solution structure of LBDB was determined using nuclear magnetic resonance. Furthermore, our study involved a comprehensive series of experiments, including morphological studies and in vitro and in vivo antibacterial tests. Results: The NMR solution structure showed that LBDB possesses a circular extended structure with a disulfide crosslink at the terminus and two 310-helices and exhibits a broad antimicrobial spectrum. In addition, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that LBDB induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. Finally, coinjection of LBDB, Vibrio alginolyticus and Staphylococcus aureus in vivo improved the survival of M. japonicus, demonstrating the promising therapeutic role of LBDB for treating infectious disease. Conclusions: The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.http://www.sciencedirect.com/science/article/pii/S209012322400078XMachine learningGenetic algorithmDirected evolutionAntimicrobial peptide
spellingShingle Heqian Zhang
Yihan Wang
Yanran Zhu
Pengtao Huang
Qiandi Gao
Xiaojie Li
Zhaoying Chen
Yu Liu
Jiakun Jiang
Yuan Gao
Jiaquan Huang
Zhiwei Qin
Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides
Journal of Advanced Research
Machine learning
Genetic algorithm
Directed evolution
Antimicrobial peptide
title Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides
title_full Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides
title_fullStr Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides
title_full_unstemmed Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides
title_short Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides
title_sort machine learning and genetic algorithm guided directed evolution for the development of antimicrobial peptides
topic Machine learning
Genetic algorithm
Directed evolution
Antimicrobial peptide
url http://www.sciencedirect.com/science/article/pii/S209012322400078X
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