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

被引:6
|
作者
Zhang, Heqian [1 ]
Wang, Yihan [1 ]
Zhu, Yanran [1 ]
Huang, Pengtao [1 ]
Gao, Qiandi [1 ]
Li, Xiaojie [1 ]
Chen, Zhaoying [1 ]
Liu, Yu [2 ]
Jiang, Jiakun [3 ]
Gao, Yuan [4 ]
Huang, Jiaquan [1 ]
Qin, Zhiwei [1 ]
机构
[1] Beijing Normal Univ, Adv Inst Nat Sci, Ctr Biol Sci & Technol, Zhuhai 519087, Guangdong, Peoples R China
[2] Beijing Normal Univ, Adv Inst Nat Sci, Int Acad Ctr Complex Syst, Zhuhai 519087, Guangdong, Peoples R China
[3] Beijing Normal Univ, Adv Inst Nat Sci, Ctr Stat & Data Sci, Zhuhai 519087, Guangdong, Peoples R China
[4] Beijing Normal Univ, Instrumentat & Serv Ctr Sci & Technol, Zhuhai 519087, Guangdong, Peoples R China
关键词
Machine learning; Genetic algorithm; Directed evolution; Antimicrobial peptide; BLACK TIGER SHRIMP; ANTILIPOPOLYSACCHARIDE FACTOR ALF; TORSION ANGLE DYNAMICS; PENAEUS-MONODON; NMR STRUCTURE; SEQUENCE; IDENTIFICATION; PROGRAM; MODEL; DERMASEPTINS;
D O I
10.1016/j.jare.2024.02.016
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of Cairo University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:415 / 428
页数:14
相关论文
共 50 条
  • [31] Machine-Learning-Guided Library Design Cycle for Directed Evolution of Enzymes: The Effects of Training Data Composition on Sequence Space Exploration
    Saito, Yutaka
    Oikawa, Misaki
    Sato, Takumi
    Nakazawa, Hikaru
    Ito, Tomoyuki
    Kameda, Tomoshi
    Tsuda, Koji
    Umetsu, Mitsuo
    ACS CATALYSIS, 2021, 11 (23) : 14615 - 14624
  • [32] Using Evolutionary Algorithms and Machine Learning to Explore Sequence Space for the Discovery of Antimicrobial Peptides
    Yoshida, Mari
    Hinkley, Trevor
    Tsuda, Soichiro
    Abul-Haija, Yousef M.
    McBurney, Roy T.
    Kulikov, Vladislav
    Mathieson, Jennifer S.
    Reyes, Sabrina Galinanes
    Castro, Maria D.
    Cronin, Leroy
    CHEM, 2018, 4 (03): : 533 - 543
  • [33] Waste to resource: Mining antimicrobial peptides in sludge from metagenomes using machine learning
    Xu, Jiaqi
    Xu, Xin
    Jiang, Yunhan
    Fu, Yulong
    Shen, Chaofeng
    ENVIRONMENT INTERNATIONAL, 2024, 186
  • [34] Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides
    Zakharova, Elena
    Orsi, Markus
    Capecchi, Alice
    Reymond, Jean-Louis
    CHEMMEDCHEM, 2022, 17 (17)
  • [35] Genetic-algorithm-based machine learning for crop management
    Kurata, K
    Iida, Y
    ARTIFICIAL INTELLIGENCE IN AGRICULTURE 1998, 1998, : 109 - 114
  • [36] Designing optical glasses by machine learning coupled with a genetic algorithm
    Cassar, Daniel R.
    Santos, Gisele G.
    Zanotto, Edgar D.
    CERAMICS INTERNATIONAL, 2021, 47 (08) : 10555 - 10564
  • [37] Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning
    Basak Olcay
    Gizem D. Ozdemir
    Mehmet A. Ozdemir
    Utku K. Ercan
    Onan Guren
    Ozan Karaman
    BMC Biomedical Engineering, 6 (1):
  • [38] Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection
    Malakar, Samir
    Banerjee, Nirwan
    Prasad, Dilip K.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 139
  • [39] Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides
    Medina-Ortiz, David
    Contreras, Seba
    Fernandez, Diego
    Soto-Garcia, Nicole
    Moya, Ivan
    Cabas-Mora, Gabriel
    Olivera-Nappa, Alvaro
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2024, 25 (16)
  • [40] Combinatorial synthesis and directed evolution applied to the production of α-helix forming antimicrobial peptides analogues
    Castro, Mariana S.
    Cilli, Eduardo M.
    Fontes, Wagner
    CURRENT PROTEIN & PEPTIDE SCIENCE, 2006, 7 (06) : 473 - 478