全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
Bioprocess  2025 

抗菌肽发现的策略及进展
Discovery Strategies and Advances in Antimicrobial Peptides

DOI: 10.12677/bp.2025.151011, PP. 80-85

Keywords: 抗菌肽,抗菌肽挖掘,天然抗菌肽,抗菌肽改造,机器学习,人工智能,深度学习
Antimicrobial Peptides
, Antimicrobial Peptide Mining, Natural Antimicrobial Peptides, Antimicrobial Peptide Engineering, Machine Learning, Artificial Intelligence, Deep Learning

Full-Text   Cite this paper   Add to My Lib

Abstract:

随着抗生素的不合理使用,微生物耐药性问题日益严重,成为人类健康的巨大威胁。世界卫生组织(WHO)和美国传染病学会(IDSA)已将抗生素耐药问题列为威胁公共卫生的三大问题之一,迫切需要发现新型抗菌物质。抗菌肽是一类具有广谱抗菌活性、低耐药性倾向和多种作用机制的天然小分子,具有抗多重耐药菌、抗真菌、抗病毒、抗癌等多种生物活性,在治疗疾病方面有广阔的应用前景。由于氨基酸的多样性排列以及复杂的结构,发现、识别和筛选抗菌肽十分困难。计算机技术和人工智能的发展使抗菌肽的挖掘方法取得进展。本文旨在系统总结抗菌肽发现方法的研究进展,为新型方法的应用提供参考,促进抗菌肽领域的创新和发展。
With the irrational use of antibiotics, the problem of microbial resistance has become increasingly serious and a great threat to human health. The World Health Organization (WHO) and the Infectious Diseases Society of America (IDSA) have listed antibiotic resistance as one of the three major problems threatening public health, and there is an urgent need to discover new antibacterial substances. Antimicrobial peptides (AMPs), a class of natural small molecules with broad-spectrum antimicrobial activity, low resistance potential, and diverse mechanisms of action, exhibit various biological activities such as anti-multidrug-resistant bacteria, antifungal, antiviral, and anticancer properties, showing promising potential in disease treatment. However, the discovery, identification, and screening of AMPs are challenging due to the diverse arrangements of amino acids and their complex structures. Advances in computer technology and artificial intelligence have facilitated progress in AMPs mining methods. This article aims to systematically summarize the research progress in AMPs discovery methods, provide references for the application of novel approaches, and promote innovation and development in the field of antimicrobial peptides.

References

[1]  Miller, W.R. and Arias, C.A. (2024) ESKAPE Pathogens: Antimicrobial Resistance, Epidemiology, Clinical Impact and Therapeutics. Nature Reviews Microbiology, 22, 598-616.
https://doi.org/10.1038/s41579-024-01054-w

[2]  Laxminarayan, R., Impalli, I., Rangarajan, R., Cohn, J., Ramjeet, K., Trainor, B.W., et al. (2024) Expanding Antibiotic, Vaccine, and Diagnostics Development and Access to Tackle Antimicrobial Resistance. The Lancet, 403, 2534-2550.
https://doi.org/10.1016/s0140-6736(24)00878-x

[3]  Chen, N. and Jiang, C. (2023) Antimicrobial Peptides: Structure, Mechanism, and Modification. European Journal of Medicinal Chemistry, 255, Article ID: 115377.
https://doi.org/10.1016/j.ejmech.2023.115377

[4]  Zasloff, M. (2002) Antimicrobial Peptides of Multicellular Organisms. Nature, 415, 389-395.
https://doi.org/10.1038/415389a

[5]  Epand, R.M., Walker, C., Epand, R.F. and Magarvey, N.A. (2016) Molecular Mechanisms of Membrane Targeting Antibiotics. Biochimica et Biophysica Acta (BBA)—Biomembranes, 1858, 980-987.
https://doi.org/10.1016/j.bbamem.2015.10.018

[6]  Ma, Y., Guo, Z., Xia, B., Zhang, Y., Liu, X., Yu, Y., et al. (2022) Identification of Antimicrobial Peptides from the Human Gut Microbiome Using Deep Learning. Nature Biotechnology, 40, 921-931.
https://doi.org/10.1038/s41587-022-01226-0

[7]  Alexander, P.J., Oyama, L.B., Olleik, H., Godoy Santos, F., O’Brien, S., Cookson, A., et al. (2024) Microbiome-Derived Antimicrobial Peptides Show Therapeutic Activity against the Critically Important Priority Pathogen, Acinetobacter baumannii. npj Biofilms and Microbiomes, 10, Article No. 92.
https://doi.org/10.1038/s41522-024-00560-2

[8]  Santos-Júnior, C.D., Torres, M.D.T., Duan, Y., Rodríguez del Río, Á., Schmidt, T.S.B., Chong, H., et al. (2024) Discovery of Antimicrobial Peptides in the Global Microbiome with Machine Learning. Cell, 187, 3761-3778.e16.
https://doi.org/10.1016/j.cell.2024.05.013

[9]  Boman, H.G., Nilsson, I. and Rasmuson, B. (1972) Inducible Antibacterial Defence System in Drosophila. Nature, 237, 232-235.
https://doi.org/10.1038/237232a0

[10]  Zasloff, M. (1987) Magainins, a Class of Antimicrobial Peptides from Xenopus Skin: Isolation, Characterization of Two Active Forms, and Partial cDNA Sequence of a Precursor. Proceedings of the National Academy of Sciences of the United States of America, 84, 5449-5453.
https://doi.org/10.1073/pnas.84.15.5449

[11]  Bevier, C.R., Sonnevend, A., Kolodziejek, J., Nowotny, N., Nielsen, P.F. and Michael Conlon, J. (2004) Purification and Characterization of Antimicrobial Peptides from the Skin Secretions of the Mink Frog (Rana septentrionalis). Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 139, 31-38.
https://doi.org/10.1016/j.cca.2004.08.019

[12]  Conlon, J.M., Demandt, A., Nielsen, P.F., Leprince, J., Vaudry, H. and Woodhams, D.C. (2009) The Alyteserins: Two Families of Antimicrobial Peptides from the Skin Secretions of the Midwife Toad Alytes Obstetricans (Alytidae). Peptides, 30, 1069-1073.
https://doi.org/10.1016/j.peptides.2009.03.004

[13]  Conlon, J.M., Sonnevend, A., Pál, T. and Vila-Farrés, X. (2012) Efficacy of Six Frog Skin-Derived Antimicrobial Peptides against Colistin-Resistant Strains of the Acinetobacter baumannii Group. International Journal of Antimicrobial Agents, 39, 317-320.
https://doi.org/10.1016/j.ijantimicag.2011.12.005

[14]  McLean, D.T.F., McCrudden, M.T.C., Linden, G.J., Irwin, C.R., Conlon, J.M. and Lundy, F.T. (2014) Antimicrobial and Immunomodulatory Properties of Pgla-Am1, CPF-AM1, and Magainin-Am1: Potent Activity against Oral Pathogens. Regulatory Peptides, 194, 63-68.
https://doi.org/10.1016/j.regpep.2014.11.002

[15]  Neshani, A., Zare, H., Akbari Eidgahi, M.R., Kamali Kakhki, R., Safdari, H., Khaledi, A., et al. (2019) LL-37: Review of Antimicrobial Profile against Sensitive and Antibiotic-Resistant Human Bacterial Pathogens. Gene Reports, 17, Article ID: 100519.
https://doi.org/10.1016/j.genrep.2019.100519

[16]  Barksdale, S.M., Hrifko, E.J. and van Hoek, M.L. (2017) Cathelicidin Antimicrobial Peptide from Alligator mississippiensis Has Antibacterial Activity against Multi-Drug Resistant Acinetobacter baumanii and Klebsiella pneumoniae. Developmental & Comparative Immunology, 70, 135-144.
https://doi.org/10.1016/j.dci.2017.01.011

[17]  Zanetti, M., Litteri, L., Griffiths, G., Gennaro, R. and Romeo, D. (1991) Stimulus-Induced Maturation of Probactenecins, Precursors of Neutrophil Antimicrobial Polypeptides. The Journal of Immunology, 146, 4295-4300.
https://doi.org/10.4049/jimmunol.146.12.4295

[18]  Michael Conlon, J., Galadari, S., Raza, H. and Condamine, E. (2008) Design of Potent, Non‐Toxic Antimicrobial Agents Based Upon the Naturally Occurring Frog Skin Peptides, Ascaphin‐8 and Peptide XT‐7. Chemical Biology & Drug Design, 72, 58-64.
https://doi.org/10.1111/j.1747-0285.2008.00671.x

[19]  de Breij, A., Riool, M., Cordfunke, R.A., Malanovic, N., de Boer, L., Koning, R.I., et al. (2018) The Antimicrobial Peptide SAAP-148 Combats Drug-Resistant Bacteria and Biofilms. Science Translational Medicine, 10, eaan4044.
https://doi.org/10.1126/scitranslmed.aan4044

[20]  Ye, Z., Xu, Z., Ouyang, J., Shi, W., Li, S., Wang, X., et al. (2024) Improving the Stability and Anti-Infective Activity of Sea Turtle Amps Using Multiple Structural Modification Strategies. Journal of Medicinal Chemistry, 67, 22104-22123.
https://doi.org/10.1021/acs.jmedchem.4c02039

[21]  Song, J., Wang, J., Zhan, N., Sun, T., Yu, W., Zhang, L., et al. (2019) Therapeutic Potential of Trp-Rich Engineered Amphiphiles by Single Hydrophobic Amino Acid End-tagging. ACS Applied Materials & Interfaces, 11, 43820-43834.
https://doi.org/10.1021/acsami.9b12706

[22]  Wei, X., Wu, R., Si, D., Liao, X., Zhang, L. and Zhang, R. (2016) Novel Hybrid Peptide Cecropin a (1-8)-LL37 (17-30) with Potential Antibacterial Activity. International Journal of Molecular Sciences, 17, Article 983.
https://doi.org/10.3390/ijms17070983

[23]  Avitabile, C., Capparelli, R., Rigano, M.M., Fulgione, A., Barone, A., Pedone, C., et al. (2013) Antimicrobial Peptides from Plants: Stabilization of the γ Core of a Tomato Defensin by Intramolecular Disulfide Bond. Journal of Peptide Science, 19, 240-245.
https://doi.org/10.1002/psc.2479

[24]  Rozek, A., Powers, J.S., Friedrich, C.L. and Hancock, R.E.W. (2003) Structure-Based Design of an Indolicidin Peptide Analogue with Increased Protease Stability. Biochemistry, 42, 14130-14138.
https://doi.org/10.1021/bi035643g

[25]  Zhang, Y. and Sanner, M.F. (2019) Docking Flexible Cyclic Peptides with Autodock CrankPep. Journal of Chemical Theory and Computation, 15, 5161-5168.
https://doi.org/10.1021/acs.jctc.9b00557

[26]  Morris, C.J., Beck, K., Fox, M.A., Ulaeto, D., Clark, G.C. and Gumbleton, M. (2012) Pegylation of Antimicrobial Peptides Maintains the Active Peptide Conformation, Model Membrane Interactions, and Antimicrobial Activity While Improving Lung Tissue Biocompatibility Following Airway Delivery. Antimicrobial Agents and Chemotherapy, 56, 3298-3308.
https://doi.org/10.1128/aac.06335-11

[27]  Chen, L., Shen, T., Liu, Y., Zhou, J., Shi, S., Wang, Y., et al. (2020) Enhancing the Antibacterial Activity of Antimicrobial Peptide PMAP-37(F34-R) by Cholesterol Modification. BMC Veterinary Research, 16, Article No. 419.
https://doi.org/10.1186/s12917-020-02630-x

[28]  Yao, S., You, R., Wang, S., Xiong, Y., Huang, X. and Zhu, S. (2021) Netgo 2.0: Improving Large-Scale Protein Function Prediction with Massive Sequence, Text, Domain, Family and Network Information. Nucleic Acids Research, 49, W469-W475.
https://doi.org/10.1093/nar/gkab398

[29]  Wang, P., Hu, L., Liu, G., Jiang, N., Chen, X., Xu, J., et al. (2011) Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods. PLOS ONE, 6, e18476.
https://doi.org/10.1371/journal.pone.0018476

[30]  Torres, M.D.T., Melo, M.C.R., Crescenzi, O., Notomista, E. and de la Fuente-Nunez, C. (2021) Mining for Encrypted Peptide Antibiotics in the Human Proteome. Nature Biomedical Engineering, 6, 67-75.
https://doi.org/10.1038/s41551-021-00801-1

[31]  Bhadra, P., Yan, J., Li, J., Fong, S. and Siu, S.W.I. (2018) AmPEP: Sequence-Based Prediction of Antimicrobial Peptides Using Distribution Patterns of Amino Acid Properties and Random Forest. Scientific Reports, 8, Article No. 1697.
https://doi.org/10.1038/s41598-018-19752-w

[32]  Loose, C., Jensen, K., Rigoutsos, I. and Stephanopoulos, G. (2006) A Linguistic Model for the Rational Design of Antimicrobial Peptides. Nature, 443, 867-869.
https://doi.org/10.1038/nature05233

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133