1
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Bello-Madruga R, Torrent Burgas M. The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides. Comput Struct Biotechnol J 2024; 23:972-981. [PMID: 38404711 PMCID: PMC10884422 DOI: 10.1016/j.csbj.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/10/2024] [Accepted: 02/10/2024] [Indexed: 02/27/2024] Open
Abstract
Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting with bacterial membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) with AMP-like properties could exhibit antimicrobial activity. We tested 14 peptides from different IDRs predicted to have antimicrobial activity and found that nearly all of them did not display the anticipated effects. These peptides failed to adopt a defined secondary structure and had compromised membrane interactions, resulting in a lack of antimicrobial activity. We hypothesize that evolutionary constraints may prevent IDRs from folding, even in membrane-like environments, limiting their antimicrobial potential. Moreover, our research reveals that current antimicrobial predictors fail to accurately capture the structural features of peptides when dealing with intrinsically unstructured sequences. Hence, the results presented here may have far-reaching implications for designing and improving antimicrobial strategies and therapies against infectious diseases.
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Affiliation(s)
- Roberto Bello-Madruga
- The Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
| | - Marc Torrent Burgas
- The Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
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2
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Liang Q, Liu Z, Liang Z, Zhu C, Li D, Kong Q, Mou H. Development strategies and application of antimicrobial peptides as future alternatives to in-feed antibiotics. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172150. [PMID: 38580107 DOI: 10.1016/j.scitotenv.2024.172150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/14/2024] [Accepted: 03/30/2024] [Indexed: 04/07/2024]
Abstract
The use of in-feed antibiotics has been widely restricted due to the significant environmental pollution and food safety concerns they have caused. Antimicrobial peptides (AMPs) have attracted widespread attention as potential future alternatives to in-feed antibiotics owing to their demonstrated antimicrobial activity and environment friendly characteristics. However, the challenges of weak bioactivity, immature stability, and low production yields of natural AMPs impede practical application in the feed industry. To address these problems, efforts have been made to develop strategies for approaching the AMPs with enhanced properties. Herein, we summarize approaches to improving the properties of AMPs as potential alternatives to in-feed antibiotics, mainly including optimization of structural parameters, sequence modification, selection of microbial hosts, fusion expression, and industrially fermentation control. Additionally, the potential for application of AMPs in animal husbandry is discussed. This comprehensive review lays a strong theoretical foundation for the development of in-feed AMPs to achieve the public health globally.
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Affiliation(s)
- Qingping Liang
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Zhemin Liu
- Fundamental Science R&D Center of Vazyme Biotech Co. Ltd., Nanjing 210000, China
| | - Ziyu Liang
- Section of Neurobiology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Changliang Zhu
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Dongyu Li
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Qing Kong
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Haijin Mou
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China.
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3
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Zhang J, Sun X, Zhao H, Zhou X, Zhang Y, Xie F, Li B, Guo G. In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity. J Chem Inf Model 2024; 64:4277-4285. [PMID: 38743449 DOI: 10.1021/acs.jcim.4c00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.
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Affiliation(s)
- Jin Zhang
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Xinhao Sun
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Hongwei Zhao
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Xu Zhou
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Yiling Zhang
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Feng Xie
- Moutai Institute, Renhuai 564507, China
| | - Boyan Li
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Guo Guo
- The Key and Characteristic Laboratory of Modern Pathogen Biology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang 561113, China
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4
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Goles M, Daza A, Cabas-Mora G, Sarmiento-Varón L, Sepúlveda-Yañez J, Anvari-Kazemabad H, Davari MD, Uribe-Paredes R, Olivera-Nappa Á, Navarrete MA, Medina-Ortiz D. Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides. Brief Bioinform 2024; 25:bbae275. [PMID: 38856172 PMCID: PMC11163380 DOI: 10.1093/bib/bbae275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/23/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024] Open
Abstract
With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.
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Affiliation(s)
- Montserrat Goles
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Anamaría Daza
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Gabriel Cabas-Mora
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Lindybeth Sarmiento-Varón
- Centro Asistencial de Docencia e Investigación, CADI, Universidad de Magallanes, Av. Los Flamencos 01364, 6210005, Punta Arenas, Chile
| | - Julieta Sepúlveda-Yañez
- Facultad de Ciencias de la Salud, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Hoda Anvari-Kazemabad
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Mehdi D Davari
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
| | - Roberto Uribe-Paredes
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Marcelo A Navarrete
- Centro Asistencial de Docencia e Investigación, CADI, Universidad de Magallanes, Av. Los Flamencos 01364, 6210005, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - David Medina-Ortiz
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
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5
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Zervou MA, Doutsi E, Pantazis Y, Tsakalides P. De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks. Int J Mol Sci 2024; 25:5506. [PMID: 38791544 PMCID: PMC11122239 DOI: 10.3390/ijms25105506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.
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Affiliation(s)
- Michaela Areti Zervou
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Effrosyni Doutsi
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Yannis Pantazis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Panagiotis Tsakalides
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
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6
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Salvati B, Flórez-Castillo JM, Santagapita PR, Barja BC, Perullini M. One-pot synthesis of alginate-antimicrobial peptide nanogel. Photochem Photobiol Sci 2024; 23:665-679. [PMID: 38443738 DOI: 10.1007/s43630-024-00542-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/23/2024] [Indexed: 03/07/2024]
Abstract
Nanosized alginate-based particles (NAPs) were obtained in a one-pot solvent-free synthesis procedure, achieving the design of a biocompatible nanocarrier for the encapsulation of IbM6 antimicrobial peptide (IbM6). IbM6 is integrated in the nascent nanosized hydrogel self-assembly guided by electrostatic interactions and by weak interactions, typical of soft matter. The formation of the nanogel is a dynamic and complex process, which presents an interesting temporal evolution. In this work, we optimized the synthesis conditions of IbM6-NAPs based on small-angle X-ray scattering (SAXS) measurements and evaluated its time evolution over several weeks by sensing the IbM6 environment in IbM6-NAPs from photochemical experiments. Fluorescence deactivation experiments revealed that the accessibility of different quenchers to the IbM6 peptide embedded in NAPs is dependent on the aging time of the alginate network. Lifetimes measurements indicate that the deactivation paths of the excited state of the IbM6 in the nanoaggregates are reduced when compared with those exhibited by the peptide in aqueous solution, and are also dependent on the aging time of the nanosized alginate network. Finally, the entrapment of IbM6 in NAPs hinders the degradation of the peptide by trypsin, increasing its antimicrobial activity against Escherichia coli K-12 in simulated operation conditions.
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Affiliation(s)
- Brianne Salvati
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Inorgánica, Analítica y Química Física (DQIAQF), Universidad de Buenos Aires, Buenos Aires, Argentina
- Instituto de Química de Materiales medio Ambiente y Energía (INQUIMAE), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Johanna Marcela Flórez-Castillo
- Universidad de Magdalena, Santa Marta, Colombia
- Universidad de Santander UDES, Grupo de Investigación en Ciencias Básicas y Aplicadas para la Sostenibilidad-CIBAS, Santander, Colombia
| | - Patricio Román Santagapita
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Orgánica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Centro de Investigaciones en Hidratos de Carbono (CIHIDECAR), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Beatriz C Barja
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Inorgánica, Analítica y Química Física (DQIAQF), Universidad de Buenos Aires, Buenos Aires, Argentina.
- Instituto de Química de Materiales medio Ambiente y Energía (INQUIMAE), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina.
| | - Mercedes Perullini
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Inorgánica, Analítica y Química Física (DQIAQF), Universidad de Buenos Aires, Buenos Aires, Argentina.
- Instituto de Química de Materiales medio Ambiente y Energía (INQUIMAE), CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina.
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7
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Zhang H, Wang Y, Zhu Y, Huang P, Gao Q, Li X, Chen Z, Liu Y, Jiang J, Gao Y, Huang J, Qin Z. Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides. J Adv Res 2024:S2090-1232(24)00078-X. [PMID: 38431124 DOI: 10.1016/j.jare.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
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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Affiliation(s)
- Heqian Zhang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yihan Wang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yanran Zhu
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Pengtao Huang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Qiandi Gao
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Xiaojie Li
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Zhaoying Chen
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yu Liu
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Jiakun Jiang
- Center for Statistics and Data Science, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yuan Gao
- Instrumentation and Service Center for Science and Technology, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Jiaquan Huang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China.
| | - Zhiwei Qin
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China.
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8
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de la Fuente-Nunez C. AI in infectious diseases: The role of datasets. Drug Resist Updat 2024; 73:101067. [PMID: 38387282 DOI: 10.1016/j.drup.2024.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Affiliation(s)
- Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States of America; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States of America.
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9
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Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, Liu XF. Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res 2024; 11:7. [PMID: 38254241 PMCID: PMC10804841 DOI: 10.1186/s40779-024-00510-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.
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Affiliation(s)
- Guang-Yu Liu
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Dan Yu
- National Key Discipline of Pediatrics Key Laboratory of Major Diseases in Children Ministry of Education, Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Mei-Mei Fan
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ze-Yu Jin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christoph Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
| | - Xiao-Fen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Key Laboratory of Clinical Pharmacology of Antibiotics, National Health Commission of the People's Republic of China, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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10
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Purohit K, Reddy N, Sunna A. Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics. Int J Mol Sci 2024; 25:1391. [PMID: 38338676 PMCID: PMC10855437 DOI: 10.3390/ijms25031391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/18/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.
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Affiliation(s)
- Kruttika Purohit
- School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia;
- Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (FAAB), Sydney, NSW 2109, Australia;
| | - Narsimha Reddy
- Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (FAAB), Sydney, NSW 2109, Australia;
- School of Science, Parramatta Campus, Western Sydney University, Penrith, NSW 2751, Australia
| | - Anwar Sunna
- School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia;
- Australian Research Council Industrial Transformation Training Centre for Facilitated Advancement of Australia’s Bioactives (FAAB), Sydney, NSW 2109, Australia;
- Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW 2109, Australia
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11
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Wang R, Wang T, Zhuo L, Wei J, Fu X, Zou Q, Yao X. Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization. Brief Bioinform 2024; 25:bbae078. [PMID: 38446739 PMCID: PMC10939340 DOI: 10.1093/bib/bbae078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.
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Affiliation(s)
- Rui Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Jinhang Wei
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012 Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 611730 Chengdu, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078 Macao, China
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12
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Aguilera-Puga MDC, Cancelarich NL, Marani MM, de la Fuente-Nunez C, Plisson F. Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. Methods Mol Biol 2024; 2714:329-352. [PMID: 37676607 DOI: 10.1007/978-1-0716-3441-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico
| | - Natalia L Cancelarich
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Mariela M Marani
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Fabien Plisson
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico.
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico.
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13
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Szymczak P, Szczurek E. Artificial intelligence-driven antimicrobial peptide discovery. Curr Opin Struct Biol 2023; 83:102733. [PMID: 37992451 DOI: 10.1016/j.sbi.2023.102733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/06/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023]
Abstract
Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
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14
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Gallardo-Becerra L, Cervantes-Echeverría M, Cornejo-Granados F, Vazquez-Morado LE, Ochoa-Leyva A. Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota. MICROBIAL ECOLOGY 2023; 87:8. [PMID: 38036921 PMCID: PMC10689560 DOI: 10.1007/s00248-023-02313-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023]
Abstract
Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.
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Affiliation(s)
- Luigui Gallardo-Becerra
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Melany Cervantes-Echeverría
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Fernanda Cornejo-Granados
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Luis E Vazquez-Morado
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Adrian Ochoa-Leyva
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
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15
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Pandi A, Adam D, Zare A, Trinh VT, Schaefer SL, Burt M, Klabunde B, Bobkova E, Kushwaha M, Foroughijabbari Y, Braun P, Spahn C, Preußer C, Pogge von Strandmann E, Bode HB, von Buttlar H, Bertrams W, Jung AL, Abendroth F, Schmeck B, Hummer G, Vázquez O, Erb TJ. Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides. Nat Commun 2023; 14:7197. [PMID: 37938588 PMCID: PMC10632401 DOI: 10.1038/s41467-023-42434-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/10/2023] [Indexed: 11/09/2023] Open
Abstract
Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell-free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for high throughput and low-cost production and testing of bioactive peptides within less than 24 h.
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Affiliation(s)
- Amir Pandi
- Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
| | - David Adam
- Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Bundeswehr Institute of Microbiology, Munich, Germany
| | - Amir Zare
- Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Van Tuan Trinh
- Department of Chemistry, Philipps-University Marburg, Marburg, Germany
| | - Stefan L Schaefer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Marie Burt
- Institute for Lung Research, Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL), Marburg, Germany
| | - Björn Klabunde
- Institute for Lung Research, Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL), Marburg, Germany
| | - Elizaveta Bobkova
- Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Manish Kushwaha
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Yeganeh Foroughijabbari
- Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Peter Braun
- Bundeswehr Institute of Microbiology, Munich, Germany
- German Center for Infection Research (DZIF), Munich, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Immunology, Infection and Pandemic Research, Munich, Germany
| | - Christoph Spahn
- Department of Natural Products in Organismic Interactions, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Christian Preußer
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps-University Marburg, Marburg, Germany
- Core Facility Extracellular Vesicles, Center for Tumor Biology and Immunology, Philipps-University of Marburg, Marburg, Germany
| | - Elke Pogge von Strandmann
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps-University Marburg, Marburg, Germany
- Core Facility Extracellular Vesicles, Center for Tumor Biology and Immunology, Philipps-University of Marburg, Marburg, Germany
| | - Helge B Bode
- Department of Natural Products in Organismic Interactions, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Molecular Biotechnology, Department of Biosciences, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Chemistry, Chemical Biology, Philipps-University Marburg, Marburg, Germany
- Senckenberg Gesellschaft für Naturforschung, Frankfurt, Germany
- SYNMIKRO Center of Synthetic Microbiology, Marburg, Germany
| | - Heiner von Buttlar
- Bundeswehr Institute of Microbiology, Munich, Germany
- German Center for Infection Research (DZIF), Munich, Germany
| | - Wilhelm Bertrams
- Institute for Lung Research, Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL), Marburg, Germany
| | - Anna Lena Jung
- Institute for Lung Research, Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL), Marburg, Germany
- Core Facility Flow Cytometry - Bacterial Vesicles, Philipps-University Marburg, Marburg, Germany
| | - Frank Abendroth
- Department of Chemistry, Philipps-University Marburg, Marburg, Germany
| | - Bernd Schmeck
- Institute for Lung Research, Universities of Giessen and Marburg Lung Center, Philipps-University Marburg, German Center for Lung Research (DZL), Marburg, Germany
- SYNMIKRO Center of Synthetic Microbiology, Marburg, Germany
- Core Facility Flow Cytometry - Bacterial Vesicles, Philipps-University Marburg, Marburg, Germany
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Marburg, Philipps-University Marburg, Marburg, Germany
- Institute for Lung Health (ILH), Giessen, Germany
- Member of the German Center for Infectious Disease Research (DZIF), Marburg, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
- Institute for Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Olalla Vázquez
- Department of Chemistry, Philipps-University Marburg, Marburg, Germany
- SYNMIKRO Center of Synthetic Microbiology, Marburg, Germany
| | - Tobias J Erb
- Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
- SYNMIKRO Center of Synthetic Microbiology, Marburg, Germany.
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16
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Guo L, Tang M, Luo S, Zhou X. Screening and Functional Analyses of Novel Cecropins from Insect Transcriptome. INSECTS 2023; 14:794. [PMID: 37887806 PMCID: PMC10607850 DOI: 10.3390/insects14100794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/27/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023]
Abstract
Antibiotic resistance is a significant and growing threat to global public health. However, antimicrobial peptides (AMPs) have shown promise as they exhibit a broad spectrum of antibacterial activities with low potential for resistance development. Insects, which inhabit a wide range of environments and are incredibly diverse, remain largely unexplored as a source of novel AMPs. To address this, we conducted a screening of the representative transcriptomes from the 1000 Insect Transcriptome Evolution (1KITE) dataset, focusing on the homologous reference genes of Cecropins, the first identified AMPs in insects known for its high efficiency. Our analysis identified 108 Cecropin genes from 105 insect transcriptomes, covering all major hexapod lineages. We validated the gene sequences and synthesized mature peptides for three identified Cecropin genes. Through minimal inhibition concentration and agar diffusion assays, we confirmed that these peptides exhibited antimicrobial activities against Gram-negative bacteria. Similar to the known Cecropin, the three Cecropins adopted an alpha-helical conformation in membrane-like environments, efficiently disrupting bacterial membranes through permeabilization. Importantly, none of the three Cecropins demonstrated cytotoxicity in erythrocyte hemolysis tests, suggesting their safety in real-world applications. Overall, this newly developed methodology provides a high-throughput bioinformatic pipeline for the discovery of AMP, taking advantage of the expanding genomic resources available for diverse organisms.
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Affiliation(s)
- Lizhen Guo
- Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China; (L.G.); (M.T.)
- Sanya Institute of China Agricultural University, Sanya 572000, China
| | - Min Tang
- Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China; (L.G.); (M.T.)
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Shiqi Luo
- Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China; (L.G.); (M.T.)
| | - Xin Zhou
- Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China; (L.G.); (M.T.)
- Sanya Institute of China Agricultural University, Sanya 572000, China
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17
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Szot-Karpińska K, Kudła P, Orzeł U, Narajczyk M, Jönsson-Niedziółka M, Pałys B, Filipek S, Ebner A, Niedziółka-Jönsson J. Investigation of Peptides for Molecular Recognition of C-Reactive Protein-Theoretical and Experimental Studies. Anal Chem 2023; 95:14475-14483. [PMID: 37695838 PMCID: PMC10535004 DOI: 10.1021/acs.analchem.3c03127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 08/29/2023] [Indexed: 09/13/2023]
Abstract
We investigate the interactions between C-reactive protein (CRP) and new CRP-binding peptide materials using experimental (biological and physicochemical) methods with the support of theoretical simulations (computational modeling analysis). Three specific CRP-binding peptides (P2, P3, and P9) derived from an M13 bacteriophage have been identified using phage-display technology. The binding efficiency of the peptides exposed on phages toward the CRP protein was demonstrated via biological methods. Fibers of the selected phages/peptides interact differently due to different compositions of amino acid sequences on the exposed peptides, which was confirmed by transmission electron microscopy. Numerical and experimental studies consistently showed that the P3 peptide is the best CRP binder. A combination of theoretical and experimental methods demonstrates that identifying the best binder can be performed simply, cheaply, and fast. Such an approach has not been reported previously for peptide screening and demonstrates a new trend in science where calculations can replace or support laborious experimental techniques. Finally, the best CRP binder─the P3 peptide─was used for CRP recognition on silicate-modified indium tin oxide-coated glass electrodes. The obtained electrodes exhibit a wide range of operation (1.0-100 μg mL-1) with a detection limit (LOD = 3σ/S) of 0.34 μg mL-1. Moreover, the dissociation constant Kd of 4.2 ± 0.144 μg mL-1 (35 ± 1.2 nM) was evaluated from the change in the current. The selectivity of the obtained electrode was demonstrated in the presence of three interfering proteins. These results prove that the presented P3 peptide is a potential candidate as a receptor for CRP, which can replace specific antibodies.
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Affiliation(s)
- Katarzyna Szot-Karpińska
- Institute
of Physical Chemistry, Polish Academy of
Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Patryk Kudła
- Institute
of Physical Chemistry, Polish Academy of
Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
| | - Urszula Orzeł
- Biological
and Chemical Research Centre, University
of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Magdalena Narajczyk
- Department
of Electron Microscopy, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland
| | | | - Barbara Pałys
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Sławomir Filipek
- Biological
and Chemical Research Centre, University
of Warsaw, Zwirki i Wigury 101, 02-089 Warsaw, Poland
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Andreas Ebner
- Institute
of Biophysics, Johannes Kepler University, Gruberstrasse 40, 4020 Linz, Austria
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18
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Cesaro A, Bagheri M, Torres MDT, Wan F, de la Fuente-Nunez C. Deep learning tools to accelerate antibiotic discovery. Expert Opin Drug Discov 2023; 18:1245-1257. [PMID: 37794737 PMCID: PMC10790350 DOI: 10.1080/17460441.2023.2250721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
INTRODUCTION As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity. AREAS COVERED This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics. EXPERT OPINION Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.
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Affiliation(s)
- Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Mojtaba Bagheri
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marcelo D. T. Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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