1
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Voss H, Robert Engel D, Wagenlehner F, Shevchuk O. Discovery of Antimicrobial Peptides in Urinary Tract Infections. Eur Urol Focus 2024:S2405-4569(24)00166-4. [PMID: 39227205 DOI: 10.1016/j.euf.2024.08.010] [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: 07/04/2024] [Revised: 08/06/2024] [Accepted: 08/25/2024] [Indexed: 09/05/2024]
Abstract
Antimicrobial peptides (AMPs) play a pivotal role in the innate immune system as a frontline defense against microbial threats. AMPs can serve as biomarkers and alternative antibiotics, overcoming mortality related to multidrug-resistant pathogens in urinary tract infections (UTIs). While the relevance of AMPs in UTIs has been validated and AMP drugs approved by the US Food and Drug Administration are in clinical use, information about their modification status, regulation, and mechanism of action remains sparse. Only a small fraction of sequences with potential AMP activity, predicted on the basis of known AMP characteristics, have been validated. Elucidation of the global profile of AMPs in the bladder, kidney, and urine under UTI conditions would facilitate an in-depth, disease-specific understanding of the innate immune system and the development of tailored AMP biomarkers and antibiotics. This mini-review focuses on a comprehensive strategy for global profiling and validation of AMPs in UTIs that incorporates AMP data repositories, prediction algorithms, and proteomics for healthy individuals and UTI patients. PATIENT SUMMARY: Short protein molecules called peptides that have antimicrobial activity show promise for the treatment of urinary tract infections. More research and testing of naturally occurring and synthetic peptides with this activity are needed to fully understand how they can help in patient care.
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Affiliation(s)
- Hannah Voss
- Institute of Experimental Immunology and Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Daniel Robert Engel
- Institute of Experimental Immunology and Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Florian Wagenlehner
- Clinic for Urology, Paediatric Urology and Andrology, Justus-Liebig University Giessen, Giessen, Germany
| | - Olga Shevchuk
- Institute of Experimental Immunology and Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
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2
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Ye Z, Fu L, Li S, Chen Z, Ouyang J, Shang X, Liu Y, Gao L, Wang Y. Synergistic collaboration between AMPs and non-direct antimicrobial cationic peptides. Nat Commun 2024; 15:7319. [PMID: 39183339 PMCID: PMC11345435 DOI: 10.1038/s41467-024-51730-x] [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: 01/03/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024] Open
Abstract
Non-direct antimicrobial cationic peptides (NDACPs) are components of the animal innate immune system. But their functions and association with antimicrobial peptides (AMPs) are incompletely understood. Here, we reveal a synergistic interaction between the AMP AW1 and the NDACP AW2, which are co-expressed in the frog Amolops wuyiensis. AW2 enhances the antibacterial activity of AW1 both in vitro and in vivo, while mitigating the development of bacterial resistance and eradicating biofilms. AW1 and AW2 synergistically damage bacterial membranes, facilitating cellular uptake and interaction of AW2 with the intracellular target bacterial genomic DNA. Simultaneously, they trigger the generation of ROS in bacteria, contributing to cell death upon reaching a threshold level. Moreover, we demonstrate that this synergistic antibacterial effect between AMPs and NDACPs is prevalent across diverse animal species. These findings unveil a robust and previously unknown correlation between AMPs and NDACPs as a widespread antibacterial immune defense strategy in animals.
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Affiliation(s)
- Zifan Ye
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Lei Fu
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China
| | - Shuangyu Li
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Ziying Chen
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China
| | - Jianhong Ouyang
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Xinci Shang
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Yanli Liu
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China
| | - Lianghui Gao
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China.
| | - Yipeng Wang
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, China.
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China.
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3
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Deb R, Torres MDT, Boudný M, Koběrská M, Cappiello F, Popper M, Dvořáková
Bendová K, Drabinová M, Hanáčková A, Jeannot K, Petřík M, Mangoni ML, Balíková Novotná G, Mráz M, de la Fuente-Nunez C, Vácha R. Computational Design of Pore-Forming Peptides with Potent Antimicrobial and Anticancer Activities. J Med Chem 2024; 67:14040-14061. [PMID: 39116273 PMCID: PMC11345766 DOI: 10.1021/acs.jmedchem.4c00912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 07/05/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024]
Abstract
Peptides that form transmembrane barrel-stave pores are potential alternative therapeutics for bacterial infections and cancer. However, their optimization for clinical translation is hampered by a lack of sequence-function understanding. Recently, we have de novo designed the first synthetic barrel-stave pore-forming antimicrobial peptide with an identified function of all residues. Here, we systematically mutate the peptide to improve pore-forming ability in anticipation of enhanced activity. Using computer simulations, supported by liposome leakage and atomic force microscopy experiments, we find that pore-forming ability, while critical, is not the limiting factor for improving activity in the submicromolar range. Affinity for bacterial and cancer cell membranes needs to be optimized simultaneously. Optimized peptides more effectively killed antibiotic-resistant ESKAPEE bacteria at submicromolar concentrations, showing low cytotoxicity to human cells and skin model. Peptides showed systemic anti-infective activity in a preclinical mouse model of Acinetobacter baumannii infection. We also demonstrate peptide optimization for pH-dependent antimicrobial and anticancer activity.
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Affiliation(s)
- Rahul Deb
- CEITEC
− Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
- National
Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno 625 00, Czech Republic
| | - 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 19104, United States
- Departments
of Bioengineering and Chemical and Biomolecular Engineering, School
of Engineering and Applied Science, University
of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn
Institute for Computational Science, University
of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department
of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Miroslav Boudný
- CEITEC
− Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
- Department
of Internal Medicine, Hematology and Oncology, University Hospital
Brno and Faculty of Medicine, Masaryk University, Brno 625 00, Czech Republic
| | - Markéta Koběrská
- Institute
of Microbiology, Czech Academy of Sciences,
BIOCEV, Vestec 252 50, Czech Republic
| | - Floriana Cappiello
- Department
of Biochemical Sciences, Laboratory Affiliated to Istituto Pasteur
Italia-Fondazione Cenci Bolognetti, Sapienza
University of Rome, Rome 00185, Italy
| | - Miroslav Popper
- Institute
of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Olomouc 779 00, Czech Republic
| | - Kateřina Dvořáková
Bendová
- Institute
of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Olomouc 779 00, Czech Republic
| | - Martina Drabinová
- CEITEC
− Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
| | - Adelheid Hanáčková
- CEITEC
− Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
| | - Katy Jeannot
- University
of Franche-Comté, CNRS, Chrono-environment, Besançon 25030, France
- National Reference Centre for Antibiotic
Resistance, Besançon 25030, France
| | - Miloš Petřík
- Institute
of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Olomouc 779 00, Czech Republic
- Czech
Advanced Technology and Research Institute, Palacký University, Olomouc 779 00, Czech Republic
| | - Maria Luisa Mangoni
- Department
of Biochemical Sciences, Laboratory Affiliated to Istituto Pasteur
Italia-Fondazione Cenci Bolognetti, Sapienza
University of Rome, Rome 00185, Italy
| | | | - Marek Mráz
- CEITEC
− Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
- Department
of Internal Medicine, Hematology and Oncology, University Hospital
Brno and Faculty of Medicine, Masaryk University, Brno 625 00, Czech Republic
| | - 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 19104, United States
- Departments
of Bioengineering and Chemical and Biomolecular Engineering, School
of Engineering and Applied Science, University
of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn
Institute for Computational Science, University
of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department
of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Robert Vácha
- CEITEC
− Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
- National
Centre for Biomolecular Research, Faculty of Science, Masaryk University, Brno 625 00, Czech Republic
- Department
of Condensed Matter Physics, Faculty of Science, Masaryk University, Brno 611 37, Czech Republic
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4
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Torres MDT, Brooks EF, Cesaro A, Sberro H, Gill MO, Nicolaou C, Bhatt AS, de la Fuente-Nunez C. Mining human microbiomes reveals an untapped source of peptide antibiotics. Cell 2024:S0092-8674(24)00802-X. [PMID: 39163860 DOI: 10.1016/j.cell.2024.07.027] [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: 08/08/2023] [Revised: 05/09/2024] [Accepted: 07/17/2024] [Indexed: 08/22/2024]
Abstract
Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.
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Affiliation(s)
- 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, PA 19104, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erin F Brooks
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA 94305, USA
| | - 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, PA 19104, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hila Sberro
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA 94305, USA
| | - Matthew O Gill
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Cosmos Nicolaou
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA 94305, USA
| | - Ami S Bhatt
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
| | - 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 19104, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
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5
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Chen J, Wang W, Hu X, Yue Y, Lu X, Wang C, Wei B, Zhang H, Wang H. Medium-sized peptides from microbial sources with potential for antibacterial drug development. Nat Prod Rep 2024; 41:1235-1263. [PMID: 38651516 DOI: 10.1039/d4np00002a] [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: 04/25/2024]
Abstract
Covering: 1993 to the end of 2022As the rapid development of antibiotic resistance shrinks the number of clinically available antibiotics, there is an urgent need for novel options to fill the existing antibiotic pipeline. In recent years, antimicrobial peptides have attracted increased interest due to their impressive broad-spectrum antimicrobial activity and low probability of antibiotic resistance. However, macromolecular antimicrobial peptides of plant and animal origin face obstacles in antibiotic development because of their extremely short elimination half-life and poor chemical stability. Herein, we focus on medium-sized antibacterial peptides (MAPs) of microbial origin with molecular weights below 2000 Da. The low molecular weight is not sufficient to form complex protein conformations and is also associated to a better chemical stability and easier modifications. Microbially-produced peptides are often composed of a variety of non-protein amino acids and terminal modifications, which contribute to improving the elimination half-life of compounds. Therefore, MAPs have great potential for drug discovery and are likely to become key players in the development of next-generation antibiotics. In this review, we provide a detailed exploration of the modes of action demonstrated by 45 MAPs and offer a concise summary of the structure-activity relationships observed in these MAPs.
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Affiliation(s)
- Jianwei Chen
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Wei Wang
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xubin Hu
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yujie Yue
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xingyue Lu
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Chenjie Wang
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Bin Wei
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Huawei Zhang
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hong Wang
- College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310014, China
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6
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Broschat SL, Siu SWI, de la Fuente-Nunez C. Editorial: Machine learning approaches to antimicrobials: discovery and resistance. FRONTIERS IN BIOINFORMATICS 2024; 4:1458237. [PMID: 39184338 PMCID: PMC11341447 DOI: 10.3389/fbinf.2024.1458237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Accepted: 07/22/2024] [Indexed: 08/27/2024] Open
Affiliation(s)
- Shira L. Broschat
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States
- Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, United States
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, United States
| | - Shirley W. I. Siu
- Centre for Artificial Intelligence Driven Drug Discovery, Macao Polytechnic University, Macao, Macao SAR, China
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, Macao SAR, China
| | - 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
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
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7
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Torrance AW, de la Fuente-Nunez C. The patentability and bioethics of molecular de-extinction. Nat Biotechnol 2024; 42:1179-1180. [PMID: 39143159 DOI: 10.1038/s41587-024-02332-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Affiliation(s)
- Andrew W Torrance
- University of Kansas School of Law, Lawrence, KS, USA
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - 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.
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
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8
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Dance A. Five ways science is tackling the antibiotic resistance crisis. Nature 2024; 632:494-496. [PMID: 39138306 DOI: 10.1038/d41586-024-02601-4] [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] [Indexed: 08/15/2024]
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9
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Chaudhary S, Ali Z, Mahfouz M. Molecular farming for sustainable production of clinical-grade antimicrobial peptides. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:2282-2300. [PMID: 38685599 PMCID: PMC11258990 DOI: 10.1111/pbi.14344] [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: 10/20/2023] [Revised: 02/26/2024] [Accepted: 03/11/2024] [Indexed: 05/02/2024]
Abstract
Antimicrobial peptides (AMPs) are emerging as next-generation therapeutics due to their broad-spectrum activity against drug-resistant bacterial strains and their ability to eradicate biofilms, modulate immune responses, exert anti-inflammatory effects and improve disease management. They are produced through solid-phase peptide synthesis or in bacterial or yeast cells. Molecular farming, i.e. the production of biologics in plants, offers a low-cost, non-toxic, scalable and simple alternative platform to produce AMPs at a sustainable cost. In this review, we discuss the advantages of molecular farming for producing clinical-grade AMPs, advances in expression and purification systems and the cost advantage for industrial-scale production. We further review how 'green' production is filling the sustainability gap, streamlining patent and regulatory approvals and enabling successful clinical translations that demonstrate the future potential of AMPs produced by molecular farming. Finally, we discuss the regulatory challenges that need to be addressed to fully realize the potential of molecular farming-based AMP production for therapeutics.
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Affiliation(s)
- Shahid Chaudhary
- Laboratory for Genome Engineering and Synthetic Biology, Division of Biological Sciences4700 King Abdullah University of Science and TechnologyThuwalSaudi Arabia
| | - Zahir Ali
- Laboratory for Genome Engineering and Synthetic Biology, Division of Biological Sciences4700 King Abdullah University of Science and TechnologyThuwalSaudi Arabia
| | - Magdy Mahfouz
- Laboratory for Genome Engineering and Synthetic Biology, Division of Biological Sciences4700 King Abdullah University of Science and TechnologyThuwalSaudi Arabia
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10
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Santos-Júnior CD, Torres MDT, Duan Y, Rodríguez Del Río Á, Schmidt TSB, Chong H, Fullam A, Kuhn M, Zhu C, Houseman A, Somborski J, Vines A, Zhao XM, Bork P, Huerta-Cepas J, de la Fuente-Nunez C, Coelho LP. Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 2024; 187:3761-3778.e16. [PMID: 38843834 DOI: 10.1016/j.cell.2024.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 06/25/2024]
Abstract
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Laboratory of Microbial Processes & Biodiversity - LMPB, Department of Hydrobiology, Universidade Federal de São Carlos - UFSCar, São Carlos, São Paulo 13565-905, Brazil
| | - 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, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; APC Microbiome & School of Medicine, University College Cork, Cork, Ireland
| | - Hui Chong
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Chengkai Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Amy Houseman
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Jelena Somborski
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anna Vines
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - 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; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, QLD, Australia.
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11
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Wan F, Torres MDT, Peng J, de la Fuente-Nunez C. Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nat Biomed Eng 2024; 8:854-871. [PMID: 38862735 PMCID: PMC11310081 DOI: 10.1038/s41551-024-01201-x] [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: 10/04/2023] [Accepted: 03/25/2024] [Indexed: 06/13/2024]
Abstract
Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.
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Affiliation(s)
- 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, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - 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, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline Peng
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - 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.
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
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12
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Coelho LP, Santos-Júnior CD, de la Fuente-Nunez C. Challenges in computational discovery of bioactive peptides in 'omics data. Proteomics 2024; 24:e2300105. [PMID: 38458994 DOI: 10.1002/pmic.202300105] [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: 10/13/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/10/2024]
Abstract
Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.
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Affiliation(s)
- Luis Pedro Coelho
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Woolloongabba, Queensland, Australia
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
- Laboratory of Microbial Processes & Biodiversity - LMPB, Hydrobiology Department, Federal University of São Carlos - UFSCar, São Paulo, Brazil
| | - 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, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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13
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Torres MDT, Cesaro A, de la Fuente-Nunez C. Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.25.586636. [PMID: 38585860 PMCID: PMC10996515 DOI: 10.1101/2024.03.25.586636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Encrypted peptides have been recently described as a new class of antimicrobial molecules. They have been proposed to play a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these peptides are found embedded in proteins unrelated to the immune system, suggesting that immunological responses may extend beyond traditional host immunity proteins. To test this idea, here we synthesized and tested representative peptides derived from non-immune proteins for their ability to exert antimicrobial and immunomodulatory properties. Our experiments revealed that most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators such as IL-6, TNF-α, and MCP-1. Moreover, eight of the peptides identified, collagenin 3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may play a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.
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14
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Hourigan D, Stefanovic E, Hill C, Ross RP. Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy. BMC Microbiol 2024; 24:103. [PMID: 38539119 PMCID: PMC10976773 DOI: 10.1186/s12866-024-03243-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 02/28/2024] [Indexed: 04/19/2024] Open
Abstract
Vancomycin-resistant enterococci (VRE) are major opportunistic pathogens and the causative agents of serious diseases, such as urinary tract infections and endocarditis. VRE strains mainly include species of Enterococcus faecium and E. faecalis which can colonise the gastrointestinal tract (GIT) of patients and, following growth and persistence in the gut, can transfer to blood resulting in systemic dissemination in the body. Advancements in genomics have revealed that hospital-associated VRE strains are characterised by increased numbers of mobile genetic elements, higher numbers of antibiotic resistance genes and often lack active CRISPR-Cas systems. Additionally, comparative genomics have increased our understanding of dissemination routes among patients and healthcare workers. Since the efficiency of currently available antibiotics is rapidly declining, new measures to control infection and dissemination of these persistent pathogens are urgently needed. These approaches include combinatory administration of antibiotics, strengthening colonisation resistance of the gut microbiota to reduce VRE proliferation through commensals or probiotic bacteria, or switching to non-antibiotic bacterial killers, such as bacteriophages or bacteriocins. In this review, we discuss the current knowledge of the genomics of VRE isolates and state-of-the-art therapeutic advances against VRE infections.
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Affiliation(s)
- David Hourigan
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland
- School of Microbiology, University College Cork, College Rd, University College, Cork, Ireland
| | - Ewelina Stefanovic
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland
- Teagasc Food Research Centre, Moorepark, Moorepark West, Fermoy, Co. Cork, Ireland
| | - Colin Hill
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland
- School of Microbiology, University College Cork, College Rd, University College, Cork, Ireland
| | - R Paul Ross
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland.
- School of Microbiology, University College Cork, College Rd, University College, Cork, Ireland.
- Teagasc Food Research Centre, Moorepark, Moorepark West, Fermoy, Co. Cork, Ireland.
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15
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De los Santos L, Beckman RL, DeBarro C, Keener JE, Torres MD, de la Fuente-Nunez C, Brodbelt JS, Fleeman RM. Polyproline peptide targets Klebsiella pneumoniae polysaccharides to collapse biofilms. CELL REPORTS. PHYSICAL SCIENCE 2024; 5:101869. [PMID: 38605913 PMCID: PMC11008256 DOI: 10.1016/j.xcrp.2024.101869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Hypervirulent Klebsiella pneumoniae is known for its increased extracellular polysaccharide production. Biofilm matrices of hypervirulent K. pneumoniae have increased polysaccharide abundance and are uniquely susceptible to disruption by peptide bactenecin 7 (bac7 (1-35)). Here, using confocal microscopy, we show that polysaccharides within the biofilm matrix collapse following bac7 (1-35) treatment. This collapse led to the release of cells from the biofilm, which were then killed by the peptide. Characterization of truncated peptide analogs revealed that their interactions with polysaccharide were responsible for the biofilm matrix changes that accompany bac7 (1-35) treatment. Ultraviolet photodissociation mass spectrometry with the parental peptide or a truncated analog bac7 (10-35) reveal the important regions for bac7 (1-35) complexing with polysaccharides. Finally, we tested bac7 (1-35) using a murine skin abscess model and observed a significant decrease in the bacterial burden. These findings unveil the potential of bac7 (1-35) polysaccharide interactions to collapse K. pneumoniae biofilms.
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Affiliation(s)
- Laura De los Santos
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
| | - Robert L. Beckman
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
| | - Christina DeBarro
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
| | - James E. Keener
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA
| | - 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, PA 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - 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 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer S. Brodbelt
- Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA
| | - Renee M. Fleeman
- Division of Immunity and Pathogenesis, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
- X (formerly Twitter): @FleemanLab
- Lead contact
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16
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Monsalve D, Mesa A, Mira LM, Mera C, Orduz S, Branch-Bedoya JW. Antimicrobial peptides designed by computational analysis of proteomes. Antonie Van Leeuwenhoek 2024; 117:55. [PMID: 38488950 DOI: 10.1007/s10482-024-01946-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 02/06/2024] [Indexed: 03/17/2024]
Abstract
Antimicrobial peptides (AMPs) are promising cationic and amphipathic molecules to fight antibiotic resistance. To search for novel AMPs, we applied a computational strategy to identify peptide sequences within the organisms' proteome, including in-house developed software and artificial intelligence tools. After analyzing 150.450 proteins from eight proteomes of bacteria, plants, a protist, and a nematode, nine peptides were selected and modified to increase their antimicrobial potential. The 18 resulting peptides were validated by bioassays with four pathogenic bacterial species, one yeast species, and two cancer cell-lines. Fourteen of the 18 tested peptides were antimicrobial, with minimum inhibitory concentrations (MICs) values under 10 µM against at least three bacterial species; seven were active against Candida albicans with MICs values under 10 µM; six had a therapeutic index above 20; two peptides were active against A549 cells, and eight were active against MCF-7 cells under 30 µM. This study's most active antimicrobial peptides damage the bacterial cell membrane, including grooves, dents, membrane wrinkling, cell destruction, and leakage of cytoplasmic material. The results confirm that the proposed approach, which uses bioinformatic tools and rational modifications, is highly efficient and allows the discovery, with high accuracy, of potent AMPs encrypted in proteins.
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Affiliation(s)
- Dahiana Monsalve
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Andrea Mesa
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Laura M Mira
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Carlos Mera
- Departamento de Sistemas de Información, Instituto Tecnológico Metropolitano, Calle 54A # 30-01, 050013, Medellín, Antioquia, Colombia.
- Departamento de Ingeniería de Sistemas, Facultad de Ingenierías, Universidad de Antioquia, Calle 70 # 52-21, 050010, Medellín, Antioquia, Colombia.
| | - Sergio Orduz
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - John W Branch-Bedoya
- Departamento de Ciencias de la Computación y de la Decisión, Facultad de Minas, Universidad Nacional de Colombia, sede Medellín, Av. 80 # 65-223, 050041, Medellín, Antioquia, Colombia
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17
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Yang S, Zhang W, Yang B, Feng X, Li Y, Li X, Liu Q. Metagenomic evidence for antibiotic-associated actinomycetes in the Karamay Gobi region. Front Microbiol 2024; 15:1330880. [PMID: 38505550 PMCID: PMC10949947 DOI: 10.3389/fmicb.2024.1330880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/07/2024] [Indexed: 03/21/2024] Open
Abstract
Due to the misuse of antibiotics, there is an increasing emergence and spread of multidrug-resistant (MDR) bacteria, leading to a human health crisis. To address clinical antibiotic resistance and prevent/control pathogenic microorganisms, the development of novel antibiotics is essential. This also offers a new approach to discovering valuable actinobacterial flora capable of producing natural bioactive products. In this study, we employed bioinformatics and macro-genome sequencing to collect 15 soil samples from three different locations in the Karamay Gobi region. First, we assessed the diversity of microorganisms in soil samples from different locations, analyzing the content of bacteria, archaea, actinomycetes, and fungi. The biodiversity of soil samples from outside the Gobi was found to be higher than that of soil samples from within and in the center of the Gobi. Second, through microbial interaction network analysis, we identified actinomycetes as the dominant group in the system. We have identified the top four antibiotic genes, such as Ecol_fabG_TRC, Efac_liaR_DAP, tetA (58), and macB, by CARD. These genes are associated with peptide antibiotics, disinfecting agents and antiseptics, tetracycline antibiotics, and macrolide antibiotics. In addition, we also obtained 40 other antibiotic-related genes through CARD alignment. Through in-depth analysis of desert soil samples, we identified several unstudied microbial species belonging to different families, including Erythrobacteriaceae, Solirubrobacterales, Thermoleophilaceae, Gaiellaceae, Nocardioidaceae, Actinomycetia, Egibacteraceae, and Acidimicrobiales. These species have the capability to produce peptide antibiotics, macrolide antibiotics, and tetracycline antibiotics, as well as disinfectants and preservatives. This study provides valuable theoretical support for future in-depth research.
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Affiliation(s)
- Shuai Yang
- Xinjiang Second Medical College, Xinjiang, China
- Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin Co-funded by Xinjiang Production & Construction Corps and The Ministry of Science & Technology, Tarim University, Alar, China
| | - Wei Zhang
- Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin Co-funded by Xinjiang Production & Construction Corps and The Ministry of Science & Technology, Tarim University, Alar, China
| | - Bo Yang
- Xinjiang Second Medical College, Xinjiang, China
| | - Xin Feng
- Xinjiang Second Medical College, Xinjiang, China
| | - Yiyang Li
- Xinjiang Second Medical College, Xinjiang, China
| | - Xiaolin Li
- Xinjiang Second Medical College, Xinjiang, China
| | - Qin Liu
- Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin Co-funded by Xinjiang Production & Construction Corps and The Ministry of Science & Technology, Tarim University, Alar, China
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18
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Nazarian-Firouzabadi F, Torres MDT, de la Fuente-Nunez C. Recombinant production of antimicrobial peptides in plants. Biotechnol Adv 2024; 71:108296. [PMID: 38042311 DOI: 10.1016/j.biotechadv.2023.108296] [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: 09/06/2023] [Revised: 11/10/2023] [Accepted: 11/26/2023] [Indexed: 12/04/2023]
Abstract
Classical plant breeding methods are limited in their ability to confer disease resistance on plants. However, in recent years, advancements in molecular breeding and biotechnological have provided new approaches to overcome these limitations and protect plants from disease. Antimicrobial peptides (AMPs) constitute promising agents that may be able to protect against infectious agents. Recently, peptides have been recombinantly produced in plants at scale and low cost. Because AMPs are less likely than conventional antimicrobials to elicit resistance of pathogenic bacteria, they open up exciting new avenues for agricultural applications. Here, we review recent advances in the design and production of bioactive recombinant AMPs that can effectively protect crop plants from diseases.
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Affiliation(s)
- Farhad Nazarian-Firouzabadi
- Production Engineering and Plant Genetics Department, Faculty of Agriculture, Lorestan University, P.O. Box, 465, Khorramabad, Iran.
| | - Marcelo Der Torossian 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, 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; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, 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, 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; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States of America.
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19
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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] [Grants] [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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20
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Wang Y, Stebe KJ, de la Fuente-Nunez C, Radhakrishnan R. Computational Design of Peptides for Biomaterials Applications. ACS APPLIED BIO MATERIALS 2024; 7:617-625. [PMID: 36971822 PMCID: PMC11190638 DOI: 10.1021/acsabm.2c01023] [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: 03/29/2023]
Abstract
Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.
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Affiliation(s)
- Yiming Wang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Kathleen J Stebe
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Cesar de la Fuente-Nunez
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Machine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Ravi Radhakrishnan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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21
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Di Napoli M, Castagliuolo G, Pio S, Di Nardo I, Russo T, Antonini D, Notomista E, Varcamonti M, Zanfardino A. Study of the Antimicrobial Activity of the Human Peptide SQQ30 against Pathogenic Bacteria. Antibiotics (Basel) 2024; 13:145. [PMID: 38391531 PMCID: PMC10886087 DOI: 10.3390/antibiotics13020145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 02/24/2024] Open
Abstract
Given the continuous increase in antibiotic resistance, research has been driven towards the isolation of new antimicrobial molecules. Short, charged, and very hydrophobic antimicrobial peptides have a direct action against biological membranes, which are less prone to developing resistance. Using a bioinformatic tool, we chose the SQQ30 peptide, isolated from the human SOGA1 protein. The antimicrobial activity of this peptide against various Gram-negative and Gram-positive bacterial strains and against a fungal strain was studied. A mechanism of action directed against biological membranes was outlined. When administered in combination with the antibiotic ciprofloxacin and with the TRS21 (buforin II), another antimicrobial peptide, SQQ30 can be used with a lower MIC, showing additivity and synergism, respectively. Particularly interesting is the ability of SQQ30 to bind LPS in Gram-negative strains, preventing the eukaryotic cell from releasing inflammatory mediators. Our study indicates SQQ30 as a novel and promising antimicrobial agent.
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Affiliation(s)
- Michela Di Napoli
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
| | - Giusy Castagliuolo
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
| | - Sara Pio
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
| | - Ilaria Di Nardo
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
| | - Teresa Russo
- IPCB-Institute for Polymers, Composites and Biomaterials, National Research Council of Italy, 80125 Naples, Italy
| | - Dario Antonini
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
| | - Eugenio Notomista
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
| | - Mario Varcamonti
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
| | - Anna Zanfardino
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy
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22
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Costa ISD, Junot T, Silva FL, Felix W, Cardozo Fh JL, Pereira de Araujo AF, Pais do Amaral C, Gonçalves S, Santos NC, Leite JRSA, Bloch C, Brand GD. Occurrence and evolutionary conservation analysis of α-helical cationic amphiphilic segments in the human proteome. FEBS J 2024; 291:547-565. [PMID: 37945538 DOI: 10.1111/febs.16997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/14/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023]
Abstract
The existence of encrypted fragments with antimicrobial activity in human proteins has been thoroughly demonstrated in the literature. Recently, algorithms for the large-scale identification of these segments in whole proteomes were developed, and the pervasiveness of this phenomenon was stated. These algorithms typically mine encrypted cationic and amphiphilic segments of proteins, which, when synthesized as individual polypeptide sequences, exert antimicrobial activity by membrane disruption. In the present report, the human reference proteome was submitted to the software kamal for the uncovering of protein segments that correspond to putative intragenic antimicrobial peptides (IAPs). The assessment of the identity of these segments, frequency, functional classes of parent proteins, structural relevance, and evolutionary conservation of amino acid residues within their corresponding proteins was conducted in silico. Additionally, the antimicrobial and anticancer activity of six selected synthetic peptides was evaluated. Our results indicate that cationic and amphiphilic segments can be found in 2% of all human proteins, but are more common in transmembrane and peripheral membrane proteins. These segments are surface-exposed basic patches whose amino acid residues present similar conservation scores to other residues with similar solvent accessibility. Moreover, the antimicrobial and anticancer activity of the synthetic putative IAP sequences was irrespective to whether these are associated to membranes in the cellular setting. Our study discusses these findings in light of the current understanding of encrypted peptide sequences, offering some insights into the relevance of these segments to the organism in the context of their harboring proteins or as separate polypeptide sequences.
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Affiliation(s)
- Igor S D Costa
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
| | - Tiago Junot
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
| | - Fernanda L Silva
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
| | - Wanessa Felix
- Núcleo de Pesquisa em Morfologia e Imunologia Aplicada - NuPMIA, Faculdade de Medicina, Universidade de Brasília, Brazil
| | - José L Cardozo Fh
- Laboratório de Espectrometria de Massa - LEM, Embrapa Recursos Genéticos e Biotecnologia, Brasília, Brazil
| | - Antonio F Pereira de Araujo
- Laboratório de Biofísica Teórica e Computacional, Departamento de Biologia Celular, Universidade de Brasília, Brazil
| | | | - Sónia Gonçalves
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Nuno C Santos
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - José R S A Leite
- Núcleo de Pesquisa em Morfologia e Imunologia Aplicada - NuPMIA, Faculdade de Medicina, Universidade de Brasília, Brazil
| | - Carlos Bloch
- Laboratório de Espectrometria de Massa - LEM, Embrapa Recursos Genéticos e Biotecnologia, Brasília, Brazil
| | - Guilherme D Brand
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
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Busari AA, Efejene IO, Olayemi SO, Orororo OC, Egbune EO. Evaluation of antibiotic use and analysis of ciprofloxacin and gentamicin residue in fish samples from farms in Lagos, Nigeria. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:127. [PMID: 38195963 DOI: 10.1007/s10661-024-12303-z] [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: 03/22/2023] [Accepted: 01/02/2024] [Indexed: 01/11/2024]
Abstract
Antibiotic use in aquaculture for increase yield has been established over time; however, consumption of such fish may lead to inadvertent intake of sub therapeutic doses of antibiotics. Therefore, this study aimed to investigate the extent of antibiotic use in fish farming and ciprofloxacin and gentamicin residues in fish that are consumed in Lagos, Nigeria. It was conducted in two parts: a survey of 60 fish producers to assess their use of antibiotics and an analysis of ciprofloxacin and gentamicin residues in fish organs using high-performance liquid chromatography (HPLC). The survey found that twenty-nine (48%) of the farms used antibiotics, with oxytetracycline being the most common (20%), followed by ciprofloxacin (15%); gentamicin (5%); neomycin (8.33%); and probiotics (13%). HPLC analysis of fish liver and fillet samples showed that Lagos Island had the lowest residual ciprofloxacin dose (0.3014ug/g), while Lagos mainland had the greatest residual ciprofloxacin dosage (113.78765ug/g). The mean gentamicin residue in flesh of fish from farms in Lagos Island LGA was 0.37ug/g while that obtained for liver of fish samples from Surulere LGA was 2.12ug/g). The largest (5.3240ug/g) and lowest (0.2661ug/g) residual gentamicin antibiotic concentrations were found in fishes harvested from farms located in Surulere and Lagos Island, respectively. The residue levels were within the permitted range set by the WHO, but continuous regulatory surveillance is necessary to prevent antimicrobial resistance spread and improved food safety. The study has revealed that fish produced in the study area contained residues of the commonly used antibiotics hence farmers should be encouraged to consult veterinarians in cases of disease treatment for judicious use of antibiotics while self-medication and purchase of antibiotics from vendors should be discouraged.
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Affiliation(s)
- Abdulwasiu A Busari
- Department of Pharmacology, Therapeutics and Toxicology, Faculty of Basic Medical Sciences, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Israel O Efejene
- Deparment of Pharmacology, College of Medical and Health Sciences, Novena University, Ogume, Delta State, Nigeria
| | - Sunday O Olayemi
- Department of Pharmacology, Therapeutics and Toxicology, Faculty of Basic Medical Sciences, College of Medicine, University of Lagos, Lagos, Nigeria
| | - Osuvwe C Orororo
- Department of Medical Biochemistry, Faculty of Basic Medical Sciences, Delta State University, P.M.B. 1, Abraka, Nigeria
| | - Egoamaka O Egbune
- Department of Biochemistry, Faculty of Science, Delta State University, P.M.B. 1, Abraka, Nigeria.
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24
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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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25
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Xue Y, Zhao Z, Lei Y, Qiu Z, Li X, Wang C, Cui R, Shen S, Fang L, Wang Y, Ji J, Chen Z, Zhu H, Zhu B. Influence of the linkage between long alkyl tails and cationic groups on membrane activity of nano-sized hyperbranched polyquaterniums. J Colloid Interface Sci 2024; 653:894-907. [PMID: 37774653 DOI: 10.1016/j.jcis.2023.09.131] [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: 02/12/2023] [Revised: 09/14/2023] [Accepted: 09/21/2023] [Indexed: 10/01/2023]
Abstract
The recurrent emergence of serious pathogens necessitates novel insights and highly efficient antibacterial agents. However, the innate inability of metal ions and reactive oxygen species (ROS) to differentiate between bacteria and mammalian cells presents a challenge, limiting the selectivity crucial for an ideal antimicrobial solution. Herein, we present a systematic exploration involving two variants of nano-sized hyperbranched polyquaterniums (NHBPQs) - one featuring a lengthy alkyl tail linked to the ammonium unit at the N-atom center (NHBPQ-A), and the other in a segregated configuration (NHBPQ-B). The exterior alkyl chain chains act as a barrier to the cationic group's non-specific adsorption due to spatial site resistance, causing NHBPQ-A in broad-spectrum cytotoxicity. Conversely, the distinct molecular configuration of NHBPQ-B in the segregated state affords greater flexibility, allowing the cationic groups to be released and interact non-specifically, finally resulting in selective bactericidal activity. Leveraging this selectivity, the optimized NHBPQ-B exhibits robust anti-infectious performance in a model of methicillin-resistant Staphylococcus aureus (MRSA)-infected wounds. This work establishes a promising avenue for biocompatible NHBPQs, holding significant potential in addressing MRSA infections and ameliorating both genetically encoded and phenotypic antibiotic resistance mechanisms.
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Affiliation(s)
- Yunyun Xue
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China; Center of Healthcare Materials, Shaoxing Institute, Zhejiang University, Shaoxing, 312000, China
| | - Zihao Zhao
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China; Center of Healthcare Materials, Shaoxing Institute, Zhejiang University, Shaoxing, 312000, China
| | - Yuqing Lei
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zelin Qiu
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Xinfang Li
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Chuyao Wang
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Ronglu Cui
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Shuyang Shen
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Lifeng Fang
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China.
| | - Youxiang Wang
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jian Ji
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhi Chen
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310027, China
| | - Haihong Zhu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310027, China
| | - Baoku Zhu
- Key Laboratory of Macromolecular Synthesis and Functionalization (Ministry of Education), Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China; Center of Healthcare Materials, Shaoxing Institute, Zhejiang University, Shaoxing, 312000, China.
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26
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Huynh K, Kibrom A, Donald BR, Zhou P. Discovery, characterization, and redesign of potent antimicrobial thanatin orthologs from Chinavia ubica and Murgantia histrionica targeting E. coli LptA. J Struct Biol X 2023; 8:100091. [PMID: 37416832 PMCID: PMC10320583 DOI: 10.1016/j.yjsbx.2023.100091] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 07/08/2023] Open
Abstract
Podisus maculiventris thanatin has been reported as a potent antimicrobial peptide with antibacterial and antifungal activity. Its antibiotic activity has been most thoroughly characterized against E. coli and shown to interfere with multiple pathways, such as the lipopolysaccharide transport (LPT) pathway comprised of seven different Lpt proteins. Thanatin binds to E. coli LptA and LptD, thus disrupting the LPT complex formation and inhibiting cell wall synthesis and microbial growth. Here, we performed a genomic database search to uncover novel thanatin orthologs, characterized their binding to E. coli LptA using bio-layer interferometry, and assessed their antimicrobial activity against E. coli. We found that thanatins from Chinavia ubica and Murgantia histrionica bound tighter (by 3.6- and 2.2-fold respectively) to LptA and exhibited more potent antibiotic activity (by 2.1- and 2.8-fold respectively) than the canonical thanatin from P. maculiventris. We crystallized and determined the LptA-bound complex structures of thanatins from C. ubica (1.90 Å resolution), M. histrionica (1.80 Å resolution), and P. maculiventris (2.43 Å resolution) to better understand their mechanism of action. Our structural analysis revealed that residues A10 and I21 in C. ubica and M. histrionica thanatin are important for improving the binding interface with LptA, thus overall improving the potency of thanatin against E. coli. We also designed a stapled variant of thanatin that removes the need for a disulfide bond but retains the ability to bind LptA and antibiotic activity. Our discovery presents a library of novel thanatin sequences to serve as starting scaffolds for designing more potent antimicrobial therapeutics.
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Affiliation(s)
- Kelly Huynh
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, United States
| | - Amanuel Kibrom
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, United States
| | - Bruce R. Donald
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, United States
- Department of Computer Science, Duke University, Durham, NC, United States
| | - Pei Zhou
- Department of Biochemistry, Duke University School of Medicine, Durham, NC, United States
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27
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Louis M, Tahrioui A, Tremlett CJ, Clamens T, Leprince J, Lefranc B, Kipnis E, Grandjean T, Bouffartigues E, Barreau M, Defontaine F, Cornelis P, Feuilloley MG, Harmer NJ, Chevalier S, Lesouhaitier O. The natriuretic peptide receptor agonist osteocrin disperses Pseudomonas aeruginosa biofilm. Biofilm 2023; 5:100131. [PMID: 37252226 PMCID: PMC10220261 DOI: 10.1016/j.bioflm.2023.100131] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/02/2023] [Accepted: 05/18/2023] [Indexed: 05/31/2023] Open
Abstract
Biofilms are highly tolerant to antimicrobials and host immune defense, enabling pathogens to thrive in hostile environments. The diversity of microbial biofilm infections requires alternative and complex treatment strategies. In a previous work we demonstrated that the human Atrial Natriuretic Peptide (hANP) displays a strong anti-biofilm activity toward Pseudomonas aeruginosa and that the binding of hANP by the AmiC protein supports this effect. This AmiC sensor has been identified as an analog of the human natriuretic peptide receptor subtype C (h-NPRC). In the present study, we evaluated the anti-biofilm activity of the h-NPRC agonist, osteocrin (OSTN), a hormone that displays a strong affinity for the AmiC sensor at least in vitro. Using molecular docking, we identified a pocket in the AmiC sensor that OSTN reproducibly docks into, suggesting that OSTN might possess an anti-biofilm activity as well as hANP. This hypothesis was validated since we observed that OSTN dispersed established biofilm of P. aeruginosa PA14 strain at the same concentrations as hANP. However, the OSTN dispersal effect is less marked than that observed for the hANP (-61% versus -73%). We demonstrated that the co-exposure of P. aeruginosa preformed biofilm to hANP and OSTN induced a biofilm dispersion with a similar effect to that observed with hANP alone suggesting a similar mechanism of action of these two peptides. This was confirmed by the observation that OSTN anti-biofilm activity requires the activation of the complex composed by the sensor AmiC and the regulator AmiR of the ami pathway. Using a panel of both P. aeruginosa laboratory reference strains and clinical isolates, we observed that the OSTN capacity to disperse established biofilms is highly variable from one strain to another. Taken together, these results show that similarly to the hANP hormone, OSTN has a strong potential to be used as a tool to disperse P. aeruginosa biofilms.
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Affiliation(s)
- Melissande Louis
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Ali Tahrioui
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Courtney J. Tremlett
- Living Systems Institute, Stocker Road, University of Exeter, Exeter, EX4 4QD, UK
| | - Thomas Clamens
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Jérôme Leprince
- PRIMACEN, University of Rouen Normandy, 76821, Mont-Saint-Aignan, France
| | - Benjamin Lefranc
- PRIMACEN, University of Rouen Normandy, 76821, Mont-Saint-Aignan, France
| | - Eric Kipnis
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019-UMR9017-CIIL-Centre d'Infection et d'Immunité de Lille, University Lille, F-59000, Lille, France
| | - Teddy Grandjean
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019-UMR9017-CIIL-Centre d'Infection et d'Immunité de Lille, University Lille, F-59000, Lille, France
| | - Emeline Bouffartigues
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Magalie Barreau
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Florian Defontaine
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Pierre Cornelis
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Marc G.J. Feuilloley
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Nicholas J. Harmer
- Living Systems Institute, Stocker Road, University of Exeter, Exeter, EX4 4QD, UK
| | - Sylvie Chevalier
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
| | - Olivier Lesouhaitier
- Univ Rouen Normandie, Unité de Recherche Communication Bactérienne et Stratégies Anti-infectieuses, CBSA UR4312, 27000, Evreux, France
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28
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Martinez ZA, Murray RM, Thomson MW. TRILL: ORCHESTRATING MODULAR DEEP-LEARNING WORKFLOWS FOR DEMOCRATIZED, SCALABLE PROTEIN ANALYSIS AND ENGINEERING. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563881. [PMID: 37986952 PMCID: PMC10659302 DOI: 10.1101/2023.10.24.563881] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Deep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the "language of life." However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds. TRILL (TRaining and Inference using the Language of Life) is a platform for creative protein design and discovery. Leveraging several state-of-the-art models such as ESM-2, DiffDock, and RFDiffusion, TRILL allows researchers to generate novel proteins, predict 3-D structures, extract high-dimensional representations of proteins, functionally classify proteins and more. What sets TRILL apart is its ability to enable complex pipelines by chaining together models and effectively merging the capabilities of different models to achieve a sum greater than its individual parts. Whether using Google Colab with one GPU or a supercomputer with hundreds, TRILL allows scientists to effectively utilize models with millions to billions of parameters by using optimized training strategies such as ZeRO-Offload and distributed data parallel. Therefore, TRILL not only bridges the gap between complex deep-learning models and their practical application in the field of biology, but also simplifies the orchestration of these models into comprehensive workflows, democratizing access to powerful methods. Documentation: https://trill.readthedocs.io/en/latest/home.html.
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Affiliation(s)
- Zachary A Martinez
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125
| | - Richard M Murray
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125
| | - Matt W Thomson
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125
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29
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Lee YCJ, Javdan B. Ice Cores as a Source for Antimicrobials: From Bioprospecting to Biodesign. BIODESIGN RESEARCH 2023; 5:0024. [PMID: 37928441 PMCID: PMC10623340 DOI: 10.34133/bdr.0024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/21/2023] [Indexed: 11/07/2023] Open
Abstract
The golden age has passed for antibiotic discovery, and while some antibiotics are currently in various phases of clinical trials in the United States, many pharmaceutical companies have abandoned antibiotic research. With the need for antibiotics, we should expand our horizon for therapeutic mining and can look toward understudied sources such as ice cores. Ice cores contain microorganisms and genetic material that have been frozen in time for thousands of years. The antibiotics used by these organisms are encoded in their genomes, which can be unlocked, identified, and characterized with modern advances in molecular biology, genetic sequencing, various computational approaches, and established natural product discovery pipelines. While synthetic biology can be used in natural product discovery approaches, synthetic biology and bioengineering efforts can also be leveraged in the selection and biodesign of increased compound yields, potency, and stability. Here, we provide the perspective that ice cores can be a source of novel antibiotic compounds and that the tools of synthetic biology can be used to design better antimicrobials.
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Affiliation(s)
| | - Bahar Javdan
- Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
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30
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Pedron CN, Torres MDT, Oliveira CS, Silva AF, Andrade GP, Wang Y, Pinhal MAS, Cerchiaro G, da Silva Junior PI, da Silva FD, Radhakrishnan R, de la Fuente-Nunez C, Oliveira Junior VX. Molecular hybridization strategy for tuning bioactive peptide function. Commun Biol 2023; 6:1067. [PMID: 37857855 PMCID: PMC10587126 DOI: 10.1038/s42003-023-05254-7] [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: 05/18/2022] [Accepted: 08/17/2023] [Indexed: 10/21/2023] Open
Abstract
The physicochemical and structural properties of antimicrobial peptides (AMPs) determine their mechanism of action and biological function. However, the development of AMPs as therapeutic drugs has been traditionally limited by their toxicity for human cells. Tuning the physicochemical properties of such molecules may abolish toxicity and yield synthetic molecules displaying optimal safety profiles and enhanced antimicrobial activity. Here, natural peptides were modified to improve their activity by the hybridization of sequences from two different active peptide sequences. Hybrid AMPs (hAMPs) were generated by combining the amphipathic faces of the highly toxic peptide VmCT1, derived from scorpion venom, with parts of four other naturally occurring peptides having high antimicrobial activity and low toxicity against human cells. This strategy led to the design of seven synthetic bioactive variants, all of which preserved their structure and presented increased antimicrobial activity (3.1-128 μmol L-1). Five of the peptides (three being hAMPs) presented high antiplasmodial at 0.8 μmol L-1, and virtually no undesired toxic effects against red blood cells. In sum, we demonstrate that peptide hybridization is an effective strategy for redirecting biological activity to generate novel bioactive molecules with desired properties.
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Affiliation(s)
- Cibele Nicolaski Pedron
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
- Departamento de Bioquímica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Marcelo Der Torossian 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, 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
| | - Cyntia Silva Oliveira
- Departamento de Bioquímica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Adriana Farias Silva
- Departamento de Biofísica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Gislaine Patricia Andrade
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
- Departamento de Biofísica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil
| | - Yiming Wang
- 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
| | | | - Giselle Cerchiaro
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
| | | | - Fernanda Dias da Silva
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil
| | - Ravi Radhakrishnan
- 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
| | - 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.
| | - Vani Xavier Oliveira Junior
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil.
- Departamento de Bioquímica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil.
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Cafaro V, Bosso A, Di Nardo I, D’Amato A, Izzo I, De Riccardis F, Siepi M, Culurciello R, D’Urzo N, Chiarot E, Torre A, Pizzo E, Merola M, Notomista E. The Antimicrobial, Antibiofilm and Anti-Inflammatory Activities of P13#1, a Cathelicidin-like Achiral Peptoid. Pharmaceuticals (Basel) 2023; 16:1386. [PMID: 37895857 PMCID: PMC10610514 DOI: 10.3390/ph16101386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/22/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
Cationic antimicrobial peptides (CAMPs) are powerful molecules with antimicrobial, antibiofilm and endotoxin-scavenging activities. These properties make CAMPs very attractive drugs in the face of the rapid increase in multidrug-resistant (MDR) pathogens, but they are limited by their susceptibility to proteolytic degradation. An intriguing solution to this issue could be the development of functional mimics of CAMPs with structures that enable the evasion of proteases. Peptoids (N-substituted glycine oligomers) are an important class of peptidomimetics with interesting benefits: easy synthetic access, intrinsic proteolytic stability and promising bioactivities. Here, we report the characterization of P13#1, a 13-residue peptoid specifically designed to mimic cathelicidins, the best-known and most widespread family of CAMPs. P13#1 showed all the biological activities typically associated with cathelicidins: bactericidal activity over a wide spectrum of strains, including several ESKAPE pathogens; the ability to act in combination with different classes of conventional antibiotics; antibiofilm activity against preformed biofilms of Pseudomonas aeruginosa, comparable to that of human cathelicidin LL-37; limited toxicity; and an ability to inhibit LPS-induced proinflammatory effects which is comparable to that of "the last resource" antibiotic colistin. We further studied the interaction of P13#1 with SDS, LPSs and bacterial cells by using a fluorescent version of P13#1. Finally, in a subcutaneous infection mouse model, it showed antimicrobial and anti-inflammatory activities comparable to ampicillin and gentamicin without apparent toxicity. The collected data indicate that P13#1 is an excellent candidate for the formulation of new antimicrobial therapies.
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Affiliation(s)
- Valeria Cafaro
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | - Andrea Bosso
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | - Ilaria Di Nardo
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | - Assunta D’Amato
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, 84084 Fisciano, Italy; (A.D.); (I.I.); (F.D.R.)
| | - Irene Izzo
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, 84084 Fisciano, Italy; (A.D.); (I.I.); (F.D.R.)
| | - Francesco De Riccardis
- Department of Chemistry and Biology “A. Zambelli”, University of Salerno, 84084 Fisciano, Italy; (A.D.); (I.I.); (F.D.R.)
| | - Marialuisa Siepi
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | - Rosanna Culurciello
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | - Nunzia D’Urzo
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | | | | | - Elio Pizzo
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | - Marcello Merola
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
| | - Eugenio Notomista
- Department of Biology, University of Naples Federico II, 80126 Naples, Italy; (V.C.); (A.B.); (I.D.N.); (M.S.); (R.C.); (N.D.); (E.P.)
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32
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Santos-Júnior CD, Der Torossian Torres M, Duan Y, del Río ÁR, Schmidt TS, Chong H, Fullam A, Kuhn M, Zhu C, Houseman A, Somborski J, Vines A, Zhao XM, Bork P, Huerta-Cepas J, de la Fuente-Nunez C, Coelho LP. Computational exploration of the global microbiome for antibiotic discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.31.555663. [PMID: 37693522 PMCID: PMC10491242 DOI: 10.1101/2023.08.31.555663] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Marcelo Der Torossian 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
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Álvaro Rodríguez del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Thomas S.B. Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Hui Chong
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Chengkai Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Amy Houseman
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Jelena Somborski
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Anna Vines
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- International Human Phenome Institute, Shanghai, China
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - 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
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
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33
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Torres MDT, Brooks E, Cesaro A, Sberro H, Nicolaou C, Bhatt AS, de la Fuente-Nunez C. Human gut metagenomic mining reveals an untapped source of peptide antibiotics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.31.555711. [PMID: 37693399 PMCID: PMC10491270 DOI: 10.1101/2023.08.31.555711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally mined 444,054 families of putative small proteins from 1,773 human gut metagenomes, identifying 323 peptide antibiotics encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 59% displaying activity against either pathogens or commensals. Since these peptides were unique compared to previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergized with each other, and modulated gut commensals, indicating that they may play a role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. We report the discovery of hundreds of peptide sequences in the human gut.
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Affiliation(s)
- 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 19104, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
| | - Erin Brooks
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
| | - 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 19104, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
| | - Hila Sberro
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
| | - Cosmos Nicolaou
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
| | - Ami S. Bhatt
- Department of Medicine (Hematology; Blood and Marrow Transplantation), Stanford University, Stanford, CA, United States of America
- Department of Genetics, Stanford University, Stanford, CA, 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 19104, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States of America
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34
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Maasch JRMA, Torres MDT, Melo MCR, de la Fuente-Nunez C. Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. Cell Host Microbe 2023; 31:1260-1274.e6. [PMID: 37516110 DOI: 10.1016/j.chom.2023.07.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/12/2023] [Accepted: 07/06/2023] [Indexed: 07/31/2023]
Abstract
Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.
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Affiliation(s)
- Jacqueline R M A Maasch
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; 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 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - 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, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo C R Melo
- 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 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - 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 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
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35
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Wu J, Xu C, Ye Z, Chen H, Wang Y, Yang K, Yuan B. Transition between Different Diffusion Modes of Individual Lipids during the Membrane-Specific Action of As-CATH4 Peptides. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2301713. [PMID: 37093200 DOI: 10.1002/smll.202301713] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/02/2023] [Indexed: 05/03/2023]
Abstract
The cell membrane permeabilization ability of immune defense antimicrobial peptides (AMPs) is widely applied in biomedicine. Although the mechanisms of peptide-membrane interactions have been widely investigated, analyses at the molecular level are still lacking. Herein, the membrane-specific action of a native AMP, As-CATH4, is investigated using a single-lipid tracking method in combination with live cell and model membrane assays conducted at different scales. The peptide-membrane interaction process is characterized by analyzing single-lipid diffusion behaviors. As-CATH4 exhibits potent antimicrobial activity through bacterial membrane permeabilization, with moderate cytotoxicity against mammalian cells. In-plane diffusion analyses of individual lipids show that the lipid molecules exhibit non-Gaussian and heterogeneous diffusion behaviors in both pristine and peptide-treated membranes, which can be decomposed into two Gaussian subgroups corresponding to normal- and slow-diffusive lipids. Assessment of the temporal evolution of these two diffusion modes of lipids reveal that the peptide action states of As-CATH4 include surface binding, transmembrane defect formation, and dynamic equilibrium. The action mechanisms of As-CATH4 at varying concentrations and against different membranes are distinguished. This work resolves the simultaneous mixed diffusion mechanisms of single lipids in biomimetic cell membranes, especially during dynamic membrane permeabilization by AMPs.
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Affiliation(s)
- Jinfeng Wu
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, P. R. China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, P. R. China
| | - Cheng Xu
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, P. R. China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, P. R. China
| | - Zifan Ye
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, 215123, P. R. China
| | - Haibo Chen
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, P. R. China
| | - Yipeng Wang
- Department of Biopharmaceutical Sciences, College of Pharmaceutical Sciences, Soochow University, Suzhou, 215123, P. R. China
| | - Kai Yang
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, P. R. China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, P. R. China
| | - Bing Yuan
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, P. R. China
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Boaro A, Ageitos L, Torres MDT, Blasco EB, Oztekin S, de la Fuente-Nunez C. Structure-function-guided design of synthetic peptides with anti-infective activity derived from wasp venom. CELL REPORTS. PHYSICAL SCIENCE 2023; 4:101459. [PMID: 38239869 PMCID: PMC10795512 DOI: 10.1016/j.xcrp.2023.101459] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2024]
Abstract
Antimicrobial peptides (AMPs) derived from natural toxins and venoms offer a promising alternative source of antibiotics. Here, through structure-function-guided design, we convert two natural AMPs derived from the venom of the solitary eumenine wasp Eumenes micado into α-helical AMPs with reduced toxicity that kill Gram-negative bacteria in vitro and in a preclinical mouse model. To identify the sequence determinants conferring antimicrobial activity, an alanine scan screen and strategic single lysine substitutions are made to the amino acid sequence of these natural peptides. These efforts yield a total of 34 synthetic derivatives, including alanine substituted and lysine-substituted sequences with stabilized α-helical structures and increased net positive charge. The resulting lead synthetic peptides kill the Gram-negative pathogens Escherichia coli and Pseudomonas aeruginosa (PAO1 and PA14) by rapidly permeabilizing both their outer and cytoplasmic membranes, exhibit anti-infective efficacy in a mouse model by reducing bacterial loads by up to three orders of magnitude, and do not readily select for bacterial resistance.
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Affiliation(s)
- Andreia Boaro
- 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 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Present address: Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, São Paulo 09210-580, Brazil
- These authors contributed equally
| | - Lucía Ageitos
- 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 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Present address: CICA - Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15008 A Coruña, Spain
- These authors contributed equally
| | - Marcelo Der Torossian 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, PA 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Esther Broset Blasco
- 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 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sebahat Oztekin
- 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 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Present address: Faculty of Engineering, Department of Food Engineering, Bayburt University, Bayburt 69000, Turkey
| | - 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 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lead contact
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37
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Wong F, de la Fuente-Nunez C, Collins JJ. Leveraging artificial intelligence in the fight against infectious diseases. Science 2023; 381:164-170. [PMID: 37440620 PMCID: PMC10663167 DOI: 10.1126/science.adh1114] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/05/2023] [Indexed: 07/15/2023]
Abstract
Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.
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Affiliation(s)
- Felix Wong
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - 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 19104, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James J. Collins
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
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Fernandes FC, Cardoso MH, Gil-Ley A, Luchi LV, da Silva MGL, Macedo MLR, de la Fuente-Nunez C, Franco OL. Geometric deep learning as a potential tool for antimicrobial peptide prediction. FRONTIERS IN BIOINFORMATICS 2023; 3:1216362. [PMID: 37521317 PMCID: PMC10374423 DOI: 10.3389/fbinf.2023.1216362] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/13/2023] [Indexed: 08/01/2023] Open
Abstract
Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.
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Affiliation(s)
- Fabiano C. Fernandes
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- Departamento de Ciência da Computação, Instituto Federal de Brasília, Brasília, Brazil
| | - Marlon H. Cardoso
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Campo Grande, Mato Grosso do Sul, Brazil
| | - Abel Gil-Ley
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Lívia V. Luchi
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Maria G. L. da Silva
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
| | - Maria L. R. Macedo
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Campo Grande, Mato Grosso do Sul, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Perelman School of Medicine, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Octavio L. Franco
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
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Zhao L, Islam MS, Song P, Zhu L, Dong W. Isolation and Optimization of a Broad-Spectrum Synthetic Antimicrobial Peptide, Ap920-WI, from Arthrobacter sp. H5 for the Biological Control of Plant Diseases. Int J Mol Sci 2023; 24:10598. [PMID: 37445776 DOI: 10.3390/ijms241310598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/13/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Antimicrobial peptides (AMPs) are naturally occurring molecules found in various organisms that can help to defend against invading microorganisms and reduce the likelihood of drug resistance development. This study focused on the isolation of new AMPs from the genome library of a Gram-positive bacterium called Arthrobacter sp. H5. To achieve this, we used the Bacillus subtilis expression system and employed bioinformatics techniques to optimize and modify the peptides, resulting in the development of a new synthetic antimicrobial peptide (SAMP). Ap920 is expected to be a new antimicrobial peptide with a high positive charge (+12.5). Through optimization, a new synthetic antimicrobial peptide, Ap920-WI, containing only 15 amino acids, was created. Thereafter, the antimicrobial and antifungal activities of Ap920-WI were determined using minimum inhibitory concentration (MIC) and the concentration for 50% of maximal effect (EC50). The Ap920-WI peptide was observed to target the outer membrane of fungal hyphae, leading to inhibition of growth in Rhizoctonia Solani, Sclerotinia sclerotiorum, and Botrytis cinerea. In plants, Ap920-WI showed significant antifungal activity and inhibited the infestation of S. sclerotiorum on rape leaves. Importantly, Ap920-WI was found to be safe for mammalian cells since it did not show any hemolytic activity against sheep red blood cells. Overall, the study found that the new synthetic antimicrobial peptide Ap920-WI exhibits broad-spectrum activity against microorganisms and may offer a new solution for controlling plant diseases, as well as hold potential for drug development.
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Affiliation(s)
- Li Zhao
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease, Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan 430070, China
| | - Md Samiul Islam
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease, Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan 430070, China
| | - Pei Song
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease, Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhu
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease, Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan 430070, China
| | - Wubei Dong
- Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease, Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan 430070, China
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Wan F, de la Fuente-Nunez C. Mining for antimicrobial peptides in sequence space. Nat Biomed Eng 2023; 7:707-708. [PMID: 37095317 DOI: 10.1038/s41551-023-01027-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Affiliation(s)
- 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, 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
| | - 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.
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Li L. Accessing hidden microbial biosynthetic potential from underexplored sources for novel drug discovery. Biotechnol Adv 2023:108176. [PMID: 37211187 DOI: 10.1016/j.biotechadv.2023.108176] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/23/2023]
Abstract
Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently needed to be developed to combat the growing antimicrobial resistance and other public health problems. The advances in next-generation sequencing technologies and powerful computational tools open up new opportunities to explore microbial biosynthetic potential from underexplored sources, with millions of secondary metabolites awaiting discovery. The review highlights challenges associated with discovery of new chemical entities, rich reservoirs provided by untapped taxa, ecological niches or host microbiomes, emerging synthetic biotechnologies to unearth the hidden microbial biosynthetic potential for novel drug discovery at scale and speed.
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Affiliation(s)
- Lei Li
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China.
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Chen B, Zhang Z, Zhang Q, Xu N, Lu T, Wang T, Hong W, Fu Z, Penuelas J, Gillings M, Qian H. Antimicrobial Peptides in the Global Microbiome: Biosynthetic Genes and Resistance Determinants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:7698-7708. [PMID: 37161271 DOI: 10.1021/acs.est.3c01664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Antimicrobial peptides are a promising new class of antimicrobials that could address the antibiotic resistance crisis, which poses a major threat to human health. These peptides are present in all kingdoms of life, but especially in microorganisms, having multiple origins in diverse taxa. To date, there has been no global study on the diversity of antimicrobial peptides, the hosts in which these occur, and the potential for resistance to these agents. Here, we investigated the diversity and number of antimicrobial peptides in four main habitats (aquatic, terrestrial, human, and engineered) by analyzing 52,515 metagenome-assembled genomes. The number of antimicrobial peptides was higher in the human gut microbiome than in other habitats, and most hosts of antimicrobial peptides were habitat-specific. The relative abundance of genes that confer resistance to antimicrobial peptides varied between habitats and was generally low, except for the built environment and on human skin. The horizontal transfer of potential resistance genes among these habitats was probably constrained by ecological barriers. We systematically quantified the risk of each resistance determinant to human health and found that nearly half of them pose a threat, especially those that confer resistance to multiple AMPs and polymyxin B. Our results help identify the biosynthetic potential of antimicrobial peptides in the global microbiome, further identifying peptides with a low risk of developing resistance.
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Affiliation(s)
- Bingfeng Chen
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, P. R. China
| | - Zhenyan Zhang
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, P. R. China
| | - Qi Zhang
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, P. R. China
| | - Nuohan Xu
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, P. R. China
| | - Tao Lu
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, P. R. China
| | - Tingzhang Wang
- Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou 310012, P. R. China
| | - Wenjie Hong
- Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou 310012, P. R. China
| | - Zhengwei Fu
- College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou 310032, P. R. China
| | - Josep Penuelas
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona 08193, Catalonia, Spain
- CREAF, Campus Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona 08193, Catalonia, Spain
| | - Michael Gillings
- ARC Centre of Excellence in Synthetic Biology, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Haifeng Qian
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, P. R. China
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Cesaro A, Lin S, Pardi N, de la Fuente-Nunez C. Advanced delivery systems for peptide antibiotics. Adv Drug Deliv Rev 2023; 196:114733. [PMID: 36804008 PMCID: PMC10771258 DOI: 10.1016/j.addr.2023.114733] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 02/07/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
Antimicrobial peptides (AMPs) hold promise as alternatives to traditional antibiotics for preventing and treating multidrug-resistant infections. Although they have potent antimicrobial efficacy, AMPs are mainly limited by their susceptibility to proteases and potential off-site cytotoxicity. Designing the right delivery system for peptides can help to overcome such limitations, thus improving the pharmacokinetic and pharmacodynamic profiles of these drugs. The versatility of peptides and their genetically encodable structure make them suitable for both conventional and nucleoside-based formulations. In this review, we describe the main drug delivery procedures developed so far for peptide antibiotics: lipid nanoparticles, polymeric nanoparticles, hydrogels, functionalized surfaces, and DNA- and RNA-based delivery systems.
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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, PA, United States; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Shuangzhe Lin
- 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; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Norbert Pardi
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - 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; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States.
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Humpola MV, Spinelli R, Erben M, Perdomo V, Tonarelli GG, Albericio F, Siano AS. D- and N-Methyl Amino Acids for Modulating the Therapeutic Properties of Antimicrobial Peptides and Lipopeptides. Antibiotics (Basel) 2023; 12:antibiotics12050821. [PMID: 37237724 DOI: 10.3390/antibiotics12050821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
Here we designed and synthesized analogs of two antimicrobial peptides, namely C10:0-A2, a lipopeptide, and TA4, a cationic α-helical amphipathic peptide, and used non-proteinogenic amino acids to improve their therapeutic properties. The physicochemical properties of these analogs were analyzed, including their retention time, hydrophobicity, and critical micelle concentration, as well as their antimicrobial activity against gram-positive and gram-negative bacteria and yeast. Our results showed that substitution with D- and N-methyl amino acids could be a useful strategy to modulate the therapeutic properties of antimicrobial peptides and lipopeptides, including enhancing stability against enzymatic degradation. The study provides insights into the design and optimization of antimicrobial peptides to achieve improved stability and therapeutic efficacy. TA4(dK), C10:0-A2(6-NMeLys), and C10:0-A2(9-NMeLys) were identified as the most promising molecules for further studies.
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Affiliation(s)
- Maria Veronica Humpola
- Laboratorio de Péptidos Bioactivos, Departamento de Química Orgánica, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Santa Fe S3000ZAA, Argentina
| | - Roque Spinelli
- Laboratorio de Péptidos Bioactivos, Departamento de Química Orgánica, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Santa Fe S3000ZAA, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425FQB, Argentina
| | - Melina Erben
- Laboratorio de Péptidos Bioactivos, Departamento de Química Orgánica, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Santa Fe S3000ZAA, Argentina
| | - Virginia Perdomo
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425FQB, Argentina
- Área Parasitología, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Rosario S2002KTT, Argentina
| | - Georgina Guadalupe Tonarelli
- Laboratorio de Péptidos Bioactivos, Departamento de Química Orgánica, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Santa Fe S3000ZAA, Argentina
| | - Fernando Albericio
- School of Chemistry and Physics, University of KwaZulu-Natal, Durban 4001, South Africa
- Consorcio Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Networking Centre on Bioengineering, Biomaterials and Nanomedicine, Department of Organic Chemistry, University of Barcelona, 08028 Barcelona, Spain
| | - Alvaro Sebastian Siano
- Laboratorio de Péptidos Bioactivos, Departamento de Química Orgánica, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Santa Fe S3000ZAA, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425FQB, Argentina
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Yeh JC, Hazam PK, Hsieh CY, Hsu PH, Lin WC, Chen YR, Li CC, Chen JY. Rational Design of Stapled Antimicrobial Peptides to Enhance Stability and In Vivo Potency against Polymicrobial Sepsis. Microbiol Spectr 2023; 11:e0385322. [PMID: 36877022 PMCID: PMC10101059 DOI: 10.1128/spectrum.03853-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/17/2023] [Indexed: 03/07/2023] Open
Abstract
In this work, we sought to develop a TP4-based stapled peptide that can be used to counter polymicrobial sepsis. First, we segregated the TP4 sequence into hydrophobic and cationic/hydrophilic zones and substituted the preferred residue, lysine, as the sole cationic amino acid. These modifications minimized the intensity of cationic or hydrophobic characteristics within small segments. Then, we incorporated single or multiple staples into the peptide chain, bracketing the cationic/hydrophilic segments to improve pharmacological suitability. Using this approach, we were able to develop an AMP with low toxicity and notable in vivo efficacy. IMPORTANCE In our in vitro studies, one dual stapled peptide out of the series of candidates (TP4-3: FIIXKKSXGLFKKKAGAXKKKXIKK) showed significant activity, low toxicity, and high stability (in 50% human serum). When tested in cecal ligation and puncture (CLP) mouse models of polymicrobial sepsis, TP4-3 improved survival (87.5% on day 7). Furthermore, TP4-3 enhanced the activity of meropenem against polymicrobial sepsis (100% survival on day 7) compared to meropenem alone (37.5% survival on day 7). Molecules such as TP4-3 may be well suited for a wide variety of clinical applications.
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Affiliation(s)
- Jih-Chao Yeh
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, Jiaushi, Ilan, Taiwan
| | - Prakash Kishore Hazam
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, Jiaushi, Ilan, Taiwan
| | - Chu-Yi Hsieh
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, Jiaushi, Ilan, Taiwan
| | - Po-Hsien Hsu
- Institute of Fisheries Science, National Taiwan University, Taipei, Taiwan
| | - Wen-Chun Lin
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, Jiaushi, Ilan, Taiwan
| | - Yun-Ru Chen
- Academia Sinica Protein Clinic, Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Chao-Chin Li
- Institute of Cellular and Organismic Biology, Academia Sinica, Nankang, Taipei, Taiwan
| | - Jyh-Yih Chen
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, Jiaushi, Ilan, Taiwan
- The iEGG and Animal Biotechnology Center and the Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, Taiwan
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Hazam PK, Cheng CC, Lin WC, Hsieh CY, Hsu PH, Chen YR, Li CC, Hsueh PR, Chen JY. Strategic modification of low-activity natural antimicrobial peptides confers antibacterial potential in vitro and in vivo. Eur J Med Chem 2023; 249:115131. [PMID: 36669399 DOI: 10.1016/j.ejmech.2023.115131] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 01/09/2023] [Accepted: 01/15/2023] [Indexed: 01/19/2023]
Abstract
Antimicrobial peptides (AMPs) show great promise for clinical applications, but the utility of naturally occurring AMPs is often limited by their stability. Here, we used a rational design approach to improve the characteristics of a pair of inactive peptides, tilapia piscidin 1 and 2 (TP1 and TP2). From each starting peptide, we generated a series of novel derivatives by substituting residues to adjust cationic charge density, percent hydrophobicity and hydrophilicity/hydrophobicity coefficients. This approach yielded a novel peptide, TP2-5 (KKCIAKAILKKAKKLLKKLVNP), that exhibits significant bactericidal potency, low cytotoxicity and high stability. The designed peptide further showed antibiofilm activity, rapid antibacterial action and a low capacity to induce bacterial resistance. Importantly, we also demonstrated that TP2-5 can protect mice in a Vibrio vulnificus-infected wound model. Therefore, our peptide modification strategy successfully generated a novel AMP with high potential for future clinical application.
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Affiliation(s)
- Prakash Kishore Hazam
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, 23-10, Dahuen Rd., Jiaushi, Ilan, 262, Taiwan
| | - Chih-Cheng Cheng
- Institute of Fisheries Science, National Taiwan University, 1 Roosevelt Road, Sec. 4, Taipei, 106, Taiwan
| | - Wen-Chun Lin
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, 23-10, Dahuen Rd., Jiaushi, Ilan, 262, Taiwan
| | - Chu-Yi Hsieh
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, 23-10, Dahuen Rd., Jiaushi, Ilan, 262, Taiwan
| | - Po-Hsien Hsu
- Institute of Fisheries Science, National Taiwan University, 1 Roosevelt Road, Sec. 4, Taipei, 106, Taiwan
| | - Yun-Ru Chen
- Academia Sinica Protein Clinic, Institute of Biological Chemistry, Academia Sinica, 128, Academia Road, Section 2, Nankang District, Taipei, 115, Taiwan
| | - Chao-Chin Li
- Institute of Cellular and Organismic Biology, Academia Sinica, Nankang, Taipei, 115, Taiwan
| | - Po-Ren Hsueh
- Departments of Laboratory Medicine and Internal Medicine, China Medical University Hospital, School of Medicine, China Medical University, Taichung, Taiwan; PhD Program for Aging, School of Medicine, China Medical University, Taichung, Taiwan; Departments of Laboratory Medicine and Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jyh-Yih Chen
- Marine Research Station, Institute of Cellular and Organismic Biology, Academia Sinica, 23-10, Dahuen Rd., Jiaushi, Ilan, 262, Taiwan; The iEGG and Animal Biotechnology Center and the Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, 402, Taiwan.
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47
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Li T, Wang Z, Guo J, de la Fuente-Nunez C, Wang J, Han B, Tao H, Liu J, Wang X. Bacterial resistance to antibacterial agents: Mechanisms, control strategies, and implications for global health. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160461. [PMID: 36435256 DOI: 10.1016/j.scitotenv.2022.160461] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/19/2022] [Accepted: 11/20/2022] [Indexed: 06/16/2023]
Abstract
The spread of bacterial drug resistance has posed a severe threat to public health globally. Here, we cover bacterial resistance to current antibacterial drugs, including traditional herbal medicines, conventional antibiotics, and antimicrobial peptides. We summarize the influence of bacterial drug resistance on global health and its economic burden while highlighting the resistance mechanisms developed by bacteria. Based on the One Health concept, we propose 4A strategies to combat bacterial resistance, including prudent Application of antibacterial agents, Administration, Assays, and Alternatives to antibiotics. Finally, we identify several opportunities and unsolved questions warranting future exploration for combating bacterial resistance, such as predicting genetic bacterial resistance through the use of more effective techniques, surveying both genetic determinants of bacterial resistance and the transmission dynamics of antibiotic resistance genes (ARGs).
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Affiliation(s)
- Ting Li
- Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, PR China; State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, No. 20, Dongda Street, Fengtai District, Beijing 100071, PR China
| | - Zhenlong Wang
- Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, PR China
| | - Jianhua Guo
- Australian Centre for Water and Environmental Biotechnology (ACWEB, formerly AWMC), The University of Queensland, St Lucia, Queensland 4072, Australia.
| | - 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; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States of America.
| | - Jinquan Wang
- Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, PR China
| | - Bing Han
- Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, PR China
| | - Hui Tao
- Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, PR China
| | - Jie Liu
- Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, PR China
| | - Xiumin Wang
- Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China; Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, PR China.
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Yang N, Aminov R, Franco OL, de la Fuente-Nunez C, Wang J. Editorial: Community series in antimicrobial peptides: Molecular design, structure function relationship and biosynthesis optimization. Front Microbiol 2023; 14:1125426. [PMID: 36726373 PMCID: PMC9885265 DOI: 10.3389/fmicb.2023.1125426] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Affiliation(s)
- Na Yang
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Rustam Aminov
- The School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Octavio Luiz Franco
- S-Inova Biotech, Universidade Católica Dom Bosco, Campo Grande, MS, Brazil
- Centro de Análises Proteômicas e Bioquímicas Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil
| | - 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
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Jianhua Wang
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, China
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Huang J, Xu Y, Xue Y, Huang Y, Li X, Chen X, Xu Y, Zhang D, Zhang P, Zhao J, Ji J. Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. Nat Biomed Eng 2023:10.1038/s41551-022-00991-2. [PMID: 36635418 DOI: 10.1038/s41551-022-00991-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 11/29/2022] [Indexed: 01/14/2023]
Abstract
Systematically identifying functional peptides is difficult owing to the vast combinatorial space of peptide sequences. Here we report a machine-learning pipeline that mines the hundreds of billions of sequences in the entire virtual library of peptides made of 6-9 amino acids to identify potent antimicrobial peptides. The pipeline consists of trainable machine-learning modules (for performing empirical selection, classification, ranking and regression tasks) assembled sequentially following a coarse-to-fine design principle to gradually narrow down the search space. The leading three antimicrobial hexapeptides identified by the pipeline showed strong activities against a wide range of clinical isolates of multidrug-resistant pathogens. In mice with bacterial pneumonia, aerosolized formulations of the identified peptides showed therapeutic efficacy comparable to penicillin, negligible toxicity and a low propensity to induce drug resistance. The machine-learning pipeline may accelerate the discovery of new functional peptides.
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Affiliation(s)
- Junjie Huang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China.,International Research Center for X Polymers, International Campus, Zhejiang University, Haining, China
| | - Yanchao Xu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yunfan Xue
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China.,International Research Center for X Polymers, International Campus, Zhejiang University, Haining, China
| | - Yue Huang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China
| | - Xu Li
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China
| | - Xiaohui Chen
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China
| | - Yao Xu
- R&D Department of AtaGenix Laboratories Co., Ltd. (Wuhan), Wuhan, China
| | - Dongxiang Zhang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Peng Zhang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China. .,International Research Center for X Polymers, International Campus, Zhejiang University, Haining, China.
| | - Junbo Zhao
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
| | - Jian Ji
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China. .,International Research Center for X Polymers, International Campus, Zhejiang University, Haining, China.
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50
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Feng W, Chittò M, Moriarty TF, Li G, Wang X. Targeted Drug Delivery Systems for Eliminating Intracellular Bacteria. Macromol Biosci 2023; 23:e2200311. [PMID: 36189899 DOI: 10.1002/mabi.202200311] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/08/2022] [Indexed: 01/19/2023]
Abstract
The intracellular survival of pathogenic bacteria requires a range of survival strategies and virulence factors. These infections are a significant clinical challenge, wherein treatment frequently fails because of poor antibiotic penetration, stability, and retention in host cells. Drug delivery systems (DDSs) are promising tools to overcome these shortcomings and enhance the efficacy of antibiotic therapy. In this review, the classification and the mechanisms of intracellular bacterial persistence are elaborated. Furthermore, the systematic design strategies applied to DDSs to eliminate intracellular bacteria are also described, and the strategies used for internalization, intracellular activation, bacterial targeting, and immune enhancement are highlighted. Finally, this overview provides guidance for constructing functionalized DDSs to effectively eliminate intracellular bacteria.
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Affiliation(s)
- Wenli Feng
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.,AO Research Institute Davos, Davos, 7270, Switzerland
| | - Marco Chittò
- AO Research Institute Davos, Davos, 7270, Switzerland
| | | | - Guofeng Li
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
| | - Xing Wang
- State Key Laboratory of Organic-Inorganic Composites, Beijing Laboratory of Biomedical Materials, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
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