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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. [PMID: 38651516 DOI: 10.1039/d4np00002a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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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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Coelho LP, Santos-Júnior CD, de la Fuente-Nunez C. Challenges in computational discovery of bioactive peptides in 'omics data. Proteomics 2024:e2300105. [PMID: 38458994 DOI: 10.1002/pmic.202300105] [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: 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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de la Fuente-Nunez C. AI in infectious diseases: The role of datasets. Drug Resist Updat 2024; 73:101067. [PMID: 38387282 DOI: 10.1016/j.drup.2024.101067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Affiliation(s)
- Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States of America; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States of America.
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Kamel M, Aleya S, Alsubih M, Aleya L. Microbiome Dynamics: A Paradigm Shift in Combatting Infectious Diseases. J Pers Med 2024; 14:217. [PMID: 38392650 PMCID: PMC10890469 DOI: 10.3390/jpm14020217] [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: 12/26/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 02/24/2024] Open
Abstract
Infectious diseases have long posed a significant threat to global health and require constant innovation in treatment approaches. However, recent groundbreaking research has shed light on a previously overlooked player in the pathogenesis of disease-the human microbiome. This review article addresses the intricate relationship between the microbiome and infectious diseases and unravels its role as a crucial mediator of host-pathogen interactions. We explore the remarkable potential of harnessing this dynamic ecosystem to develop innovative treatment strategies that could revolutionize the management of infectious diseases. By exploring the latest advances and emerging trends, this review aims to provide a new perspective on combating infectious diseases by targeting the microbiome.
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Affiliation(s)
- Mohamed Kamel
- Department of Medicine and Infectious Diseases, Faculty of Veterinary Medicine, Cairo University, Giza 11221, Egypt
| | - Sami Aleya
- Faculty of Medecine, Université de Bourgogne Franche-Comté, Hauts-du-Chazal, 25030 Besançon, France
| | - Majed Alsubih
- Department of Civil Engineering, King Khalid University, Guraiger, Abha 62529, Saudi Arabia
| | - Lotfi Aleya
- Laboratoire de Chrono-Environnement, Université de Bourgogne Franche-Comté, UMR CNRS 6249, La Bouloie, 25030 Besançon, France
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Lee YJ. Examining the functional space of gut microbiome-derived peptides. Microbiologyopen 2023; 12:e1393. [PMID: 38129980 PMCID: PMC10714122 DOI: 10.1002/mbo3.1393] [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: 09/07/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
The human gut microbiome contains thousands of small, novel peptides that could play a role in microbe-microbe and host-microbe interactions, contributing to human health and disease. Although these peptides have not yet been systematically characterized, computational tools can be used to elucidate the bioactivities they may have. This article proposes probing the functional space of gut microbiome-derived peptides (MDPs) using in silico approaches for three bioactivities: antimicrobial, anticancer, and nucleomodulins. Machine learning programs that support peptide and protein queries are provided for each bioactivity. Considering the biases of an activity-centric approach, activity-agnostic tools using structural and chemical similarity and target prediction are also described. Gut MDPs represent a vast functional space that can not only contribute to our understanding of microbiome interactions but potentially even serve as a source of life-changing therapeutics.
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Affiliation(s)
- Ying‐Chiang J. Lee
- Department of Molecular BiologyPrinceton UniversityPrincetonNew JerseyUSA
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Coxe T, Azad RK. Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance. Antibiotics (Basel) 2023; 12:1604. [PMID: 37998806 PMCID: PMC10669088 DOI: 10.3390/antibiotics12111604] [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: 09/30/2023] [Revised: 10/30/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023] Open
Abstract
In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance.
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Affiliation(s)
- Tallon Coxe
- Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA;
- BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA
| | - Rajeev K. Azad
- Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA;
- BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA
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de la Fuente-Nunez C, Cesaro A, Hancock REW. Antibiotic failure: Beyond antimicrobial resistance. Drug Resist Updat 2023; 71:101012. [PMID: 37924726 DOI: 10.1016/j.drup.2023.101012] [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: 07/31/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/06/2023]
Abstract
Despite significant progress in antibiotic discovery, millions of lives are lost annually to infections. Surprisingly, the failure of antimicrobial treatments to effectively eliminate pathogens frequently cannot be attributed to genetically-encoded antibiotic resistance. This review aims to shed light on the fundamental mechanisms contributing to clinical scenarios where antimicrobial therapies are ineffective (i.e., antibiotic failure), emphasizing critical factors impacting this under-recognized issue. Explored aspects include biofilm formation and sepsis, as well as the underlying microbiome. Therapeutic strategies beyond antibiotics, are examined to address the dimensions and resolution of antibiotic failure, actively contributing to this persistent but escalating crisis. We discuss the clinical relevance of antibiotic failure beyond resistance, limited availability of therapies, potential of new antibiotics to be ineffective, and the urgent need for novel anti-infectives or host-directed therapies directly addressing antibiotic failure. Particularly noteworthy is multidrug adaptive resistance in biofilms that represent 65 % of infections, due to the lack of approved therapies. Sepsis, responsible for 19.7 % of all deaths (as well as severe COVID-19 deaths), is a further manifestation of this issue, since antibiotics are the primary frontline therapy, and yet 23 % of patients succumb to this condition.
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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, 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.
| | - 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, 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
| | - Robert E W Hancock
- Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, Canada.
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