1
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Bucataru C, Ciobanasu C. Antimicrobial peptides: Opportunities and challenges in overcoming resistance. Microbiol Res 2024; 286:127822. [PMID: 38986182 DOI: 10.1016/j.micres.2024.127822] [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: 04/09/2024] [Revised: 06/20/2024] [Accepted: 06/25/2024] [Indexed: 07/12/2024]
Abstract
Antibiotic resistance represents a global health threat, challenging the efficacy of traditional antimicrobial agents and necessitating innovative approaches to combat infectious diseases. Among these alternatives, antimicrobial peptides have emerged as promising candidates against resistant pathogens. Unlike traditional antibiotics with only one target, these peptides can use different mechanisms to destroy bacteria, with low toxicity to mammalian cells compared to many conventional antibiotics. Antimicrobial peptides (AMPs) have encouraging antibacterial properties and are currently employed in the clinical treatment of pathogen infection, cancer, wound healing, cosmetics, or biotechnology. This review summarizes the mechanisms of antimicrobial peptides against bacteria, discusses the mechanisms of drug resistance, the limitations and challenges of AMPs in peptide drug applications for combating drug-resistant bacterial infections, and strategies to enhance their capabilities.
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Affiliation(s)
- Cezara Bucataru
- Alexandru I. Cuza University, Institute of Interdisciplinary Research, Department of Exact and Natural Sciences, Bulevardul Carol I, Nr.11, Iasi 700506, Romania
| | - Corina Ciobanasu
- Alexandru I. Cuza University, Institute of Interdisciplinary Research, Department of Exact and Natural Sciences, Bulevardul Carol I, Nr.11, Iasi 700506, Romania.
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2
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Du J, Yang C, Deng Y, Guo H, Gu M, Chen D, Liu X, Huang J, Yan W, Liu J. Discovery of AMPs from random peptides via deep learning-based model and biological activity validation. Eur J Med Chem 2024; 277:116797. [PMID: 39197254 DOI: 10.1016/j.ejmech.2024.116797] [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: 06/24/2024] [Revised: 07/31/2024] [Accepted: 08/22/2024] [Indexed: 09/01/2024]
Abstract
The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent advances in artificial intelligence (AI) have significantly improved the efficiency of identifying antimicrobial peptides from large libraries, whereas using random peptides as negative data increases the difficulty of discovering antimicrobial peptides from random peptides using discriminative models. In this study, we constructed three multi-discriminator models using deep learning and successfully screened twelve AMPs from a library of 30,000 random peptides. three candidate peptides (P2, P11, and P12) were screened by antimicrobial experiments, and further experiments showed that they not only possessed excellent antimicrobial activity but also had extremely low hemolytic activity. Mechanistic studies showed that these peptides exerted their bactericidal effects through membrane disruption, thus reducing the possibility of bacterial resistance. Notably, peptide 12 (P12) showed significant efficacy in a mouse model of Staphylococcus aureus wound infection with low toxicity to major organs at the highest tested dose (400 mg/kg). These results suggest deep learning-based multi-discriminator models can identify AMPs from random peptides with potential clinical applications.
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Affiliation(s)
- Jun Du
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China; Gansu Provincial Maternity and Child Care Hospital, North Road 143, Qilihe District, Lanzhou, 730000, China
| | - Changyan Yang
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China; Gansu Provincial Maternity and Child Care Hospital, North Road 143, Qilihe District, Lanzhou, 730000, China
| | - Yabo Deng
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China
| | - Hai Guo
- The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730000, China
| | - Mengyun Gu
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China
| | - Danna Chen
- Department of Hematology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China
| | - Xia Liu
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China.
| | - Jinqi Huang
- Department of Hematology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
| | - Wenjin Yan
- School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China.
| | - Jian Liu
- Gansu Provincial Maternity and Child Care Hospital, North Road 143, Qilihe District, Lanzhou, 730000, China.
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3
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de la Lastra JMP, Wardell SJT, Pal T, de la Fuente-Nunez C, Pletzer D. From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review. J Med Syst 2024; 48:71. [PMID: 39088151 PMCID: PMC11294375 DOI: 10.1007/s10916-024-02089-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/12/2024] [Indexed: 08/02/2024]
Abstract
The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.
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Affiliation(s)
- José M Pérez de la Lastra
- Biotechnology of Macromolecules, Instituto de Productos Naturales y Agrobiología, IPNA (CSIC), Avda. Astrofísico Francisco Sánchez, 3, 38206, San Cristóbal de la Laguna, (Santa Cruz de Tenerife), Spain.
| | - Samuel J T Wardell
- Department of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, 9054, Dunedin, New Zealand
| | - Tarun Pal
- School of Bioengineering and Food Technology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, 173229, Himachal Pradesh, India
| | - 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
| | - Daniel Pletzer
- Department of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, 9054, Dunedin, New Zealand.
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4
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Xu Y, Ma S, Cui H, Chen J, Xu S, Gong F, Golubovic A, Zhou M, Wang KC, Varley A, Lu RXZ, Wang B, Li B. AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery. Nat Commun 2024; 15:6305. [PMID: 39060305 PMCID: PMC11282250 DOI: 10.1038/s41467-024-50619-z] [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: 01/29/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE's potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.
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Affiliation(s)
- Yue Xu
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Shihao Ma
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Haotian Cui
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
| | - Jingan Chen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Shufen Xu
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Fanglin Gong
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Alex Golubovic
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Muye Zhou
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Kevin Chang Wang
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Andrew Varley
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Rick Xing Ze Lu
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Bo Wang
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada.
- Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada.
- Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada.
| | - Bowen Li
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada.
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
- Department of Chemistry, University of Toronto, Toronto, ON, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
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5
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Zeng P, Wang H, Zhang P, Leung SSY. Unearthing naturally-occurring cyclic antibacterial peptides and their structural optimization strategies. Biotechnol Adv 2024; 73:108371. [PMID: 38704105 DOI: 10.1016/j.biotechadv.2024.108371] [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: 11/10/2023] [Revised: 03/08/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
Natural products with antibacterial activity are highly desired globally to combat against multidrug-resistant (MDR) bacteria. Antibacterial peptide (ABP), especially cyclic ABP (CABP), is one of the abundant classes. Most of them were isolated from microbes, demonstrating excellent bactericidal effects. With the improved proteolytic stability, CABPs are normally considered to have better druggability than linear peptides. However, most clinically-used CABP-based antibiotics, such as colistin, also face the challenges of drug resistance soon after they reached the market, urgently requiring the development of next-generation succedaneums. We present here a detail review on the novel naturally-occurring CABPs discovered in the past decade and some of them are under clinical trials, exhibiting anticipated application potential. According to their chemical structures, they were broadly classified into five groups, including (i) lactam/lactone-based CABPs, (ii) cyclic lipopeptides, (iii) glycopeptides, (iv) cyclic sulfur-rich peptides and (v) multiple-modified CABPs. Their chemical structures, antibacterial spectrums and proposed mechanisms are discussed. Moreover, engineered analogs of these novel CABPs are also summarized to preliminarily analyze their structure-activity relationship. This review aims to provide a global perspective on research and development of novel CABPs to highlight the effectiveness of derivatives design in identifying promising antibacterial agents. Further research efforts in this area are believed to play important roles in fighting against the multidrug-resistance crisis.
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Affiliation(s)
- Ping Zeng
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Honglan Wang
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Pengfei Zhang
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Sharon Shui Yee Leung
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong.
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6
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Panjla A, Joshi S, Singh G, Bamford SE, Mechler A, Verma S. Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance. Chem Asian J 2024:e202400102. [PMID: 38948939 DOI: 10.1002/asia.202400102] [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: 01/30/2024] [Revised: 06/30/2024] [Accepted: 07/01/2024] [Indexed: 07/02/2024]
Abstract
Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.
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Affiliation(s)
- Apurva Panjla
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
| | - Saurabh Joshi
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
| | - Geetanjali Singh
- Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
| | - Sarah E Bamford
- Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - Adam Mechler
- Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - Sandeep Verma
- Mehta Family Center for Engineering in Medicine, Center for Nanoscience, Gangwal School of Medical Sciences and Technology, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India
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7
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Zhang X, Liu P, Zhang R, Zheng W, Qin D, Liu Y, Wang X, Sun T, Gao Y, Li LL. Action Programmed Nanoantibiotics with pH-Induced Collapse and Negative-Charged-Surface-Induced Deformation against Antibiotic-Resistant Bacterial Peritonitis. Adv Healthc Mater 2024:e2401470. [PMID: 38924797 DOI: 10.1002/adhm.202401470] [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: 04/22/2024] [Revised: 06/19/2024] [Indexed: 06/28/2024]
Abstract
The incorporation of well-designed antibiotic nanocarriers, along with an antibiotic adjuvant effect, in combination with various antibiotics, offers an opportunity to combat drug-resistant strains. However, precise control over morphology and encapsulated payload release can significantly impact their antibacterial efficacy and synergistic effects when used alongside antibiotics. Here, this study focuses on developing lipopeptide-based nanoantibiotics, which demonstrate an antibiotic adjuvant effect by inducing pH-induced collapse and negative-charged-surface-induced deformation. This enhances the disruption of the bacterial outer membrane and facilitates drug penetration, effectively boosting the antimicrobial activity against drug-resistant strains. The modulation regulations of the lipopeptide nanocarriers with modular design are governed by the authors. The nanoantibiotics, made from lipopeptide and ciprofloxacin (Cip), have a drug loading efficiency of over 80%. The combination with Cip results in a significantly low fractional inhibitory concentration index of 0.375 and a remarkable reduction in the minimum inhibitory concentration of Cip against multidrug-resistant (MDR) Escherichia coli (clinical isolated strains) by up to 32-fold. The survival rate of MDR E. coli peritonitis treated with nanoantibiotics is significantly higher, reaching over 87%, compared to only 25% for Cip and no survival for the control group. Meanwhile, the nanoantibiotic shows no obvious toxicity to major organs.
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Affiliation(s)
- Xiao Zhang
- School of Pharmacy, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
| | - Penghui Liu
- School of Bioscience and Technology, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology (NCNST), No. 11 Beiyitiao, Zhongguancun, Beijing, 100190, P. R. China
| | - Ran Zhang
- School of Pharmacy, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
| | - Wenhong Zheng
- School of Bioscience and Technology, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology (NCNST), No. 11 Beiyitiao, Zhongguancun, Beijing, 100190, P. R. China
| | - Di Qin
- School of Bioscience and Technology, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
| | - Yinghang Liu
- School of Pharmacy, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
| | - Xin Wang
- School of Bioscience and Technology, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology (NCNST), No. 11 Beiyitiao, Zhongguancun, Beijing, 100190, P. R. China
| | - Tongyi Sun
- School of Bioscience and Technology, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
| | - Yuanyuan Gao
- School of Pharmacy, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
| | - Li-Li Li
- School of Bioscience and Technology, Shandong Second Medical University, Weifang, Shandong, 261053, P. R. China
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology (NCNST), No. 11 Beiyitiao, Zhongguancun, Beijing, 100190, P. R. China
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8
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Groover KE, Randall JR, Davies BW. Development of a Selective and Stable Antimicrobial Peptide. ACS Infect Dis 2024; 10:2151-2160. [PMID: 38712889 PMCID: PMC11185160 DOI: 10.1021/acsinfecdis.4c00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/17/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
Antimicrobial peptides (AMPs) are presented as potential scaffolds for antibiotic development due to their desirable qualities including broad-spectrum activity, rapid action, and general lack of susceptibility to current resistance mechanisms. However, they often lose antibacterial activity under physiological conditions and/or display mammalian cell toxicity, which limits their potential use. Identification of AMPs that overcome these barriers will help develop rules for how this antibacterial class can be developed to treat infection. Here we describe the development of our novel synthetic AMP, from discovery through in vivo application. Our evolved AMP, DTr18-dab, has broad-spectrum antibacterial activity and is nonhemolytic. It is active against planktonic bacteria and biofilm, is unaffected by colistin resistance, and importantly is active in both human serum and a Galleria mellonella infection model. Several modifications, including the incorporation of noncanonical amino acids, were used to arrive at this robust sequence. We observed that the impact on antibacterial activity with noncanonical amino acids was dependent on assay conditions and therefore not entirely predictable. Overall, our results demonstrate how a relatively weak lead can be developed into a robust AMP with qualities important for potential therapeutic translation.
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Affiliation(s)
- Kyra E. Groover
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
| | - Justin R. Randall
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
| | - Bryan W. Davies
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
- John
Ring LaMontagne Center for Infectious Diseases, The University of Texas at Austin, Austin, Texas 78712, United States
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9
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Cheng Z, Aitha M, Thomas CA, Sturgill A, Fairweather M, Hu A, Bethel CR, Rivera DD, Dranchak P, Thomas PW, Li H, Feng Q, Tao K, Song M, Sun N, Wang S, Silwal SB, Page RC, Fast W, Bonomo RA, Weese M, Martinez W, Inglese J, Crowder MW. Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase. J Chem Inf Model 2024; 64:3977-3991. [PMID: 38727192 PMCID: PMC11129921 DOI: 10.1021/acs.jcim.3c02015] [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] [Indexed: 05/13/2024]
Abstract
The worldwide spread of the metallo-β-lactamases (MBL), especially New Delhi metallo-β-lactamase-1 (NDM-1), is threatening the efficacy of β-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV-vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4H-pyrido[1,2-a]pyrimidin-4-one.
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Affiliation(s)
- Zishuo Cheng
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Mahesh Aitha
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Caitlyn A. Thomas
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Aidan Sturgill
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Mitch Fairweather
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Amy Hu
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Christopher R. Bethel
- Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106, USA
| | - Dann D. Rivera
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, University of Texas, Austin, TX 78712, USA
| | - Patricia Dranchak
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
| | - Pei W. Thomas
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, University of Texas, Austin, TX 78712, USA
| | - Han Li
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Qi Feng
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Kaicheng Tao
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Minshuai Song
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Na Sun
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Shuo Wang
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | | | - Richard C. Page
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Walt Fast
- Division of Chemical Biology and Medicinal Chemistry, College of Pharmacy, University of Texas, Austin, TX 78712, USA
| | - Robert A. Bonomo
- Research Service, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106, USA
- Departments of Medicine, Biochemistry, Molecular Biology and Microbiology, Pharmacology, and Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Clinician Scientist Investigator, Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106, USA
- CWRU-Cleveland VAMC Center for Antimicrobial Resistance and Epidemiology (Case VA CARES) Cleveland, OH 44106, USA
| | - Maria Weese
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - Waldyn Martinez
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
| | - James Inglese
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA
- Metabolic Medicine Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817, USA
| | - Michael W. Crowder
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA
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10
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Aguilera-Puga MDC, Plisson F. Structure-aware machine learning strategies for antimicrobial peptide discovery. Sci Rep 2024; 14:11995. [PMID: 38796582 PMCID: PMC11127937 DOI: 10.1038/s41598-024-62419-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024] Open
Abstract
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico
| | - Fabien Plisson
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico.
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Ralhan K, Iyer KA, Diaz LL, Bird R, Maind A, Zhou QA. Navigating Antibacterial Frontiers: A Panoramic Exploration of Antibacterial Landscapes, Resistance Mechanisms, and Emerging Therapeutic Strategies. ACS Infect Dis 2024; 10:1483-1519. [PMID: 38691668 PMCID: PMC11091902 DOI: 10.1021/acsinfecdis.4c00115] [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: 02/10/2024] [Revised: 04/11/2024] [Accepted: 04/15/2024] [Indexed: 05/03/2024]
Abstract
The development of effective antibacterial solutions has become paramount in maintaining global health in this era of increasing bacterial threats and rampant antibiotic resistance. Traditional antibiotics have played a significant role in combating bacterial infections throughout history. However, the emergence of novel resistant strains necessitates constant innovation in antibacterial research. We have analyzed the data on antibacterials from the CAS Content Collection, the largest human-curated collection of published scientific knowledge, which has proven valuable for quantitative analysis of global scientific knowledge. Our analysis focuses on mining the CAS Content Collection data for recent publications (since 2012). This article aims to explore the intricate landscape of antibacterial research while reviewing the advancement from traditional antibiotics to novel and emerging antibacterial strategies. By delving into the resistance mechanisms, this paper highlights the need to find alternate strategies to address the growing concern.
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Affiliation(s)
| | | | - Leilani Lotti Diaz
- CAS,
A Division of the American Chemical Society, Columbus, Ohio 43210, United States
| | - Robert Bird
- CAS,
A Division of the American Chemical Society, Columbus, Ohio 43210, United States
| | - Ankush Maind
- ACS
International India Pvt. Ltd., Pune 411044, India
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12
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Carini C, Seyhan AA. Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med 2024; 22:411. [PMID: 38702711 PMCID: PMC11069149 DOI: 10.1186/s12967-024-05067-0] [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: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 05/06/2024] Open
Abstract
Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.
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Affiliation(s)
- Claudio Carini
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, New Hunt's House, King's College London, Guy's Campus, London, UK.
- Biomarkers Consortium, Foundation of the National Institute of Health, Bethesda, MD, USA.
| | - Attila A Seyhan
- Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Joint Program in Cancer Biology, Lifespan Health System and Brown University, Providence, RI, USA.
- Legorreta Cancer Center at Brown University, Providence, RI, USA.
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [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: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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14
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Wallach I, Bernard D, Nguyen K, Ho G, Morrison A, Stecula A, Rosnik A, O’Sullivan AM, Davtyan A, Samudio B, Thomas B, Worley B, Butler B, Laggner C, Thayer D, Moharreri E, Friedland G, Truong H, van den Bedem H, Ng HL, Stafford K, Sarangapani K, Giesler K, Ngo L, Mysinger M, Ahmed M, Anthis NJ, Henriksen N, Gniewek P, Eckert S, de Oliveira S, Suterwala S, PrasadPrasad SVK, Shek S, Contreras S, Hare S, Palazzo T, O’Brien TE, Van Grack T, Williams T, Chern TR, Kenyon V, Lee AH, Cann AB, Bergman B, Anderson BM, Cox BD, Warrington JM, Sorenson JM, Goldenberg JM, Young MA, DeHaan N, Pemberton RP, Schroedl S, Abramyan TM, Gupta T, Mysore V, Presser AG, Ferrando AA, Andricopulo AD, Ghosh A, Ayachi AG, Mushtaq A, Shaqra AM, Toh AKL, Smrcka AV, Ciccia A, de Oliveira AS, Sverzhinsky A, de Sousa AM, Agoulnik AI, Kushnir A, Freiberg AN, Statsyuk AV, Gingras AR, Degterev A, Tomilov A, Vrielink A, Garaeva AA, Bryant-Friedrich A, Caflisch A, Patel AK, Rangarajan AV, Matheeussen A, Battistoni A, Caporali A, Chini A, Ilari A, Mattevi A, Foote AT, Trabocchi A, Stahl A, Herr AB, Berti A, Freywald A, Reidenbach AG, Lam A, Cuddihy AR, White A, Taglialatela A, Ojha AK, Cathcart AM, Motyl AAL, Borowska A, D’Antuono A, Hirsch AKH, Porcelli AM, Minakova A, Montanaro A, Müller A, Fiorillo A, Virtanen A, O’Donoghue AJ, Del Rio Flores A, Garmendia AE, Pineda-Lucena A, Panganiban AT, Samantha A, Chatterjee AK, Haas AL, Paparella AS, John ALS, Prince A, ElSheikh A, Apfel AM, Colomba A, O’Dea A, Diallo BN, Ribeiro BMRM, Bailey-Elkin BA, Edelman BL, Liou B, Perry B, Chua BSK, Kováts B, Englinger B, Balakrishnan B, Gong B, Agianian B, Pressly B, Salas BPM, Duggan BM, Geisbrecht BV, Dymock BW, Morten BC, Hammock BD, Mota BEF, Dickinson BC, Fraser C, Lempicki C, Novina CD, Torner C, Ballatore C, Bon C, Chapman CJ, Partch CL, Chaton CT, Huang C, Yang CY, Kahler CM, Karan C, Keller C, Dieck CL, Huimei C, Liu C, Peltier C, Mantri CK, Kemet CM, Müller CE, Weber C, Zeina CM, Muli CS, Morisseau C, Alkan C, Reglero C, Loy CA, Wilson CM, Myhr C, Arrigoni C, Paulino C, Santiago C, Luo D, Tumes DJ, Keedy DA, Lawrence DA, Chen D, Manor D, Trader DJ, Hildeman DA, Drewry DH, Dowling DJ, Hosfield DJ, Smith DM, Moreira D, Siderovski DP, Shum D, Krist DT, Riches DWH, Ferraris DM, Anderson DH, Coombe DR, Welsbie DS, Hu D, Ortiz D, Alramadhani D, Zhang D, Chaudhuri D, Slotboom DJ, Ronning DR, Lee D, Dirksen D, Shoue DA, Zochodne DW, Krishnamurthy D, Duncan D, Glubb DM, Gelardi ELM, Hsiao EC, Lynn EG, Silva EB, Aguilera E, Lenci E, Abraham ET, Lama E, Mameli E, Leung E, Christensen EM, Mason ER, Petretto E, Trakhtenberg EF, Rubin EJ, Strauss E, Thompson EW, Cione E, Lisabeth EM, Fan E, Kroon EG, Jo E, García-Cuesta EM, Glukhov E, Gavathiotis E, Yu F, Xiang F, Leng F, Wang F, Ingoglia F, van den Akker F, Borriello F, Vizeacoumar FJ, Luh F, Buckner FS, Vizeacoumar FS, Bdira FB, Svensson F, Rodriguez GM, Bognár G, Lembo G, Zhang G, Dempsey G, Eitzen G, Mayer G, Greene GL, Garcia GA, Lukacs GL, Prikler G, Parico GCG, Colotti G, De Keulenaer G, Cortopassi G, Roti G, Girolimetti G, Fiermonte G, Gasparre G, Leuzzi G, Dahal G, Michlewski G, Conn GL, Stuchbury GD, Bowman GR, Popowicz GM, Veit G, de Souza GE, Akk G, Caljon G, Alvarez G, Rucinski G, Lee G, Cildir G, Li H, Breton HE, Jafar-Nejad H, Zhou H, Moore HP, Tilford H, Yuan H, Shim H, Wulff H, Hoppe H, Chaytow H, Tam HK, Van Remmen H, Xu H, Debonsi HM, Lieberman HB, Jung H, Fan HY, Feng H, Zhou H, Kim HJ, Greig IR, Caliandro I, Corvo I, Arozarena I, Mungrue IN, Verhamme IM, Qureshi IA, Lotsaris I, Cakir I, Perry JJP, Kwiatkowski J, Boorman J, Ferreira J, Fries J, Kratz JM, Miner J, Siqueira-Neto JL, Granneman JG, Ng J, Shorter J, Voss JH, Gebauer JM, Chuah J, Mousa JJ, Maynes JT, Evans JD, Dickhout J, MacKeigan JP, Jossart JN, Zhou J, Lin J, Xu J, Wang J, Zhu J, Liao J, Xu J, Zhao J, Lin J, Lee J, Reis J, Stetefeld J, Bruning JB, Bruning JB, Coles JG, Tanner JJ, Pascal JM, So J, Pederick JL, Costoya JA, Rayman JB, Maciag JJ, Nasburg JA, Gruber JJ, Finkelstein JM, Watkins J, Rodríguez-Frade JM, Arias JAS, Lasarte JJ, Oyarzabal J, Milosavljevic J, Cools J, Lescar J, Bogomolovas J, Wang J, Kee JM, Kee JM, Liao J, Sistla JC, Abrahão JS, Sishtla K, Francisco KR, Hansen KB, Molyneaux KA, Cunningham KA, Martin KR, Gadar K, Ojo KK, Wong KS, Wentworth KL, Lai K, Lobb KA, Hopkins KM, Parang K, Machaca K, Pham K, Ghilarducci K, Sugamori KS, McManus KJ, Musta K, Faller KME, Nagamori K, Mostert KJ, Korotkov KV, Liu K, Smith KS, Sarosiek K, Rohde KH, Kim KK, Lee KH, Pusztai L, Lehtiö L, Haupt LM, Cowen LE, Byrne LJ, Su L, Wert-Lamas L, Puchades-Carrasco L, Chen L, Malkas LH, Zhuo L, Hedstrom L, Hedstrom L, Walensky LD, Antonelli L, Iommarini L, Whitesell L, Randall LM, Fathallah MD, Nagai MH, Kilkenny ML, Ben-Johny M, Lussier MP, Windisch MP, Lolicato M, Lolli ML, Vleminckx M, Caroleo MC, Macias MJ, Valli M, Barghash MM, Mellado M, Tye MA, Wilson MA, Hannink M, Ashton MR, Cerna MVC, Giorgis M, Safo MK, Maurice MS, McDowell MA, Pasquali M, Mehedi M, Serafim MSM, Soellner MB, Alteen MG, Champion MM, Skorodinsky M, O’Mara ML, Bedi M, Rizzi M, Levin M, Mowat M, Jackson MR, Paige M, Al-Yozbaki M, Giardini MA, Maksimainen MM, De Luise M, Hussain MS, Christodoulides M, Stec N, Zelinskaya N, Van Pelt N, Merrill NM, Singh N, Kootstra NA, Singh N, Gandhi NS, Chan NL, Trinh NM, Schneider NO, Matovic N, Horstmann N, Longo N, Bharambe N, Rouzbeh N, Mahmoodi N, Gumede NJ, Anastasio NC, Khalaf NB, Rabal O, Kandror O, Escaffre O, Silvennoinen O, Bishop OT, Iglesias P, Sobrado P, Chuong P, O’Connell P, Martin-Malpartida P, Mellor P, Fish PV, Moreira POL, Zhou P, Liu P, Liu P, Wu P, Agogo-Mawuli P, Jones PL, Ngoi P, Toogood P, Ip P, von Hundelshausen P, Lee PH, Rowswell-Turner RB, Balaña-Fouce R, Rocha REO, Guido RVC, Ferreira RS, Agrawal RK, Harijan RK, Ramachandran R, Verma R, Singh RK, Tiwari RK, Mazitschek R, Koppisetti RK, Dame RT, Douville RN, Austin RC, Taylor RE, Moore RG, Ebright RH, Angell RM, Yan R, Kejriwal R, Batey RA, Blelloch R, Vandenberg RJ, Hickey RJ, Kelm RJ, Lake RJ, Bradley RK, Blumenthal RM, Solano R, Gierse RM, Viola RE, McCarthy RR, Reguera RM, Uribe RV, do Monte-Neto RL, Gorgoglione R, Cullinane RT, Katyal S, Hossain S, Phadke S, Shelburne SA, Geden SE, Johannsen S, Wazir S, Legare S, Landfear SM, Radhakrishnan SK, Ammendola S, Dzhumaev S, Seo SY, Li S, Zhou S, Chu S, Chauhan S, Maruta S, Ashkar SR, Shyng SL, Conticello SG, Buroni S, Garavaglia S, White SJ, Zhu S, Tsimbalyuk S, Chadni SH, Byun SY, Park S, Xu SQ, Banerjee S, Zahler S, Espinoza S, Gustincich S, Sainas S, Celano SL, Capuzzi SJ, Waggoner SN, Poirier S, Olson SH, Marx SO, Van Doren SR, Sarilla S, Brady-Kalnay SM, Dallman S, Azeem SM, Teramoto T, Mehlman T, Swart T, Abaffy T, Akopian T, Haikarainen T, Moreda TL, Ikegami T, Teixeira TR, Jayasinghe TD, Gillingwater TH, Kampourakis T, Richardson TI, Herdendorf TJ, Kotzé TJ, O’Meara TR, Corson TW, Hermle T, Ogunwa TH, Lan T, Su T, Banjo T, O’Mara TA, Chou T, Chou TF, Baumann U, Desai UR, Pai VP, Thai VC, Tandon V, Banerji V, Robinson VL, Gunasekharan V, Namasivayam V, Segers VFM, Maranda V, Dolce V, Maltarollo VG, Scoffone VC, Woods VA, Ronchi VP, Van Hung Le V, Clayton WB, Lowther WT, Houry WA, Li W, Tang W, Zhang W, Van Voorhis WC, Donaldson WA, Hahn WC, Kerr WG, Gerwick WH, Bradshaw WJ, Foong WE, Blanchet X, Wu X, Lu X, Qi X, Xu X, Yu X, Qin X, Wang X, Yuan X, Zhang X, Zhang YJ, Hu Y, Aldhamen YA, Chen Y, Li Y, Sun Y, Zhu Y, Gupta YK, Pérez-Pertejo Y, Li Y, Tang Y, He Y, Tse-Dinh YC, Sidorova YA, Yen Y, Li Y, Frangos ZJ, Chung Z, Su Z, Wang Z, Zhang Z, Liu Z, Inde Z, Artía Z, Heifets A. AI is a viable alternative to high throughput screening: a 318-target study. Sci Rep 2024; 14:7526. [PMID: 38565852 PMCID: PMC10987645 DOI: 10.1038/s41598-024-54655-z] [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: 09/15/2023] [Accepted: 02/15/2024] [Indexed: 04/04/2024] Open
Abstract
High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.
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Agu PC, Obulose CN. Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications. Drug Dev Res 2024; 85:e22159. [PMID: 38375772 DOI: 10.1002/ddr.22159] [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: 11/08/2023] [Revised: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024]
Abstract
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.
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Affiliation(s)
- Peter Chinedu Agu
- Department of Biochemistry, College of Science, Evangel University, Akaeze, Ebonyi State, Nigeria
| | - Chidiebere Nwiboko Obulose
- Department of Computer Sciences, Our Savior Institute of Science, Agriculture, and Technology (OSISATECH Polytechnic), Enugu, Nigeria
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16
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Singh H, Nim DK, Randhawa AS, Ahluwalia S. Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists. Expert Rev Clin Pharmacol 2024; 17:381-391. [PMID: 38340012 DOI: 10.1080/17512433.2024.2317963] [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: 10/27/2023] [Accepted: 02/08/2024] [Indexed: 02/12/2024]
Abstract
INTRODUCTION The integration of artificial intelligence (AI) into clinical pharmacology could be a potential approach for accelerating drug discovery and development, improving patient care, and streamlining medical research processes. AREAS COVERED We reviewed the current state of AI applications in clinical pharmacology, focusing on drug discovery and development, precision medicine, pharmacovigilance, and other ventures. Key AI applications in clinical pharmacology are examined, including machine learning, natural language processing, deep learning, and reinforcement learning etc. Additionally, the evolving role of clinical pharmacologists, ethical considerations, and challenges in implementing AI in clinical pharmacology are discussed. EXPERT OPINION The AI could be instrumental in accelerating drug discovery, predicting drug safety and efficacy, and optimizing clinical trial designs. It can play a vital role in precision medicine by helping in personalized drug dosing, treatment selection, and predicting drug response based on genetic, clinical, and environmental factors. The role of AI in pharmacovigilance, such as signal detection and adverse event prediction, is also promising. The collaboration between clinical pharmacologists and AI experts also poses certain ethical and practical challenges. Clinical pharmacologists can be instrumental in shaping the future of AI-driven clinical pharmacology and contribute to the improvement of healthcare systems.
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Affiliation(s)
- Harmanjit Singh
- Department of Pharmacology, Government Medical College & Hospital, Chandigarh, India
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17
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Dral PO. AI in computational chemistry through the lens of a decade-long journey. Chem Commun (Camb) 2024; 60:3240-3258. [PMID: 38444290 DOI: 10.1039/d4cc00010b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.
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Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
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18
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Tsai CT, Lin CW, Ye GL, Wu SC, Yao P, Lin CT, Wan L, Tsai HHG. Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models. ACS OMEGA 2024; 9:9357-9374. [PMID: 38434814 PMCID: PMC10905719 DOI: 10.1021/acsomega.3c08676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/19/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024]
Abstract
The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.
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Affiliation(s)
- Cheng-Ting Tsai
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Chia-Wei Lin
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Gen-Lin Ye
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Shao-Chi Wu
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Philip Yao
- Aurora
High School, 109 W Pioneer Trail, Aurora, Ohio 44202, United States
| | - Ching-Ting Lin
- School
of Chinese Medicine, China Medical University, No. 91 Hsueh-Shih Road, Taichung 40402, Taiwan
| | - Lei Wan
- School
of Chinese Medicine, China Medical University, No. 91 Hsueh-Shih Road, Taichung 40402, Taiwan
| | - Hui-Hsu Gavin Tsai
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
- Research
Center of New Generation Light Driven Photovoltaic Modules, National Central University, Taoyuan 32001, Taiwan
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19
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Iqbal S, Begum F, Ullah I, Jalal N, Shaw P. Peeling off the layers from microbial dark matter (MDM): recent advances, future challenges, and opportunities. Crit Rev Microbiol 2024:1-21. [PMID: 38385313 DOI: 10.1080/1040841x.2024.2319669] [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: 07/07/2023] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
Microbes represent the most common organisms on Earth; however, less than 2% of microbial species in the environment can undergo cultivation for study under laboratory conditions, and the rest of the enigmatic, microbial world remains mysterious, constituting a kind of "microbial dark matter" (MDM). In the last two decades, remarkable progress has been made in culture-dependent and culture-independent techniques. More recently, studies of MDM have relied on culture-independent techniques to recover genetic material through either unicellular genomics or shotgun metagenomics to construct single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs), respectively, which provide information about evolution and metabolism. Despite the remarkable progress made in the past decades, the functional diversity of MDM still remains uncharacterized. This review comprehensively summarizes the recently developed culture-dependent and culture-independent techniques for characterizing MDM, discussing major challenges, opportunities, and potential applications. These activities contribute to expanding our knowledge of the microbial world and have implications for various fields including Biotechnology, Bioprospecting, Functional genomics, Medicine, Evolutionary and Planetary biology. Overall, this review aims to peel off the layers from MDM, shed light on recent advancements, identify future challenges, and illuminate the exciting opportunities that lie ahead in unraveling the secrets of this intriguing microbial realm.
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Affiliation(s)
- Sajid Iqbal
- Oujiang Lab (Zhejiang Laboratory for Regenerative Medicine, Vision, and Brain Health), Wenzhou, China
- School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, China
| | - Farida Begum
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Ihsan Ullah
- College of Chemical Engineering, Fuzhou University, Fuzhou, China
| | - Nasir Jalal
- Oujiang Lab (Zhejiang Laboratory for Regenerative Medicine, Vision, and Brain Health), Wenzhou, China
| | - Peter Shaw
- Oujiang Lab (Zhejiang Laboratory for Regenerative Medicine, Vision, and Brain Health), Wenzhou, China
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20
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Shen T, Guo J, Han Z, Zhang G, Liu Q, Si X, Wang D, Wu S, Xia J. AutoMolDesigner for Antibiotic Discovery: An AI-Based Open-Source Software for Automated Design of Small-Molecule Antibiotics. J Chem Inf Model 2024; 64:575-583. [PMID: 38265916 DOI: 10.1021/acs.jcim.3c01562] [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: 01/26/2024]
Abstract
Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in holistic and efficient tools specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative-deep-learning-enabled molecular generation and automated machine-learning-based antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole-cell-based antibiotic screening and design. It is open-source, thus allowing for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with a Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/10097899).
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Affiliation(s)
- Tao Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jiale Guo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zunsheng Han
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Gao Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Qingxin Liu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
- School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China
| | - Xinxin Si
- School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China
| | - Dongmei Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
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21
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Ji S, An F, Zhang T, Lou M, Guo J, Liu K, Zhu Y, Wu J, Wu R. Antimicrobial peptides: An alternative to traditional antibiotics. Eur J Med Chem 2024; 265:116072. [PMID: 38147812 DOI: 10.1016/j.ejmech.2023.116072] [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: 10/16/2023] [Revised: 12/04/2023] [Accepted: 12/17/2023] [Indexed: 12/28/2023]
Abstract
As antibiotic-resistant bacteria and genes continue to emerge, the identification of effective alternatives to traditional antibiotics has become a pressing issue. Antimicrobial peptides are favored for their safety, low residue, and low resistance properties, and their unique antimicrobial mechanisms show significant potential in combating antibiotic resistance. However, the high production cost and weak activity of antimicrobial peptides limit their application. Moreover, traditional laboratory methods for identifying and designing new antimicrobial peptides are time-consuming and labor-intensive, hindering their development. Currently, novel technologies, such as artificial intelligence (AI) are being employed to develop and design new antimicrobial peptide resources, offering new opportunities for the advancement of antimicrobial peptides. This article summarizes the basic characteristics and antimicrobial mechanisms of antimicrobial peptides, as well as their advantages and limitations, and explores the application of AI in antimicrobial peptides prediction amd design. This highlights the crucial role of AI in enhancing the efficiency of antimicrobial peptide research and provides a reference for antimicrobial drug development.
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Affiliation(s)
- Shuaiqi Ji
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Feiyu An
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China
| | - Taowei Zhang
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Mengxue Lou
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China
| | - Jiawei Guo
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Kexin Liu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Yi Zhu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China
| | - Junrui Wu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China.
| | - Rina Wu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China.
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22
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Hemmati S, Saeidikia Z, Seradj H, Mohagheghzadeh A. Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents. Pharmaceuticals (Basel) 2024; 17:201. [PMID: 38399416 PMCID: PMC10892805 DOI: 10.3390/ph17020201] [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: 01/06/2024] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 02/25/2024] Open
Abstract
The underdevelopment of adjuvant discovery and diversity, compared to core vaccine technology, is evident. On the other hand, antibiotic resistance is on the list of the top ten threats to global health. Immunomodulatory peptides that target a pathogen and modulate the immune system simultaneously are promising for the development of preventive and therapeutic molecules. Since investigating innate immunity in insects has led to prominent achievements in human immunology, such as toll-like receptor (TLR) discovery, we used the capacity of the immunomodulatory peptides of arthropods with concomitant antimicrobial or antitumor activity. An SVM-based machine learning classifier identified short immunomodulatory sequences encrypted in 643 antimicrobial peptides from 55 foe-to-friend arthropods. The critical features involved in efficacy and safety were calculated. Finally, 76 safe immunomodulators were identified. Then, molecular docking and simulation studies defined the target of the most optimal peptide ligands among all human cell-surface TLRs. SPalf2-453 from a crab is a cell-penetrating immunoadjuvant with antiviral properties. The peptide interacts with the TLR1/2 heterodimer. SBsib-711 from a blackfly is a TLR4/MD2 ligand used as a cancer vaccine immunoadjuvant. In addition, SBsib-711 binds CD47 and PD-L1 on tumor cells, which is applicable in cancer immunotherapy as a checkpoint inhibitor. MRh4-679 from a shrimp is a broad-spectrum or universal immunoadjuvant with a putative Th1/Th2-balanced response. We also implemented a pathway enrichment analysis to define fingerprints or immunological signatures for further in vitro and in vivo immunogenicity and reactogenicity measurements. Conclusively, combinatorial machine learning, molecular docking, and simulation studies, as well as systems biology, open a new opportunity for the discovery and development of multifunctional prophylactic and therapeutic lead peptides.
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Affiliation(s)
- Shiva Hemmati
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran
- Biotechnology Research Center, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran
- Department of Pharmaceutical Biology, Faculty of Pharmaceutical Sciences, UCSI University, Cheras, Kuala Lumpur 56000, Malaysia
| | - Zahra Saeidikia
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran;
| | - Hassan Seradj
- Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran;
| | - Abdolali Mohagheghzadeh
- Department of Phytopharmaceuticals, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71345-1583, Iran;
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23
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Wong F, Zheng EJ, Valeri JA, Donghia NM, Anahtar MN, Omori S, Li A, Cubillos-Ruiz A, Krishnan A, Jin W, Manson AL, Friedrichs J, Helbig R, Hajian B, Fiejtek DK, Wagner FF, Soutter HH, Earl AM, Stokes JM, Renner LD, Collins JJ. Discovery of a structural class of antibiotics with explainable deep learning. Nature 2024; 626:177-185. [PMID: 38123686 PMCID: PMC10866013 DOI: 10.1038/s41586-023-06887-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 11/21/2023] [Indexed: 12/23/2023]
Abstract
The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.
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Affiliation(s)
- Felix Wong
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Integrated Biosciences, San Carlos, CA, USA
| | - Erica J Zheng
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Chemical Biology, Harvard University, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Jacqueline A Valeri
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Nina M Donghia
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Melis N Anahtar
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Satotaka Omori
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Integrated Biosciences, San Carlos, CA, USA
| | - Alicia Li
- Integrated Biosciences, San Carlos, CA, USA
| | - Andres Cubillos-Ruiz
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
| | - Aarti Krishnan
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Wengong Jin
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Abigail L Manson
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jens Friedrichs
- Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany
| | - Ralf Helbig
- Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany
| | - Behnoush Hajian
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dawid K Fiejtek
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Florence F Wagner
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Holly H Soutter
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ashlee M Earl
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan M Stokes
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research and David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Lars D Renner
- Leibniz Institute of Polymer Research and the Max Bergmann Center of Biomaterials, Dresden, Germany
| | - James J Collins
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
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24
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Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, Liu XF. Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res 2024; 11:7. [PMID: 38254241 PMCID: PMC10804841 DOI: 10.1186/s40779-024-00510-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.
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Affiliation(s)
- Guang-Yu Liu
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Dan Yu
- National Key Discipline of Pediatrics Key Laboratory of Major Diseases in Children Ministry of Education, Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Mei-Mei Fan
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ze-Yu Jin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christoph Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
| | - Xiao-Fen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Key Laboratory of Clinical Pharmacology of Antibiotics, National Health Commission of the People's Republic of China, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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25
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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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26
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Guzman-Pando A, Ramirez-Alonso G, Arzate-Quintana C, Camarillo-Cisneros J. Deep learning algorithms applied to computational chemistry. Mol Divers 2023:10.1007/s11030-023-10771-y. [PMID: 38151697 DOI: 10.1007/s11030-023-10771-y] [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: 09/20/2023] [Accepted: 11/14/2023] [Indexed: 12/29/2023]
Abstract
Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.
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Affiliation(s)
- Abimael Guzman-Pando
- Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico
| | - Graciela Ramirez-Alonso
- Faculty of Engineering, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico
| | - Carlos Arzate-Quintana
- Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico
| | - Javier Camarillo-Cisneros
- Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomédicas, Universidad Autónoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.
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27
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Amsterdam D. Perspective: Limiting Antimicrobial Resistance with Artificial Intelligence/Machine Learning. BME FRONTIERS 2023; 4:0033. [PMID: 38188353 PMCID: PMC10769497 DOI: 10.34133/bmef.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 10/31/2023] [Indexed: 01/09/2024] Open
Abstract
The author traces his experience with the application of computers in clinical microbiology over the past 60 years, specifically in directing clinicians to treat bacterial infections diagnosed by the laboratory and the antibacterial agent(s) that could be used to treat those infections. Appropriate use of antibiotics will result in reduced antimicrobial resistance, which is increasing worldwide. An early form of AI, Mycin (1976), a system based on rules provided by experts designed to propose antibiotic regimens for central nervous system infections, was never applied due to the limitations in the number of rules that could be incorporated into the clinical workflow. Machine learning (ML) was developed to overcome the limitations of expert systems. Several variables that influence the outcome bacteria/drug interaction, such as the source of the infection, absence of antimicrobial resistance markers, patients' health profile, and the historical susceptibility within the hospital and the local area are incorporated in the proposed comprehensive AI/ML program. The role of AI in the discovery of new antimicrobial agents is also addressed.
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Affiliation(s)
- Daniel Amsterdam
- Jacobs School of Medicine & Biomedical Sciences, State University of New York, Buffalo, NY, USA
- Departments of Microbiology and Immunology, Medicine, and Pathology at the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
- Department of Laboratory Medicine, ECMC, Buffalo, NY, USA. Address correspondence to:
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28
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Szymczak P, Szczurek E. Artificial intelligence-driven antimicrobial peptide discovery. Curr Opin Struct Biol 2023; 83:102733. [PMID: 37992451 DOI: 10.1016/j.sbi.2023.102733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/06/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023]
Abstract
Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
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Muteeb G, Rehman MT, Shahwan M, Aatif M. Origin of Antibiotics and Antibiotic Resistance, and Their Impacts on Drug Development: A Narrative Review. Pharmaceuticals (Basel) 2023; 16:1615. [PMID: 38004480 PMCID: PMC10675245 DOI: 10.3390/ph16111615] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/08/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
Antibiotics have revolutionized medicine, saving countless lives since their discovery in the early 20th century. However, the origin of antibiotics is now overshadowed by the alarming rise in antibiotic resistance. This global crisis stems from the relentless adaptability of microorganisms, driven by misuse and overuse of antibiotics. This article explores the origin of antibiotics and the subsequent emergence of antibiotic resistance. It delves into the mechanisms employed by bacteria to develop resistance, highlighting the dire consequences of drug resistance, including compromised patient care, increased mortality rates, and escalating healthcare costs. The article elucidates the latest strategies against drug-resistant microorganisms, encompassing innovative approaches such as phage therapy, CRISPR-Cas9 technology, and the exploration of natural compounds. Moreover, it examines the profound impact of antibiotic resistance on drug development, rendering the pursuit of new antibiotics economically challenging. The limitations and challenges in developing novel antibiotics are discussed, along with hurdles in the regulatory process that hinder progress in this critical field. Proposals for modifying the regulatory process to facilitate antibiotic development are presented. The withdrawal of major pharmaceutical firms from antibiotic research is examined, along with potential strategies to re-engage their interest. The article also outlines initiatives to overcome economic challenges and incentivize antibiotic development, emphasizing international collaborations and partnerships. Finally, the article sheds light on government-led initiatives against antibiotic resistance, with a specific focus on the Middle East. It discusses the proactive measures taken by governments in the region, such as Saudi Arabia and the United Arab Emirates, to combat this global threat. In the face of antibiotic resistance, a multifaceted approach is imperative. This article provides valuable insights into the complex landscape of antibiotic development, regulatory challenges, and collaborative efforts required to ensure a future where antibiotics remain effective tools in safeguarding public health.
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Affiliation(s)
- Ghazala Muteeb
- Department of Nursing, College of Applied Medical Science, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Md Tabish Rehman
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh 11437, Saudi Arabia;
- Center for Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman 346, United Arab Emirates;
| | - Moayad Shahwan
- Center for Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman 346, United Arab Emirates;
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman 346, United Arab Emirates
| | - Mohammad Aatif
- Department of Public Health, College of Applied Medical Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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Lu N, Chen J, Rao Z, Guo B, Xu Y. Recent Advances of Biosensors for Detection of Multiple Antibiotics. BIOSENSORS 2023; 13:850. [PMID: 37754084 PMCID: PMC10526323 DOI: 10.3390/bios13090850] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 09/28/2023]
Abstract
The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.
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Affiliation(s)
| | | | | | | | - Ying Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
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Wozniak RAF, El-Herte R. Editorial: Women in antimicrobial resistance and new antimicrobial drugs. Front Cell Infect Microbiol 2023; 13:1263568. [PMID: 37692172 PMCID: PMC10486009 DOI: 10.3389/fcimb.2023.1263568] [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: 07/20/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Affiliation(s)
- Rachel A. F. Wozniak
- Department of Ophthalmology, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
| | - Rima El-Herte
- Department of Medicine, Division of Infectious Diseases, Creighton University School of Medicine, Omaha, NE, United States
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Melo MCR, Bernardi RC. Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike. Biophys J 2023; 122:2833-2840. [PMID: 36738105 PMCID: PMC10398237 DOI: 10.1016/j.bpj.2023.01.042] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/24/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.
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Affiliation(s)
- Marcelo C R Melo
- Auburn University, Department of Physics, Auburn University, Auburn, Alabama
| | - Rafael C Bernardi
- Auburn University, Department of Physics, Auburn University, Auburn, Alabama.
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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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Cesaro A, Bagheri M, Torres MDT, Wan F, de la Fuente-Nunez C. Deep learning tools to accelerate antibiotic discovery. Expert Opin Drug Discov 2023; 18:1245-1257. [PMID: 37794737 PMCID: PMC10790350 DOI: 10.1080/17460441.2023.2250721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
INTRODUCTION As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity. AREAS COVERED This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics. EXPERT OPINION Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.
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Affiliation(s)
- Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Mojtaba Bagheri
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marcelo D. T. Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Irvine A, McKenzie D, McCoy CJ, Graham RLJ, Graham C, Huws SA, Atkinson LE, Mousley A. Novel integrated computational AMP discovery approaches highlight diversity in the helminth AMP repertoire. PLoS Pathog 2023; 19:e1011508. [PMID: 37523405 PMCID: PMC10414684 DOI: 10.1371/journal.ppat.1011508] [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: 02/02/2023] [Revised: 08/10/2023] [Accepted: 06/23/2023] [Indexed: 08/02/2023] Open
Abstract
Antimicrobial Peptides (AMPs) are immune effectors that are key components of the invertebrate innate immune system providing protection against pathogenic microbes. Parasitic helminths (phylum Nematoda and phylum Platyhelminthes) share complex interactions with their hosts and closely associated microbiota that are likely regulated by a diverse portfolio of antimicrobial immune effectors including AMPs. Knowledge of helminth AMPs has largely been derived from nematodes, whereas the flatworm AMP repertoire has not been described. This study highlights limitations in the homology-based approaches, used to identify putative nematode AMPs, for the characterisation of flatworm AMPs, and reveals that innovative algorithmic AMP prediction approaches provide an alternative strategy for novel helminth AMP discovery. The data presented here: (i) reveal that flatworms do not encode traditional lophotrochozoan AMP groups (Big Defensin, CSαβ peptides and Myticalin); (ii) describe a unique integrated computational pipeline for the discovery of novel helminth AMPs; (iii) reveal >16,000 putative AMP-like peptides across 127 helminth species; (iv) highlight that cysteine-rich peptides dominate helminth AMP-like peptide profiles; (v) uncover eight novel helminth AMP-like peptides with diverse antibacterial activities, and (vi) demonstrate the detection of AMP-like peptides from Ascaris suum biofluid. These data represent a significant advance in our understanding of the putative helminth AMP repertoire and underscore a potential untapped source of antimicrobial diversity which may provide opportunities for the discovery of novel antimicrobials. Further, unravelling the role of endogenous worm-derived antimicrobials and their potential to influence host-worm-microbiome interactions may be exploited for the development of unique helminth control approaches.
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Affiliation(s)
- Allister Irvine
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Darrin McKenzie
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Ciaran J. McCoy
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Robert L. J. Graham
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Ciaren Graham
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Sharon A. Huws
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Louise E. Atkinson
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
| | - Angela Mousley
- Microbes & Pathogen Biology, The Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, United Kingdom
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O'Sullivan C, Tsai DHT, Wu ICY, Boselli E, Hughes C, Padmanabhan D, Hsia Y. Machine learning applications on neonatal sepsis treatment: a scoping review. BMC Infect Dis 2023; 23:441. [PMID: 37386442 DOI: 10.1186/s12879-023-08409-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023] Open
Abstract
INTRODUCTION Neonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are non-specific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment. METHODS PubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning. RESULTS There were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks. CONCLUSION Despite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis.
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Affiliation(s)
| | - Daniel Hsiang-Te Tsai
- Centre for Neonatal and Paediatric Infection, St. George's, University of London, London, UK
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ian Chang-Yen Wu
- Centre for Neonatal and Paediatric Infection, St. George's, University of London, London, UK
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Emanuela Boselli
- Department of Pediatrics, V. Buzzi Children's Hospital, University of Milan, Milan, Italy
| | - Carmel Hughes
- School of Pharmacy, Queen's University Belfast, Belfast, UK
| | - Deepak Padmanabhan
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
| | - Yingfen Hsia
- School of Pharmacy, Queen's University Belfast, Belfast, UK
- Centre for Neonatal and Paediatric Infection, St. George's, University of London, London, UK
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Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P, Piñeiro Á, Garcia-Fandino R. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel) 2023; 16:891. [PMID: 37375838 DOI: 10.3390/ph16060891] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.
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Affiliation(s)
- Alexandre Blanco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, 15782 Santiago de Compostela, Spain
| | - Alfonso Cabezón
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Alejandro Seco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Daniel Conde-Torres
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Paula Antelo-Riveiro
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Ángel Piñeiro
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Rebeca Garcia-Fandino
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
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Helmy NM, Parang K. Cyclic Peptides with Antifungal Properties Derived from Bacteria, Fungi, Plants, and Synthetic Sources. Pharmaceuticals (Basel) 2023; 16:892. [PMID: 37375840 DOI: 10.3390/ph16060892] [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: 05/29/2023] [Revised: 06/13/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
Fungal infections remain a significant concern for human health. The emergence of microbial resistance, the improper use of antimicrobial drugs, and the need for fewer toxic antifungal treatments in immunocompromised patients have sparked substantial interest in antifungal research. Cyclic peptides, classified as antifungal peptides, have been in development as potential antifungal agents since 1948. In recent years, there has been growing attention from the scientific community to explore cyclic peptides as a promising strategy for combating antifungal infections caused by pathogenic fungi. The identification of antifungal cyclic peptides from various sources has been possible due to the widespread interest in peptide research in recent decades. It is increasingly important to evaluate narrow- to broad-spectrum antifungal activity and the mode of action of synthetic and natural cyclic peptides for both synthesized and extracted peptides. This short review aims to highlight some of the antifungal cyclic peptides isolated from bacteria, fungi, and plants. This brief review is not intended to present an exhaustive catalog of all known antifungal cyclic peptides but rather seeks to showcase selected cyclic peptides with antifungal properties that have been isolated from bacteria, fungi, plants, and synthetic sources. The addition of commercially available cyclic antifungal peptides serves to corroborate the notion that cyclic peptides can serve as a valuable source for the development of antifungal drugs. Additionally, this review discusses the potential future of utilizing combinations of antifungal peptides from different sources. The review underscores the need for the further exploration of the novel antifungal therapeutic applications of these abundant and diverse cyclic peptides.
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Affiliation(s)
- Naiera M Helmy
- Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, Irvine, CA 92618, USA
- Microbial Biotechnology Department, Biotechnology Research Institute, National Research Centre, Giza 3751134, Egypt
| | - Keykavous Parang
- Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, Irvine, CA 92618, USA
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Hallsworth JE, Udaondo Z, Pedrós‐Alió C, Höfer J, Benison KC, Lloyd KG, Cordero RJB, de Campos CBL, Yakimov MM, Amils R. Scientific novelty beyond the experiment. Microb Biotechnol 2023; 16:1131-1173. [PMID: 36786388 PMCID: PMC10221578 DOI: 10.1111/1751-7915.14222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 02/15/2023] Open
Abstract
Practical experiments drive important scientific discoveries in biology, but theory-based research studies also contribute novel-sometimes paradigm-changing-findings. Here, we appraise the roles of theory-based approaches focusing on the experiment-dominated wet-biology research areas of microbial growth and survival, cell physiology, host-pathogen interactions, and competitive or symbiotic interactions. Additional examples relate to analyses of genome-sequence data, climate change and planetary health, habitability, and astrobiology. We assess the importance of thought at each step of the research process; the roles of natural philosophy, and inconsistencies in logic and language, as drivers of scientific progress; the value of thought experiments; the use and limitations of artificial intelligence technologies, including their potential for interdisciplinary and transdisciplinary research; and other instances when theory is the most-direct and most-scientifically robust route to scientific novelty including the development of techniques for practical experimentation or fieldwork. We highlight the intrinsic need for human engagement in scientific innovation, an issue pertinent to the ongoing controversy over papers authored using/authored by artificial intelligence (such as the large language model/chatbot ChatGPT). Other issues discussed are the way in which aspects of language can bias thinking towards the spatial rather than the temporal (and how this biased thinking can lead to skewed scientific terminology); receptivity to research that is non-mainstream; and the importance of theory-based science in education and epistemology. Whereas we briefly highlight classic works (those by Oakes Ames, Francis H.C. Crick and James D. Watson, Charles R. Darwin, Albert Einstein, James E. Lovelock, Lynn Margulis, Gilbert Ryle, Erwin R.J.A. Schrödinger, Alan M. Turing, and others), the focus is on microbiology studies that are more-recent, discussing these in the context of the scientific process and the types of scientific novelty that they represent. These include several studies carried out during the 2020 to 2022 lockdowns of the COVID-19 pandemic when access to research laboratories was disallowed (or limited). We interviewed the authors of some of the featured microbiology-related papers and-although we ourselves are involved in laboratory experiments and practical fieldwork-also drew from our own research experiences showing that such studies can not only produce new scientific findings but can also transcend barriers between disciplines, act counter to scientific reductionism, integrate biological data across different timescales and levels of complexity, and circumvent constraints imposed by practical techniques. In relation to urgent research needs, we believe that climate change and other global challenges may require approaches beyond the experiment.
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Affiliation(s)
- John E. Hallsworth
- Institute for Global Food Security, School of Biological SciencesQueen's University BelfastBelfastUK
| | - Zulema Udaondo
- Department of Biomedical InformaticsUniversity of Arkansas for Medical SciencesLittle RockArkansasUSA
| | - Carlos Pedrós‐Alió
- Department of Systems BiologyCentro Nacional de Biotecnología (CSIC)MadridSpain
| | - Juan Höfer
- Escuela de Ciencias del MarPontificia Universidad Católica de ValparaísoValparaísoChile
| | - Kathleen C. Benison
- Department of Geology and GeographyWest Virginia UniversityMorgantownWest VirginiaUSA
| | - Karen G. Lloyd
- Microbiology DepartmentUniversity of TennesseeKnoxvilleTennesseeUSA
| | - Radamés J. B. Cordero
- Department of Molecular Microbiology and ImmunologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Claudia B. L. de Campos
- Institute of Science and TechnologyUniversidade Federal de Sao Paulo (UNIFESP)São José dos CamposSPBrazil
| | | | - Ricardo Amils
- Department of Molecular Biology, Centro de Biología Molecular Severo Ochoa (CSIC‐UAM)Nicolás Cabrera n° 1, Universidad Autónoma de MadridMadridSpain
- Department of Planetology and HabitabilityCentro de Astrobiología (INTA‐CSIC)Torrejón de ArdozSpain
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Farhat F, Athar MT, Ahmad S, Madsen DØ, Sohail SS. Antimicrobial resistance and machine learning: past, present, and future. Front Microbiol 2023; 14:1179312. [PMID: 37303800 PMCID: PMC10250749 DOI: 10.3389/fmicb.2023.1179312] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.
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Affiliation(s)
- Faiza Farhat
- Department of Zoology, Aligarh Muslim University, Aligarh, India
| | - Md Tanwir Athar
- Department of Pharmacognosy and Pharmaceutical Chemistry, College of Dentistry and Pharmacy, Buraydah Colleges, Buraydah, Al-Qassim, Saudi Arabia
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Computer Science and Engineering, University Center for Research and Development (UCRD), Chandigarh University, Mohali, Punjab, India
| | - Dag Øivind Madsen
- School of Business, University of South-Eastern Norway, Hønefoss, Norway
| | - Shahab Saquib Sohail
- Department of Computer Science and Engineering, Jamia Hamdard University, New Delhi, India
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Irrgang C, Eckmanns T, V Kleist M, Antão EM, Ladewig K, Wieler LH, Körber N. [Application areas of artificial intelligence in the context of One Health with a focus on antimicrobial resistance]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2023:10.1007/s00103-023-03707-2. [PMID: 37140603 PMCID: PMC10157576 DOI: 10.1007/s00103-023-03707-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/21/2023] [Indexed: 05/05/2023]
Abstract
Societal health is facing a number of new challenges, largely driven by ongoing climate change, demographic ageing, and globalization. The One Health approach links human, animal, and environmental sectors with the goal of achieving a holistic understanding of health in general. To implement this approach, diverse and heterogeneous data streams and types must be combined and analyzed. To this end, artificial intelligence (AI) techniques offer new opportunities for cross-sectoral assessment of current and future health threats. Using the example of antimicrobial resistance as a global threat in the One Health context, we demonstrate potential applications and challenges of AI techniques.This article provides an overview of different applications of AI techniques in the context of One Health and highlights their challenges. Using the spread of antimicrobial resistance (AMR), an increasing global threat, as an example, existing and future AI-based approaches to AMR containment and prevention are described. These range from novel drug development and personalized therapy, to targeted monitoring of antibiotic use in livestock and agriculture, to comprehensive environmental surveillance.
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Affiliation(s)
- Christopher Irrgang
- Zentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland.
| | - Tim Eckmanns
- FG 37: Nosokomiale Infektionen, Surveillance von Antibiotikaresistenz und -verbrauch, Robert Koch-Institut, Berlin, Deutschland
| | - Max V Kleist
- Fachbereich für Mathematik und Informatik, Freie Universität Berlin, Berlin, Deutschland
- P5: Systemmedizin von Infektionskrankheiten, Robert Koch-Institut, Berlin, Deutschland
| | - Esther-Maria Antão
- Fachgebiet Digital Global Public Health, Hasso-Plattner-Institut, Potsdam, Deutschland
| | - Katharina Ladewig
- Zentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland
| | - Lothar H Wieler
- Robert Koch-Institut, Berlin, Deutschland
- Fachgebiet Digital Global Public Health, Hasso-Plattner-Institut, Potsdam, Deutschland
| | - Nils Körber
- Zentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland
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Caioni G, Benedetti E, Perugini M, Amorena M, Merola C. Personal Care Products as a Contributing Factor to Antimicrobial Resistance: Current State and Novel Approach to Investigation. Antibiotics (Basel) 2023; 12:antibiotics12040724. [PMID: 37107085 PMCID: PMC10135053 DOI: 10.3390/antibiotics12040724] [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: 03/18/2023] [Revised: 03/31/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
Antimicrobial resistance (AMR) is one of the world's industrialized nations' biggest issues. It has a significant influence on the ecosystem and negatively affects human health. The overuse of antibiotics in the healthcare and agri-food industries has historically been defined as a leading factor, although the use of antimicrobial-containing personal care products plays a significant role in the spread of AMR. Lotions, creams, shampoos, soaps, shower gels, toothpaste, fragrances, and other items are used for everyday grooming and hygiene. However, in addition to the primary ingredients, additives are included to help preserve the product by lowering its microbial load and provide disinfection properties. These same substances are released into the environment, escaping traditional wastewater treatment methods and remaining in ecosystems where they contact microbial communities and promote the spread of resistance. The study of antimicrobial compounds, which are often solely researched from a toxicological point of view, must be resumed considering the recent discoveries, to highlight their contribution to AMR. Parabens, triclocarban, and triclosan are among the most worrying chemicals. To investigate this issue, more effective models must be chosen. Among them, zebrafish is a crucial study system because it allows for the assessment of both the risks associated with exposure to these substances as well as environmental monitoring. Furthermore, artificial intelligence-based computer systems are useful in simplifying the handling of antibiotic resistance data and speeding up drug discovery processes.
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Affiliation(s)
- Giulia Caioni
- Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
| | - Elisabetta Benedetti
- Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy
| | - Monia Perugini
- Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Via Balzarini 1, 64100 Teramo, Italy
| | - Michele Amorena
- Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Via Balzarini 1, 64100 Teramo, Italy
| | - Carmine Merola
- Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Via Balzarini 1, 64100 Teramo, Italy
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Salamzade R, Cheong JZA, Sandstrom S, Swaney MH, Stubbendieck RM, Starr NL, Currie CR, Singh AM, Kalan LR. Evolutionary investigations of the biosynthetic diversity in the skin microbiome using lsaBGC. Microb Genom 2023; 9. [PMID: 37115189 DOI: 10.1099/mgen.0.000988] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023] Open
Abstract
Bacterial secondary metabolites, synthesized by enzymes encoded in biosynthetic gene clusters (BGCs), can underlie microbiome homeostasis and serve as commercialized products, which have historically been mined from a select group of taxa. While evolutionary approaches have proven beneficial for prioritizing BGCs for experimental characterization efforts to uncover new natural products, dedicated bioinformatics tools designed for comparative and evolutionary analysis of BGCs within focal taxa are limited. We thus developed lineage specific analysis of BGCs (lsaBGC; https://github.com/Kalan-Lab/lsaBGC) to aid exploration of microdiversity and evolutionary trends across homologous groupings of BGCs, gene cluster families (GCFs), in any bacterial taxa of interest. lsaBGC enables rapid and direct identification of GCFs in genomes, calculates evolutionary statistics and conservation for BGC genes, and builds a framework to allow for base resolution mining of novel variants through metagenomic exploration. Through application of the suite to four genera commonly found in skin microbiomes, we uncover new insights into the evolution and diversity of their BGCs. We show that the BGC of the virulence-associated carotenoid staphyloxanthin in
Staphylococcus aureus
is ubiquitous across the genus
Staphylococcus
. While one GCF encoding the biosynthesis of staphyloxanthin showcases evidence for plasmid-mediated horizontal gene transfer (HGT) between species, another GCF appears to be transmitted vertically amongst a sub-clade of skin-associated
Staphylococcus
. Further, the latter GCF, which is well conserved in
S. aureus
, has been lost in most
Staphylococcus epidermidis
, which is the most common
Staphylococcus
species on human skin and is also regarded as a commensal. We also identify thousands of novel single-nucleotide variants (SNVs) within BGCs from the
Corynebacterium tuberculostearicum
sp. complex, a narrow, multi-species clade that features the most prevalent
Corynebacterium
in healthy skin microbiomes. Although novel SNVs were approximately 10 times as likely to correspond to synonymous changes when located in the top five percentile of conserved sites, lsaBGC identified SNVs that defied this trend and are predicted to underlie amino acid changes within functionally key enzymatic domains. Ultimately, beyond supporting evolutionary investigations of BGCs, lsaBGC also provides important functionalities to aid efforts for the discovery or directed modification of natural products.
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Affiliation(s)
- Rauf Salamzade
- Department of Medical Microbiology and Immunology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Microbiology Doctoral Training Program, University of Wisconsin, Madison, WI, USA
| | - J Z Alex Cheong
- Department of Medical Microbiology and Immunology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Microbiology Doctoral Training Program, University of Wisconsin, Madison, WI, USA
| | - Shelby Sandstrom
- Department of Medical Microbiology and Immunology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Mary Hannah Swaney
- Department of Medical Microbiology and Immunology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Microbiology Doctoral Training Program, University of Wisconsin, Madison, WI, USA
| | - Reed M Stubbendieck
- Department of Bacteriology, University of Wisconsin, Madison, Wisconsin, USA
| | - Nicole Lane Starr
- Department of Medical Microbiology and Immunology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Cameron R Currie
- Department of Bacteriology, University of Wisconsin, Madison, Wisconsin, USA
- M.G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Anne Marie Singh
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Lindsay R Kalan
- Department of Medical Microbiology and Immunology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- M.G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, Division of Infectious Disease, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
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Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation. Antibiotics (Basel) 2023; 12:antibiotics12030523. [PMID: 36978390 PMCID: PMC10044311 DOI: 10.3390/antibiotics12030523] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.
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Fine J, Wijewardhane PR, Mohideen SDB, Smith K, Bothe JR, Krishnamachari Y, Andrews A, Liu Y, Chopra G. Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development. Pharm Res 2023; 40:701-710. [PMID: 36797504 DOI: 10.1007/s11095-023-03475-3] [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: 10/23/2022] [Accepted: 01/16/2023] [Indexed: 02/18/2023]
Abstract
PURPOSE OR OBJECTIVE Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thioflavin-T is commonly used to measure physical stability. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a drug product, we introduce a machine learning-based model for predicting the chemical stability over time using both formulation conditions as well as aggregation curves. METHODS In this work, we develop the relationships between the formulation, stability timepoint, and the chemical stability measurements and evaluated the performance on a random test set. We have developed a multilayer perceptron (MLP) for total degradation prediction and a random forest (RF) model for potency. RESULTS The coefficient of determination (R2) of 0.945 and a mean absolute error (MAE) of 0.421 were achieved on the test set when using MLP for total degradation. Similarly, we achieved a R2 of 0.908 and MAE of 1.435 when predicting potency using the RF model. When physical stability measurements are included into the MLP model, the MAE of predicting TD decreases to 0.148. Using a similar strategy for potency prediction, the MAE decreases to 0.705 for the RF model. CONCLUSIONS We conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability.
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Affiliation(s)
- Jonathan Fine
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA
| | | | | | - Katelyn Smith
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA
| | - Jameson R Bothe
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA
| | - Yogita Krishnamachari
- Sterile and Specialty Products, Pharmaceutical Sciences, MRL, Merck & Co., Inc., Rahway, NJ, USA
| | - Alexandra Andrews
- Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA
| | - Yong Liu
- Tango Therapeutics, Boston, MA, USA
| | - Gaurav Chopra
- Department of Chemistry, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science (by courtesy), Purdue University, West Lafayette, NJ, USA.
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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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48
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Sjakste N, Gajski G. A Review on Genotoxic and Genoprotective Effects of Biologically Active Compounds of Animal Origin. Toxins (Basel) 2023; 15:165. [PMID: 36828477 PMCID: PMC9961038 DOI: 10.3390/toxins15020165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Envenomation by animal venoms remains a serious medical and social problem, especially in tropical countries. On the other hand, animal venoms are widely used as a source of biologically active compounds for the development of novel drugs. Numerous derivatives of animal venoms are already used in clinical practice. When analysing the mechanisms of action of animal venoms, attention is usually focused on the main target of the venom's enzymes and peptides such as neurotoxic, cytotoxic or haemorrhagic effects. In the present review, we would like to draw attention to the "hidden" effects of animal venoms and their derivatives in regard to DNA damage and/or protection against DNA damage. Alkaloids and terpenoids isolated from sponges such as avarol, ingenamine G or variolin B manifest the capability to bind DNA in vitro and produce DNA breaks. Trabectidin, isolated from a sea squirt, also binds and damages DNA. A similar action is possible for peptides isolated from bee and wasp venoms such as mastoparan, melectin and melittin. However, DNA lesions produced by the crude venoms of jellyfish, scorpions, spiders and snakes arise as a consequence of cell membrane damage and the subsequent oxidative stress, whereas certain animal venoms or their components produce a genoprotective effect. Current research data point to the possibility of using animal venoms and their components in the development of various potential therapeutic agents; however, before their possible clinical use the route of injection, molecular target, mechanism of action, exact dosage, possible side effects and other fundamental parameters should be further investigated.
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Affiliation(s)
- Nikolajs Sjakste
- Department of Medical Biochemistry, Faculty of Medicine, University of Latvia, 1004 Riga, Latvia
- Genetics and Bioinformatics, Institute of Biology, University of Latvia, 1004 Riga, Latvia
| | - Goran Gajski
- Mutagenesis Unit, Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
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49
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Büter A, Maschkowitz G, Baum M, Mishra YK, Siebert L, Adelung R, Fickenscher H. Antibacterial Activity of Nanostructured Zinc Oxide Tetrapods. Int J Mol Sci 2023; 24:ijms24043444. [PMID: 36834854 PMCID: PMC9964984 DOI: 10.3390/ijms24043444] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/11/2023] Open
Abstract
Zinc oxide (ZnO) tetrapods as microparticles with nanostructured surfaces show peculiar physical properties and anti-infective activities. The aim of this study was to investigate the antibacterial and bactericidal properties of ZnO tetrapods in comparison to spherical, unstructured ZnO particles. Additionally, killing rates of either methylene blue-treated or untreated tetrapods and spherical ZnO particles for Gram-negative and Gram-positive bacteria species were determined. ZnO tetrapods showed considerable bactericidal activity against Staphylococcus aureus, and Klebsiella pneumoniae isolates, including multi-resistant strains, while Pseudomonas aeruginosa and Enterococcus faecalis remained unaffected. Almost complete elimination was reached after 24 h for Staphylococcus aureus at 0.5 mg/mL and Klebsiella pneumoniae at 0.25 mg/mL. Surface modifications of spherical ZnO particles by treatment with methylene blue even improved the antibacterial activity against Staphylococcus aureus. Nanostructured surfaces of ZnO particles provide active and modifiable interfaces for the contact with and killing of bacteria. The application of solid state chemistry, i.e., the direct matter-to-matter interaction between active agent and bacterium, in the form of ZnO tetrapods and non-soluble ZnO particles, can add an additional principle to the spectrum of antibacterial mechanisms, which is, in contrast to soluble antibiotics, depending on the direct local contact with the microorganisms on tissue or material surfaces.
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Affiliation(s)
- Aike Büter
- Institute for Infection Medicine, University Medical Center Schleswig-Holstein, Christian-Albrecht University of Kiel, 24105 Kiel, Germany
| | - Gregor Maschkowitz
- Institute for Infection Medicine, University Medical Center Schleswig-Holstein, Christian-Albrecht University of Kiel, 24105 Kiel, Germany
| | - Martina Baum
- Functional Nanomaterials, Institute of Material Science, Christian-Albrecht University of Kiel, 24143 Kiel, Germany
| | - Yogendra Kumar Mishra
- Smart Materials, NanoSYD, Mads Clausen Institute, University of Southern Denmark, 6400 Sønderburg, Denmark
| | - Leonard Siebert
- Functional Nanomaterials, Institute of Material Science, Christian-Albrecht University of Kiel, 24143 Kiel, Germany
- Kiel Nano, Surface and Interface Science (KiNSIS), Christian-Albrecht University of Kiel, 24118 Kiel, Germany
| | - Rainer Adelung
- Functional Nanomaterials, Institute of Material Science, Christian-Albrecht University of Kiel, 24143 Kiel, Germany
- Kiel Nano, Surface and Interface Science (KiNSIS), Christian-Albrecht University of Kiel, 24118 Kiel, Germany
| | - Helmut Fickenscher
- Institute for Infection Medicine, University Medical Center Schleswig-Holstein, Christian-Albrecht University of Kiel, 24105 Kiel, Germany
- Correspondence: ; Tel.: +49-431-500-15300
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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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