1
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Wei L, Tu W, Xu Y, Xu C, Dou Y, Ge Y, Sun S, Wei Y, Yang K, Yuan B. Assembly-Induced Membrane Selectivity of Artificial Model Peptides through Entropy-Enthalpy Competition. ACS NANO 2024. [PMID: 38959157 DOI: 10.1021/acsnano.4c05265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
Peptide design and drug development offer a promising solution for combating serious diseases or infections. In this study, using an AI-human negotiation approach, we have designed a class of minimal model peptides against tuberculosis (TB), among which K7W6 exhibits potent efficacy attributed to its assembly-induced function. Comprising lysine and tryptophan with an amphiphilic α-helical structure, the K7W6 sequence exhibits robust activity against various infectious bacteria causing TB (including clinically isolated and drug-resistant strains) both in vitro and in vivo. Moreover, it synergistically enhances the effectiveness of the first-line antibiotic rifampicin while displaying low potential for inducing drug resistance and minimal toxicity toward mammalian cells. Biophysical experiments and simulations elucidate that K7W6's exceptional performance can be ascribed to its highly selective and efficient membrane permeabilization activity induced by its distinctive self-assembly behavior. Additionally, these assemblies regulate the interplay between enthalpy and entropy during K7W6-membrane interaction, leading to the peptide's two-step mechanism of membrane interaction. These findings provide valuable insights into rational design principles for developing advanced peptide-based drugs while uncovering the functional role played by assembly.
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Affiliation(s)
- Lin Wei
- School of Life Sciences, Anhui Medical University, Hefei 230032, China
- Jiangsu Provincial Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Soochow University, Suzhou 215006, Jiangsu, China
| | - Wenqiang Tu
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Yiwei Xu
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Cheng Xu
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Yujiang Dou
- School of Electronic Information, Dongguan Polytechnic, Dongguan, Guangdong 523808, China
| | - Yuke Ge
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Shuqing Sun
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Yushuang Wei
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Kai Yang
- Center for Soft Condensed Matter Physics and Interdisciplinary Research & School of Physical Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China
| | - Bing Yuan
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
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2
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Du A, Jia W, Zhang R. Machine learning methods for unveiling the potential of antioxidant short peptides in goat milk-derived proteins during in vitro gastrointestinal digestion. J Dairy Sci 2024:S0022-0302(24)00970-6. [PMID: 38945266 DOI: 10.3168/jds.2024-24887] [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: 03/09/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024]
Abstract
Milk serves as an important dietary source of bioactive peptides, offering notable benefits to individuals. Among the antioxidant short peptides (di- and tripeptides) generated from gastrointestinal digestion are characterized by enhanced bioavailability and bioaccessibility, while assessing them individually presents a labor-intensive and expensive challenge. Based on 4 distinct types of amino acid descriptors (physicochemical, 3D structural, quantum, and topological attributes) and genetic algorithms for feature selection, 1 and 4 machine learning predicted models separately for di- and tripeptides with ABTS radical scavenging capacity exhibited excellent fitting and prediction ability with random forest regression as machine learning algorithm. Intriguingly, the electronic properties of N-terminal amino acid were considered as only factor affecting the antioxidant capacity of dipeptides containing both tyrosine and tryptophan. Four peptides from the potential di- and tripeptides exhibited highly predicted values by the constructed predicted models. Subsequently, a total of 45 dipeptides and 52 tripeptides were screened by a customized workflow in goat milk during in vitro simulated digestion. In addition to 5 known antioxidant dipeptides, 9 peptides were quantified during digestion, falling within the range of 0.04 to 1.78 mg L-1. Particularly noteworthy was the promising in vivo functionality of antioxidant dipeptides with N-terminal tyrosine, supported by in silico assays. Overall, this investigation explored crucial molecular properties influencing antioxidant short peptides and high-throughput screening potential peptides with antioxidant activity from goat milk aided by machine learning, thereby facilitating the identification of novel bioactive peptides from milk-derived proteins and paving the way for understanding their metabolites during digestion.
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Affiliation(s)
- An Du
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
| | - Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China; Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China.
| | - Rong Zhang
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
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3
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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] [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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4
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Zhang Y, Li R, Zou G, Guo Y, Wu R, Zhou Y, Chen H, Zhou R, Lavigne R, Bergen PJ, Li J, Li J. Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2404049. [PMID: 38899839 DOI: 10.1002/advs.202404049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/29/2024] [Indexed: 06/21/2024]
Abstract
The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs ("dark matter") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.
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Affiliation(s)
- Yue Zhang
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Runze Li
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Geng Zou
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yating Guo
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Renwei Wu
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yang Zhou
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
| | - Huanchun Chen
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Rui Zhou
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Rob Lavigne
- Department of Biosystems, Laboratory of Gene Technology, KU Leuven, Leuven, 3001, Belgium
| | - Phillip J Bergen
- Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3800, Australia
| | - Jian Li
- Monash Biomedicine Discovery Institute, Department of Microbiology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, 3800, Australia
| | - Jinquan Li
- National Key Laboratory of Agricultural Microbiology, Key Laboratory of Environment Correlative Dietology, College of Biomedicine and Health, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518000, China
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5
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Chen M, Hu Z, Shi J, Xie Z. Human β-defensins and their synthetic analogs: Natural defenders and prospective new drugs of oral health. Life Sci 2024; 346:122591. [PMID: 38548013 DOI: 10.1016/j.lfs.2024.122591] [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: 01/12/2024] [Revised: 03/08/2024] [Accepted: 03/24/2024] [Indexed: 04/14/2024]
Abstract
As a family of cationic host defense peptides, human β-defensins (HBDs) are ubiquitous in the oral cavity and are mainly synthesized primarily by epithelial cells, serving as the primary barrier and aiming to prevent microbial invasion, inflammation, and disease while maintaining physiological homeostasis. In recent decades, there has been great interest in their biological functions, structure-activity relationships, mechanisms of action, and therapeutic potential in oral diseases. Meanwhile, researchers are dedicated to improving the properties of HBDs for clinical application. In this review, we first describe the classification, structural characteristics, functions, and mechanisms of HBDs. Next, we cover the role of HBDs and their synthetic analogs in oral diseases, including dental caries and pulp infections, periodontitis, peri-implantitis, fungal/viral infections and oral mucosal diseases, and oral squamous cell carcinoma. Finally, we discuss the limitations and challenges of clinical translation of HBDs and their synthetic analogs, including, but not limited to, stability, bioavailability, antimicrobial activity, resistance, and toxicity. Above all, this review summarizes the biological functions, mechanisms of action, and therapeutic potential of both natural HBDs and their synthetic analogs in oral diseases, as well as the challenges associated with clinical translation, thus providing substantial insights into the laboratory development and clinical application of HBDs in oral diseases.
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Affiliation(s)
- Mumian Chen
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China.
| | - Zihe Hu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China.
| | - Jue Shi
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China.
| | - Zhijian Xie
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China.
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6
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Chen Z, Wang R, Guo J, Wang X. The role and future prospects of artificial intelligence algorithms in peptide drug development. Biomed Pharmacother 2024; 175:116709. [PMID: 38713945 DOI: 10.1016/j.biopha.2024.116709] [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: 03/10/2024] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 05/09/2024] Open
Abstract
Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.
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Affiliation(s)
- Zhiheng Chen
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Ruoxi Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Junqi Guo
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Xiaogang Wang
- Guangdong Provincial Key Laboratory of Bone and Joint Degenerative Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China.
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7
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Li F, Xu B, Lu Z, Chen J, Fu Y, Huang J, Wang Y, Li X. Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine-Learning for Tumor Therapy. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2311101. [PMID: 38234132 DOI: 10.1002/smll.202311101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 12/23/2023] [Indexed: 01/19/2024]
Abstract
Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme-based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) has provided a useful tool to enhance the accuracy and efficiency in developing synergistic substances. In this study, ML models to mine low-cytotoxicity oncolytic peptides are applied. The filtering Pipeline is constructed using a traversal design and the Autogluon framework. Through the Pipeline, 37 novel peptides with high oncolytic activity against cancer cells and low cytotoxicity to normal cells are identified from a library of 25,740 sequences. Combining dataset testing with cytotoxicity experiments, an 80% accuracy rate is achieved, verifying the reliability of ML predictions. Peptide C2 is proven to possess membranolytic functions specifically for tumor cells as targeted by Pipeline. Then Peptide C2 with CoFe hollow hydroxide nanozyme (H-CF) to form the peptide/H-CF composite is integrated. The new composite exhibited acid-triggered membranolytic function and potent peroxidase-like (POD-like) activity, which induce ferroptosis to tumor cells and inhibits tumor growth. The study suggests that this novel ML-assisted design approach can offer an accurate and efficient paradigm for developing both oncolytic peptides and synergistic peptides for catalytic materials.
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Affiliation(s)
- Feiyu Li
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
| | - Bocheng Xu
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
- Institute of Feed Science, College of Animal Science, Zhejiang University, Hangzhou, 310058, China
| | - Zijie Lu
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
| | - Jiafei Chen
- Affiliated Hospital of Stomatology, Medical College, Zhejiang University, Hangzhou, 310000, China
| | - Yike Fu
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
| | - Jie Huang
- Department of Mechanical Engineering, University College London, London, WC1E 7JE, UK
| | - Yizhen Wang
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
- Institute of Feed Science, College of Animal Science, Zhejiang University, Hangzhou, 310058, China
| | - Xiang Li
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
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8
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Lv G, Wang Y. Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis. Technol Health Care 2024:THC240119. [PMID: 38875058 DOI: 10.3233/thc-240119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
BACKGROUND The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments. OBJECTIVE To comprehensively assess the research progress of antibiotic resistance prediction models based on machine learning (ML) algorithms, providing the latest quantitative analysis and methodological evaluation. METHODS Relevant literature was systematically retrieved from databases, including PubMed, Embase and the Cochrane Library, from inception up to December 2023. Studies meeting predefined criteria were selected for inclusion. The prediction model risk of bias assessment tool was employed for methodological quality assessment, and a random-effects model was utilised for meta-analysis. RESULTS The systematic review included a total of 22 studies with a combined sample size of 43,628; 10 studies were ultimately included in the meta-analysis. Commonly used ML algorithms included random forest, decision trees and neural networks. Frequently utilised predictive variables encompassed demographics, drug use history and underlying diseases. The overall sensitivity was 0.57 (95% CI: 0.42-0.70; p< 0.001; I2= 99.7%), the specificity was 0.95 (95% CI: 0.79-0.99; p< 0.001; I2 = 99.9%), the positive likelihood ratio was 10.7 (95% CI: 2.9-39.5), the negative likelihood ratio was 0.46 (95% CI: 0.34-0.61), the diagnostic odds ratio was 23 (95% CI: 7-81) and the area under the receiver operating characteristic curve was 0.78 (95% CI: 0.74-0.81; p< 0.001), indicating a good discriminative ability of ML models for antibiotic resistance. However, methodological assessment and funnel plots suggested a high risk of bias and publication bias in the included studies. CONCLUSION This meta-analysis provides a current and comprehensive evaluation of ML models for predicting antibiotic resistance, emphasising their potential application in clinical practice. Nevertheless, stringent research design and reporting are warranted to enhance the quality and credibility of future studies. Future research should focus on methodological innovation and incorporate more high-quality studies to further advance this field.
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Affiliation(s)
- Guodong Lv
- Department of STD and AIDS Prevention and Control, Langfang Center for Disease Prevention and Control, Langfang, Hebei, China
| | - Yuntao Wang
- Department of Pharmacy, Langfang Health Vocational College, Langfang, Hebei, China
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9
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Wu S, Zhou H, Chen D, Lu Y, Li Y, Qiao J. Multi-omic analysis tools for microbial metabolites prediction. Brief Bioinform 2024; 25:bbae264. [PMID: 38859767 PMCID: PMC11165163 DOI: 10.1093/bib/bbae264] [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: 02/03/2024] [Revised: 05/08/2024] [Indexed: 06/12/2024] Open
Abstract
How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Haonan Zhou
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yutong Lu
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yanni Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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10
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Chen N, Yu J, Zhe L, Wang F, Li X, Wong KC. TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation. Brief Bioinform 2024; 25:bbae308. [PMID: 38920345 PMCID: PMC11200197 DOI: 10.1093/bib/bbae308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/28/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024] Open
Abstract
Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.
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Affiliation(s)
- Nanjun Chen
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Jixiang Yu
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Liu Zhe
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Chang Chun, Ji Lin, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guang Dong, China
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11
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Mo X, Zhang Z, Song J, Wang Y, Yu Z. Self-assembly of peptides in living cells for disease theranostics. J Mater Chem B 2024; 12:4289-4306. [PMID: 38595070 DOI: 10.1039/d4tb00365a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
The past few decades have witnessed substantial progress in biomedical materials for addressing health concerns and improving disease therapeutic and diagnostic efficacy. Conventional biomedical materials are typically created through an ex vivo approach and are usually utilized under physiological environments via transfer from preparative media. This transfer potentially gives rise to challenges for the efficient preservation of the bioactivity and implementation of theranostic goals on site. To overcome these issues, the in situ synthesis of biomedical materials on site has attracted great attention in the past few years. Peptides, which exhibit remarkable biocompability and reliable noncovalent interactions, can be tailored via tunable assembly to precisely create biomedical materials. In this review, we summarize the progress in the self-assembly of peptides in living cells for disease diagnosis and therapy. After a brief introduction to the basic design principles of peptide assembly systems in living cells, the applications of peptide assemblies for bioimaging and disease treatment are highlighted. The challenges in the field of peptide self-assembly in living cells and the prospects for novel peptide assembly systems towards next-generation biomaterials are also discussed, which will hopefully help elucidate the great potential of peptide assembly in living cells for future healthcare applications.
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Affiliation(s)
- Xiaowei Mo
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Zeyu Zhang
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Jinyan Song
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Yushi Wang
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Zhilin Yu
- Key Laboratory of Functional Polymer Materials, Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Institute of Polymer Chemistry, College of Chemistry, Nankai University, 94 Weijin Road, Tianjin 300071, China.
- Haihe Laboratory of Synthetic Biology, 21 West 15th Avenue, Tianjin 300308, China
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12
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Mancuso CP, Baker JS, Qu E, Tripp AD, Balogun IO, Lieberman TD. Intraspecies warfare restricts strain coexistence in human skin microbiomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.07.592803. [PMID: 38765968 PMCID: PMC11100718 DOI: 10.1101/2024.05.07.592803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Determining why only a fraction of encountered or applied bacterial strains engraft in a given person's microbiome is crucial for understanding and engineering these communities1. Previous work has established that metabolism can determine colonization success in vivo2-4, but relevance of bacterial warfare in preventing engraftment has been less explored. Here, we demonstrate that intraspecies warfare presents a significant barrier to strain transmission in the skin microbiome by profiling 14,884 pairwise interactions between Staphylococcus epidermidis cultured from eighteen human subjects from six families. We find that intraspecies antagonisms are abundant; these interactions are mechanistically diverse, independent of the relatedness between strains, and consistent with rapid evolution via horizontal gene transfer. Ability to antagonize more strains is associated with reaching a higher fraction of the on-person S. epidermidis community. Moreover, antagonisms are significantly depleted among strains residing on the same person relative to random assemblages. Two notable exceptions, in which bacteria evolved to become sensitive to antimicrobials found on the same host, are explained by mutations that provide phage resistance, contextualizing the importance of warfare among other lethal selective pressures. Taken together, our results emphasize that accounting for intraspecies bacterial warfare is essential to the design of long-lasting probiotic therapeutics.
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Affiliation(s)
- Christopher P. Mancuso
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - Jacob S. Baker
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - Evan Qu
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - A. Delphine Tripp
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Systems Biology, Harvard University; Cambridge, MA 02138, USA
| | - Ishaq O. Balogun
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
| | - Tami D. Lieberman
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology; Cambridge, MA 02142, USA
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Ragon Institute of MGH, MIT, and Harvard; Cambridge, MA 02142, USA
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13
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Klimovich A, Bosch TCG. Novel technologies uncover novel 'anti'-microbial peptides in Hydra shaping the species-specific microbiome. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230058. [PMID: 38497265 PMCID: PMC10945409 DOI: 10.1098/rstb.2023.0058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 03/19/2024] Open
Abstract
The freshwater polyp Hydra uses an elaborate innate immune machinery to maintain its specific microbiome. Major components of this toolkit are conserved Toll-like receptor (TLR)-mediated immune pathways and species-specific antimicrobial peptides (AMPs). Our study harnesses advanced technologies, such as high-throughput sequencing and machine learning, to uncover a high complexity of the Hydra's AMPs repertoire. Functional analysis reveals that these AMPs are specific against diverse members of the Hydra microbiome and expressed in a spatially controlled pattern. Notably, in the outer epithelial layer, AMPs are produced mainly in the neurons. The neuron-derived AMPs are secreted directly into the glycocalyx, the habitat for symbiotic bacteria, and display high selectivity and spatial restriction of expression. In the endodermal layer, in contrast, endodermal epithelial cells produce an abundance of different AMPs including members of the arminin and hydramacin families, while gland cells secrete kazal-type protease inhibitors. Since the endodermal layer lines the gastric cavity devoid of symbiotic bacteria, we assume that endodermally secreted AMPs protect the gastric cavity from intruding pathogens. In conclusion, Hydra employs a complex set of AMPs expressed in distinct tissue layers and cell types to combat pathogens and to maintain a stable spatially organized microbiome. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.
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Affiliation(s)
- Alexander Klimovich
- Zoological Institute, Christian-Albrechts University of Kiel, Am Botanischen Garten 1-9, Kiel 24118, Germany
| | - Thomas C. G. Bosch
- Zoological Institute, Christian-Albrechts University of Kiel, Am Botanischen Garten 1-9, Kiel 24118, Germany
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14
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Yi Y, An HW, Wang H. Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305099. [PMID: 37490938 DOI: 10.1002/adma.202305099] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/14/2023] [Indexed: 07/27/2023]
Abstract
Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.
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Affiliation(s)
- Yu Yi
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology (NCNST), No. 11 Beiyitiao, Zhongguancun, Haidian District, Beijing, 100190, China
| | - Hong-Wei An
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology (NCNST), No. 11 Beiyitiao, Zhongguancun, Haidian District, Beijing, 100190, China
| | - Hao Wang
- CAS Center for Excellence in Nanoscience, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology (NCNST), No. 11 Beiyitiao, Zhongguancun, Haidian District, Beijing, 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
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15
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Feng J, Sun M, Liu C, Zhang W, Xu C, Wang J, Wang G, Wan S. SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.590553. [PMID: 38712184 PMCID: PMC11071531 DOI: 10.1101/2024.04.25.590553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of features called Proportionalized Split Amino Acid Composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at around both the N-terminus and the C-terminus, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, MCC, G-measure and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package freely available at https://github.com/wan-mlab/SAMP .
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16
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Wang C, Wu Y, Xue Y, Zou L, Huang Y, Zhang P, Ji J. Combinatorial discovery of antibacterials via a feature-fusion based machine learning workflow. Chem Sci 2024; 15:6044-6052. [PMID: 38665528 PMCID: PMC11041243 DOI: 10.1039/d3sc06441g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/08/2024] [Indexed: 04/28/2024] Open
Abstract
The discovery of new antibacterials within the vast chemical space is crucial in combating drug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). However, the traditional approach of screening the entire chemical library in an ergodic manner can be laborious and time-consuming. Machine learning-assisted screening of antibacterials alleviates the exploration effort but suffers from the lack of reliable and related datasets. To address these challenges, we devised a combinatorial library comprising over 110 000 candidates based on the Ugi reaction. A focused library was subsequently generated through uniform sampling of the entire library to narrow down the preliminary screening scale. A novel feature-fusion architecture called the latent space constraint neural network was developed which incorporated both fingerprint and physicochemical molecular descriptors to predict the antibacterial properties. This integration allowed the model to leverage the complementary information provided by these descriptors and improve the accuracy of predictions. Three lead compounds that demonstrated excellent efficacy against MRSA while alleviating drug resistance were identified. This workflow highlights the integration of machine learning with the combinatorial chemical library to expedite high-quality data collection and extensive data mining for antibacterial screening.
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Affiliation(s)
- Cong Wang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University Hangzhou Zhejiang 310027 PR China
- International Research Center for X Polymers, International Campus, Zhejiang University Haining Zhejiang 314400 PR China
| | - Yuhui Wu
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University Hangzhou Zhejiang 310027 PR China
- International Research Center for X Polymers, International Campus, Zhejiang University Haining Zhejiang 314400 PR China
| | - Yunfan Xue
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University Hangzhou Zhejiang 310027 PR China
| | - Lingyun Zou
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University Hangzhou Zhejiang 310027 PR China
| | - Yue Huang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University Hangzhou Zhejiang 310027 PR China
| | - Peng Zhang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University Hangzhou Zhejiang 310027 PR China
- International Research Center for X Polymers, International Campus, Zhejiang University Haining Zhejiang 314400 PR China
- State Key Laboratory of Transvascular Implantation Devices, Zhejiang University Hangzhou Zhejiang 311202 P. R. China
| | - Jian Ji
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University Hangzhou Zhejiang 310027 PR China
- International Research Center for X Polymers, International Campus, Zhejiang University Haining Zhejiang 314400 PR China
- State Key Laboratory of Transvascular Implantation Devices, Zhejiang University Hangzhou Zhejiang 311202 P. R. China
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17
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Redondo-Gómez C, Parreira P, Martins MCL, Azevedo HS. Peptide-based self-assembled monolayers (SAMs): what peptides can do for SAMs and vice versa. Chem Soc Rev 2024; 53:3714-3773. [PMID: 38456490 DOI: 10.1039/d3cs00921a] [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: 03/09/2024]
Abstract
Self-assembled monolayers (SAMs) represent highly ordered molecular materials with versatile biochemical features and multidisciplinary applications. Research on SAMs has made much progress since the early begginings of Au substrates and alkanethiols, and numerous examples of peptide-displaying SAMs can be found in the literature. Peptides, presenting increasing structural complexity, stimuli-responsiveness, and biological relevance, represent versatile functional components in SAMs-based platforms. This review examines the major findings and progress made on the use of peptide building blocks displayed as part of SAMs with specific functions, such as selective cell adhesion, migration and differentiation, biomolecular binding, advanced biosensing, molecular electronics, antimicrobial, osteointegrative and antifouling surfaces, among others. Peptide selection and design, functionalisation strategies, as well as structural and functional characteristics from selected examples are discussed. Additionally, advanced fabrication methods for dynamic peptide spatiotemporal presentation are presented, as well as a number of characterisation techniques. All together, these features and approaches enable the preparation and use of increasingly complex peptide-based SAMs to mimic and study biological processes, and provide convergent platforms for high throughput screening discovery and validation of promising therapeutics and technologies.
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Affiliation(s)
- Carlos Redondo-Gómez
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal.
- INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal
| | - Paula Parreira
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal.
- INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal
| | - M Cristina L Martins
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal.
- INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal
- ICBAS - Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Rua de Jorge Viterbo Ferreira, 4050-313 Porto, Portugal
| | - Helena S Azevedo
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal.
- INEB - Instituto de Engenharia Biomédica, Universidade do Porto, Rua Alfredo Allen, 208, Porto, 4200-135, Portugal
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18
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Kim H, Taslakjian B, Kim S, Tirrell MV, Guler MO. Therapeutic Peptides, Proteins and their Nanostructures for Drug Delivery and Precision Medicine. Chembiochem 2024; 25:e202300831. [PMID: 38408302 DOI: 10.1002/cbic.202300831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/05/2024] [Accepted: 02/22/2024] [Indexed: 02/28/2024]
Abstract
Peptide and protein nanostructures with tunable structural features, multifunctionality, biocompatibility and biomolecular recognition capacity enable development of efficient targeted drug delivery tools for precision medicine applications. In this review article, we present various techniques employed for the synthesis and self-assembly of peptides and proteins into nanostructures. We discuss design strategies utilized to enhance their stability, drug-loading capacity, and controlled release properties, in addition to the mechanisms by which peptide nanostructures interact with target cells, including receptor-mediated endocytosis and cell-penetrating capabilities. We also explore the potential of peptide and protein nanostructures for precision medicine, focusing on applications in personalized therapies and disease-specific targeting for diagnostics and therapeutics in diseases such as cancer.
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Affiliation(s)
- HaRam Kim
- The Pritzker School of Molecular Engineering, The University of Chicago, 5640 S. Ellis Ave., Chicago, 60637, IL, USA
| | - Boghos Taslakjian
- The Pritzker School of Molecular Engineering, The University of Chicago, 5640 S. Ellis Ave., Chicago, 60637, IL, USA
| | - Sarah Kim
- The Pritzker School of Molecular Engineering, The University of Chicago, 5640 S. Ellis Ave., Chicago, 60637, IL, USA
| | - Matthew V Tirrell
- The Pritzker School of Molecular Engineering, The University of Chicago, 5640 S. Ellis Ave., Chicago, 60637, IL, USA
| | - Mustafa O Guler
- The Pritzker School of Molecular Engineering, The University of Chicago, 5640 S. Ellis Ave., Chicago, 60637, IL, USA
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19
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Wu Z, Wang C, Li C, Xu N, Cao X, Chen S, Shi Y, He Y, Zhang P, Ji J. Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides. J Phys Chem Lett 2024; 15:3748-3756. [PMID: 38551401 DOI: 10.1021/acs.jpclett.4c00393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.
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Affiliation(s)
- Zhiyu Wu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Cong Wang
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Chen Li
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Nan Xu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Xiaoyong Cao
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Shengfu Chen
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yao Shi
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
| | - Yi He
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Peng Zhang
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Transvascular Implantation Devices, Qidi Road 456, Hangzhou 310058, China
| | - Jian Ji
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Transvascular Implantation Devices, Qidi Road 456, Hangzhou 310058, China
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20
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Zeng J, Loi GWZ, Saipuljumri EN, Romero Durán MA, Silva-García O, Perez-Aguilar JM, Baizabal-Aguirre VM, Lo CH. Peptide-based allosteric inhibitor targets TNFR1 conformationally active region and disables receptor-ligand signaling complex. Proc Natl Acad Sci U S A 2024; 121:e2308132121. [PMID: 38551841 PMCID: PMC10998571 DOI: 10.1073/pnas.2308132121] [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: 05/15/2023] [Accepted: 01/23/2024] [Indexed: 04/02/2024] Open
Abstract
Tumor necrosis factor (TNF) receptor 1 (TNFR1) plays a pivotal role in mediating TNF induced downstream signaling and regulating inflammatory response. Recent studies have suggested that TNFR1 activation involves conformational rearrangements of preligand assembled receptor dimers and targeting receptor conformational dynamics is a viable strategy to modulate TNFR1 signaling. Here, we used a combination of biophysical, biochemical, and cellular assays, as well as molecular dynamics simulation to show that an anti-inflammatory peptide (FKCRRWQWRMKK), which we termed FKC, inhibits TNFR1 activation allosterically by altering the conformational states of the receptor dimer without blocking receptor-ligand interaction or disrupting receptor dimerization. We also demonstrated the efficacy of FKC by showing that the peptide inhibits TNFR1 signaling in HEK293 cells and attenuates inflammation in mice with intraperitoneal TNF injection. Mechanistically, we found that FKC binds to TNFR1 cysteine-rich domains (CRD2/3) and perturbs the conformational dynamics required for receptor activation. Importantly, FKC increases the frequency in the opening of both CRD2/3 and CRD4 in the receptor dimer, as well as induces a conformational opening in the cytosolic regions of the receptor. This results in an inhibitory conformational state that impedes the recruitment of downstream signaling molecules. Together, these data provide evidence on the feasibility of targeting TNFR1 conformationally active region and open new avenues for receptor-specific inhibition of TNFR1 signaling.
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Affiliation(s)
- Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore308232, Singapore
| | - Gavin Wen Zhao Loi
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore308232, Singapore
| | - Eka Norfaishanty Saipuljumri
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore308232, Singapore
- School of Applied Science, Republic Polytechnic, Singapore738964, Singapore
| | - Marco Antonio Romero Durán
- Centro Multidisciplinario de Estudios en Biotecnología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Michoacana de San Nicolás de Hidalgo, Morelia58893, México
| | - Octavio Silva-García
- Centro Multidisciplinario de Estudios en Biotecnología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Michoacana de San Nicolás de Hidalgo, Morelia58893, México
| | - Jose Manuel Perez-Aguilar
- School of Chemical Sciences, Meritorious Autonomous University of Puebla, University City, Puebla72570, México
| | - Víctor M. Baizabal-Aguirre
- Centro Multidisciplinario de Estudios en Biotecnología, Facultad de Medicina Veterinaria y Zootecnia, Universidad Michoacana de San Nicolás de Hidalgo, Morelia58893, México
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore308232, Singapore
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21
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Bui Thi Phuong H, Doan Ngan H, Le Huy B, Vu Dinh H, Luong Xuan H. The amphipathic design in helical antimicrobial peptides. ChemMedChem 2024; 19:e202300480. [PMID: 38408263 DOI: 10.1002/cmdc.202300480] [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: 09/07/2023] [Revised: 12/18/2023] [Indexed: 02/28/2024]
Abstract
Amphipathicity is a critical characteristic of helical antimicrobial peptides (AMPs). The hydrophilic region, primarily composed of cationic residues, plays a pivotal role in the initial binding to negatively charged components on bacterial membranes through electrostatic interactions. Subsequently, the hydrophobic region interacts with hydrophobic components, inducing membrane perturbation, ultimately leading to cell death, or inhibiting intracellular function. Due to the extensive diversity of natural and synthetic AMPs with regard to the design of amphipathicity, it is complicated to study the structure-activity relationships. Therefore, this work aims to categorize the common amphipathic design and investigate their impact on the biological properties of AMPs. Besides, the connection between current structural modification approaches and amphipathic styles was also discussed.
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Affiliation(s)
| | - Hoa Doan Ngan
- Faculty of Medical Technology, PHENIKAA University, Hanoi, 12116, Vietnam
| | - Binh Le Huy
- Center for High Technology Development, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi, 11307, Vietnam
- School of Chemical Engineering -, Hanọi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, 11615, Vietnam
| | - Hoang Vu Dinh
- School of Chemical Engineering -, Hanọi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, 11615, Vietnam
| | - Huy Luong Xuan
- Faculty of Pharmacy, PHENIKAA University, Hanoi, 12116, Vietnam
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22
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Men K, Liu M, Zhang X, Yang Y, Zhang R, Wang Y, Hu D, Zhou B, Yang L. Identification of Potent siRNA Delivery Peptides Using Computer Modeling. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308345. [PMID: 38311577 PMCID: PMC11005685 DOI: 10.1002/advs.202308345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/17/2024] [Indexed: 02/06/2024]
Abstract
Peptides with suitable aggregation behavior and electrical properties are potential siRNA delivery vectors. However, identifying suitable peptides with ideal delivery and safety features is difficult owing to the variations in amino acid sequences. Here, a holistic program based on computer modeling and single-cell RNA sequencing (scRNA-seq) is used to identify ideal siRNA delivery peptides. Stage one of this program consists of a sequential screening process for candidates with ideal assembly and delivery ability; stage two is a cell subtype-level analysis program that screens for high in vivo tissue safety. The leading candidate peptide selected from a library containing 12 amino acids showed strong lung-targeted siRNA delivery capacity after hydrophobic modification. Systemic administration of these compounds caused the least damage to liver and lung tissues and has little impact on macrophage and neutrophil numbers. By loading STAT3 siRNA, strong anticancer effects are achieved in multiple models, including patient-derived xenografts (PDX). This screening procedure may facilitate the development of peptide-based RNA interference (RNAi) therapeutics.
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Affiliation(s)
- Ke Men
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Mohan Liu
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Xueyan Zhang
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Yuling Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Rui Zhang
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Yusi Wang
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Die Hu
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Bailing Zhou
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
| | - Li Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengdu610041P. R. China
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23
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Alexander MP, Zaidi M, Larson N, Mullan A, Pavelko KD, Stegall MD, Bentall A, Wouters BG, McKee T, Taner T. Exploring the single-cell immune landscape of kidney allograft inflammation using imaging mass cytometry. Am J Transplant 2024; 24:549-563. [PMID: 37979921 DOI: 10.1016/j.ajt.2023.11.008] [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: 07/26/2023] [Revised: 11/01/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Kidney allograft inflammation, mostly attributed to rejection and infection, is an important cause of graft injury and loss. Standard histopathological assessment of allograft inflammation provides limited insights into biological processes and the immune landscape. Here, using imaging mass cytometry with a panel of 28 validated biomarkers, we explored the single-cell landscape of kidney allograft inflammation in 32 kidney transplant biopsies and 247 high-dimensional histopathology images of various phenotypes of allograft inflammation (antibody-mediated rejection, T cell-mediated rejection, BK nephropathy, and chronic pyelonephritis). Using novel analytical tools, for cell segmentation, we segmented over 900 000 cells and developed a tissue-based classifier using over 3000 manually annotated kidney microstructures (glomeruli, tubules, interstitium, and arteries). Using PhenoGraph, we identified 11 immune and 9 nonimmune clusters and found a high prevalence of memory T cell and macrophage-enriched immune populations across phenotypes. Additionally, we trained a machine learning classifier to identify spatial biomarkers that could discriminate between the different allograft inflammatory phenotypes. Further validation of imaging mass cytometry in larger cohorts and with more biomarkers will likely help interrogate kidney allograft inflammation in more depth than has been possible to date.
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Affiliation(s)
- Mariam P Alexander
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, Minnesota, USA.
| | - Mark Zaidi
- Department of Medical Biophysics, University of Toronto, Canada
| | - Nicholas Larson
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Aidan Mullan
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Kevin D Pavelko
- Immune Monitoring Core Laboratory, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark D Stegall
- Departments of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew Bentall
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Bradly G Wouters
- Department of Medical Biophysics, University of Toronto, Canada; Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Trevor McKee
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Pathomics Inc., Toronto, Ontario, Canada
| | - Timucin Taner
- Departments of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota, USA
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24
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Hourigan D, Stefanovic E, Hill C, Ross RP. Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy. BMC Microbiol 2024; 24:103. [PMID: 38539119 PMCID: PMC10976773 DOI: 10.1186/s12866-024-03243-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 02/28/2024] [Indexed: 04/19/2024] Open
Abstract
Vancomycin-resistant enterococci (VRE) are major opportunistic pathogens and the causative agents of serious diseases, such as urinary tract infections and endocarditis. VRE strains mainly include species of Enterococcus faecium and E. faecalis which can colonise the gastrointestinal tract (GIT) of patients and, following growth and persistence in the gut, can transfer to blood resulting in systemic dissemination in the body. Advancements in genomics have revealed that hospital-associated VRE strains are characterised by increased numbers of mobile genetic elements, higher numbers of antibiotic resistance genes and often lack active CRISPR-Cas systems. Additionally, comparative genomics have increased our understanding of dissemination routes among patients and healthcare workers. Since the efficiency of currently available antibiotics is rapidly declining, new measures to control infection and dissemination of these persistent pathogens are urgently needed. These approaches include combinatory administration of antibiotics, strengthening colonisation resistance of the gut microbiota to reduce VRE proliferation through commensals or probiotic bacteria, or switching to non-antibiotic bacterial killers, such as bacteriophages or bacteriocins. In this review, we discuss the current knowledge of the genomics of VRE isolates and state-of-the-art therapeutic advances against VRE infections.
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Affiliation(s)
- David Hourigan
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland
- School of Microbiology, University College Cork, College Rd, University College, Cork, Ireland
| | - Ewelina Stefanovic
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland
- Teagasc Food Research Centre, Moorepark, Moorepark West, Fermoy, Co. Cork, Ireland
| | - Colin Hill
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland
- School of Microbiology, University College Cork, College Rd, University College, Cork, Ireland
| | - R Paul Ross
- APC Microbiome Ireland, Biosciences Institute, Biosciences Research Institute, College Rd, University College, Cork, Ireland.
- School of Microbiology, University College Cork, College Rd, University College, Cork, Ireland.
- Teagasc Food Research Centre, Moorepark, Moorepark West, Fermoy, Co. Cork, Ireland.
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25
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Wu H, Chen R, Li X, Zhang Y, Zhang J, Yang Y, Wan J, Zhou Y, Chen H, Li J, Li R, Zou G. ESKtides: a comprehensive database and mining method for ESKAPE phage-derived antimicrobial peptides. Database (Oxford) 2024; 2024:baae022. [PMID: 38531599 DOI: 10.1093/database/baae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/06/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
'Superbugs' have received increasing attention from researchers, such as ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.), which directly led to about 1 270 000 death cases in 2019. Recently, phage peptidoglycan hydrolases (PGHs)-derived antimicrobial peptides were proposed as new antibacterial agents against multidrug-resistant bacteria. However, there is still a lack of methods for mining antimicrobial peptides based on phages or phage PGHs. Here, by using a collection of 6809 genomes of ESKAPE isolates and corresponding phages in public databases, based on a unified annotation process of all the genomes, PGHs were systematically identified, from which peptides were mined. As a result, a total of 12 067 248 peptides with high antibacterial activities were respectively determined. A user-friendly tool was developed to predict the phage PGHs-derived antimicrobial peptides from customized genomes, which also allows the calculation of peptide phylogeny, physicochemical properties, and secondary structure. Finally, a user-friendly and intuitive database, ESKtides (http://www.phageonehealth.cn:9000/ESKtides), was designed for data browsing, searching and downloading, which provides a rich peptide library based on ESKAPE prophages and phages. Database URL: 10.1093/database/baae022.
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Affiliation(s)
- Hongfang Wu
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Rongxian Chen
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Xuejian Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Informatics, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Yue Zhang
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jianwei Zhang
- National Key Laboratory of Crop Genetic Improvement, Shizishan Street No. 1, Wuhan 430070, China
| | - Yanbo Yang
- College of Informatics, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jun Wan
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Yang Zhou
- College of Fisheries, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Huanchun Chen
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jinquan Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Buxin Road No. 97, Shenzhen 518000, China
- Shenzhen Institute of Quality & Safety Inspection and Research, Buxin Road No. 97, Shenzhen 518000, China
| | - Runze Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Geng Zou
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
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26
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Pandey P, Srivastava A. sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides. Proteins 2024. [PMID: 38520179 DOI: 10.1002/prot.26681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/15/2024] [Accepted: 02/28/2024] [Indexed: 03/25/2024]
Abstract
During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.
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Affiliation(s)
- Poonam Pandey
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Anand Srivastava
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
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27
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Zhang H, Wang Y, Zhu Y, Huang P, Gao Q, Li X, Chen Z, Liu Y, Jiang J, Gao Y, Huang J, Qin Z. Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides. J Adv Res 2024:S2090-1232(24)00078-X. [PMID: 38431124 DOI: 10.1016/j.jare.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
INTRODUCTION Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the structure-activity relationships of AMPs can direct the synthesis of desirable peptide therapeutics. OBJECTIVES In this study, the lipopolysaccharide-binding domain (LBD) was identified through machine learning-guided directed evolution, which acts as a functional domain of the anti-lipopolysaccharide factor family of AMPs identified from Marsupenaeus japonicus. METHODS LBDA-D was identified as an output of this algorithm, in which the original LBDMj sequence was the input, and the three-dimensional solution structure of LBDB was determined using nuclear magnetic resonance. Furthermore, our study involved a comprehensive series of experiments, including morphological studies and in vitro and in vivo antibacterial tests. RESULTS The NMR solution structure showed that LBDB possesses a circular extended structure with a disulfide crosslink at the terminus and two 310-helices and exhibits a broad antimicrobial spectrum. In addition, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that LBDB induced the formation of a cluster of bacteria wrapped in a flexible coating that ruptured and consequently killed the bacteria. Finally, coinjection of LBDB, Vibrio alginolyticus and Staphylococcus aureus in vivo improved the survival of M. japonicus, demonstrating the promising therapeutic role of LBDB for treating infectious disease. CONCLUSIONS The findings of this study pave the way for the rational drug design of activity-enhanced peptide antibiotics.
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Affiliation(s)
- Heqian Zhang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yihan Wang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yanran Zhu
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Pengtao Huang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Qiandi Gao
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Xiaojie Li
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Zhaoying Chen
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yu Liu
- International Academic Center of Complex Systems, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Jiakun Jiang
- Center for Statistics and Data Science, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Yuan Gao
- Instrumentation and Service Center for Science and Technology, Beijing Normal University, Zhuhai, Guangdong 519087, China
| | - Jiaquan Huang
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China.
| | - Zhiwei Qin
- Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, China.
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28
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Yin K, Xu W, Ren S, Xu Q, Zhang S, Zhang R, Jiang M, Zhang Y, Xu D, Li R. Machine Learning Accelerates De Novo Design of Antimicrobial Peptides. Interdiscip Sci 2024:10.1007/s12539-024-00612-3. [PMID: 38416364 DOI: 10.1007/s12539-024-00612-3] [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: 10/06/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/29/2024]
Abstract
Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10 μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.
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Affiliation(s)
- Kedong Yin
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
| | - Wen Xu
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
- Law College, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China.
| | - Shiming Ren
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
| | - Qingpeng Xu
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Shaojie Zhang
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
| | - Ruiling Zhang
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- School of Economics and Trade, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Mengwan Jiang
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Yuhong Zhang
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Degang Xu
- College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
| | - Ruifang Li
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
- College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
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29
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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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30
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Zhang Z, Wang X, Liu J, Yang H, Tang H, Li J, Luan S, Yin J, Wang L, Shi H. Structural Element of Vitamin U-Mimicking Antibacterial Polypeptide with Ultrahigh Selectivity for Effectively Treating MRSA Infections. Angew Chem Int Ed Engl 2024; 63:e202318011. [PMID: 38131886 DOI: 10.1002/anie.202318011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/17/2023] [Accepted: 12/21/2023] [Indexed: 12/23/2023]
Abstract
Antimicrobial peptides (AMPs) exhibit mighty antibacterial properties without inducing drug resistance. Achieving much higher selectivity of AMPs towards bacteria and normal cells has always been a continuous goal to be pursued. Herein, a series of sulfonium-based polypeptides with different degrees of branching and polymerization were synthesized by mimicking the structure of vitamin U. The polypeptide, G2 -PM-1H+ , shows both potent antibacterial activity and the highest selectivity index of 16000 among the reported AMPs or peptoids (e.g., the known index of 9600 for recorded peptoid in "Angew. Chem. Int. Ed., 2020, 59, 6412."), which can be attributed to the high positive charge density of sulfonium and the regulation of hydrophobic chains in the structure. The antibacterial mechanisms of G2 -PM-1H+ are primarily ascribed to the interaction with the membrane, production of reactive oxygen species (ROS), and disfunction of ribosomes. Meanwhile, altering the degree of alkylation leads to selective antibacteria against either gram-positive or gram-negative bacteria in a mixed-bacteria model. Additionally, both in vitro and in vivo experiments demonstrated that G2 -PM-1H+ exhibited superior efficacy against methicillin-resistant Staphylococcus aureus (MRSA) compared to vancomycin. Together, these results show that G2 -PM-1H+ possesses high biocompatibility and is a potential pharmaceutical candidate in combating bacteria significantly threatening human health.
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Affiliation(s)
- Zhenyan Zhang
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, 230026, P. R. China
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
| | - Xiaodan Wang
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, 230026, P. R. China
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
| | - Jiaying Liu
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, 230026, P. R. China
| | - Huawei Yang
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
| | - Haoyu Tang
- Institute of Functional Nano & Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science & Technology, Soochow University, Suzhou, 215123, P. R. China
| | - Jing Li
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun, Changchun, 130022, P. R. China
| | - Shifang Luan
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, 230026, P. R. China
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
| | - Jinghua Yin
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
| | - Lei Wang
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
| | - Hengchong Shi
- School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei, 230026, P. R. China
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, P. R. China
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31
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Vincenzi M, Mercurio FA, Leone M. Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools. Int J Mol Sci 2024; 25:1798. [PMID: 38339078 PMCID: PMC10855943 DOI: 10.3390/ijms25031798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).
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Affiliation(s)
| | | | - Marilisa Leone
- Institute of Biostructures and Bioimaging, Via Pietro Castellino 111, 80131 Naples, Italy; (M.V.); (F.A.M.)
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32
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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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33
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Xu K, Zhao X, Tan Y, Wu J, Cai Y, Zhou J, Wang X. A systematical review on antimicrobial peptides and their food applications. BIOMATERIALS ADVANCES 2023; 155:213684. [PMID: 37976831 DOI: 10.1016/j.bioadv.2023.213684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023]
Abstract
Food safety issues are a major concern in food processing and packaging industries. Food spoilage is caused by microbial contamination, where antimicrobial peptides (APs) provide solutions by eliminating microorganisms. APs such as nisin have been successfully and commonly used in food processing and preservation. Here, we discuss all aspects of the functionalization of APs in food applications. We briefly review the natural sources of APs and their native functions. Recombinant expression of APs in microorganisms and their yields are described. The molecular mechanisms of AP antibacterial action are explained, and this knowledge can further benefit the design of functional APs. We highlight current utilities and challenges for the application of APs in the food industry, and address rational methods for AP design that may overcome current limitations.
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Affiliation(s)
- Kangjie Xu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - XinYi Zhao
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yameng Tan
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Junheng Wu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yiqing Cai
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China..
| | - Xinglong Wang
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
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34
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Zhang W, Xu Y, Wang A, Chen G, Zhao J. Fuse feeds as one: cross-modal framework for general identification of AMPs. Brief Bioinform 2023; 24:bbad336. [PMID: 37779248 DOI: 10.1093/bib/bbad336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.
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Affiliation(s)
- Wentao Zhang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Yanchao Xu
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Aowen Wang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Gang Chen
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
| | - Junbo Zhao
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, 310027, Hangzhou,Zhejiang, P.R.China
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35
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Wang H, Li Q, Alam P, Bai H, Bhalla V, Bryce MR, Cao M, Chen C, Chen S, Chen X, Chen Y, Chen Z, Dang D, Ding D, Ding S, Duo Y, Gao M, He W, He X, Hong X, Hong Y, Hu JJ, Hu R, Huang X, James TD, Jiang X, Konishi GI, Kwok RTK, Lam JWY, Li C, Li H, Li K, Li N, Li WJ, Li Y, Liang XJ, Liang Y, Liu B, Liu G, Liu X, Lou X, Lou XY, Luo L, McGonigal PR, Mao ZW, Niu G, Owyong TC, Pucci A, Qian J, Qin A, Qiu Z, Rogach AL, Situ B, Tanaka K, Tang Y, Wang B, Wang D, Wang J, Wang W, Wang WX, Wang WJ, Wang X, Wang YF, Wu S, Wu Y, Xiong Y, Xu R, Yan C, Yan S, Yang HB, Yang LL, Yang M, Yang YW, Yoon J, Zang SQ, Zhang J, Zhang P, Zhang T, Zhang X, Zhang X, Zhao N, Zhao Z, Zheng J, Zheng L, Zheng Z, Zhu MQ, Zhu WH, Zou H, Tang BZ. Aggregation-Induced Emission (AIE), Life and Health. ACS NANO 2023; 17:14347-14405. [PMID: 37486125 PMCID: PMC10416578 DOI: 10.1021/acsnano.3c03925] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 07/12/2023] [Indexed: 07/25/2023]
Abstract
Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health.
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Affiliation(s)
- Haoran Wang
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, China
- Department
of Chemistry, Hong Kong Branch of Chinese National Engineering Research
Center for Tissue Restoration and Reconstruction, Division of Life
Science, State Key Laboratory of Molecular Neuroscience, Guangdong-Hong
Kong-Macau Joint Laboratory of Optoelectronic and Magnetic Functional
Materials, The Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
| | - Qiyao Li
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, China
- State
Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial
Key Laboratory of Luminescence from Molecular Aggregates, South China University of Technology, Guangzhou 510640, China
| | - Parvej Alam
- Clinical
Translational Research Center of Aggregation-Induced Emission, School
of Medicine, The Second Affiliated Hospital, School of Science and
Engineering, The Chinese University of Hong
Kong, Shenzhen (CUHK- Shenzhen), Guangdong 518172, China
| | - Haotian Bai
- Beijing
National Laboratory for Molecular Sciences, Key Laboratory of Organic
Solids, Institute of Chemistry, Chinese
Academy of Sciences, Beijing 100190, China
| | - Vandana Bhalla
- Department
of Chemistry, Guru Nanak Dev University, Amritsar 143005, India
| | - Martin R. Bryce
- Department
of Chemistry, Durham University, South Road, Durham DH1 3LE, United Kingdom
| | - Mingyue Cao
- State
Key Laboratory of Crystal Materials, Shandong
University, Jinan 250100, China
| | - Chao Chen
- Department
of Chemistry, Hong Kong Branch of Chinese National Engineering Research
Center for Tissue Restoration and Reconstruction, Division of Life
Science, State Key Laboratory of Molecular Neuroscience, Guangdong-Hong
Kong-Macau Joint Laboratory of Optoelectronic and Magnetic Functional
Materials, The Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
| | - Sijie Chen
- Ming
Wai Lau Centre for Reparative Medicine, Karolinska Institutet, Sha Tin, Hong Kong SAR 999077, China
| | - Xirui Chen
- State Key
Laboratory of Food Science and Resources, School of Food Science and
Technology, Nanchang University, Nanchang 330047, China
| | - Yuncong Chen
- State
Key Laboratory of Coordination Chemistry, School of Chemistry and
Chemical Engineering, Chemistry and Biomedicine Innovation Center
(ChemBIC), Department of Cardiothoracic Surgery, Nanjing Drum Tower
Hospital, Medical School, Nanjing University, Nanjing 210023, China
| | - Zhijun Chen
- Engineering
Research Center of Advanced Wooden Materials and Key Laboratory of
Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin 150040, China
| | - Dongfeng Dang
- School
of Chemistry, Xi’an Jiaotong University, Xi’an 710049 China
| | - Dan Ding
- State
Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Bioactive
Materials, Ministry of Education, and College of Life Sciences, Nankai University, Tianjin 300071, China
| | - Siyang Ding
- Department
of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yanhong Duo
- Department
of Radiation Oncology, Shenzhen People’s Hospital (The Second
Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong 518020, China
| | - Meng Gao
- National
Engineering Research Center for Tissue Restoration and Reconstruction,
Key Laboratory of Biomedical Engineering of Guangdong Province, Key
Laboratory of Biomedical Materials and Engineering of the Ministry
of Education, Innovation Center for Tissue Restoration and Reconstruction,
School of Materials Science and Engineering, South China University of Technology, Guangzhou 510006, China
| | - Wei He
- Department
of Chemistry, Hong Kong Branch of Chinese National Engineering Research
Center for Tissue Restoration and Reconstruction, Division of Life
Science, State Key Laboratory of Molecular Neuroscience, Guangdong-Hong
Kong-Macau Joint Laboratory of Optoelectronic and Magnetic Functional
Materials, The Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
| | - Xuewen He
- The
Key Lab of Health Chemistry and Molecular Diagnosis of Suzhou, College
of Chemistry, Chemical Engineering and Materials Science, Soochow University, 199 Ren’ai Road, Suzhou 215123, China
| | - Xuechuan Hong
- State
Key Laboratory of Virology, Department of Cardiology, Zhongnan Hospital
of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China
| | - Yuning Hong
- Department
of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Jing-Jing Hu
- State
Key Laboratory of Biogeology and Environmental Geology, Engineering
Research Center of Nano-Geomaterials of Ministry of Education, Faculty
of Materials Science and Chemistry, China
University of Geosciences, Wuhan 430074, China
| | - Rong Hu
- School
of Chemistry and Chemical Engineering, University
of South China, Hengyang 421001, China
| | - Xiaolin Huang
- State Key
Laboratory of Food Science and Resources, School of Food Science and
Technology, Nanchang University, Nanchang 330047, China
| | - Tony D. James
- Department
of Chemistry, University of Bath, Bath BA2 7AY, United Kingdom
| | - Xingyu Jiang
- Guangdong
Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Key Laboratory
of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088 Xueyuan Road, Nanshan District, Shenzhen, Guangdong 518055, China
| | - Gen-ichi Konishi
- Department
of Chemical Science and Engineering, Tokyo
Institute of Technology, O-okayama, Meguro-ku, Tokyo 152-8552, Japan
| | - Ryan T. K. Kwok
- Department
of Chemistry, Hong Kong Branch of Chinese National Engineering Research
Center for Tissue Restoration and Reconstruction, Division of Life
Science, State Key Laboratory of Molecular Neuroscience, Guangdong-Hong
Kong-Macau Joint Laboratory of Optoelectronic and Magnetic Functional
Materials, The Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
| | - Jacky W. Y. Lam
- Department
of Chemistry, Hong Kong Branch of Chinese National Engineering Research
Center for Tissue Restoration and Reconstruction, Division of Life
Science, State Key Laboratory of Molecular Neuroscience, Guangdong-Hong
Kong-Macau Joint Laboratory of Optoelectronic and Magnetic Functional
Materials, The Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
| | - Chunbin Li
- College
of Chemistry and Chemical Engineering, Inner Mongolia Key Laboratory
of Fine Organic Synthesis, Inner Mongolia
University, Hohhot 010021, China
| | - Haidong Li
- State
Key Laboratory of Fine Chemicals, School of Bioengineering, Dalian University of Technology, 2 Linggong Road, Dalian 116024, China
| | - Kai Li
- College
of Chemistry, Zhengzhou University, 100 Science Road, Zhengzhou 450001, China
| | - Nan Li
- Key
Laboratory of Macromolecular Science of Shaanxi Province, Key Laboratory
of Applied Surface and Colloid Chemistry of Ministry of Education,
School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi’an 710119, China
| | - Wei-Jian Li
- Shanghai
Key Laboratory of Green Chemistry and Chemical Processes & Chang-Kung
Chuang Institute, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, China
| | - Ying Li
- Innovation
Research Center for AIE Pharmaceutical Biology, Guangzhou Municipal
and Guangdong Provincial Key Laboratory of Molecular Target &
Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory
Disease, School of Pharmaceutical Sciences and the Fifth Affiliated
Hospital, Guangzhou Medical University, Guangzhou 511436, China
| | - Xing-Jie Liang
- CAS
Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety,
CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- School
of Biomedical Engineering, Guangzhou Medical
University, Guangzhou 511436, China
| | - Yongye Liang
- Department
of Materials Science and Engineering, Shenzhen Key Laboratory of Printed
Organic Electronics, Southern University
of Science and Technology, Shenzhen 518055, China
| | - Bin Liu
- Department
of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
| | - Guozhen Liu
- Ciechanover
Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen (CUHK- Shenzhen), Guangdong 518172, China
| | - Xingang Liu
- Department
of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
| | - Xiaoding Lou
- State
Key Laboratory of Biogeology and Environmental Geology, Engineering
Research Center of Nano-Geomaterials of Ministry of Education, Faculty
of Materials Science and Chemistry, China
University of Geosciences, Wuhan 430074, China
| | - Xin-Yue Lou
- International
Joint Research Laboratory of Nano-Micro Architecture Chemistry, College
of Chemistry, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Liang Luo
- National
Engineering Research Center for Nanomedicine, College of Life Science
and Technology, Huazhong University of Science
and Technology, Wuhan 430074, China
| | - Paul R. McGonigal
- Department
of Chemistry, University of York, Heslington, York YO10 5DD, United
Kingdom
| | - Zong-Wan Mao
- MOE
Key Laboratory of Bioinorganic and Synthetic Chemistry, School of
Chemistry, Sun Yat-Sen University, Guangzhou 510006, China
| | - Guangle Niu
- State
Key Laboratory of Crystal Materials, Shandong
University, Jinan 250100, China
| | - Tze Cin Owyong
- Department
of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Andrea Pucci
- Department
of Chemistry and Industrial Chemistry, University
of Pisa, Via Moruzzi 13, Pisa 56124, Italy
| | - Jun Qian
- State
Key Laboratory of Modern Optical Instrumentations, Centre for Optical
and Electromagnetic Research, College of Optical Science and Engineering,
International Research Center for Advanced Photonics, Zhejiang University, Hangzhou 310058, China
| | - Anjun Qin
- State
Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial
Key Laboratory of Luminescence from Molecular Aggregates, South China University of Technology, Guangzhou 510640, China
| | - Zijie Qiu
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, China
| | - Andrey L. Rogach
- Department
of Materials Science and Engineering, City
University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
| | - Bo Situ
- Department
of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Kazuo Tanaka
- Department
of Polymer Chemistry, Graduate School of Engineering, Kyoto University, Katsura,
Nishikyo-ku, Kyoto 615-8510, Japan
| | - Youhong Tang
- Institute
for NanoScale Science and Technology, College of Science and Engineering, Flinders University, Bedford Park, South Australia 5042, Australia
| | - Bingnan Wang
- State
Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial
Key Laboratory of Luminescence from Molecular Aggregates, South China University of Technology, Guangzhou 510640, China
| | - Dong Wang
- Center
for AIE Research, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Jianguo Wang
- College
of Chemistry and Chemical Engineering, Inner Mongolia Key Laboratory
of Fine Organic Synthesis, Inner Mongolia
University, Hohhot 010021, China
| | - Wei Wang
- Shanghai
Key Laboratory of Green Chemistry and Chemical Processes & Chang-Kung
Chuang Institute, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, China
| | - Wen-Xiong Wang
- School
of Energy and Environment and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon, Hong Kong SAR 999077, China
| | - Wen-Jin Wang
- MOE
Key Laboratory of Bioinorganic and Synthetic Chemistry, School of
Chemistry, Sun Yat-Sen University, Guangzhou 510006, China
- Central
Laboratory of The Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen (CUHK-
Shenzhen), & Longgang District People’s Hospital of Shenzhen, Guangdong 518172, China
| | - Xinyuan Wang
- Department
of Materials Science and Engineering, Shenzhen Key Laboratory of Printed
Organic Electronics, Southern University
of Science and Technology, Shenzhen 518055, China
| | - Yi-Feng Wang
- CAS
Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety,
CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China
- School
of Biomedical Engineering, Guangzhou Medical
University, Guangzhou 511436, China
| | - Shuizhu Wu
- State
Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial
Key Laboratory of Luminescence from Molecular Aggregates, College
of Materials Science and Engineering, South
China University of Technology, Wushan Road 381, Guangzhou 510640, China
| | - Yifan Wu
- Innovation
Research Center for AIE Pharmaceutical Biology, Guangzhou Municipal
and Guangdong Provincial Key Laboratory of Molecular Target &
Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory
Disease, School of Pharmaceutical Sciences and the Fifth Affiliated
Hospital, Guangzhou Medical University, Guangzhou 511436, China
| | - Yonghua Xiong
- State Key
Laboratory of Food Science and Resources, School of Food Science and
Technology, Nanchang University, Nanchang 330047, China
| | - Ruohan Xu
- School
of Chemistry, Xi’an Jiaotong University, Xi’an 710049 China
| | - Chenxu Yan
- Key
Laboratory for Advanced Materials and Joint International Research,
Laboratory of Precision Chemistry and Molecular Engineering, Feringa
Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals,
Frontiers Science Center for Materiobiology and Dynamic Chemistry,
School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Saisai Yan
- Center
for AIE Research, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Hai-Bo Yang
- Shanghai
Key Laboratory of Green Chemistry and Chemical Processes & Chang-Kung
Chuang Institute, East China Normal University, 3663 N. Zhongshan Road, Shanghai 200062, China
| | - Lin-Lin Yang
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, China
| | - Mingwang Yang
- Department
of Chemistry, Hong Kong Branch of Chinese National Engineering Research
Center for Tissue Restoration and Reconstruction, Division of Life
Science, State Key Laboratory of Molecular Neuroscience, Guangdong-Hong
Kong-Macau Joint Laboratory of Optoelectronic and Magnetic Functional
Materials, The Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
| | - Ying-Wei Yang
- International
Joint Research Laboratory of Nano-Micro Architecture Chemistry, College
of Chemistry, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Juyoung Yoon
- Department
of Chemistry and Nanoscience, Ewha Womans
University, Seoul 03760, Korea
| | - Shuang-Quan Zang
- College
of Chemistry, Zhengzhou University, 100 Science Road, Zhengzhou 450001, China
| | - Jiangjiang Zhang
- Guangdong
Provincial Key Laboratory of Advanced Biomaterials, Shenzhen Key Laboratory
of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088 Xueyuan Road, Nanshan District, Shenzhen, Guangdong 518055, China
- Key
Laboratory of Molecular Medicine and Biotherapy, the Ministry of Industry
and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Pengfei Zhang
- Guangdong
Key Laboratory of Nanomedicine, Shenzhen, Engineering Laboratory of
Nanomedicine and Nanoformulations, CAS Key Lab for Health Informatics,
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, University Town of Shenzhen, 1068 Xueyuan Avenue, Shenzhen 518055, China
| | - Tianfu Zhang
- School
of Biomedical Engineering, Guangzhou Medical
University, Guangzhou 511436, China
| | - Xin Zhang
- Department
of Chemistry, Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang Province 310030, China
- Westlake
Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang Province 310024, China
| | - Xin Zhang
- Ciechanover
Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen (CUHK- Shenzhen), Guangdong 518172, China
| | - Na Zhao
- Key
Laboratory of Macromolecular Science of Shaanxi Province, Key Laboratory
of Applied Surface and Colloid Chemistry of Ministry of Education,
School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi’an 710119, China
| | - Zheng Zhao
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, China
| | - Jie Zheng
- Department
of Chemical, Biomolecular, and Corrosion Engineering The University of Akron, Akron, Ohio 44325, United States
| | - Lei Zheng
- Department
of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Zheng Zheng
- School of
Chemistry and Chemical Engineering, Hefei
University of Technology, Hefei 230009, China
| | - Ming-Qiang Zhu
- Wuhan
National
Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wei-Hong Zhu
- Key
Laboratory for Advanced Materials and Joint International Research,
Laboratory of Precision Chemistry and Molecular Engineering, Feringa
Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals,
Frontiers Science Center for Materiobiology and Dynamic Chemistry,
School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Hang Zou
- Department
of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Ben Zhong Tang
- School
of Science and Engineering, Shenzhen Institute of Aggregate Science
and Technology, The Chinese University of
Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, China
- Department
of Chemistry, Hong Kong Branch of Chinese National Engineering Research
Center for Tissue Restoration and Reconstruction, Division of Life
Science, State Key Laboratory of Molecular Neuroscience, Guangdong-Hong
Kong-Macau Joint Laboratory of Optoelectronic and Magnetic Functional
Materials, The Hong Kong University of Science
and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China
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36
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Wan F, de la Fuente-Nunez C. Mining for antimicrobial peptides in sequence space. Nat Biomed Eng 2023:10.1038/s41551-023-01027-z. [PMID: 37095317 DOI: 10.1038/s41551-023-01027-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Affiliation(s)
- Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
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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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