1
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Kim N, Ma J, Kim W, Kim J, Belenky P, Lee I. Genome-resolved metagenomics: a game changer for microbiome medicine. Exp Mol Med 2024:10.1038/s12276-024-01262-7. [PMID: 38945961 DOI: 10.1038/s12276-024-01262-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/06/2024] [Accepted: 03/25/2024] [Indexed: 07/02/2024] Open
Abstract
Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.
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Affiliation(s)
- Nayeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Junyeong Ma
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Wonjong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jungyeon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Peter Belenky
- Department of Molecular Microbiology and Immunology, Brown University, Providence, RI, 02912, USA.
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea.
- POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
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2
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Ramos-Llorens M, Bello-Madruga R, Valle J, Andreu D, Torrent M. PyAMPA: a high-throughput prediction and optimization tool for antimicrobial peptides. mSystems 2024:e0135823. [PMID: 38934543 DOI: 10.1128/msystems.01358-23] [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: 12/23/2023] [Accepted: 05/27/2024] [Indexed: 06/28/2024] Open
Abstract
The alarming rise of antibiotic-resistant bacterial infections is driving efforts to develop alternatives to conventional antibiotics. In this context, antimicrobial peptides (AMPs) have emerged as promising candidates for their ability to target a broad range of microorganisms. However, the development of AMPs with optimal potency, selectivity, and/or stability profiles remains a challenge. To address it, computational tools for predicting AMP properties and designing novel peptides have gained increasing attention. PyAMPA is a novel platform for AMP discovery. It consists of five modules, namely AMPScreen, AMPValidate, AMPSolve, AMPMutate, and AMPOptimize, that allow high-throughput proteome inspection, candidate screening, and optimization through point-mutation and genetic algorithms. The platform also offers additional tools for predicting and evaluating AMP properties, including antimicrobial and cytotoxic activity, and peptide half-life. By providing innovative and accessible inroads into AMP motifs in proteomes, PyAMPA will enable advances in AMP development and potential translation into clinically useful molecules. PyAMPA is available at: https://github.com/SysBioUAB/PyAMPA. IMPORTANCE This paper introduces PyAMPA, a new bioinformatics platform designed for the discovery and optimization of antimicrobial peptides (AMPs). It addresses the urgent need for new antimicrobials due to the rise of antibiotic-resistant infections. PyAMPA, with its five predictive modules -AMPScreen, AMPValidate, AMPSolve, AMPMutate and AMPOptimize, enables high-throughput screening of proteomes to identify potential AMP motifs and optimize them for clinical use. Its unique approach, combining prediction, design, and optimization tools, makes PyAMPA a robust solution for developing new AMP-based therapies, offering a significant advance in combatting antibiotic resistance.
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Affiliation(s)
- Marc Ramos-Llorens
- Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Roberto Bello-Madruga
- Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Javier Valle
- Barcelona Biomedical Research Park, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - David Andreu
- Barcelona Biomedical Research Park, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Marc Torrent
- Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, Barcelona, Spain
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3
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Orsi M, Reymond JL. Can large language models predict antimicrobial peptide activity and toxicity? RSC Med Chem 2024; 15:2030-2036. [PMID: 38911166 PMCID: PMC11187562 DOI: 10.1039/d4md00159a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/19/2024] [Indexed: 06/25/2024] Open
Abstract
Antimicrobial peptides (AMPs) are naturally occurring or designed peptides up to a few tens of amino acids which may help address the antimicrobial resistance crisis. However, their clinical development is limited by toxicity to human cells, a parameter which is very difficult to control. Given the similarity between peptide sequences and words, large language models (LLMs) might be able to predict AMP activity and toxicity. To test this hypothesis, we fine-tuned LLMs using data from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). GPT-3 performed well but not reproducibly for activity prediction and hemolysis, taken as a proxy for toxicity. The later GPT-3.5 performed more poorly and was surpassed by recurrent neural networks (RNN) trained on sequence-activity data or support vector machines (SVM) trained on MAP4C molecular fingerprint-activity data. These simpler models are therefore recommended, although the rapid evolution of LLMs warrants future re-evaluation of their prediction abilities.
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Affiliation(s)
- Markus Orsi
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
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4
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Iglesias V, Bárcenas O, Pintado-Grima C, Burdukiewicz M, Ventura S. Structural information in therapeutic peptides: Emerging applications in biomedicine. FEBS Open Bio 2024. [PMID: 38877295 DOI: 10.1002/2211-5463.13847] [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: 02/29/2024] [Revised: 05/08/2024] [Accepted: 05/27/2024] [Indexed: 06/16/2024] Open
Abstract
Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.
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Affiliation(s)
- Valentín Iglesias
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Institute of Advanced Chemistry of Catalonia (IQAC), CSIC, Barcelona, Spain
| | - Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Michał Burdukiewicz
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
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5
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Shaon MSH, Karim T, Sultan MF, Ali MM, Ahmed K, Hasan MZ, Moustafa A, Bui FM, Al-Zahrani FA. AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features. Sci Rep 2024; 14:12892. [PMID: 38839785 PMCID: PMC11153637 DOI: 10.1038/s41598-024-63461-6] [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: 02/16/2024] [Accepted: 05/29/2024] [Indexed: 06/07/2024] Open
Abstract
Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .
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Affiliation(s)
- Md Shazzad Hossain Shaon
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Tasmin Karim
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Md Fahim Sultan
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Md Mamun Ali
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
- Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Kawsar Ahmed
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada.
- Group of Bio-photomatiχ, Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh.
| | - Md Zahid Hasan
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Ahmed Moustafa
- Department of Human Anatomy and Physiology, The Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
- School of Psychology, Centre for Data Analytics, Bond University, Gold Coast, QLD, Australia
| | - Francis M Bui
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
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6
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Nguyen QH, Nguyen-Vo TH, Do TTT, Nguyen BP. An efficient hybrid deep learning architecture for predicting short antimicrobial peptides. Proteomics 2024:e2300382. [PMID: 38837544 DOI: 10.1002/pmic.202300382] [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: 10/02/2023] [Revised: 05/02/2024] [Accepted: 05/07/2024] [Indexed: 06/07/2024]
Abstract
Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.
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Affiliation(s)
- Quang H Nguyen
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
| | - Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
- School of Innovation, Design and Technology, Wellington Institute of Technology, Lower Hutt, New Zealand
| | - Trang T T Do
- Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington, New Zealand
- Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
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7
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Yao L, Guan J, Xie P, Chung C, Deng J, Huang Y, Chiang Y, Lee T. AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest. Protein Sci 2024; 33:e5006. [PMID: 38723168 PMCID: PMC11081525 DOI: 10.1002/pro.5006] [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: 04/10/2024] [Accepted: 04/13/2024] [Indexed: 05/13/2024]
Abstract
The emergence and spread of antibiotic-resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three-stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state-of-the-art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.
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Affiliation(s)
- Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of MedicineThe Chinese University of Hong KongShenzhenChina
- School of Science and EngineeringThe Chinese University of Hong KongShenzhenChina
| | - Jiahui Guan
- Kobilka Institute of Innovative Drug Discovery, School of MedicineThe Chinese University of Hong KongShenzhenChina
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Peilin Xie
- Kobilka Institute of Innovative Drug Discovery, School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Chia‐Ru Chung
- Department of Computer Science and Information EngineeringNational Central UniversityTaoyuanTaiwan
| | - Junyang Deng
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Yixian Huang
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Ying‐Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of MedicineThe Chinese University of Hong KongShenzhenChina
- School of MedicineThe Chinese University of Hong KongShenzhenChina
| | - Tzong‐Yi Lee
- Institute of Bioinformatics and Systems BiologyNational Yang Ming Chiao Tung UniversityHsinchuTaiwan
- Center for Intelligent Drug Systems and Smart Bio‐devices (IDS2B)National Yang Ming Chiao Tung UniversityHsinchuTaiwan
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8
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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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9
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Liang Q, Liu Z, Liang Z, Zhu C, Li D, Kong Q, Mou H. Development strategies and application of antimicrobial peptides as future alternatives to in-feed antibiotics. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172150. [PMID: 38580107 DOI: 10.1016/j.scitotenv.2024.172150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/14/2024] [Accepted: 03/30/2024] [Indexed: 04/07/2024]
Abstract
The use of in-feed antibiotics has been widely restricted due to the significant environmental pollution and food safety concerns they have caused. Antimicrobial peptides (AMPs) have attracted widespread attention as potential future alternatives to in-feed antibiotics owing to their demonstrated antimicrobial activity and environment friendly characteristics. However, the challenges of weak bioactivity, immature stability, and low production yields of natural AMPs impede practical application in the feed industry. To address these problems, efforts have been made to develop strategies for approaching the AMPs with enhanced properties. Herein, we summarize approaches to improving the properties of AMPs as potential alternatives to in-feed antibiotics, mainly including optimization of structural parameters, sequence modification, selection of microbial hosts, fusion expression, and industrially fermentation control. Additionally, the potential for application of AMPs in animal husbandry is discussed. This comprehensive review lays a strong theoretical foundation for the development of in-feed AMPs to achieve the public health globally.
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Affiliation(s)
- Qingping Liang
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Zhemin Liu
- Fundamental Science R&D Center of Vazyme Biotech Co. Ltd., Nanjing 210000, China
| | - Ziyu Liang
- Section of Neurobiology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Changliang Zhu
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Dongyu Li
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Qing Kong
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
| | - Haijin Mou
- College of Food Science and Engineering, Ocean University of China, Qingdao 266404, China.
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10
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Santos-Júnior CD, Torres MDT, Duan Y, Rodríguez Del Río Á, Schmidt TSB, Chong H, Fullam A, Kuhn M, Zhu C, Houseman A, Somborski J, Vines A, Zhao XM, Bork P, Huerta-Cepas J, de la Fuente-Nunez C, Coelho LP. Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 2024:S0092-8674(24)00522-1. [PMID: 38843834 DOI: 10.1016/j.cell.2024.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 06/25/2024]
Abstract
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Laboratory of Microbial Processes & Biodiversity - LMPB, Department of Hydrobiology, Universidade Federal de São Carlos - UFSCar, São Carlos, São Paulo 13565-905, Brazil
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; APC Microbiome & School of Medicine, University College Cork, Cork, Ireland
| | - Hui Chong
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Chengkai Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Amy Houseman
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Jelena Somborski
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anna Vines
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, QLD, Australia.
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11
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Cordoves-Delgado G, García-Jacas CR. Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning. J Chem Inf Model 2024; 64:4310-4321. [PMID: 38739853 DOI: 10.1021/acs.jcim.3c02061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.
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Affiliation(s)
- Greneter Cordoves-Delgado
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - César R García-Jacas
- Cátedras CONAHCYT - Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
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12
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Zervou MA, Doutsi E, Pantazis Y, Tsakalides P. De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks. Int J Mol Sci 2024; 25:5506. [PMID: 38791544 PMCID: PMC11122239 DOI: 10.3390/ijms25105506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.
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Affiliation(s)
- Michaela Areti Zervou
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Effrosyni Doutsi
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Yannis Pantazis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Panagiotis Tsakalides
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
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13
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Wang SC. E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides. PLoS One 2024; 19:e0300125. [PMID: 38722967 PMCID: PMC11081394 DOI: 10.1371/journal.pone.0300125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 02/22/2024] [Indexed: 05/13/2024] Open
Abstract
With the increasing problem of antimicrobial drug resistance, the search for new antimicrobial agents has become a crucial task in the field of medicine. Antimicrobial peptides, as a class of naturally occurring antimicrobial agents, possess broad-spectrum antimicrobial activity and lower risk of resistance development. However, traditional screening methods for antimicrobial peptides are inefficient, necessitating the development of an efficient screening model. In this study, we aimed to develop an ensemble learning model for the identification of antimicrobial peptides, named E-CLEAP, based on the Multilayer Perceptron Classifier (MLP Classifier). By considering multiple features, including amino acid composition (AAC) and pseudo amino acid composition (PseAAC) of antimicrobial peptides, we aimed to improve the accuracy and generalization ability of the identification process. To validate the superiority of our model, we employed five-fold cross-validation and compared it with other commonly used methods for antimicrobial peptide identification. In the experimental results on an independent test set, E-CLEAP achieved accuracies of 97.33% and 84% for the AAC and PseAAC features, respectively. The results demonstrated that our model outperformed other methods in all evaluation metrics. The findings of this study highlight the potential of the E-CLEAP model in enhancing the efficiency and accuracy of antimicrobial peptide screening, which holds significant implications for drug development, disease treatment, and biotechnology advancement. Future research can further optimize the model by incorporating additional features and information, as well as validating its reliability on larger datasets and in real-world environments. The source code and all datasets are publicly available at https://github.com/Wangsicheng52/E-CLEAP.
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Affiliation(s)
- Si-Cheng Wang
- School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China
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14
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Lobanov MY, Slizen MV, Dovidchenko NV, Panfilov AV, Surin AA, Likhachev IV, Galzitskaya OV. Comparison of deep learning models with simple method to assess the problem of antimicrobial peptides prediction. Mol Inform 2024; 43:e202200181. [PMID: 36961202 DOI: 10.1002/minf.202200181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 03/25/2023]
Abstract
Antibiotic-resistant strains are an emerging threat to public health. The usage of antimicrobial peptides (AMPs) is one of the promising approaches to solve this problem. For the development of new AMPs, it is necessary to have reliable prediction methods. Recently, deep learning approaches have been used to predict AMP. In this paper, we want to compare simple and complex methods for these purposes. We used the BERT transformer to create sequence embeddings and the multilayer perceptron (MLP) and light attention (LA) approaches for classification. One of them reached about 80 % accuracy and specificity in benchmark testing, which is on par with the best available methods. For comparison, we proposed a simple method using only the amino acid composition of proteins or peptides. This method has shown good results, at the level of the best methods. We have prepared a special server for predicting the ability of AMPs by amino acid composition: http://bioproteom.protres.ru/antimicrob/.
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Affiliation(s)
- M Y Lobanov
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - M V Slizen
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - N V Dovidchenko
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - A V Panfilov
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
| | - A A Surin
- Faculty of Applied math, MIREA - Russian Technological University, Moscow, 119454, Russia
| | - I V Likhachev
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
- Institute of Mathematical Problems of Biology branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 142290, Pushchino, Russia
| | - O V Galzitskaya
- Laboratory of Bioinformatics and Proteomics, Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
- Laboratory of Structure and Function of Muscle Proteins, Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia
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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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Jacquier JC, Duffy C, O'Sullivan M, Dillon E. Copper-Chelated Chitosan Microgels for the Selective Enrichment of Small Cationic Peptides. Gels 2024; 10:289. [PMID: 38786205 PMCID: PMC11121711 DOI: 10.3390/gels10050289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Abstract
Copper-chelated chitosan microgels were investigated as an immobilized metal affinity chromatography (IMAC) phase for peptide separation. The copper-crosslinked chitosan beads were shown to strongly interact with a range of amino acids, in a wide range of pH and saline conditions. The beads exhibited an affinity that seemed to depend on the isoelectric point of the amino acid, with the extent of uptake increasing with decreasing isoelectric point. This selective interaction with anionic amino acids resulted in a significant relative enrichment of the supernatant solution in cationic amino acids. The beads were then studied as a novel fractionation system for complex milk hydrolysates. The copper chitosan beads selectively removed larger peptides from the hydrolysate aqueous solution, yielding a solution relatively enriched in medium and smaller peptides, which was characterized both quantitatively and qualitatively by size exclusion chromatography (SEC). Liquid chromatography-mass spectrometry (LCMS) work provided comprehensive data on a peptide sequence level and showed that a depletion of the anionic peptides by the beads resulted in a relative enrichment of the cationic peptides in the supernatant solution. It could be concluded that after fractionation a dramatic relative enrichment in respect to small- and medium-sized cationic peptides in the solution, characteristics that have been linked to bioactivities, such as anti-microbial and cell-penetrating properties. The results demonstrate the use of the chitosan copper gel bead system in lab scale fractionation of complex hydrolysate mixtures, with the potential to enhance milk hydrolysate bioactivity.
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Affiliation(s)
- Jean-Christophe Jacquier
- UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Ciara Duffy
- UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Michael O'Sullivan
- UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Eugène Dillon
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, D04 V1W8 Dublin, Ireland
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17
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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18
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Chen J, Wu H, Wang N. KEGG orthology prediction of bacterial proteins using natural language processing. BMC Bioinformatics 2024; 25:146. [PMID: 38600441 PMCID: PMC11007918 DOI: 10.1186/s12859-024-05766-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 04/03/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights. RESULTS In this work, we propose a novel pipeline for KEGG orthology annotation of bacterial protein sequences that uses natural language processing and deep learning. To assess the effectiveness of our pipeline, we conducted evaluations using the genomes of two randomly selected species from the KEGG database. In our evaluation, we obtain competitive results on precision, recall, and F1 score, with values of 0.948, 0.947, and 0.947, respectively. CONCLUSIONS Our experimental results suggest that our pipeline demonstrates performance comparable to traditional methods and excels in identifying distant relatives with low sequence identity. This demonstrates the potential of our pipeline to significantly improve the accuracy and comprehensiveness of KEGG orthology annotation, thereby advancing our understanding of functional relationships within biological systems.
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Affiliation(s)
- Jing Chen
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
- Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence, Jiangnan University, Wuxi, China
| | - Haoyu Wu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Ning Wang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
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19
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Li C, Zou Q, Jia C, Zheng J. AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and Multihead Attention. J Chem Inf Model 2024; 64:2393-2404. [PMID: 37799091 DOI: 10.1021/acs.jcim.3c01017] [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: 10/07/2023]
Abstract
Antimicrobial peptides (AMPs) are small molecular polypeptides that can be widely used in the prevention and treatment of microbial infections. Although many computational models have been proposed to help identify AMPs, a high-performance and interpretable model is still lacking. In this study, new benchmark data sets are collected and processed, and a stacking deep architecture named AMPpred-MFA is carefully designed to discover and identify AMPs. Multiple features and a multihead attention mechanism are utilized on the basis of a bidirectional long short-term memory (LSTM) network and a convolutional neural network (CNN). The effectiveness of AMPpred-MFA is verified through five independent tests conducted in batches. Experimental results show that AMPpred-MFA achieves a state-of-the-art performance. The visualization interpretability analyses and ablation experiments offer a further understanding of the model behavior and performance, validating the importance of our feature representation and stacking architecture, especially the multihead attention mechanism. Therefore, AMPpred-MFA can be considered a reliable and efficient approach to understanding and predicting AMPs.
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Affiliation(s)
- Changjiang Li
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Jia Zheng
- School of Science, Dalian Maritime University, Dalian 116026, China
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20
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Martínez‐Mauricio KL, García‐Jacas CR, Cordoves‐Delgado G. Examining evolutionary scale modeling-derived different-dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow. Protein Sci 2024; 33:e4928. [PMID: 38501511 PMCID: PMC10949403 DOI: 10.1002/pro.4928] [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: 01/28/2024] [Accepted: 01/30/2024] [Indexed: 03/20/2024]
Abstract
Molecular features play an important role in different bio-chem-informatics tasks, such as the Quantitative Structure-Activity Relationships (QSAR) modeling. Several pre-trained models have been recently created to be used in downstream tasks, either by fine-tuning a specific model or by extracting features to feed traditional classifiers. In this regard, a new family of Evolutionary Scale Modeling models (termed as ESM-2 models) was recently introduced, demonstrating outstanding results in protein structure prediction benchmarks. Herein, we studied the usefulness of the different-dimensional embeddings derived from the ESM-2 models to classify antimicrobial peptides (AMPs). To this end, we built a KNIME workflow to use the same modeling methodology across experiments in order to guarantee fair analyses. As a result, the 640- and 1280-dimensional embeddings derived from the 30- and 33-layer ESM-2 models, respectively, are the most valuable since statistically better performances were achieved by the QSAR models built from them. We also fused features of the different ESM-2 models, and it was concluded that the fusion contributes to getting better QSAR models than using features of a single ESM-2 model. Frequency studies revealed that only a portion of the ESM-2 embeddings is valuable for modeling tasks since between 43% and 66% of the features were never used. Comparisons regarding state-of-the-art deep learning (DL) models confirm that when performing methodologically principled studies in the prediction of AMPs, non-DL based QSAR models yield comparable-to-superior performances to DL-based QSAR models. The developed KNIME workflow is available-freely at https://github.com/cicese-biocom/classification-QSAR-bioKom. This workflow can be valuable to avoid unfair comparisons regarding new computational methods, as well as to propose new non-DL based QSAR models.
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Affiliation(s)
- Karla L. Martínez‐Mauricio
- Departamento de Ciencias de la ComputaciónCentro de Investigación Científica y de Educación Superior de Ensenada (CICESE)EnsenadaMexico
| | - César R. García‐Jacas
- Cátedras CONAHCYT – Departamento de Ciencias de la ComputaciónCentro de Investigación Científica y de Educación Superior de Ensenada (CICESE)EnsenadaMexico
| | - Greneter Cordoves‐Delgado
- Departamento de Ciencias de la ComputaciónCentro de Investigación Científica y de Educación Superior de Ensenada (CICESE)EnsenadaMexico
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21
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Fukunaga I, Matsukiyo Y, Kaitoh K, Yamanishi Y. Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization. Mol Inform 2024; 43:e202300148. [PMID: 38182544 DOI: 10.1002/minf.202300148] [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: 06/20/2023] [Revised: 02/03/2024] [Accepted: 02/03/2024] [Indexed: 01/07/2024]
Abstract
Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.
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Affiliation(s)
- Itsuki Fukunaga
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yuki Matsukiyo
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
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22
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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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Kanwal S, Arif R, Ahmed S, Kabir M. A novel stacking-based predictor for accurate prediction of antimicrobial peptides. J Biomol Struct Dyn 2024:1-12. [PMID: 38500243 DOI: 10.1080/07391102.2024.2329298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/06/2024] [Indexed: 03/20/2024]
Abstract
Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sameera Kanwal
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Roha Arif
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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24
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Monsalve D, Mesa A, Mira LM, Mera C, Orduz S, Branch-Bedoya JW. Antimicrobial peptides designed by computational analysis of proteomes. Antonie Van Leeuwenhoek 2024; 117:55. [PMID: 38488950 DOI: 10.1007/s10482-024-01946-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 02/06/2024] [Indexed: 03/17/2024]
Abstract
Antimicrobial peptides (AMPs) are promising cationic and amphipathic molecules to fight antibiotic resistance. To search for novel AMPs, we applied a computational strategy to identify peptide sequences within the organisms' proteome, including in-house developed software and artificial intelligence tools. After analyzing 150.450 proteins from eight proteomes of bacteria, plants, a protist, and a nematode, nine peptides were selected and modified to increase their antimicrobial potential. The 18 resulting peptides were validated by bioassays with four pathogenic bacterial species, one yeast species, and two cancer cell-lines. Fourteen of the 18 tested peptides were antimicrobial, with minimum inhibitory concentrations (MICs) values under 10 µM against at least three bacterial species; seven were active against Candida albicans with MICs values under 10 µM; six had a therapeutic index above 20; two peptides were active against A549 cells, and eight were active against MCF-7 cells under 30 µM. This study's most active antimicrobial peptides damage the bacterial cell membrane, including grooves, dents, membrane wrinkling, cell destruction, and leakage of cytoplasmic material. The results confirm that the proposed approach, which uses bioinformatic tools and rational modifications, is highly efficient and allows the discovery, with high accuracy, of potent AMPs encrypted in proteins.
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Affiliation(s)
- Dahiana Monsalve
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Andrea Mesa
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Laura M Mira
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - Carlos Mera
- Departamento de Sistemas de Información, Instituto Tecnológico Metropolitano, Calle 54A # 30-01, 050013, Medellín, Antioquia, Colombia.
- Departamento de Ingeniería de Sistemas, Facultad de Ingenierías, Universidad de Antioquia, Calle 70 # 52-21, 050010, Medellín, Antioquia, Colombia.
| | - Sergio Orduz
- Escuela de Biociencias, Departamento de Ciencias, Universidad Nacional de Colombia, sede Medellín, Carrera 65 # 59A-110, 050034, Medellín, Antioquia, Colombia
| | - John W Branch-Bedoya
- Departamento de Ciencias de la Computación y de la Decisión, Facultad de Minas, Universidad Nacional de Colombia, sede Medellín, Av. 80 # 65-223, 050041, Medellín, Antioquia, Colombia
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25
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Shao J, Zhao Y, Wei W, Vaisman II. AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria. Front Microbiol 2024; 15:1304044. [PMID: 38516021 PMCID: PMC10955071 DOI: 10.3389/fmicb.2024.1304044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/12/2024] [Indexed: 03/23/2024] Open
Abstract
Introduction Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria. Methods N-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness. Results The models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models. Discussion Hydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.
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Affiliation(s)
- Jonathan Shao
- Statistics and Bioinformatics Group - Northeast Area, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD, United States
- School of Systems Biology, George Mason University, Manassas, VA, United States
| | - Yan Zhao
- Molecular Plant Pathology Laboratory, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD, United States
| | - Wei Wei
- Molecular Plant Pathology Laboratory, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD, United States
| | - Iosif I. Vaisman
- School of Systems Biology, George Mason University, Manassas, VA, United States
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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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27
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Tsai CT, Lin CW, Ye GL, Wu SC, Yao P, Lin CT, Wan L, Tsai HHG. Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models. ACS OMEGA 2024; 9:9357-9374. [PMID: 38434814 PMCID: PMC10905719 DOI: 10.1021/acsomega.3c08676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/19/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024]
Abstract
The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.
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Affiliation(s)
- Cheng-Ting Tsai
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Chia-Wei Lin
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Gen-Lin Ye
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Shao-Chi Wu
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
| | - Philip Yao
- Aurora
High School, 109 W Pioneer Trail, Aurora, Ohio 44202, United States
| | - Ching-Ting Lin
- School
of Chinese Medicine, China Medical University, No. 91 Hsueh-Shih Road, Taichung 40402, Taiwan
| | - Lei Wan
- School
of Chinese Medicine, China Medical University, No. 91 Hsueh-Shih Road, Taichung 40402, Taiwan
| | - Hui-Hsu Gavin Tsai
- Department
of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan
- Research
Center of New Generation Light Driven Photovoltaic Modules, National Central University, Taoyuan 32001, Taiwan
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Bajiya N, Choudhury S, Dhall A, Raghava GPS. AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria. Antibiotics (Basel) 2024; 13:168. [PMID: 38391554 PMCID: PMC10885866 DOI: 10.3390/antibiotics13020168] [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: 12/26/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.
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Affiliation(s)
- Nisha Bajiya
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Shubham Choudhury
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
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29
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Ji S, An F, Zhang T, Lou M, Guo J, Liu K, Zhu Y, Wu J, Wu R. Antimicrobial peptides: An alternative to traditional antibiotics. Eur J Med Chem 2024; 265:116072. [PMID: 38147812 DOI: 10.1016/j.ejmech.2023.116072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/04/2023] [Accepted: 12/17/2023] [Indexed: 12/28/2023]
Abstract
As antibiotic-resistant bacteria and genes continue to emerge, the identification of effective alternatives to traditional antibiotics has become a pressing issue. Antimicrobial peptides are favored for their safety, low residue, and low resistance properties, and their unique antimicrobial mechanisms show significant potential in combating antibiotic resistance. However, the high production cost and weak activity of antimicrobial peptides limit their application. Moreover, traditional laboratory methods for identifying and designing new antimicrobial peptides are time-consuming and labor-intensive, hindering their development. Currently, novel technologies, such as artificial intelligence (AI) are being employed to develop and design new antimicrobial peptide resources, offering new opportunities for the advancement of antimicrobial peptides. This article summarizes the basic characteristics and antimicrobial mechanisms of antimicrobial peptides, as well as their advantages and limitations, and explores the application of AI in antimicrobial peptides prediction amd design. This highlights the crucial role of AI in enhancing the efficiency of antimicrobial peptide research and provides a reference for antimicrobial drug development.
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Affiliation(s)
- Shuaiqi Ji
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Feiyu An
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China
| | - Taowei Zhang
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Mengxue Lou
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China
| | - Jiawei Guo
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Kexin Liu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China
| | - Yi Zhu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China
| | - Junrui Wu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China.
| | - Rina Wu
- College of Food Science, Shenyang Agricultural University, Shenyang, 110866, PR China; Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang, 110866, PR China; Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang, 110866, PR China.
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30
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Zhuang J, Gao W, Su R. EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features. J Bioinform Comput Biol 2024; 22:2450001. [PMID: 38406833 DOI: 10.1142/s021972002450001x] [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] [Indexed: 02/27/2024]
Abstract
Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers. The average of four classifiers is the final prediction result. Compared with other state-of-the-art algorithms on six datasets, EnAMP outperforms most existing models with similar computational costs, even when compared with high computational cost algorithms based on Bidirectional Encoder Representation from Transformers (BERT), the performance of our model is comparable. EnAMP source code and the data are available at https://github.com/ruisue/EnAMP.
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Affiliation(s)
- Jujuan Zhuang
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Wanquan Gao
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
| | - Rui Su
- School of Science, Dalian Maritime University, Dalian, Liaoning, P. R. China
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31
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Costa ISD, Junot T, Silva FL, Felix W, Cardozo Fh JL, Pereira de Araujo AF, Pais do Amaral C, Gonçalves S, Santos NC, Leite JRSA, Bloch C, Brand GD. Occurrence and evolutionary conservation analysis of α-helical cationic amphiphilic segments in the human proteome. FEBS J 2024; 291:547-565. [PMID: 37945538 DOI: 10.1111/febs.16997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/14/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023]
Abstract
The existence of encrypted fragments with antimicrobial activity in human proteins has been thoroughly demonstrated in the literature. Recently, algorithms for the large-scale identification of these segments in whole proteomes were developed, and the pervasiveness of this phenomenon was stated. These algorithms typically mine encrypted cationic and amphiphilic segments of proteins, which, when synthesized as individual polypeptide sequences, exert antimicrobial activity by membrane disruption. In the present report, the human reference proteome was submitted to the software kamal for the uncovering of protein segments that correspond to putative intragenic antimicrobial peptides (IAPs). The assessment of the identity of these segments, frequency, functional classes of parent proteins, structural relevance, and evolutionary conservation of amino acid residues within their corresponding proteins was conducted in silico. Additionally, the antimicrobial and anticancer activity of six selected synthetic peptides was evaluated. Our results indicate that cationic and amphiphilic segments can be found in 2% of all human proteins, but are more common in transmembrane and peripheral membrane proteins. These segments are surface-exposed basic patches whose amino acid residues present similar conservation scores to other residues with similar solvent accessibility. Moreover, the antimicrobial and anticancer activity of the synthetic putative IAP sequences was irrespective to whether these are associated to membranes in the cellular setting. Our study discusses these findings in light of the current understanding of encrypted peptide sequences, offering some insights into the relevance of these segments to the organism in the context of their harboring proteins or as separate polypeptide sequences.
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Affiliation(s)
- Igor S D Costa
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
| | - Tiago Junot
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
| | - Fernanda L Silva
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
| | - Wanessa Felix
- Núcleo de Pesquisa em Morfologia e Imunologia Aplicada - NuPMIA, Faculdade de Medicina, Universidade de Brasília, Brazil
| | - José L Cardozo Fh
- Laboratório de Espectrometria de Massa - LEM, Embrapa Recursos Genéticos e Biotecnologia, Brasília, Brazil
| | - Antonio F Pereira de Araujo
- Laboratório de Biofísica Teórica e Computacional, Departamento de Biologia Celular, Universidade de Brasília, Brazil
| | | | - Sónia Gonçalves
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Nuno C Santos
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - José R S A Leite
- Núcleo de Pesquisa em Morfologia e Imunologia Aplicada - NuPMIA, Faculdade de Medicina, Universidade de Brasília, Brazil
| | - Carlos Bloch
- Laboratório de Espectrometria de Massa - LEM, Embrapa Recursos Genéticos e Biotecnologia, Brasília, Brazil
| | - Guilherme D Brand
- Laboratório de Síntese e Análise de Biomoléculas - LSAB, Instituto de Química, Universidade de Brasília, Brazil
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Yao L, Guan J, Li W, Chung CR, Deng J, Chiang YC, Lee TY. Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation. Anal Chem 2024; 96:1538-1546. [PMID: 38226973 DOI: 10.1021/acs.analchem.3c04196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).
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Affiliation(s)
- Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Wenshuo Li
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, 320317 Taoyuan, Taiwan
| | - Junyang Deng
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 300093 Hsinchu, Taiwan
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Wang R, Wang T, Zhuo L, Wei J, Fu X, Zou Q, Yao X. Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization. Brief Bioinform 2024; 25:bbae078. [PMID: 38446739 PMCID: PMC10939340 DOI: 10.1093/bib/bbae078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.
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Affiliation(s)
- Rui Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Jinhang Wei
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012 Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 611730 Chengdu, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078 Macao, China
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Feijoo-Coronel ML, Mendes B, Ramírez D, Peña-Varas C, de los Monteros-Silva NQE, Proaño-Bolaños C, de Oliveira LC, Lívio DF, da Silva JA, da Silva JMSF, Pereira MGAG, Rodrigues MQRB, Teixeira MM, Granjeiro PA, Patel K, Vaiyapuri S, Almeida JR. Antibacterial and Antiviral Properties of Chenopodin-Derived Synthetic Peptides. Antibiotics (Basel) 2024; 13:78. [PMID: 38247637 PMCID: PMC10812719 DOI: 10.3390/antibiotics13010078] [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: 11/29/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Antimicrobial peptides have been developed based on plant-derived molecular scaffolds for the treatment of infectious diseases. Chenopodin is an abundant seed storage protein in quinoa, an Andean plant with high nutritional and therapeutic properties. Here, we used computer- and physicochemical-based strategies and designed four peptides derived from the primary structure of Chenopodin. Two peptides reproduce natural fragments of 14 amino acids from Chenopodin, named Chen1 and Chen2, and two engineered peptides of the same length were designed based on the Chen1 sequence. The two amino acids of Chen1 containing amide side chains were replaced by arginine (ChenR) or tryptophan (ChenW) to generate engineered cationic and hydrophobic peptides. The evaluation of these 14-mer peptides on Staphylococcus aureus and Escherichia coli showed that Chen1 does not have antibacterial activity up to 512 µM against these strains, while other peptides exhibited antibacterial effects at lower concentrations. The chemical substitutions of glutamine and asparagine by amino acids with cationic or aromatic side chains significantly favoured their antibacterial effects. These peptides did not show significant hemolytic activity. The fluorescence microscopy analysis highlighted the membranolytic nature of Chenopodin-derived peptides. Using molecular dynamic simulations, we found that a pore is formed when multiple peptides are assembled in the membrane. Whereas, some of them form secondary structures when interacting with the membrane, allowing water translocations during the simulations. Finally, Chen2 and ChenR significantly reduced SARS-CoV-2 infection. These findings demonstrate that Chenopodin is a highly useful template for the design, engineering, and manufacturing of non-toxic, antibacterial, and antiviral peptides.
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Affiliation(s)
- Marcia L. Feijoo-Coronel
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
| | - Bruno Mendes
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
| | - David Ramírez
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
| | - Carlos Peña-Varas
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
| | | | - Carolina Proaño-Bolaños
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
| | - Leonardo Camilo de Oliveira
- Centro de Pesquisa e Desenvolvimento de Fármacos, Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Diego Fernandes Lívio
- Campus Centro Oeste, Federal University of São João Del-Rei, Rua Sebastião Gonçalves Filho, n 400, Chanadour, Divinópolis 35501-296, Brazil
| | - José Antônio da Silva
- Campus Centro Oeste, Federal University of São João Del-Rei, Rua Sebastião Gonçalves Filho, n 400, Chanadour, Divinópolis 35501-296, Brazil
| | - José Maurício S. F. da Silva
- Departamento de Bioquímica, Centro de Ciências Biomédicas, Federal University of Alfenas, Rua Gabriel Monteiro da Silva, 700, Sala E209, Alfenas 37130-001, Brazil
| | - Marília Gabriella A. G. Pereira
- Departamento de Bioquímica, Centro de Ciências Biomédicas, Federal University of Alfenas, Rua Gabriel Monteiro da Silva, 700, Sala E209, Alfenas 37130-001, Brazil
| | - Marina Q. R. B. Rodrigues
- Departamento de Bioquímica, Centro de Ciências Biomédicas, Federal University of Alfenas, Rua Gabriel Monteiro da Silva, 700, Sala E209, Alfenas 37130-001, Brazil
- Departamento de Engenharia de Biossistemas, Campus Dom Bosco, Federal University of São João Del-Rei, Praça Dom Helvécio, 74, Fábricas, São João del-Rei 36301-160, Brazil
| | - Mauro M. Teixeira
- Centro de Pesquisa e Desenvolvimento de Fármacos, Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Paulo Afonso Granjeiro
- Campus Centro Oeste, Federal University of São João Del-Rei, Rua Sebastião Gonçalves Filho, n 400, Chanadour, Divinópolis 35501-296, Brazil
| | - Ketan Patel
- School of Biological Sciences, University of Reading, Reading RG6 6UB, UK
| | | | - José R. Almeida
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
- School of Pharmacy, University of Reading, Reading RG6 6UB, UK
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Matias LLR, Damasceno KSFDSC, Pereira AS, Passos TS, Morais AHDA. Innovative Biomedical and Technological Strategies for the Control of Bacterial Growth and Infections. Biomedicines 2024; 12:176. [PMID: 38255281 PMCID: PMC10813423 DOI: 10.3390/biomedicines12010176] [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: 11/28/2023] [Revised: 01/05/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
Antibiotics comprise one of the most successful groups of pharmaceutical products. Still, they have been associated with developing bacterial resistance, which has become one of the most severe problems threatening human health today. This context has prompted the development of new antibiotics or co-treatments using innovative tools to reverse the resistance context, combat infections, and offer promising antibacterial therapy. For the development of new alternatives, strategies, and/or antibiotics for controlling bacterial growth, it is necessary to know the target bacteria, their classification, morphological characteristics, the antibiotics currently used for therapies, and their respective mechanisms of action. In this regard, genomics, through the sequencing of bacterial genomes, has generated information on diverse genetic resources, aiding in the discovery of new molecules or antibiotic compounds. Nanotechnology has been applied to propose new antimicrobials, revitalize existing drug options, and use strategic encapsulating agents with their biochemical characteristics, making them more effective against various bacteria. Advanced knowledge in bacterial sequencing contributes to the construction of databases, resulting in advances in bioinformatics and the development of new antimicrobials. Moreover, it enables in silico antimicrobial susceptibility testing without the need to cultivate the pathogen, reducing costs and time. This review presents new antibiotics and biomedical and technological innovations studied in recent years to develop or improve natural or synthetic antimicrobial agents to reduce bacterial growth, promote well-being, and benefit users.
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Affiliation(s)
- Lídia Leonize Rodrigues Matias
- Biochemistry and Molecular Biology Postgraduate Program, Biosciences Center, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil;
| | | | - Annemberg Salvino Pereira
- Nutrition Course, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil;
| | - Thaís Souza Passos
- Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (K.S.F.d.S.C.D.); (T.S.P.)
| | - Ana Heloneida de Araujo Morais
- Biochemistry and Molecular Biology Postgraduate Program, Biosciences Center, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil;
- Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (K.S.F.d.S.C.D.); (T.S.P.)
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36
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Yue L, Song L, Zhu S, Fu X, Li X, He C, Li J. Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization. Sci Rep 2024; 14:947. [PMID: 38200054 PMCID: PMC10781772 DOI: 10.1038/s41598-023-50832-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.
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Affiliation(s)
- Lizhi Yue
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China
- School of Chemistry and Chemical Engineering, Qilu Normal University, Shandong, China
| | - Liya Song
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China
| | - Siyu Zhu
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Xiaolei Fu
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Xuhui Li
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Zhejiang Provincial Key Laboratory of Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
| | - Congfen He
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China.
| | - Junxiang Li
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China.
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China.
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37
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Bass L, Elder LH, Folescu DE, Forouzesh N, Tolokh IS, Karpatne A, Onufriev AV. Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment. J Chem Theory Comput 2024; 20:396-410. [PMID: 38149593 PMCID: PMC10950260 DOI: 10.1021/acs.jctc.3c00981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.
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Affiliation(s)
- Lewis Bass
- Department of Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Luke H Elder
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Dan E Folescu
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department of Mathematics, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, California 90032, United States
| | - Igor S Tolokh
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Anuj Karpatne
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Alexey V Onufriev
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
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38
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Aguilera-Puga MDC, Cancelarich NL, Marani MM, de la Fuente-Nunez C, Plisson F. Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. Methods Mol Biol 2024; 2714:329-352. [PMID: 37676607 DOI: 10.1007/978-1-0716-3441-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico
| | - Natalia L Cancelarich
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Mariela M Marani
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Fabien Plisson
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico.
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico.
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Wu Z, Wu Y, Zhu C, Wu X, Zhai S, Wang X, Su Z, Duan H. Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides. J Chem Inf Model 2023; 63:7655-7668. [PMID: 38049371 DOI: 10.1021/acs.jcim.3c01385] [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: 12/06/2023]
Abstract
The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.
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Affiliation(s)
- Zhipeng Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yejian Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Cheng Zhu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinyi Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Silong Zhai
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinqiao Wang
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zhihao Su
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
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40
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La Paglia L, Vazzana M, Mauro M, Urso A, Arizza V, Vizzini A. Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence. Mar Drugs 2023; 22:6. [PMID: 38276644 PMCID: PMC10817596 DOI: 10.3390/md22010006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.
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Affiliation(s)
- Laura La Paglia
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Mirella Vazzana
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Manuela Mauro
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Alfonso Urso
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Vincenzo Arizza
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Aiti Vizzini
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
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41
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Genth J, Schäfer K, Cassidy L, Graspeuntner S, Rupp J, Tholey A. Identification of proteoforms of short open reading frame-encoded peptides in Blautia producta under different cultivation conditions. Microbiol Spectr 2023; 11:e0252823. [PMID: 37782090 PMCID: PMC10715070 DOI: 10.1128/spectrum.02528-23] [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: 06/16/2023] [Accepted: 08/14/2023] [Indexed: 10/03/2023] Open
Abstract
IMPORTANCE The identification of short open reading frame-encoded peptides (SEP) and different proteoforms in single cultures of gut microbes offers new insights into a largely neglected part of the microbial proteome landscape. This is of particular importance as SEP provide various predicted functions, such as acting as antimicrobial peptides, maintaining cell homeostasis under stress conditions, or even contributing to the virulence pattern. They are, thus, taking a poorly understood role in structure and function of microbial networks in the human body. A better understanding of SEP in the context of human health requires a precise understanding of the abundance of SEP both in commensal microbes as well as pathogens. For the gut beneficial B. producta, we demonstrate the importance of specific environmental conditions for biosynthesis of SEP expanding previous findings about their role in microbial interactions.
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Affiliation(s)
- Jerome Genth
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Kathrin Schäfer
- Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany
| | - Liam Cassidy
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Simon Graspeuntner
- Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany
- German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany
| | - Jan Rupp
- Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany
- German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany
| | - Andreas Tholey
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
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Yu H, Deng H, He J, Keasling JD, Luo X. UniKP: a unified framework for the prediction of enzyme kinetic parameters. Nat Commun 2023; 14:8211. [PMID: 38081905 PMCID: PMC10713628 DOI: 10.1038/s41467-023-44113-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.
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Affiliation(s)
- Han Yu
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Huaxiang Deng
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jiahui He
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jay D Keasling
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Department of Chemical and Biomolecular Engineering & Department of Bioengineering, University of California, Berkeley, CA, 94720, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs, Lyngby, Denmark
| | - Xiaozhou Luo
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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Chang DH, Lee MR, Wang N, Lynn DM, Palecek SP. Establishing Quantifiable Guidelines for Antimicrobial α/β-Peptide Design: A Partial Least-Squares Approach to Improve Antimicrobial Activity and Reduce Mammalian Cell Toxicity. ACS Infect Dis 2023; 9:2632-2651. [PMID: 38014670 PMCID: PMC10807133 DOI: 10.1021/acsinfecdis.3c00468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Antimicrobial peptides (AMPs) are promising candidates to combat pathogens that are resistant to conventional antimicrobial drugs because they operate through mechanisms that involve membrane disruption. However, the use of AMPs in clinical settings has been limited, at least in part, by their susceptibility to proteolytic degradation and their lack of selectivity toward pathogenic microbes vs mammalian cells. We recently reported on the design of α- and β-peptide oligomers structurally templated upon the naturally occurring α-helical AMP aurein 1.2. These α/β-peptide oligomers are more proteolytically stable than aurein 1.2 and have several other attributes that render them attractive as alternatives to conventional AMPs. This study describes the influence of peptide physicochemical properties on the broad-spectrum activity of aurein 1.2-based α/β-peptide mimics against nine bacterial, fungal, and mammalian cell lines. We used a partial least-squares regression (PLSR)-supervised machine learning model to quantify and visualize relationships between experimentally determined physicochemical properties (e.g., hydrophobicity, charge, and helicity) and experimentally measured cell-type-specific activities of 21 peptides in a 149-member α/β-peptide library. Using this approach, we identified several peptides that were predicted to exhibit enhanced broad-spectrum selectivity, a measure that evaluates antimicrobial activity relative to mammalian cell toxicity compared to aurein 1.2. Experimental validation demonstrated high model predictive performance, and characterization of compounds with the highest broad-spectrum selectivity revealed peptide hydrophobicity, helicity, and helical rigidity to be strong predictors of broad-spectrum selectivity. The most selective peptide identified from the model prediction has more than a 13-fold improvement in broad-spectrum selectivity than that of aurein 1.2, demonstrating the ability of using PLSR models to identify quantitative structure-function relationships for nonstandard amino acid-containing peptides. Overall, this work establishes quantifiable guidelines for the rational design of helical antimicrobial α/β-peptides and identifies promising new α/β-peptides with significantly reduced mammalian toxicities and improved antifungal and antibacterial activities relative to aurein 1.2.
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Affiliation(s)
- Douglas H. Chang
- Department of Chemical & Biological Engineering, University of Wisconsin–Madison, 1415 Engineering Dr., Madison, WI 53706, USA
| | - Myung-Ryul Lee
- Department of Chemical & Biological Engineering, University of Wisconsin–Madison, 1415 Engineering Dr., Madison, WI 53706, USA
| | - Nathan Wang
- Department of Chemical & Biological Engineering, University of Wisconsin–Madison, 1415 Engineering Dr., Madison, WI 53706, USA
| | - David M. Lynn
- Department of Chemical & Biological Engineering, University of Wisconsin–Madison, 1415 Engineering Dr., Madison, WI 53706, USA
- Department of Chemistry, University of Wisconsin–Madison, 1101 University Ave., Madison, WI 53706, USA
| | - Sean P. Palecek
- Department of Chemical & Biological Engineering, University of Wisconsin–Madison, 1415 Engineering Dr., Madison, WI 53706, USA
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Lee YJ. Examining the functional space of gut microbiome-derived peptides. Microbiologyopen 2023; 12:e1393. [PMID: 38129980 PMCID: PMC10714122 DOI: 10.1002/mbo3.1393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
The human gut microbiome contains thousands of small, novel peptides that could play a role in microbe-microbe and host-microbe interactions, contributing to human health and disease. Although these peptides have not yet been systematically characterized, computational tools can be used to elucidate the bioactivities they may have. This article proposes probing the functional space of gut microbiome-derived peptides (MDPs) using in silico approaches for three bioactivities: antimicrobial, anticancer, and nucleomodulins. Machine learning programs that support peptide and protein queries are provided for each bioactivity. Considering the biases of an activity-centric approach, activity-agnostic tools using structural and chemical similarity and target prediction are also described. Gut MDPs represent a vast functional space that can not only contribute to our understanding of microbiome interactions but potentially even serve as a source of life-changing therapeutics.
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Affiliation(s)
- Ying‐Chiang J. Lee
- Department of Molecular BiologyPrinceton UniversityPrincetonNew JerseyUSA
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45
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Gallardo-Becerra L, Cervantes-Echeverría M, Cornejo-Granados F, Vazquez-Morado LE, Ochoa-Leyva A. Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota. MICROBIAL ECOLOGY 2023; 87:8. [PMID: 38036921 PMCID: PMC10689560 DOI: 10.1007/s00248-023-02313-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023]
Abstract
Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.
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Affiliation(s)
- Luigui Gallardo-Becerra
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Melany Cervantes-Echeverría
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Fernanda Cornejo-Granados
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Luis E Vazquez-Morado
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Adrian Ochoa-Leyva
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
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Xing W, Zhang J, Li C, Huo Y, Dong G. iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model. Brief Bioinform 2023; 25:bbad443. [PMID: 38055840 PMCID: PMC10699745 DOI: 10.1093/bib/bbad443] [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: 09/08/2023] [Revised: 10/31/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023] Open
Abstract
As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.
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Affiliation(s)
- Wenxuan Xing
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Jie Zhang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Chen Li
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Yujia Huo
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Gaifang Dong
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
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Mwangi J, Kamau PM, Thuku RC, Lai R. Design methods for antimicrobial peptides with improved performance. Zool Res 2023; 44:1095-1114. [PMID: 37914524 PMCID: PMC10802102 DOI: 10.24272/j.issn.2095-8137.2023.246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 09/20/2023] [Indexed: 11/03/2023] Open
Abstract
The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult. In this regard, developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority. Antimicrobial peptides (AMPs), a ubiquitous class of naturally occurring compounds with broad-spectrum antipathogenic activity, hold significant promise as an effective solution to the current antimicrobial resistance (AMR) crisis. Several AMPs have been identified and evaluated for their therapeutic application, with many already in the drug development pipeline. Their distinct properties, such as high target specificity, potency, and ability to bypass microbial resistance mechanisms, make AMPs a promising alternative to traditional antibiotics. Nonetheless, several challenges, such as high toxicity, lability to proteolytic degradation, low stability, poor pharmacokinetics, and high production costs, continue to hamper their clinical applicability. Therefore, recent research has focused on optimizing the properties of AMPs to improve their performance. By understanding the physicochemical properties of AMPs that correspond to their activity, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies used to optimize the performance of AMPs, including rational design and de novo synthesis. We also discuss the growing role of predictive computational tools, utilizing artificial intelligence and machine learning, in the design and synthesis of highly efficacious lead drug candidates.
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Affiliation(s)
- James Mwangi
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Peter Muiruri Kamau
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Rebecca Caroline Thuku
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Ren Lai
- Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Peptides of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Centre for Non-Human Primates, Kunming Primate Research Centre, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Sino-African Joint Research Centre, New Cornerstone Science Institute, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650107, China
- Centre for Evolution and Conservation Biology, Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, Guangdong 511458, China. E-mail:
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Putri RA, Rohman MS, Swasono RT, Raharjo TJ. A novel synthetic peptide analog enhanced antibacterial activity of the frog-derived skin peptide wuchuanin-A1. J Biomol Struct Dyn 2023:1-11. [PMID: 37968993 DOI: 10.1080/07391102.2023.2281633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/04/2023] [Indexed: 11/17/2023]
Abstract
In recent years, there has been a growing focus on the development of novel antibacterial compounds for clinical applications, such as antimicrobial peptide (AMP). Among the developed AMP, wuchuanin-A1, a coil-shaped bioactive peptide derived from Odorrana wuchuanensis frog skin, has been reported to exhibit antibacterial, antifungal, and antioxidant activity, but there are limited studies on its potential as an antibacterial agent. Therefore, this study aims to molecularly modify the sequence of wuchuanin-A1 to enhance its antibacterial properties. The interaction of both the native and analog peptide with bacterial inner membranes was initially assessed using computational methods. Specific amino acid substitutions were then used to enhance the modified peptide's antibacterial efficacy, followed by several preliminary tests to evaluate its activity. This study bridges the gap in exploring the potential of wuchuanin-A1 for antibacterial purposes, providing insights into the design of effective antimicrobial agents.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | | | - Tri Joko Raharjo
- Department of Chemistry, Universitas Gadjah Mada, Bulaksumur, Indonesia
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Tripathi AK, Singh J, Trivedi R, Ranade P. Shaping the Future of Antimicrobial Therapy: Harnessing the Power of Antimicrobial Peptides in Biomedical Applications. J Funct Biomater 2023; 14:539. [PMID: 37998108 PMCID: PMC10672284 DOI: 10.3390/jfb14110539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023] Open
Abstract
Antimicrobial peptides (AMPs) have emerged as a promising class of bioactive molecules with the potential to combat infections associated with medical implants and biomaterials. This review article aims to provide a comprehensive analysis of the role of antimicrobial peptides in medical implants and biomaterials, along with their diverse clinical applications. The incorporation of AMPs into various medical implants and biomaterials has shown immense potential in mitigating biofilm formation and preventing implant-related infections. We review the latest advancements in biomedical sciences and discuss the AMPs that were immobilized successfully to enhance their efficacy and stability within the implant environment. We also highlight successful examples of AMP coatings for the treatment of surgical site infections (SSIs), contact lenses, dental applications, AMP-incorporated bone grafts, urinary tract infections (UTIs), medical implants, etc. Additionally, we discuss the potential challenges and prospects of AMPs in medical implants, such as effectiveness, instability and implant-related complications. We also discuss strategies that can be employed to overcome the limitations of AMP-coated biomaterials for prolonged longevity in clinical settings.
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Affiliation(s)
- Amit Kumar Tripathi
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (R.T.); (P.R.)
| | - Jyotsana Singh
- Hematopoietic Biology and Malignancy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Rucha Trivedi
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (R.T.); (P.R.)
| | - Payal Ranade
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (R.T.); (P.R.)
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Guerrero-Vázquez K, Del Rio G, Brizuela CA. Cell-penetrating peptides predictors: A comparative analysis of methods and datasets. Mol Inform 2023; 42:e202300104. [PMID: 37672879 DOI: 10.1002/minf.202300104] [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: 05/06/2023] [Revised: 07/24/2023] [Accepted: 08/18/2023] [Indexed: 09/08/2023]
Abstract
Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors. We present a systematic study of the influence of the peptide sequence's similarity of the datasets on the predictors' performance. The analysis reveals that the datasets used for training have a stronger influence on the predictors performance than the model or descriptors employed. We show that datasets with low sequence similarity between the positive and negative examples can be easily separated, and the tested classifiers showed good performance on them. On the other hand, a dataset with high sequence similarity between CPP and non-CPP will be a hard dataset, and it should be the one to be used for assessing the performance of new predictors.
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Affiliation(s)
- Karen Guerrero-Vázquez
- Department of Computer Science, CICESE Research Center, Ensenada, 22860, Mexico
- Current address: School of Mathematics & Statistical Sciences, University of Galway, Galway, H91 TK33, Ireland
| | - Gabriel Del Rio
- Department of Biochemistry and Structural Biology, Instituto de Fisiologia Celular, UNAM, Mexico City, 04510, Mexico
| | - Carlos A Brizuela
- Department of Computer Science, CICESE Research Center, Ensenada, 22860, Mexico
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