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Zhang K, Yang N, Teng D, Mao R, Hao Y, Wang J. Expression and characterization of the new antimicrobial peptide AP138L-arg26 anti Staphylococcus aureus. Appl Microbiol Biotechnol 2024; 108:111. [PMID: 38229298 DOI: 10.1007/s00253-023-12947-w] [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: 08/08/2023] [Revised: 10/18/2023] [Accepted: 10/25/2023] [Indexed: 01/18/2024]
Abstract
The low activity and yield of antimicrobial peptides (AMPs) are pressing problems. The improvement of activity and yield through modification and heterologous expression, a potential way to solve the problem, is a research hot-pot. In this work, a new plectasin-derived variant L-type AP138 (AP138L-arg26) was constructed for the study of recombination expression and druggablity. As a result, the total protein concentration of AP138L-arg26 was 3.1 mg/mL in Pichia pastoris X-33 supernatant after 5 days of induction expression in a 5-L fermenter. The recombinant peptide AP138L-arg26 has potential antibacterial activity against selected standard and clinical Gram-positive bacteria (G+, minimum inhibitory concentration (MIC) 2-16 µg/mL) and high stability under different conditions (temperature, pH, ion concentration) and 2 × MIC of AP138L-arg26 could rapidly kill Staphylococcus aureus (S. aureus) (> 99.99%) within 1.5 h. It showed a high safety in vivo and in vivo and a long post-antibiotic effect (PAE, 1.91 h) compared with vancomycin (1.2 h). Furthermore, the bactericidal mechanism was revealed from two dimensions related to its disruption of the cell membrane resulting in intracellular potassium leakage (2.5-fold higher than control), and an increase in intracellular adenosine triphosphate (ATP), and reactive oxygen species (ROS), the decrease of lactate dehydrogenase (LDH) and further intervening metabolism in S. aureus. These results indicate that AP138L-arg26 as a new peptide candidate could be used for more in-depth development in the future. KEY POINTS: • The AP138L-arg26 was expressed in the P. pastoris expression system with high yield • The AP138 L-arg26 showed high stability and safety in vitro and in vivo • The AP138L-arg26 killed S. aureus by affecting cell membranes and metabolism.
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Affiliation(s)
- Kun Zhang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun Nandajie St., Haidian District, Beijing, 100081, People's Republic of China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, People's Republic of China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, 100081, People's Republic of China
| | - Na Yang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun Nandajie St., Haidian District, Beijing, 100081, People's Republic of China.
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, People's Republic of China.
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, 100081, People's Republic of China.
| | - Da Teng
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun Nandajie St., Haidian District, Beijing, 100081, People's Republic of China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, People's Republic of China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, 100081, People's Republic of China
| | - Ruoyu Mao
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun Nandajie St., Haidian District, Beijing, 100081, People's Republic of China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, People's Republic of China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, 100081, People's Republic of China
| | - Ya Hao
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun Nandajie St., Haidian District, Beijing, 100081, People's Republic of China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, People's Republic of China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, 100081, People's Republic of China
| | - Jianhua Wang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun Nandajie St., Haidian District, Beijing, 100081, People's Republic of China.
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, People's Republic of China.
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing, 100081, People's Republic of China.
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Fong-Coronado PA, Ramirez V, Quintero-Hernández V, Balleza D. A Critical Review of Short Antimicrobial Peptides from Scorpion Venoms, Their Physicochemical Attributes, and Potential for the Development of New Drugs. J Membr Biol 2024; 257:165-205. [PMID: 38990274 DOI: 10.1007/s00232-024-00315-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/08/2024] [Indexed: 07/12/2024]
Abstract
Scorpion venoms have proven to be excellent sources of antimicrobial agents. However, although many of them have been functionally characterized, they remain underutilized as pharmacological agents, despite their evident therapeutic potential. In this review, we discuss the physicochemical properties of short scorpion venom antimicrobial peptides (ssAMPs). Being generally short (13-25 aa) and amidated, their proven antimicrobial activity is generally explained by parameters such as their net charge, the hydrophobic moment, or the degree of helicity. However, for a complete understanding of their biological activities, also considering the properties of the target membranes is of great relevance. Here, with an extensive analysis of the physicochemical, structural, and thermodynamic parameters associated with these biomolecules, we propose a theoretical framework for the rational design of new antimicrobial drugs. Through a comparison of these physicochemical properties with the bioactivity of ssAMPs in pathogenic bacteria such as Staphylococcus aureus or Acinetobacter baumannii, it is evident that in addition to the net charge, the hydrophobic moment, electrostatic energy, or intrinsic flexibility are determining parameters to understand their performance. Although the correlation between these parameters is very complex, the consensus of our analysis suggests that there is a delicate balance between them and that modifying one affects the rest. Understanding the contribution of lipid composition to their bioactivities is also underestimated, which suggests that for each peptide, there is a physiological context to consider for the rational design of new drugs.
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Affiliation(s)
- Pedro Alejandro Fong-Coronado
- Ecology and Survival of Microorganisms Group (ESMG), Laboratorio de Ecología Molecular Microbiana (LEMM), Centro de Investigaciones en Ciencias Microbiológicas (CICM), Instituto de Ciencias (IC), Benemérita Universidad Autónoma de Puebla (BUAP), Puebla, México
| | - Verónica Ramirez
- Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla (FCQ-BUAP), Ciudad Universitaria, Puebla, México
| | | | - Daniel Balleza
- Laboratorio de Microbiología, Unidad de Investigación y Desarrollo en Alimentos, Instituto Tecnológico de Veracruz, Tecnológico Nacional de México, Veracruz, México.
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3
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Rathore AS, Choudhury S, Arora A, Tijare P, Raghava GPS. ToxinPred 3.0: An improved method for predicting the toxicity of peptides. Comput Biol Med 2024; 179:108926. [PMID: 39038391 DOI: 10.1016/j.compbiomed.2024.108926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 05/17/2024] [Accepted: 07/17/2024] [Indexed: 07/24/2024]
Abstract
Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).
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Affiliation(s)
- Anand Singh Rathore
- 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.
| | - Akanksha Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Purva Tijare
- 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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4
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Zhao F, Qiu J, Xiang D, Jiao P, Cao Y, Xu Q, Qiao D, Xu H, Cao Y. deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model. PeerJ 2024; 12:e17729. [PMID: 39040937 PMCID: PMC11262304 DOI: 10.7717/peerj.17729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps. Results In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.
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Affiliation(s)
- Fei Zhao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Junhui Qiu
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Dongyou Xiang
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Pengrui Jiao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Yu Cao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Qingrui Xu
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Dairong Qiao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Hui Xu
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
| | - Yi Cao
- Microbiology and Metabolic Engineering Laboratory of Sichuan Province, College of Life Science, Sichuan University, Chengdu, Sichuan, China
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5
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Cheong HH, Zuo W, Chen J, Un CW, Si YW, Wong KH, Kwok HF, Siu SWI. Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations. J Chem Inf Model 2024. [PMID: 39008832 DOI: 10.1021/acs.jcim.4c00501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.
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Affiliation(s)
- Hong-Hin Cheong
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Weimin Zuo
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Jiarui Chen
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Chon-Wai Un
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Yain-Whar Si
- Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Koon Ho Wong
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Hang Fai Kwok
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR 999078, China
| | - Shirley W I Siu
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macau SAR 999078, China
- Institute of Science and Environment, University of Saint Joseph, Estrada Marginal da Ilha Verde 14-17, Macau SAR 999078, China
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6
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Santos-Júnior CD, Torres MDT, Duan Y, Rodríguez Del Río Á, Schmidt TSB, Chong H, Fullam A, Kuhn M, Zhu C, Houseman A, Somborski J, Vines A, Zhao XM, Bork P, Huerta-Cepas J, de la Fuente-Nunez C, Coelho LP. Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 2024; 187:3761-3778.e16. [PMID: 38843834 DOI: 10.1016/j.cell.2024.05.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 04/11/2024] [Accepted: 05/06/2024] [Indexed: 06/25/2024]
Abstract
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Laboratory of Microbial Processes & Biodiversity - LMPB, Department of Hydrobiology, Universidade Federal de São Carlos - UFSCar, São Carlos, São Paulo 13565-905, Brazil
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Yiqian Duan
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; APC Microbiome & School of Medicine, University College Cork, Cork, Ireland
| | - Hui Chong
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anthony Fullam
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Chengkai Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Amy Houseman
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Jelena Somborski
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Anna Vines
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Campus de Montegancedo-UPM, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai 200433, China; Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, QLD, Australia.
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Bhattarai S, Tayara H, Chong KT. Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models. J Chem Inf Model 2024; 64:4941-4957. [PMID: 38874445 DOI: 10.1021/acs.jcim.4c00295] [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: 06/15/2024]
Abstract
Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.
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Affiliation(s)
- Sadik Bhattarai
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
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8
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Yang S, Xu P. HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information. Anal Biochem 2024; 690:115523. [PMID: 38552762 DOI: 10.1016/j.ab.2024.115523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/02/2024]
Abstract
Hemolytic peptides can trigger hemolysis by rupturing red blood cells' membranes and triggering cell disruption. Due to the labor-intensive and time-consuming in-lab identification process, accurate, high-throughput hemolytic peptide prediction is crucial for the growth of peptide sequence data in proteomics and peptidomics. In this study, we offer the HemoDL ensemble learning model, which learns the distinct distribution of sequence characteristics for predicting the hemolytic activity of peptides using a double LightGBM framework. To determine the most informative encoding features, we compare 17 widely used features across four benchmark datasets. Our investigation reveals that CTD, BPF, Charge, AAC, GDPC, ATC, QSO, and transformer-based features exhibit more positive contributions to detecting the hemolytic activity of peptides. Comparison with eight state-of-the-art methods demonstrates that HemoDL outperforms other models, attaining higher Matthews Correlation Coefficient values on four test datasets, ranging from 6.30% to 16.04%, 6.63%-11.26%, 4.76%-9.92%, and 7.41%-15.03%, respectively. Additionally, we provide the HemoDL with a user-friendly graphical interface available at https://github.com/abcair/HemoDL. In summary, the HemoDL model, leveraging CTD, BPF, Charge, AAC, GDPC, ATC, QSO and transformer-based encoding features within a double LightGBM learning framework, achieves high accuracy in predicting the hemolytic activity of peptides.
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Affiliation(s)
- Sen Yang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China; The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213164, China
| | - Piao Xu
- College of Economics and Management, Nanjing Forestry University, China.
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9
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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; 24:e2300382. [PMID: 38837544 DOI: 10.1002/pmic.202300382] [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: 10/02/2023] [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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10
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Alimbarashvili E, Samsonidze N, Grigolava M, Pirtskhalava M. Small Natural Cyclic Peptides from DBAASP Database. Pharmaceuticals (Basel) 2024; 17:845. [PMID: 39065696 PMCID: PMC11279581 DOI: 10.3390/ph17070845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 05/30/2024] [Accepted: 06/01/2024] [Indexed: 07/28/2024] Open
Abstract
Antimicrobial peptides (AMPs) are promising tools for combating microbial resistance. However, their therapeutic potential is hindered by two intrinsic drawbacks-low target affinity and poor in vivo stability. Macrocyclization, a process that improves the pharmacological properties and bioactivity of peptides, can address these limitations. As a result, macrocyclic peptides represent attractive drug candidates. Moreover, many drugs are macrocycles that originated from natural product scaffolds, suggesting that nature offers solutions to the challenges faced by AMPs. In this review, we explore natural cyclic peptides from the DBAASP database. DBAASP is a comprehensive repository of data on antimicrobial/cytotoxic activities and structures of peptides. We analyze the data on small (≤25 AA) ribosomal and non-ribosomal cyclic peptides from DBAASP according to their amino acid composition, bonds used for cyclization, targets they act on, and mechanisms of action. This analysis will enhance our understanding of the small cyclic peptides that nature has provided to defend living organisms.
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Affiliation(s)
- Evgenia Alimbarashvili
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia; (N.S.); (M.G.)
| | | | | | - Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia; (N.S.); (M.G.)
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11
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van Teijlingen A, Edwards DC, Hu L, Lilienkampf A, Cockroft SL, Tuttle T. An active machine learning discovery platform for membrane-disrupting and pore-forming peptides. Phys Chem Chem Phys 2024; 26:17745-17752. [PMID: 38873737 PMCID: PMC11202314 DOI: 10.1039/d4cp01404a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/30/2024] [Indexed: 06/15/2024]
Abstract
Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents is of interest in treating malfunctioning cells. Known PFPs tend to be up to 50 residues in length, which is commensurate with the thickness of a lipid bilayer. Accordingly, few short PFPs are known. Here we show that the discovery of PFPs can be accelerated via an active machine learning approach. The approach identified 71 potential PFPs from the 25.6 billion octapeptide sequence space; 13 sequences were tested experimentally, and all were found to have the predicted membrane-disrupting ability, with 1 forming highly stable pores. Experimental verification of the predicted pore-forming ability demonstrated that a range of short peptides can form pores in membranes, while the positioning and characteristics of residues that favour pore-forming behaviour were identified. This approach identified more ultrashort (8-residues, unmodified, non-cyclic) PFPs than previously known. We anticipate our findings and methodology will be useful in discovering new pore-forming and membrane-disrupting peptides for a range of applications from nanoreactors to therapeutics.
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Affiliation(s)
- Alexander van Teijlingen
- 1Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow, G1 1XL, UK.
| | - Daniel C Edwards
- EaStCHEM School of Chemistry, Joseph Black Building, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Liao Hu
- EaStCHEM School of Chemistry, Joseph Black Building, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Annamaria Lilienkampf
- EaStCHEM School of Chemistry, Joseph Black Building, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Scott L Cockroft
- EaStCHEM School of Chemistry, Joseph Black Building, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Tell Tuttle
- 1Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow, G1 1XL, UK.
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12
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Tian Y, Wei H, Lu F, Wu H, Lou D, Wang S, Geng T. Antibacterial mechanism and structure-activity relationships of Bombyx mori cecropin A. INSECT MOLECULAR BIOLOGY 2024. [PMID: 38898565 DOI: 10.1111/imb.12934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024]
Abstract
Bombyx mori cecropin A (Bmcecropin A) has antibacterial, antiviral, anti-filamentous fungal and tumour cell inhibition activities and is considered a potential succedaneum for antibiotics. We clarified the antibacterial mechanism and structure-activity relationships and then directed the structure-activity optimization of Bmcecropin A. Firstly, we found Bmcecropin A shows a strong binding force and permeability to cell membranes like a detergent; Bmcecropin A could competitively bind to the cell membrane with the cell membrane-specific dye DiI, then damaged the membrane for the access of DiI into the cytoplasm and leading to the leakage of electrolyte and proteins. Secondly, we found Bmcopropin A could also bind to and degrade DNA; furthermore, DNA library polymerase chain reaction (PCR) results indicated that Bmcecropin A inhibited DNA replication by non-specific binding. In addition, we have identified C-terminus amidation and serine-lysine- glycine (SLG) amino acids of Bmcecropin A played critical roles in the membrane damage and DNA degradation. Based on the above results, we designed a mutant of Bmcecropin A (E9 to H, D17 to K, K33 to A), which showed higher antibacterial activity, thermostability and pH stability than ampicillin but no haemolytic activity. Finally, we speculated that Bmcecropin A damaged the cell membrane through a carpet model and drew the schematic diagram of its antibacterial mechanism, based on the antibacterial mechanism and the three-dimensional configuration. These findings yield insights into the mechanism of antimicrobial peptide-pathogen interaction and beneficial for the development of new antibiotics.
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Affiliation(s)
- Yuyuan Tian
- State Key Laboratory of Green Pesticide, Guizhou University, Guiyang, China
- Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, China
- Center for R&D of Fine Chemicals, Guizhou University, Guiyang, China
| | - Hongxian Wei
- Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Fuping Lu
- Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Huazhou Wu
- Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Dezhao Lou
- Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Shuchang Wang
- Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Tao Geng
- Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
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13
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Marissen J, Reichert L, Härtel C, Fortmann MI, Faust K, Msanga D, Harder J, Zemlin M, Gomez de Agüero M, Masjosthusmann K, Humberg A. Antimicrobial Peptides (AMPs) and the Microbiome in Preterm Infants: Consequences and Opportunities for Future Therapeutics. Int J Mol Sci 2024; 25:6684. [PMID: 38928389 PMCID: PMC11203687 DOI: 10.3390/ijms25126684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Antimicrobial peptides (AMPs) are crucial components of the innate immune system in various organisms, including humans. Beyond their direct antimicrobial effects, AMPs play essential roles in various physiological processes. They induce angiogenesis, promote wound healing, modulate immune responses, and serve as chemoattractants for immune cells. AMPs regulate the microbiome and combat microbial infections on the skin, lungs, and gastrointestinal tract. Produced in response to microbial signals, AMPs help maintain a balanced microbial community and provide a first line of defense against infection. In preterm infants, alterations in microbiome composition have been linked to various health outcomes, including sepsis, necrotizing enterocolitis, atopic dermatitis, and respiratory infections. Dysbiosis, or an imbalance in the microbiome, can alter AMP profiles and potentially lead to inflammation-mediated diseases such as chronic lung disease and obesity. In the following review, we summarize what is known about the vital role of AMPs as multifunctional peptides in protecting newborn infants against infections and modulating the microbiome and immune response. Understanding their roles in preterm infants and high-risk populations offers the potential for innovative approaches to disease prevention and treatment.
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Affiliation(s)
- Janina Marissen
- Department of Pediatrics, University Hospital Würzburg, 97080 Würzburg, Germany; (J.M.); (L.R.)
- Würzburg Institute of Systems Immunology, Max-Planck Research Group, University of Würzburg, 97078 Würzburg, Germany;
| | - Lilith Reichert
- Department of Pediatrics, University Hospital Würzburg, 97080 Würzburg, Germany; (J.M.); (L.R.)
| | - Christoph Härtel
- Department of Pediatrics, University Hospital Würzburg, 97080 Würzburg, Germany; (J.M.); (L.R.)
- German Center for Infection Research, Site Hamburg-Lübeck-Borstel-Riems, 23538 Lübeck, Germany
| | - Mats Ingmar Fortmann
- Department of Pediatrics, University Hospital Schleswig-Holstein, 23538 Lübeck, Germany; (M.I.F.); (K.F.)
| | - Kirstin Faust
- Department of Pediatrics, University Hospital Schleswig-Holstein, 23538 Lübeck, Germany; (M.I.F.); (K.F.)
| | - Delfina Msanga
- Department of Pediatrics, Bugando Hospital, Catholic University of Health and Allied Sciences, Mwanza 33109, Tanzania;
| | - Jürgen Harder
- Department of Dermatology, Venerology and Allergology, Quincke Research Center, Kiel University, 24105 Kiel, Germany;
| | - Michael Zemlin
- Department of General Pediatrics and Neonatology, Saarland University Medical Center, 66421 Homburg, Germany;
| | - Mercedes Gomez de Agüero
- Würzburg Institute of Systems Immunology, Max-Planck Research Group, University of Würzburg, 97078 Würzburg, Germany;
| | - Katja Masjosthusmann
- Department of General Pediatrics, University Children’s Hospital Münster, 48149 Münster, Germany; (K.M.); (A.H.)
| | - Alexander Humberg
- Department of General Pediatrics, University Children’s Hospital Münster, 48149 Münster, Germany; (K.M.); (A.H.)
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14
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Roque-Borda CA, Primo LMDG, Franzyk H, Hansen PR, Pavan FR. Recent advances in the development of antimicrobial peptides against ESKAPE pathogens. Heliyon 2024; 10:e31958. [PMID: 38868046 PMCID: PMC11167364 DOI: 10.1016/j.heliyon.2024.e31958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/14/2024] Open
Abstract
Multi-drug resistant ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are a global health threat. The severity of the problem lies in its impact on mortality, therapeutic limitations, the threat to public health, and the costs associated with managing infections caused by these resistant strains. Effectively addressing this challenge requires innovative approaches to research, the development of new antimicrobials, and more responsible antibiotic use practices globally. Antimicrobial peptides (AMPs) are a part of the innate immune system of all higher organisms. They are short, cationic and amphipathic molecules with broad-spectrum activity. AMPs interact with the negatively charged bacterial membrane. In recent years, AMPs have attracted considerable interest as potential antibiotics. However, AMPs have low bioavailability and short half-lives, which may be circumvented by chemical modification. This review presents recent in vitro and in silico strategies for the modification of AMPs to improve their stability and application in preclinical experiments.
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Affiliation(s)
- Cesar Augusto Roque-Borda
- São Paulo State University (UNESP), Tuberculosis Research Laboratory, School of Pharmaceutical Sciences, Araraquara, Brazil
- Universidad Católica de Santa María, Vicerrectorado de Investigación, Arequipa, Peru
| | | | - Henrik Franzyk
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Drug Design and Pharmacology, Denmark
| | - Paul Robert Hansen
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Drug Design and Pharmacology, Denmark
| | - Fernando Rogério Pavan
- São Paulo State University (UNESP), Tuberculosis Research Laboratory, School of Pharmaceutical Sciences, Araraquara, Brazil
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15
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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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16
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Wan F, Torres MDT, Peng J, de la Fuente-Nunez C. Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nat Biomed Eng 2024:10.1038/s41551-024-01201-x. [PMID: 38862735 DOI: 10.1038/s41551-024-01201-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/25/2024] [Indexed: 06/13/2024]
Abstract
Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.
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Affiliation(s)
- Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline Peng
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
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17
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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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18
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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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19
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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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20
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Carpenter AM, van Hoek ML. Development of a defibrinated human blood hemolysis assay for rapid testing of hemolytic activity compared to computational prediction. J Immunol Methods 2024; 529:113670. [PMID: 38604530 DOI: 10.1016/j.jim.2024.113670] [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: 12/02/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/13/2024]
Abstract
Cytotoxicity studies determining hemolytic properties of antimicrobial peptides or other drugs are an important step in the development of novel therapeutics for clinical use. Hemolysis is an affordable, accessible, and rapid method for initial assessment of cellular toxicity for all drugs under development. However, variability in species of red blood cells and protocols used may result in significant differences in results. AMPs generally possess higher selectivity for bacterial cells but can have toxicity against host cells at high concentrations. Knowing the hemolytic activity of the peptides we are developing contributes to our understanding of their potential toxicity. Computational approaches for predicting hemolytic activity of AMPs exist and were tested head-to-head with our experimental results. RESULTS Starting with an observation of high hemolytic activity of LL-37 peptide against human red blood cells that were collected in EDTA, we explored alternative approaches to develop a more robust, accurate and simple hemolysis assay using defibrinated human blood. We found significant differences between the sensitivity of defibrinated red blood cells and EDTA treated red blood cells. SIGNIFICANCE Accurately determining the hemolytic activity using human red blood cells will allow for a more robust calculation of the therapeutic index of our potential antimicrobial compounds, a critical measure in their pre-clinical development. CONCLUSION We introduce a standardized, more accurate protocol for assessing hemolytic activity using defibrinated human red blood cells. This approach, facilitated by the increased commercial availability of de-identified human blood and defibrination methods, offers a robust tool for evaluating toxicity of emerging drug compounds, especially AMPs.
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Affiliation(s)
- Ashley M Carpenter
- School of Systems Biology, George Mason University, Manassas, VA 20110, United States of America
| | - Monique L van Hoek
- School of Systems Biology, George Mason University, Manassas, VA 20110, United States of America; Center for Infectious Disease Research, George Mason University, Manassas, VA 20110, United States of America.
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21
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Zhang J, Sun X, Zhao H, Zhou X, Zhang Y, Xie F, Li B, Guo G. In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity. J Chem Inf Model 2024; 64:4277-4285. [PMID: 38743449 DOI: 10.1021/acs.jcim.4c00142] [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
Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 μM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.
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Affiliation(s)
- Jin Zhang
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Xinhao Sun
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Hongwei Zhao
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Xu Zhou
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Yiling Zhang
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Feng Xie
- Moutai Institute, Renhuai 564507, China
| | - Boyan Li
- School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 561113, China
| | - Guo Guo
- The Key and Characteristic Laboratory of Modern Pathogen Biology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang 561113, China
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22
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Sun X, Liu Y, Ma T, Zhu N, Lao X, Zheng H. DCTPep, the data of cancer therapy peptides. Sci Data 2024; 11:541. [PMID: 38796630 PMCID: PMC11128002 DOI: 10.1038/s41597-024-03388-9] [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: 07/19/2023] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.
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Affiliation(s)
- Xin Sun
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Yanchao Liu
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Tianyue Ma
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Ning Zhu
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Xingzhen Lao
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
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23
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Aguilera-Puga MDC, Plisson F. Structure-aware machine learning strategies for antimicrobial peptide discovery. Sci Rep 2024; 14:11995. [PMID: 38796582 PMCID: PMC11127937 DOI: 10.1038/s41598-024-62419-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024] Open
Abstract
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico
| | - Fabien Plisson
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico.
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24
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Ansari M, White AD. Learning peptide properties with positive examples only. DIGITAL DISCOVERY 2024; 3:977-986. [PMID: 38756224 PMCID: PMC11094695 DOI: 10.1039/d3dd00218g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 03/30/2024] [Indexed: 05/18/2024]
Abstract
Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.
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Affiliation(s)
- Mehrad Ansari
- Department of Chemical Engineering, University of Rochester Rochester NY 14627 USA
| | - Andrew D White
- Department of Chemical Engineering, University of Rochester Rochester NY 14627 USA
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25
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Nobakht MS, Bazyar K, Langeroudi MSG, Mirzaei M, Goudarzi M, Shivaee A. Investigating the Antimicrobial Effects of a Novel Peptide Derived From Listeriolysin S on S aureus, E coli, and L plantarum: An In Silico and In Vitro Study. Bioinform Biol Insights 2024; 18:11779322241252513. [PMID: 38765021 PMCID: PMC11100392 DOI: 10.1177/11779322241252513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 04/17/2024] [Indexed: 05/21/2024] Open
Abstract
Aims The emergence of antibiotic resistance is one of the most significant issues today. Modifying antimicrobial peptides (AMPs) can improve their effects. In this study, the active region of Listeriolysin S (LLS) as a peptidic toxin has been recognized, and its antibacterial properties have been evaluated by modifying that region. Methods After extracting the sequence, the structure of LLS was predicted by PEP-FOLD3. AntiBP and AMPA servers identified its antimicrobial active site. It was modified by adding arginine residue to its 3- and N-terminal regions. Its antimicrobial properties on Staphylococcus aureus, Escherichia coli, and Lactobacillus Plantarum were estimated. Findings The results of AntiBP and AntiBP servers demonstrated that a region of 15 amino acids has the most antimicrobial properties (score = 1.696). After adding arginine to the chosen region, the physicochemical evaluation and antimicrobial properties revealed that the designed peptide is a stable AMP with a positive charge of 4, which is not toxic to human erythrocyte cells and has antigenic properties. The results of in vitro and colony counting indicated that at different hours, it caused a significant reduction in the count of S aureus, E coli, and L Plantarum compared with the control sample. Conclusions Upcoming research implies that identifying and enhancing the active sites of natural peptides can help combat bacteria.
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Affiliation(s)
- Mojgan Sarabi Nobakht
- Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Microbiology, Faculty of Basic Sciences, Islamic Azad University, Sirjan, Iran
| | - Kaveh Bazyar
- Department of Clinical Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Mandana Mirzaei
- Department of Microbiology, Faculty of Science, Islamic Azad University, Karaj, Iran
| | - Mehdi Goudarzi
- Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Shivaee
- Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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26
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Klimovich A, Bosch TCG. Novel technologies uncover novel 'anti'-microbial peptides in Hydra shaping the species-specific microbiome. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230058. [PMID: 38497265 PMCID: PMC10945409 DOI: 10.1098/rstb.2023.0058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 03/19/2024] Open
Abstract
The freshwater polyp Hydra uses an elaborate innate immune machinery to maintain its specific microbiome. Major components of this toolkit are conserved Toll-like receptor (TLR)-mediated immune pathways and species-specific antimicrobial peptides (AMPs). Our study harnesses advanced technologies, such as high-throughput sequencing and machine learning, to uncover a high complexity of the Hydra's AMPs repertoire. Functional analysis reveals that these AMPs are specific against diverse members of the Hydra microbiome and expressed in a spatially controlled pattern. Notably, in the outer epithelial layer, AMPs are produced mainly in the neurons. The neuron-derived AMPs are secreted directly into the glycocalyx, the habitat for symbiotic bacteria, and display high selectivity and spatial restriction of expression. In the endodermal layer, in contrast, endodermal epithelial cells produce an abundance of different AMPs including members of the arminin and hydramacin families, while gland cells secrete kazal-type protease inhibitors. Since the endodermal layer lines the gastric cavity devoid of symbiotic bacteria, we assume that endodermally secreted AMPs protect the gastric cavity from intruding pathogens. In conclusion, Hydra employs a complex set of AMPs expressed in distinct tissue layers and cell types to combat pathogens and to maintain a stable spatially organized microbiome. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.
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Affiliation(s)
- Alexander Klimovich
- Zoological Institute, Christian-Albrechts University of Kiel, Am Botanischen Garten 1-9, Kiel 24118, Germany
| | - Thomas C. G. Bosch
- Zoological Institute, Christian-Albrechts University of Kiel, Am Botanischen Garten 1-9, Kiel 24118, Germany
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27
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Hasannejad-Asl B, Heydari S, Azod F, Pooresmaeil F, Esmaeili A, Bolhassani A. Peptide-Membrane Docking and Molecular Dynamic Simulation of In Silico Detected Antimicrobial Peptides from Portulaca oleracea's Transcriptome. Probiotics Antimicrob Proteins 2024:10.1007/s12602-024-10261-z. [PMID: 38704476 DOI: 10.1007/s12602-024-10261-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2024] [Indexed: 05/06/2024]
Abstract
The main issue with clinical infections is multidrug resistance to traditional antibiotics. As they are essential to innate immunity, shielding hosts from pathogenic microbes, traditional herbal remedies are an excellent supplier of antimicrobial peptides (AMPs), vital parts of defensive systems. Nevertheless, little is known about the bioactive peptide components of most ethnobotanical species. Our goal in this study was to find new, likely AMPs from Portulaca oleracea (P. oleracea) using in silico studies. The P. oleracea transcriptome was gained from Sequence Read Archive (SRA) and quality controlled, then adapters and other low-quality reads were trimmed. Afterward, de novo assembled and translated open reading frames (ORFs) were determined. Next, the ORFs were filtered based on AMP physiochemical criteria and deep learning methods. Finally, the five selected putative AMPs docked with E. coli and S. aureus membranes that showed penetration in bilayers. In this step, PO2 was chosen as a candidate AMP to analyze with molecular dynamics (MD) simulations. Our data demonstrated that PO2 is more stable in E. coli than in S. aureus. Moreover, these predicted AMPs can be good candidates for in vitro and in vivo analysis.
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Affiliation(s)
- Behnam Hasannejad-Asl
- Department of Hepatitis and AIDS, Pasteur Institute of Iran, Tehran, Iran
- Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Salimeh Heydari
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Fahime Azod
- Department of Biology, Faculty of Science, University of Yazd, Yazd, Iran
| | - Farkhondeh Pooresmaeil
- Department of Hepatitis and AIDS, Pasteur Institute of Iran, Tehran, Iran
- Department of Medical Biotechnology, School of Allied Medicine, Iran , University of Medical Science, Tehran, Iran
| | - Ali Esmaeili
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azam Bolhassani
- Department of Hepatitis and AIDS, Pasteur Institute of Iran, Tehran, Iran.
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28
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Luebbert L, Sullivan DK, Carilli M, Hjörleifsson KE, Winnett AV, Chari T, Pachter L. Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.11.571168. [PMID: 38168363 PMCID: PMC10760059 DOI: 10.1101/2023.12.11.571168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
There are an estimated 300,000 mammalian viruses from which infectious diseases in humans may arise. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify putative novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.
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Affiliation(s)
- Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | - Delaney K. Sullivan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Maria Carilli
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | | | - Alexander Viloria Winnett
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
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29
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Arakal BS, Rowlands RS, McCarthy M, Whitworth DE, Maddocks SE, James PE, Livingstone PG. Corallococcus senghenyddensis sp. nov., a myxobacterium with potent antimicrobial activity. J Appl Microbiol 2024; 135:lxae102. [PMID: 38649930 DOI: 10.1093/jambio/lxae102] [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: 03/11/2024] [Revised: 04/10/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024]
Abstract
AIM Corallococcus species are diverse in the natural environment with 10 new Corallococcus species having been characterized in just the last 5 years. As well as being an abundant myxobacterial genus, they produce several secondary metabolites, including Corallopyronin, Corramycin, Coralmycin, and Corallorazine. We isolated a novel strain Corallococcus spp RDP092CA from soil in South Wales, UK, using Candida albicans as prey bait and characterized its predatory activities against pathogenic bacteria and yeast. METHODS AND RESULTS The size of the RDP092CA genome was 8.5 Mb with a G + C content of 71.4%. Phylogenetically, RDP092CA is closely related to Corallococcus interemptor, C. coralloides, and C. exiguus. However, genome average nucleotide identity and digital DNA-DNA hybridization values are lower than 95% and 70% when compared to those type strains, implying that it belongs to a novel species. The RDP092CA genome harbours seven types of biosynthetic gene clusters (BGCs) and 152 predicted antimicrobial peptides. In predation assays, RDP092CA showed good predatory activity against Escherichia coli, Pseudomonas aeruginosa, Citrobacter freundii, and Staphylococcus aureus but not against Enterococcus faecalis. It also showed good antibiofilm activity against all five bacteria in biofilm assays. Antifungal activity against eight Candida spp. was variable, with particularly good activity against Meyerozyma guillermondii DSM 6381. Antimicrobial peptide RDP092CA_120 exhibited potent antibiofilm activity with >50% inhibition and >60% dispersion of biofilms at concentrations down to 1 μg/ml. CONCLUSIONS We propose that strain RDP092CA represents a novel species with promising antimicrobial activities, Corallococcus senghenyddensis sp. nov. (=NBRC 116490T =CCOS 2109T), based on morphological, biochemical, and genomic features.
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Affiliation(s)
- Benita S Arakal
- School of Sports and Health Sciences, Department of Biomedical Sciences, Cardiff Metropolitan University, Llandaff Campus, Cardiff CF5 2YB, United Kingdom
| | - Richard S Rowlands
- School of Sports and Health Sciences, Department of Biomedical Sciences, Cardiff Metropolitan University, Llandaff Campus, Cardiff CF5 2YB, United Kingdom
| | - Michael McCarthy
- School of Sports and Health Sciences, Department of Biomedical Sciences, Cardiff Metropolitan University, Llandaff Campus, Cardiff CF5 2YB, United Kingdom
| | - David E Whitworth
- Department of Life Sciences, Aberystwyth University, Aberystwyth SY23 3FL, United Kingdom
| | - Sarah E Maddocks
- School of Sports and Health Sciences, Department of Biomedical Sciences, Cardiff Metropolitan University, Llandaff Campus, Cardiff CF5 2YB, United Kingdom
| | - Philip E James
- School of Sports and Health Sciences, Department of Biomedical Sciences, Cardiff Metropolitan University, Llandaff Campus, Cardiff CF5 2YB, United Kingdom
| | - Paul G Livingstone
- School of Sports and Health Sciences, Department of Biomedical Sciences, Cardiff Metropolitan University, Llandaff Campus, Cardiff CF5 2YB, United Kingdom
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30
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Naseef Pathoor N, Viswanathan A, Wadhwa G, Ganesh PS. Understanding the biofilm development of Acinetobacter baumannii and novel strategies to combat infection. APMIS 2024; 132:317-335. [PMID: 38444124 DOI: 10.1111/apm.13399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 02/22/2024] [Indexed: 03/07/2024]
Abstract
Acinetobacter baumannii (A. baumannii) is a Gram-negative, nonmotile, and aerobic bacillus emerged as a superbug, due to increasing the possibility of infection and accelerating rates of antimicrobial agents. It is recognized as a nosocomial pathogen due to its ability to form biofilms. These biofilms serve as a defensive barrier, increase antibiotic resistance, and make treatment more difficult. As a result, the current situation necessitates the rapid emergence of novel therapeutic approaches to ensure successful treatment outcomes. This review explores the intricate relationship between biofilm formation and antibiotic resistance in A. baumannii, emphasizing the role of key virulence factors and quorum sensing (QS) mechanisms that will lead to infections and facilitate insight into developing innovative method to control A. baumannii infections. Furthermore, the review article looks into promising approaches for preventing biofilm formation on medically important surfaces and potential therapeutic methods for eliminating preformed biofilms, which can address biofilm-associated A. baumannii infections. Modern advances in emerging therapeutic options such as antimicrobial peptide (AMPs), nanoparticles (NPs), bacteriophage therapy, photodynamic therapy (PDT), and other biofilm inhibitors can assist readers understand the current landscape and future prospects for effectively treating A. baumannii biofilm infections.
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Affiliation(s)
- Naji Naseef Pathoor
- Department of Microbiology, Centre for Infectious Diseases, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University (Deemed to be University), Chennai, Tamil Nadu, India
| | - Akshaya Viswanathan
- Department of Microbiology, Centre for Infectious Diseases, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University (Deemed to be University), Chennai, Tamil Nadu, India
| | - Gulshan Wadhwa
- Department of Biotechnology, Ministry of Science and Technology, New Delhi, India
| | - Pitchaipillai Sankar Ganesh
- Department of Microbiology, Centre for Infectious Diseases, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University (Deemed to be University), Chennai, Tamil Nadu, India
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31
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Higgins SA, Igwe DO, Coradetti S, Ramsey JS, DeBlasio SL, Pitino M, Shatters RG, Niedz R, Fleites LA, Heck M. Plant-Derived, Nodule-Specific Cysteine-Rich Peptides as a Novel Source of Biopesticides for Controlling Citrus Greening Disease. PHYTOPATHOLOGY 2024; 114:971-981. [PMID: 38376984 DOI: 10.1094/phyto-09-23-0322-kc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Nodule-specific cysteine-rich (NCR) peptides, encoded in the genome of the Mediterranean legume Medicago truncatula (barrelclover), are known to regulate plant-microbe interactions. A subset of computationally derived 20-mer peptide fragments from 182 NCR peptides was synthesized to identify those with activity against the unculturable vascular pathogen associated with citrus greening disease, 'Candidatus Liberibacter asiaticus' (CLas). Grounded in a design of experiments framework, we evaluated the peptides in a screening pipeline involving three distinct assays: a bacterial culture assay with Liberibacter crescens, a CLas-infected excised citrus leaf assay, and an assay to evaluate effects on bacterial acquisition by the nymphal stage of hemipteran vector Diaphorina citri. A subset of the 20-mer NCR peptide fragments inhibits both CLas growth in citrus leaves and CLas acquisition by D. citri. Two peptides induced higher levels of D. citri mortality. These findings reveal 20-mer NCR peptides as a new class of plant-derived biopesticide molecules to control citrus greening disease.
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Affiliation(s)
- Steven A Higgins
- Emerging Pests and Pathogens Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Ithaca, NY 14853
| | - David O Igwe
- Plant Pathology and Plant Microbe Interactions Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Samuel Coradetti
- Emerging Pests and Pathogens Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Ithaca, NY 14853
| | - John S Ramsey
- Emerging Pests and Pathogens Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Ithaca, NY 14853
| | - Stacy L DeBlasio
- Emerging Pests and Pathogens Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Ithaca, NY 14853
| | | | - Robert G Shatters
- U.S. Horticultural Research Laboratory, U.S. Department of Agriculture-Agricultural Research Service, Fort Pierce, FL 34945
| | - Randall Niedz
- U.S. Horticultural Research Laboratory, U.S. Department of Agriculture-Agricultural Research Service, Fort Pierce, FL 34945
| | - Laura A Fleites
- Emerging Pests and Pathogens Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Ithaca, NY 14853
| | - Michelle Heck
- Emerging Pests and Pathogens Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Ithaca, NY 14853
- Plant Pathology and Plant Microbe Interactions Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
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Chen C, Shi J, Wang D, Kong P, Wang Z, Liu Y. Antimicrobial peptides as promising antibiotic adjuvants to combat drug-resistant pathogens. Crit Rev Microbiol 2024; 50:267-284. [PMID: 36890767 DOI: 10.1080/1040841x.2023.2186215] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/19/2022] [Accepted: 10/26/2022] [Indexed: 03/10/2023]
Abstract
The widespread antimicrobial resistance (AMR) calls for the development of new antimicrobial strategies. Antibiotic adjuvant rescues antibiotic activity and increases the life span of the antibiotics, representing a more productive, timely, and cost-effective strategy in fighting drug-resistant pathogens. Antimicrobial peptides (AMPs) from synthetic and natural sources are considered new-generation antibacterial agents. Besides their direct antimicrobial activity, growing evidence shows that some AMPs effectively enhance the activity of conventional antibiotics. The combinations of AMPs and antibiotics display an improved therapeutic effect on antibiotic-resistant bacterial infections and minimize the emergence of resistance. In this review, we discuss the value of AMPs in the age of resistance, including modes of action, limiting evolutionary resistance, and their designing strategies. We summarise the recent advances in combining AMPs and antibiotics against antibiotic-resistant pathogens, as well as their synergistic mechanisms. Lastly, we highlight the challenges and opportunities associated with the use of AMPs as potential antibiotic adjuvants. This will shed new light on the deployment of synergistic combinations to address the AMR crisis.
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Affiliation(s)
- Chen Chen
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Jingru Shi
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Dejuan Wang
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Pan Kong
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
| | - Zhiqiang Wang
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
- Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Yuan Liu
- College of Veterinary Medicine, Yangzhou University, Yangzhou, China
- Institute of Comparative Medicine, Yangzhou University, Yangzhou, China
- Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education of China, Yangzhou University, Yangzhou, China
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Kao HJ, Weng TH, Chen CH, Chen YC, Huang KY, Weng SL. iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides. Proteomics 2024; 24:e2300257. [PMID: 38263811 DOI: 10.1002/pmic.202300257] [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: 06/21/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
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Affiliation(s)
- Hui-Ju Kao
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Tzu-Hsiang Weng
- Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei City, Taiwan
| | - Chia-Hung Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Yu-Chi Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Kai-Yao Huang
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Institute of Biomedical Sciences, MacKay Medical College, New Taipei City, Taiwan
| | - Shun-Long Weng
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, Taipei City, Taiwan
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Shanthappa PM, Suravajhala R, Kumar G, Melethadathil N. Computational exploration of novel antimicrobial modalities targeting fucose-binding lectins and ribosomes in Mycobacterium smegmatis using tRNA-encoded peptides. J Biomol Struct Dyn 2024:1-13. [PMID: 38676533 DOI: 10.1080/07391102.2024.2335555] [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: 03/19/2024] [Indexed: 04/29/2024]
Abstract
tRNA-Encoded Peptides (tREPs), encoded by small open reading frames (smORFs) within tRNA genes, have recently emerged as a new class of functional peptides exhibiting antiparasitic activity. The discovery of tREPs has led to a re-evaluation of the role of tRNAs in biology and has expanded our understanding of the genetic code. This presents an immense, unexplored potential in the realm of tRNA-peptide interactions, paving the way for groundbreaking discoveries and innovative applications in various biological functions. This study explores the antimicrobial potential of tREPs against protein targets by employing a computational method that uses verified data sources and highly recognized predictive algorithms to provide a sorted list of likely antimicrobial peptides, which were then filtered for toxicity, cell permeability, allergenicity and half-life. These peptides were then docked with screened protein targets and computationally validated using molecular dynamics (MD) simulations for 150 ns and the binding free energy was estimated. The peptides Pep2 (VVLWRKPRVRKTG) and Pep6 (HRLRLRRRKPWW) exhibited good binding affinities of -110.5 +/- 2.5 and -129.0 +/- 3.9, respectively, with RMSD values of 0.4 and 0.25 nm against the fucose-binding lectin (7NEF) and the 30S ribosome of Mycobacterium smegmatis (5O5J) protein targets. The 7NEF-Pep2 and 5O5J-Pep6 complexes indicated higher negative binding free energies of -52.55 kcal/mol and -55.52 kcal/mol respectively, as calculated by Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA). Thus, the tREPs derived peptides designed as a part of this study, provide novel approaches for potential anti-bacterial therapeutic modalities.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Pallavi M Shanthappa
- Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India
| | | | - Geetha Kumar
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, India
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35
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Castillo-Mendieta K, Agüero-Chapin G, Marquez E, Perez-Castillo Y, Barigye SJ, Pérez-Cárdenas M, Peréz-Giménez F, Marrero-Ponce Y. Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence. Chem Res Toxicol 2024; 37:580-589. [PMID: 38501392 DOI: 10.1021/acs.chemrestox.3c00408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.
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Affiliation(s)
- Kevin Castillo-Mendieta
- School of Biological Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador
| | - Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, University of Porto, Av. General Norton de Matos s/n, 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Edgar Marquez
- Grupo de Investigaciones en Química y Biología, Departamento de Química y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Carrera 51B, Km 5, vía Puerto Colombia, Barranquilla 081007, Colombia
| | - Yunierkis Perez-Castillo
- Bio-Chemoinformatics Research Group and Escuela de Ciencias Físicas y Matemáticas. Universidad de Las Américas, Quito 170504, Ecuador
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), 28049 Madrid, Spain
| | - Mariela Pérez-Cárdenas
- School of Biological Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador
| | - Facundo Peréz-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia 46100, Spain
| | - Yovani Marrero-Ponce
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia 46100, Spain
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, CDMX, Mexico 03920, Mexico
- Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas; and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Universidad San Francisco de Quito (USFQ), Quito, Pichincha 170157, Ecuador
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36
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Torres MDT, Cesaro A, de la Fuente-Nunez C. Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.25.586636. [PMID: 38585860 PMCID: PMC10996515 DOI: 10.1101/2024.03.25.586636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Encrypted peptides have been recently described as a new class of antimicrobial molecules. They have been proposed to play a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these peptides are found embedded in proteins unrelated to the immune system, suggesting that immunological responses may extend beyond traditional host immunity proteins. To test this idea, here we synthesized and tested representative peptides derived from non-immune proteins for their ability to exert antimicrobial and immunomodulatory properties. Our experiments revealed that most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators such as IL-6, TNF-α, and MCP-1. Moreover, eight of the peptides identified, collagenin 3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may play a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.
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37
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Kordi M, Talkhounche PG, Vahedi H, Farrokhi N, Tabarzad M. Heterologous Production of Antimicrobial Peptides: Notes to Consider. Protein J 2024; 43:129-158. [PMID: 38180586 DOI: 10.1007/s10930-023-10174-w] [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] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
Heavy and irresponsible use of antibiotics in the last century has put selection pressure on the microbes to evolve even faster and develop more resilient strains. In the confrontation with such sometimes called "superbugs", the search for new sources of biochemical antibiotics seems to have reached the limit. In the last two decades, bioactive antimicrobial peptides (AMPs), which are polypeptide chains with less than 100 amino acids, have attracted the attention of many in the control of microbial pathogens, more than the other types of antibiotics. AMPs are groups of components involved in the immune response of many living organisms, and have come to light as new frontiers in fighting with microbes. AMPs are generally produced in minute amounts within organisms; therefore, to address the market, they have to be either produced on a large scale through recombinant DNA technology or to be synthesized via chemical methods. Here, heterologous expression of AMPs within bacterial, fungal, yeast, plants, and insect cells, and points that need to be considered towards their industrialization will be reviewed.
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Affiliation(s)
- Masoumeh Kordi
- Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Parnian Ghaedi Talkhounche
- Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Helia Vahedi
- Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Naser Farrokhi
- Department of Cell & Molecular Biology, Faculty of Life Sciences & Biotechnology, Shahid Beheshti University, Tehran, Iran.
| | - Maryam Tabarzad
- Protein Technology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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38
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Karapetian M, Alimbarashvili E, Vishnepolsky B, Gabrielian A, Rosenthal A, Hurt DE, Tartakovsky M, Mchedlishvili M, Arsenadze D, Pirtskhalava M, Zaalishvili G. Evaluation of the synergistic potential and mechanisms of action for de novo designed cationic antimicrobial peptides. Heliyon 2024; 10:e27852. [PMID: 38560672 PMCID: PMC10979160 DOI: 10.1016/j.heliyon.2024.e27852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an in silico approach to design cationic AMPs de novo, followed by their experimental testing. The antibacterial potential of de novo designed cationic AMPs, along with their synergistic properties in combination with conventional antibiotics was examined. Furthermore, the effects of bacterial inoculum density and metabolic state on the antibacterial activity of AMPs were evaluated. Finally, the impact of several potent AMPs on E. coli cell envelope and genomic DNA integrity was determined. Collectively, this comprehensive analysis provides insights into the unique characteristics of cationic AMPs.
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Affiliation(s)
- Margarita Karapetian
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
| | - Evgenia Alimbarashvili
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
| | - Boris Vishnepolsky
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
| | - Andrei Gabrielian
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Darrell E. Hurt
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mariam Mchedlishvili
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
| | - Davit Arsenadze
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
| | - Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
| | - Giorgi Zaalishvili
- Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia, 240 David Aghmashenebeli Alley, 0159, Tbilisi, Georgia
- Ivane Beritashvili Center of Experimental Biomedicine, 0160, Tbilisi, Georgia
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Guan J, Yao L, Xie P, Chung CR, Huang Y, Chiang YC, Lee TY. A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy. Brief Bioinform 2024; 25:bbae208. [PMID: 38706321 PMCID: PMC11070730 DOI: 10.1093/bib/bbae208] [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: 02/04/2024] [Revised: 03/14/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.
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Affiliation(s)
- Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Lantian Yao
- 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, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Peilin Xie
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, 320317 Taoyuan, Taiwan
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 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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40
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Von Vietinghoff S, Shevchuk O, Dobrindt U, Engel DR, Jorch SK, Kurts C, Miethke T, Wagenlehner F. The global burden of antimicrobial resistance - urinary tract infections. Nephrol Dial Transplant 2024; 39:581-588. [PMID: 37891013 DOI: 10.1093/ndt/gfad233] [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: 08/04/2023] [Indexed: 10/29/2023] Open
Abstract
Antimicrobial resistance (AMR) has emerged as a significant global healthcare problem. Antibiotic use has accelerated the physiologic process of AMR, particularly in Gram-negative pathogens. Urinary tract infections (UTIs) are predominantly of a Gram-negative nature. Uropathogens are evolutionarily highly adapted and selected strains with specific virulence factors, suggesting common mechanisms in how bacterial cells acquire virulence and AMR factors. The simultaneous increase in resistance and virulence is a complex and context-dependent phenomenon. Among known AMR mechanisms, the plenitude of different β-lactamases is especially prominent. The risk for AMR in UTIs varies in different patient populations. A history of antibiotic consumption and the physiology of urinary flow are major factors that shape AMR prevalence. The urinary tract is in close crosstalk with the microbiome of other compartments, including the gut and genital tracts. In addition, pharmacokinetic properties and the physiochemical composition of urinary compartments can contribute to the emergence of AMR. Alternatives to antibiotic treatment and a broader approach to address bacterial infections are needed. Among the various alternatives studied, antimicrobial peptides and bacteriophage treatment appear to be highly promising approaches. We herein summarize the present knowledge of clinical and microbiological AMR in UTIs and discuss innovative approaches, namely new risk prediction tools and the use of non-antibiotic approaches to defend against uropathogenic microbes.
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Affiliation(s)
- Sibylle Von Vietinghoff
- University Hospital Bonn, Medical Clinic 1, Section for Nephrology and University Bonn, Germany
| | - Olga Shevchuk
- University Duisburg-Essen, University Hospital Essen, Institute of Experimental Immunology and Imaging, Department of Immunodynamics, Essen, Germany
| | - Ulrich Dobrindt
- University of Münster, Institute of Hygiene, Münster, Germany
| | - Daniel Robert Engel
- University Duisburg-Essen, University Hospital Essen, Institute of Experimental Immunology and Imaging, Department of Immunodynamics, Essen, Germany
| | | | | | - Thomas Miethke
- Medical Faculty of Mannheim University of Heidelberg, Institute for Medical Microbiology and Hygiene, Heidelberg, Germany
- Medical Faculty of Mannheim, Heidelberg University, Institute for Medical Microbiology and Hygiene, Mannheim, Germany
| | - Florian Wagenlehner
- Justus-Liebig University Giessen, Clinic for Urology, Paediatric Urology and Andrology, Giessen, Germany
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41
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Wu H, Chen R, Li X, Zhang Y, Zhang J, Yang Y, Wan J, Zhou Y, Chen H, Li J, Li R, Zou G. ESKtides: a comprehensive database and mining method for ESKAPE phage-derived antimicrobial peptides. Database (Oxford) 2024; 2024:baae022. [PMID: 38531599 DOI: 10.1093/database/baae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/06/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
'Superbugs' have received increasing attention from researchers, such as ESKAPE bacteria (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp.), which directly led to about 1 270 000 death cases in 2019. Recently, phage peptidoglycan hydrolases (PGHs)-derived antimicrobial peptides were proposed as new antibacterial agents against multidrug-resistant bacteria. However, there is still a lack of methods for mining antimicrobial peptides based on phages or phage PGHs. Here, by using a collection of 6809 genomes of ESKAPE isolates and corresponding phages in public databases, based on a unified annotation process of all the genomes, PGHs were systematically identified, from which peptides were mined. As a result, a total of 12 067 248 peptides with high antibacterial activities were respectively determined. A user-friendly tool was developed to predict the phage PGHs-derived antimicrobial peptides from customized genomes, which also allows the calculation of peptide phylogeny, physicochemical properties, and secondary structure. Finally, a user-friendly and intuitive database, ESKtides (http://www.phageonehealth.cn:9000/ESKtides), was designed for data browsing, searching and downloading, which provides a rich peptide library based on ESKAPE prophages and phages. Database URL: 10.1093/database/baae022.
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Affiliation(s)
- Hongfang Wu
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Rongxian Chen
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Xuejian Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Informatics, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Yue Zhang
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jianwei Zhang
- National Key Laboratory of Crop Genetic Improvement, Shizishan Street No. 1, Wuhan 430070, China
| | - Yanbo Yang
- College of Informatics, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jun Wan
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Yang Zhou
- College of Fisheries, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Huanchun Chen
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Jinquan Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- College of Veterinary Medicine, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Buxin Road No. 97, Shenzhen 518000, China
- Shenzhen Institute of Quality & Safety Inspection and Research, Buxin Road No. 97, Shenzhen 518000, China
| | - Runze Li
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
| | - Geng Zou
- National Key Laboratory of Agricultural Microbiology, College of Biomedicine and Health, Huazhong Agricultural University, Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
- Hubei Hongshan Laboratory, College of Food Science and Technology, Huazhong Agricultural University, Shizishan Street No. 1, Wuhan 430070, China
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42
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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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43
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Elradi M, Ahmed AI, Saleh AM, Abdel-Raouf KMA, Berika L, Daoud Y, Amleh A. Derivation of a novel antimicrobial peptide from the Red Sea Brine Pools modified to enhance its anticancer activity against U2OS cells. BMC Biotechnol 2024; 24:14. [PMID: 38491556 PMCID: PMC10943910 DOI: 10.1186/s12896-024-00835-8] [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: 11/10/2023] [Accepted: 02/06/2024] [Indexed: 03/18/2024] Open
Abstract
Cancer associated drug resistance is a major cause for cancer aggravation, particularly as conventional therapies have presented limited efficiency, low specificity, resulting in long term deleterious side effects. Peptide based drugs have emerged as potential alternative cancer treatment tools due to their selectivity, ease of design and synthesis, safety profile, and low cost of manufacturing. In this study, we utilized the Red Sea metagenomics database, generated during AUC/KAUST Red Sea microbiome project, to derive a viable anticancer peptide (ACP). We generated a set of peptide hits from our library that shared similar composition to ACPs. A peptide with a homeodomain was selected, modified to improve its anticancer properties, verified to maintain high anticancer properties, and processed for further in-silico prediction of structure and function. The peptide's anticancer properties were then assessed in vitro on osteosarcoma U2OS cells, through cytotoxicity assay (MTT assay), scratch-wound healing assay, apoptosis/necrosis detection assay (Annexin/PI assay), RNA expression analysis of Caspase 3, KI67 and Survivin, and protein expression of PARP1. L929 mouse fibroblasts were also assessed for cytotoxicity treatment. In addition, the antimicrobial activity of the peptide was also examined on E coli and S. aureus, as sample representative species of the human bacterial microbiome, by examining viability, disk diffusion, morphological assessment, and hemolytic analysis. We observed a dose dependent cytotoxic response from peptide treatment of U2OS, with a higher tolerance in L929s. Wound closure was debilitated in cells exposed to the peptide, while annexin fluorescent imaging suggested peptide treatment caused apoptosis as a major mode of cell death. Caspase 3 gene expression was not altered, while KI67 and Survivin were both downregulated in peptide treated cells. Additionally, PARP-1 protein analysis showed a decrease in expression with peptide exposure. The peptide exhibited minimal antimicrobial activity on critical human microbiome species E. coli and S. aureus, with a low inhibition rate, maintenance of structural morphology and minimal hemolytic impact. These findings suggest our novel peptide displayed preliminary ACP properties against U2OS cells, through limited specificity, while triggering apoptosis as a primary mode of cell death and while having minimal impact on the microbiological species E. coli and S. aureus.
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Affiliation(s)
- Mona Elradi
- Biotechnology Program, American University in Cairo, New Cairo, Egypt
| | - Ahmed I Ahmed
- Biology Department, American University in Cairo, New Cairo, Egypt
| | - Ahmed M Saleh
- Biology Department, American University in Cairo, New Cairo, Egypt
| | | | - Lina Berika
- Biology Department, American University in Cairo, New Cairo, Egypt
| | - Yara Daoud
- Biology Department, American University in Cairo, New Cairo, Egypt
| | - Asma Amleh
- Biotechnology Program, American University in Cairo, New Cairo, Egypt.
- Biology Department, American University in Cairo, New Cairo, Egypt.
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44
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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45
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Liu M, Wu T, Li X, Zhu Y, Chen S, Huang J, Zhou F, Liu H. ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization. Front Genet 2024; 15:1352504. [PMID: 38487252 PMCID: PMC10937565 DOI: 10.3389/fgene.2024.1352504] [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: 12/13/2023] [Accepted: 02/19/2024] [Indexed: 03/17/2024] Open
Abstract
Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.
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Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Tao Wu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Xue Li
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Yingxue Zhu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Sen Chen
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Fengfeng Zhou
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Hongmei Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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46
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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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47
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Dong Q, Wang S, Miao Y, Luo H, Weng Z, Yu L. Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning. Sci Rep 2024; 14:4529. [PMID: 38402320 PMCID: PMC10894229 DOI: 10.1038/s41598-024-55205-3] [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: 11/01/2023] [Accepted: 02/21/2024] [Indexed: 02/26/2024] Open
Abstract
The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2-4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.
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Affiliation(s)
- Qichang Dong
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Shaohua Wang
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Ying Miao
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Heng Luo
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Zuquan Weng
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Lun Yu
- Metanovas Biotech Inc., Foster City, 94404, USA.
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48
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Wang Y, Stebe KJ, de la Fuente-Nunez C, Radhakrishnan R. Computational Design of Peptides for Biomaterials Applications. ACS APPLIED BIO MATERIALS 2024; 7:617-625. [PMID: 36971822 PMCID: PMC11190638 DOI: 10.1021/acsabm.2c01023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.
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Affiliation(s)
- Yiming Wang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Kathleen J Stebe
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Cesar de la Fuente-Nunez
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Machine Biology Group, Department of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Ravi Radhakrishnan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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49
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Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
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Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
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50
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Yan Q, Wang F, Zhou B, Lin X. Hybrid 2D/3D-quantitative structure-activity relationship studies on the bioactivities and molecular mechanism of antibacterial peptides. Amino Acids 2024; 56:16. [PMID: 38358574 PMCID: PMC10869384 DOI: 10.1007/s00726-024-03381-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: 11/28/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
Antimicrobial peptide (AMP) is the polypeptide, which protects the organism avoiding attack from pathogenic bacteria. Studies have shown that there were some antimicrobial peptides with molecular action mechanism involved in crossing the cell membrane without inducing severe membrane collapse, then interacting with cytoplasmic target-nucleic acid, and exerting antibacterial activity by interfacing the transmission of genetic information of pathogenic microorganisms. However, the relationship between the antibacterial activities and peptide structures was still unclear. Therefore, in the present work, a series of AMPs with a sequence of 20 amino acids was extracted from DBAASP database, then, quantitative structure-activity relationship (QSAR) methods were conducted on these peptides. In addition, novel antimicrobial peptides with stronger antimicrobial activities were designed according to the information originated from the constructed models. Hence, the outcome of this study would lay a solid foundation for the in-silico design and exploration of novel antibacterial peptides with improved activity activities.
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Affiliation(s)
- Qingguo Yan
- School of Life Science, Linyi University, Linyi, 276000, China
| | - Fangfang Wang
- School of Life Science, Linyi University, Linyi, 276000, China.
| | - Bo Zhou
- State Key Laboratory of Functions and Applications of Medicinal Plants, College of Basic Medical, Guizhou Medical University, Guizhou, 550004, China
| | - Xiangna Lin
- School of Life Science, Linyi University, Linyi, 276000, China
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