1
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Bello-Madruga R, Torrent Burgas M. The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides. Comput Struct Biotechnol J 2024; 23:972-981. [PMID: 38404711 PMCID: PMC10884422 DOI: 10.1016/j.csbj.2024.02.008] [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: 11/28/2023] [Revised: 02/10/2024] [Accepted: 02/10/2024] [Indexed: 02/27/2024] Open
Abstract
Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting with bacterial membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) with AMP-like properties could exhibit antimicrobial activity. We tested 14 peptides from different IDRs predicted to have antimicrobial activity and found that nearly all of them did not display the anticipated effects. These peptides failed to adopt a defined secondary structure and had compromised membrane interactions, resulting in a lack of antimicrobial activity. We hypothesize that evolutionary constraints may prevent IDRs from folding, even in membrane-like environments, limiting their antimicrobial potential. Moreover, our research reveals that current antimicrobial predictors fail to accurately capture the structural features of peptides when dealing with intrinsically unstructured sequences. Hence, the results presented here may have far-reaching implications for designing and improving antimicrobial strategies and therapies against infectious diseases.
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Affiliation(s)
- Roberto Bello-Madruga
- The Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
| | - Marc Torrent Burgas
- The Systems Biology of Infection Lab, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
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2
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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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3
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Guo Y, Gao F, Rafiq M, Yu B, Cong H, Shen Y. Preparation of antimicrobial peptides and their combination with hydrogels for wound healing applications. Int J Biol Macromol 2024; 274:133494. [PMID: 38944068 DOI: 10.1016/j.ijbiomac.2024.133494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/16/2024] [Accepted: 06/26/2024] [Indexed: 07/01/2024]
Abstract
The problem of drug resistance caused by long-term use of antibiotics has been a concern for many years. As this problem worsens, there are various bacterial-induced infections that have a serious impact on human health. Currently, antimicrobial peptides are good alternatives to antibiotics, which have powerful antimicrobial activity and unique antimicrobial mechanisms. Developing bacterial resistance is not easy. In addition, how to reduce the production cost of antimicrobial peptides and improve the screening efficiency are the problems that must be solved for antimicrobial peptide application. In this study, we employed cell membrane chromatography linked with the one-bead-one-substance approach to screen and prepare the antimicrobial peptide (SALSP), which offers the benefits of fast synthetic screening and easy operation. Meanwhile, the antimicrobial peptide showed great antimicrobial activity and biocompatibility. We prepared a conjugated sodium alginate/gelatin hydrogel wound dressing incorporating antimicrobial peptides to promote wound healing. In conclusion, this research provides solutions for the development and application of antimicrobial peptides.
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Affiliation(s)
- Yuanyuan Guo
- College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao 266071, China
| | - Fengyuan Gao
- College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao 266071, China
| | - Muhammad Rafiq
- College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao 266071, China
| | - Bing Yu
- College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao 266071, China; State Key Laboratory of Bio-Fibers and Eco-Textiles, Qingdao University, Qingdao 266071, China.
| | - Hailin Cong
- College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao 266071, China; State Key Laboratory of Bio-Fibers and Eco-Textiles, Qingdao University, Qingdao 266071, China; School of Materials Science and Engineering, Shandong University of Technology, Zibo 255000, China.
| | - Youqing Shen
- College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Institute of Biomedical Materials and Engineering, Qingdao University, Qingdao 266071, China; Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Center for Bionanoengineering, Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
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4
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Iglesias V, Bárcenas O, Pintado-Grima C, Burdukiewicz M, Ventura S. Structural information in therapeutic peptides: Emerging applications in biomedicine. FEBS Open Bio 2024. [PMID: 38877295 DOI: 10.1002/2211-5463.13847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/08/2024] [Accepted: 05/27/2024] [Indexed: 06/16/2024] Open
Abstract
Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.
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Affiliation(s)
- Valentín Iglesias
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Institute of Advanced Chemistry of Catalonia (IQAC), CSIC, Barcelona, Spain
| | - Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Michał Burdukiewicz
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
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5
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Zervou MA, Doutsi E, Pantazis Y, Tsakalides P. De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks. Int J Mol Sci 2024; 25:5506. [PMID: 38791544 PMCID: PMC11122239 DOI: 10.3390/ijms25105506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.
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Affiliation(s)
- Michaela Areti Zervou
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Effrosyni Doutsi
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Yannis Pantazis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Panagiotis Tsakalides
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
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6
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Bajiya N, Choudhury S, Dhall A, Raghava GPS. AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria. Antibiotics (Basel) 2024; 13:168. [PMID: 38391554 PMCID: PMC10885866 DOI: 10.3390/antibiotics13020168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.
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Affiliation(s)
- Nisha Bajiya
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Shubham Choudhury
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
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7
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Aguilera-Puga MDC, Cancelarich NL, Marani MM, de la Fuente-Nunez C, Plisson F. Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. Methods Mol Biol 2024; 2714:329-352. [PMID: 37676607 DOI: 10.1007/978-1-0716-3441-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico
| | - Natalia L Cancelarich
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Mariela M Marani
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Fabien Plisson
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico.
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico.
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8
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Shin MK, Hwang IW, Jang BY, Bu KB, Han DH, Lee SH, Oh JW, Yoo JS, Sung JS. The Identification of a Novel Spider Toxin Peptide, Lycotoxin-Pa2a, with Antibacterial and Anti-Inflammatory Activities. Antibiotics (Basel) 2023; 12:1708. [PMID: 38136742 PMCID: PMC10740532 DOI: 10.3390/antibiotics12121708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
With the increasing challenge of controlling infectious diseases due to the emergence of antibiotic-resistant strains, the importance of discovering new antimicrobial agents is rapidly increasing. Animal venoms contain a variety of functional peptides, making them a promising platform for pharmaceutical development. In this study, a novel toxin peptide with antibacterial and anti-inflammatory activities was discovered from the spider venom gland transcriptome by implementing computational approaches. Lycotoxin-Pa2a (Lytx-Pa2a) showed homology to known-spider toxin, where functional prediction indicated the potential of both antibacterial and anti-inflammatory peptides without hemolytic activity. The colony-forming assay and minimum inhibitory concentration test showed that Lytx-Pa2a exhibited comparable or stronger antibacterial activity against pathogenic strains than melittin. Following mechanistic studies revealed that Lytx-Pa2a disrupts both cytoplasmic and outer membranes of bacteria while simultaneously inducing the accumulation of reactive oxygen species. The peptide exerted no significant toxicity when treated to human primary cells, murine macrophages, and bovine red blood cells. Moreover, Lytx-Pa2a alleviated lipopolysaccharide-induced inflammation in mouse macrophages by suppressing the expression of inflammatory mediators. These findings not only suggested that Lytx-Pa2a with dual activity can be utilized as a new antimicrobial agent for infectious diseases but also demonstrated the implementation of in silico methods for discovering a novel functional peptide, which may enhance the future utilization of biological resources.
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Affiliation(s)
- Min Kyoung Shin
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
| | - In-Wook Hwang
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
| | - Bo-Young Jang
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
| | - Kyung-Bin Bu
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
| | - Dong-Hee Han
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
| | - Seung-Ho Lee
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
| | - Jin Wook Oh
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
| | - Jung Sun Yoo
- Species Diversity Research Division, National Institute of Biological Resources, Incheon 22689, Republic of Korea;
| | - Jung-Suk Sung
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (I.-W.H.); (B.-Y.J.); (K.-B.B.); (D.-H.H.); (S.-H.L.); (J.W.O.)
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9
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Shin MK, Park HR, Hwang IW, Bu KB, Jang BY, Lee SH, Oh JW, Yoo JS, Sung JS. In Silico-Based Design of a Hybrid Peptide with Antimicrobial Activity against Multidrug-Resistant Pseudomonas aeruginosa Using a Spider Toxin Peptide. Toxins (Basel) 2023; 15:668. [PMID: 38133172 PMCID: PMC10747792 DOI: 10.3390/toxins15120668] [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: 10/16/2023] [Revised: 11/12/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
The escalating prevalence of antibiotic-resistant bacteria poses an immediate and grave threat to public health. Antimicrobial peptides (AMPs) have gained significant attention as a promising alternative to conventional antibiotics. Animal venom comprises a diverse array of bioactive compounds, which can be a rich source for identifying new functional peptides. In this study, we identified a toxin peptide, Lycotoxin-Pa1a (Lytx-Pa1a), from the transcriptome of the Pardosa astrigera spider venom gland. To enhance its functional properties, we employed an in silico approach to design a novel hybrid peptide, KFH-Pa1a, by predicting antibacterial and cytotoxic functionalities and incorporating the amino-terminal Cu(II)- and Ni(II) (ATCUN)-binding motif. KFH-Pa1a demonstrated markedly superior antimicrobial efficacy against pathogens, including multidrug-resistant (MDR) Pseudomonas aeruginosa, compared to Lytx-Pa1a. Notably, KFH-Pa1a exerted several distinct mechanisms, including the disruption of the bacterial cytoplasmic membrane, the generation of intracellular ROS, and the cleavage and inhibition of bacterial DNA. Additionally, the hybrid peptide showed synergistic activity when combined with conventional antibiotics. Our research not only identified a novel toxin peptide from spider venom but demonstrated in silico-based design of hybrid AMP with strong antimicrobial activity that can contribute to combating MDR pathogens, broadening the utilization of biological resources by incorporating computational approaches.
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Affiliation(s)
- Min Kyoung Shin
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
| | - Hye-Ran Park
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
| | - In-Wook Hwang
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
| | - Kyung-Bin Bu
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
| | - Bo-Young Jang
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
| | - Seung-Ho Lee
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
| | - Jin Wook Oh
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
| | - Jung Sun Yoo
- Species Diversity Research Division, National Institute of Biological Resources, Incheon 22689, Republic of Korea;
| | - Jung-Suk Sung
- Department of Life Science, Dongguk University-Seoul, Goyang 10326, Republic of Korea; (M.K.S.); (H.-R.P.); (I.-W.H.); (K.-B.B.); (B.-Y.J.); (S.-H.L.); (J.W.O.)
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10
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Lv H, Yan K, Liu B. TPpred-LE: therapeutic peptide function prediction based on label embedding. BMC Biol 2023; 21:238. [PMID: 37904157 PMCID: PMC10617231 DOI: 10.1186/s12915-023-01740-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: 02/17/2023] [Accepted: 10/17/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data. RESULTS In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data. CONCLUSIONS In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data.
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Affiliation(s)
- Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Haidian District, Beijing, 100081, China.
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11
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Gómez-Pérez D, Schmid M, Chaudhry V, Hu Y, Velic A, Maček B, Ruhe J, Kemen A, Kemen E. Proteins released into the plant apoplast by the obligate parasitic protist Albugo selectively repress phyllosphere-associated bacteria. THE NEW PHYTOLOGIST 2023; 239:2320-2334. [PMID: 37222268 DOI: 10.1111/nph.18995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 04/11/2023] [Indexed: 05/25/2023]
Abstract
Biotic and abiotic interactions shape natural microbial communities. The mechanisms behind microbe-microbe interactions, particularly those protein based, are not well understood. We hypothesize that released proteins with antimicrobial activity are a powerful and highly specific toolset to shape and defend plant niches. We have studied Albugo candida, an obligate plant parasite from the protist Oomycota phylum, for its potential to modulate the growth of bacteria through release of antimicrobial proteins into the apoplast. Amplicon sequencing and network analysis of Albugo-infected and uninfected wild Arabidopsis thaliana samples revealed an abundance of negative correlations between Albugo and other phyllosphere microbes. Analysis of the apoplastic proteome of Albugo-colonized leaves combined with machine learning predictors enabled the selection of antimicrobial candidates for heterologous expression and study of their inhibitory function. We found for three candidate proteins selective antimicrobial activity against Gram-positive bacteria isolated from A. thaliana and demonstrate that these inhibited bacteria are precisely important for the stability of the community structure. We could ascribe the antibacterial activity of the candidates to intrinsically disordered regions and positively correlate it with their net charge. This is the first report of protist proteins with antimicrobial activity under apoplastic conditions that therefore are potential biocontrol tools for targeted manipulation of the microbiome.
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Affiliation(s)
- Daniel Gómez-Pérez
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Monja Schmid
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Vasvi Chaudhry
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Yiheng Hu
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Ana Velic
- Department of Biology, Quantitative Proteomics Group, Interfaculty Institute of Cell Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Boris Maček
- Department of Biology, Quantitative Proteomics Group, Interfaculty Institute of Cell Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Jonas Ruhe
- Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Ariane Kemen
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Eric Kemen
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
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Wang Y, Sun F, Wang Z, Duan X, Li Q, Pang Y, Gou M. Peptidomics Analysis Reveals the Buccal Gland of Jawless Vertebrate Lamprey as a Source of Multiple Bioactive Peptides. Mar Drugs 2023; 21:389. [PMID: 37504920 PMCID: PMC10381800 DOI: 10.3390/md21070389] [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: 05/31/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 07/29/2023] Open
Abstract
Various proteins with antibacterial, anticoagulant, and anti-inflammatory properties have been identified in the buccal glands of jawless blood-sucking vertebrate lampreys. However, studies on endogenous peptides in the buccal gland of lampreys are limited. In this study, 4528 endogenous peptides were identified from 1224 precursor proteins using peptidomics and screened for bioactivity in the buccal glands of the lamprey, Lethenteron camtschaticum. We synthesized four candidate bioactive peptides (VSLNLPYSVVRGEQFVVQA, DIPVPEVPILE, VVQLPPVVLGTFG, and VPPPPLVLPPASVK), calculated their secondary structures, and validated their bioactivity. The results showed that the peptide VSLNLPYSVVRGEQFVVQA possessed anti-inflammatory activity, which significantly increased the expression of anti-inflammatory factors and decreased the expression of inflammatory factors in THP-1 cells. The peptide VVQLPPVVLGTFG showed antibacterial activity against some gram-positive bacteria. The peptide VSLNLPYSVVRGEQFVQA possessed good ACE inhibitory activity at low concentrations, but no dose-related correlation was observed. Our study revealed that the buccal glands of the jawless vertebrate lamprey are a source of multiple bioactive peptides, which will provide new insights into the blood-sucking mechanism of lamprey.
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Affiliation(s)
- Yaocen Wang
- College of Life Science, Liaoning Normal University, Dalian 116081, China
- Lamprey Research Center, Liaoning Normal University, Dalian 116081, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
| | - Feng Sun
- College of Life Science, Liaoning Normal University, Dalian 116081, China
- Lamprey Research Center, Liaoning Normal University, Dalian 116081, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
| | - Zhuoying Wang
- College of Life Science, Liaoning Normal University, Dalian 116081, China
- Lamprey Research Center, Liaoning Normal University, Dalian 116081, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
| | - Xuyuan Duan
- College of Life Science, Liaoning Normal University, Dalian 116081, China
- Lamprey Research Center, Liaoning Normal University, Dalian 116081, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
| | - Qingwei Li
- College of Life Science, Liaoning Normal University, Dalian 116081, China
- Lamprey Research Center, Liaoning Normal University, Dalian 116081, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
| | - Yue Pang
- College of Life Science, Liaoning Normal University, Dalian 116081, China
- Lamprey Research Center, Liaoning Normal University, Dalian 116081, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
| | - Meng Gou
- College of Life Science, Liaoning Normal University, Dalian 116081, China
- Lamprey Research Center, Liaoning Normal University, Dalian 116081, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian Polytechnic University, Dalian 116034, China
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13
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Ostrówka M, Duda-Madej A, Pietluch F, Mackiewicz P, Gagat P. Testing Antimicrobial Properties of Human Lactoferrin-Derived Fragments. Int J Mol Sci 2023; 24:10529. [PMID: 37445717 DOI: 10.3390/ijms241310529] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Lactoferrin, an iron-binding glycoprotein, plays a significant role in the innate immune system, with antibacterial, antivirial, antifungal, anticancer, antioxidant and immunomodulatory functions reported. It is worth emphasizing that not only the whole protein but also its derived fragments possess antimicrobial peptide (AMP) activity. Using AmpGram, a top-performing AMP classifier, we generated three novel human lactoferrin (hLF) fragments: hLF 397-412, hLF 448-464 and hLF 668-683, predicted with high probability as AMPs. For comparative studies, we included hLF 1-11, previously confirmed to kill some bacteria. With the four peptides, we treated three Gram-negative and three Gram-positive bacterial strains. Our results indicate that none of the three new lactoferrin fragments have antimicrobial properties for the bacteria tested, but hLF 1-11 was lethal against Pseudomonas aeruginosa. The addition of serine protease inhibitors with the hLF fragments did not enhance their activity, except for hLF 1-11 against P. aeruginosa, which MIC dropped from 128 to 64 µg/mL. Furthermore, we investigated the impact of EDTA with/without serine protease inhibitors and the hLF peptides on selected bacteria. We stress the importance of reporting non-AMP sequences for the development of next-generation AMP prediction models, which suffer from the lack of experimentally validated negative dataset for training and benchmarking.
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Affiliation(s)
- Michał Ostrówka
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
| | - Anna Duda-Madej
- Department of Microbiology, Faculty of Medicine, Wrocław Medical University, Chałubińskiego 4, 50-368 Wrocław, Poland
| | - Filip Pietluch
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
| | - Paweł Mackiewicz
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
| | - Przemysław Gagat
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
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14
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Galzitskaya OV. Creation of New Antimicrobial Peptides. Int J Mol Sci 2023; 24:ijms24119451. [PMID: 37298402 DOI: 10.3390/ijms24119451] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023] Open
Abstract
Antimicrobial peptides (AMPs) are natural compounds that exhibit potent antimicrobial activity against various microorganisms, including bacteria, fungi, and viruses [...].
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Affiliation(s)
- Oxana V Galzitskaya
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
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15
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Sidorczuk K, Gagat P, Kała J, Nielsen H, Pietluch F, Mackiewicz P, Burdukiewicz M. Prediction of protein subplastid localization and origin with PlastoGram. Sci Rep 2023; 13:8365. [PMID: 37225726 DOI: 10.1038/s41598-023-35296-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/16/2023] [Indexed: 05/26/2023] Open
Abstract
Due to their complex history, plastids possess proteins encoded in the nuclear and plastid genome. Moreover, these proteins localize to various subplastid compartments. Since protein localization is associated with its function, prediction of subplastid localization is one of the most important steps in plastid protein annotation, providing insight into their potential function. Therefore, we create a novel manually curated data set of plastid proteins and build an ensemble model for prediction of protein subplastid localization. Moreover, we discuss problems associated with the task, e.g. data set sizes and homology reduction. PlastoGram classifies proteins as nuclear- or plastid-encoded and predicts their localization considering: envelope, stroma, thylakoid membrane or thylakoid lumen; for the latter, the import pathway is also predicted. We also provide an additional function to differentiate nuclear-encoded inner and outer membrane proteins. PlastoGram is available as a web server at https://biogenies.info/PlastoGram and as an R package at https://github.com/BioGenies/PlastoGram . The code used for described analyses is available at https://github.com/BioGenies/PlastoGram-analysis .
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Affiliation(s)
| | - Przemysław Gagat
- Faculty of Biotechnology, University of Wrocław, 50-383, Wrocław, Poland
| | - Jakub Kała
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662, Warsaw, Poland
| | - Henrik Nielsen
- Department of Health Technology, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Filip Pietluch
- Faculty of Biotechnology, University of Wrocław, 50-383, Wrocław, Poland
| | - Paweł Mackiewicz
- Faculty of Biotechnology, University of Wrocław, 50-383, Wrocław, Poland
| | - Michał Burdukiewicz
- Institute of Biotechnology and Biomedicine, Autonomous University of Barcelona, 08193, Cerdanyola del Vallés, Spain.
- Clinical Research Centre, Medical University of Białystok, 15-089, Białystok, Poland.
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16
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Szymczak P, Możejko M, Grzegorzek T, Jurczak R, Bauer M, Neubauer D, Sikora K, Michalski M, Sroka J, Setny P, Kamysz W, Szczurek E. Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. Nat Commun 2023; 14:1453. [PMID: 36922490 PMCID: PMC10017685 DOI: 10.1038/s41467-023-36994-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marcin Możejko
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Tomasz Grzegorzek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA, 95051, USA
| | - Radosław Jurczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marta Bauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Damian Neubauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Karol Sikora
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Michał Michalski
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Jacek Sroka
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Piotr Setny
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Wojciech Kamysz
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland.
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17
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Evolutionary and in silico guided development of novel peptide analogues for antibacterial activity against ESKAPE pathogens. CURRENT RESEARCH IN MICROBIAL SCIENCES 2023; 4:100183. [PMID: 37032813 PMCID: PMC10073642 DOI: 10.1016/j.crmicr.2023.100183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
According to WHO, to combat the resistant strains, new effective anti-microbial agents are needed on an urgent basis and global researchers should focus their efforts and discovery programs on developing them against antibiotic-resistant pathogens or priority pathogens like ESKAPE. In this context, Cationic antimicrobial peptides (AMPs) are being explored extensively as promising next-generation antimicrobials due to their broad range, fast kinetics and multifunctional role. Despite recent advances, it is still a daunting challenge to identify and design a potent AMP with no cytotoxicity, but with broad specific antimicrobial activity, stability and efficacy under in vivo conditions in a cost-effective and robust manner. In this work, as a proof of concept, we designed novel potent AMPs using artificial intelligence based in silico programs. Shortlisted peptide sequences were synthesized using the fmoc chemistry approach, assessed their antimicrobial activity, cell selectivity, mode of action and in vivo efficacy using a series of experiments. The synthesized peptide analogues demonstrated their antimicrobial activity (MIC in the range of 2.5-80 μM) against bacteria. The identified potential lead molecules showed antibacterial activity in physiological conditions with no signs of cytotoxicity. We further tested the antimicrobial activity of peptide analogues for treating wounds infected with Pseudomonas aeruginosa in the mice burn wound model. In drug-development programs, the identification of lead antimicrobial agents is always challenging and involves screening a large number of molecules which is time-consuming and expensive. This work demonstrates the utility of artificial intelligence based in silico analysis programs in discovering novel antimicrobial agents in an economical, robust way.
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18
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Allogenic Adipose-Derived Stem Cells in Diabetic Foot Ulcer Treatment: Clinical Effectiveness, Safety, Survival in the Wound Site, and Proteomic Impact. Int J Mol Sci 2023; 24:ijms24021472. [PMID: 36674989 PMCID: PMC9864558 DOI: 10.3390/ijms24021472] [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: 12/06/2022] [Revised: 12/29/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023] Open
Abstract
Although encouraging results of adipose-derived stem cell (ADSC) use in wound healing are available, the mechanism of action has been studied mainly in vitro and in animals. This work aimed to examine the safety and efficacy of allogenic ADSCs in human diabetic foot ulcer treatment, in combination with the analyses of the wound. Equal groups of 23 participants each received fibrin gel with ADSCs or fibrin gel alone. The clinical effects were assessed at four time points: days 7, 14, 21 and 49. Material collected during debridement from a subset of each group was analyzed for the presence of ADSC donor DNA and proteomic changes. The reduction in wound size was greater at all subsequent visits, significantly on day 21 and 49, and the time to 50% reduction in the wound size was significantly shorter in patients who received ADSCs. Complete healing was achieved at the end of the study in seven patients treated with ADSCs vs. one treated without ADSCs. One week after ADSC application, 34 proteins significantly differentiated the material from both groups, seven of which, i.e., GAPDH, CAT, ACTN1, KRT1, KRT9, SCL4A1, and TPI, positively correlated with the healing rate. We detected ADSC donor DNA up to 21 days after administration. We confirmed ADSC-related improvement in wound healing that correlated with the molecular background, which provides insights into the role of ADSCs in wound healing-a step toward the development of cell-based therapies.
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Gagat P, Duda-Madej A, Ostrówka M, Pietluch F, Seniuk A, Mackiewicz P, Burdukiewicz M. Testing Antimicrobial Properties of Selected Short Amyloids. Int J Mol Sci 2023; 24:ijms24010804. [PMID: 36614244 PMCID: PMC9821130 DOI: 10.3390/ijms24010804] [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: 11/25/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023] Open
Abstract
Amyloids and antimicrobial peptides (AMPs) have many similarities, e.g., both kill microorganisms by destroying their membranes, form aggregates, and modulate the innate immune system. Given these similarities and the fact that the antimicrobial properties of short amyloids have not yet been investigated, we chose a group of potentially antimicrobial short amyloids to verify their impact on bacterial and eukaryotic cells. We used AmpGram, a best-performing AMP classification model, and selected ten amyloids with the highest AMP probability for our experimental research. Our results indicate that four tested amyloids: VQIVCK, VCIVYK, KCWCFT, and GGYLLG, formed aggregates under the conditions routinely used to evaluate peptide antimicrobial properties, but none of the tested amyloids exhibited antimicrobial or cytotoxic properties. Accordingly, they should be included in the negative datasets to train the next-generation AMP prediction models, based on experimentally confirmed AMP and non-AMP sequences. In the article, we also emphasize the importance of reporting non-AMPs, given that only a handful of such sequences have been officially confirmed.
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Affiliation(s)
- Przemysław Gagat
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
- Correspondence: (P.G.); (M.B.)
| | - Anna Duda-Madej
- Department of Microbiology, Faculty of Medicine, Wrocław Medical University, Chałubińskiego 4, 50-368 Wrocław, Poland
| | - Michał Ostrówka
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
| | - Filip Pietluch
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
| | - Alicja Seniuk
- Department of Microbiology, Faculty of Medicine, Wrocław Medical University, Chałubińskiego 4, 50-368 Wrocław, Poland
| | - Paweł Mackiewicz
- Faculty of Biotechnology, University of Wrocław, Fryderyka Joliot-Curie 14a, 50-137 Wrocław, Poland
| | - Michał Burdukiewicz
- Clinical Research Centre, Medical University of Bialystok, 15-089 Białystok, Poland
- Correspondence: (P.G.); (M.B.)
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20
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Specific Focus on Antifungal Peptides against Azole Resistant Aspergillus fumigatus: Current Status, Challenges, and Future Perspectives. J Fungi (Basel) 2022; 9:jof9010042. [PMID: 36675863 PMCID: PMC9864941 DOI: 10.3390/jof9010042] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/25/2022] [Accepted: 12/26/2022] [Indexed: 12/29/2022] Open
Abstract
The prevalence of fungal infections is increasing worldwide, especially that of aspergillosis, which previously only affected people with immunosuppression. Aspergillus fumigatus can cause allergic bronchopulmonary aspergillosis and endangers public health due to resistance to azole-type antimycotics such as fluconazole. Antifungal peptides are viable alternatives that combat infection by forming pores in membranes through electrostatic interactions with the phospholipids as well as cell death to peptides that inhibit protein synthesis and inhibit cell replication. Engineering antifungal peptides with nanotechnology can enhance the efficacy of these therapeutics at lower doses and reduce immune responses. This manuscript explains how antifungal peptides combat antifungal-resistant aspergillosis and also how rational peptide design with nanotechnology and artificial intelligence can engineer peptides to be a feasible antifungal alternative.
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21
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Baltutis V, O'Leary PD, Martin LL. Self-Assembly of Linear, Natural Antimicrobial Peptides: An Evolutionary Perspective. Chempluschem 2022; 87:e202200240. [PMID: 36198638 DOI: 10.1002/cplu.202200240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/29/2022] [Indexed: 01/31/2023]
Abstract
Antimicrobial peptides are an ancient and innate system of host defence against a wide range of microbial assailants. Mechanistically, unstructured peptides undergo a secondary structure transition into amphipathic α-helices, upon contact with membrane surfaces. This leads to peptide binding and removal of the membrane components in a detergent-like manner or via self-organisation into trans-membrane pores (either barrel-stave or toroidal pore) thereby destroying the microbe. Self-assembly of antimicrobial peptides into oligomers and ultimately amyloid has been mostly examined in parallel, however recent findings link diseases, such as Alzheimer's disease as an aberrant activity of a protective neuropeptide with antimicrobial activity. These self-assembled oligomers can also interact with membranes. Here, we review those antimicrobial peptides reported to self-assemble into amyloid, where supported by structural evidence. We consider their membrane activities as antimicrobial peptides and present evidence of consistent self-assembly patterns across major evolutionary groups. Trends are apparent across these groups, supporting the mounting data that self-assembly of antimicrobial peptides into amyloid should be considered as synergistic to the antimicrobial peptide response.
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Affiliation(s)
- Verity Baltutis
- School of Chemistry, Monash University, 3800, Clayton, Vic, Australia
| | - Paul D O'Leary
- School of Chemistry, Monash University, 3800, Clayton, Vic, Australia
| | - Lisandra L Martin
- School of Chemistry, Monash University, 3800, Clayton, Vic, Australia
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22
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Yan K, Lv H, Guo Y, Peng W, Liu B. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure. Bioinformatics 2022; 39:6808615. [PMID: 36342186 PMCID: PMC9805557 DOI: 10.1093/bioinformatics/btac715] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/24/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
MOTIVATION Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. RESULTS In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. AVAILABILITY AND IMPLEMENTATION A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yichen Guo
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Wei Peng
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- To whom correspondence should be addressed.
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23
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The dynamic landscape of peptide activity prediction. Comput Struct Biotechnol J 2022; 20:6526-6533. [DOI: 10.1016/j.csbj.2022.11.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
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Porosk L, Langel Ü. Approaches for evaluation of novel CPP-based cargo delivery systems. Front Pharmacol 2022; 13:1056467. [PMID: 36339538 PMCID: PMC9634181 DOI: 10.3389/fphar.2022.1056467] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/10/2022] [Indexed: 08/05/2023] Open
Abstract
Cell penetrating peptides (CPPs) can be broadly defined as relatively short synthetic, protein derived or chimeric peptides. Their most remarkable property is their ability to cross cell barriers and facilitate the translocation of cargo, such as drugs, nucleic acids, peptides, small molecules, dyes, and many others across the plasma membrane. Over the years there have been several approaches used, adapted, and developed for the evaluation of CPP efficacies as delivery systems, with the fluorophore attachment as the most widely used approach. It has become progressively evident, that the evaluation method, in order to lead to successful outcome, should concede with the specialties of the delivery. For characterization and assessment of CPP-cargo a combination of research tools of chemistry, physics, molecular biology, engineering, and other fields have been applied. In this review, we summarize the diverse, in silico, in vitro and in vivo approaches used for evaluation and characterization of CPP-based cargo delivery systems.
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Affiliation(s)
- Ly Porosk
- Laboratory of Drug Delivery, Institute of Technology, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
| | - Ülo Langel
- Laboratory of Drug Delivery, Institute of Technology, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
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25
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In Silico Prediction of Anti-Infective and Cell-Penetrating Peptides from Thalassophryne nattereri Natterin Toxins. Pharmaceuticals (Basel) 2022; 15:ph15091141. [PMID: 36145362 PMCID: PMC9501638 DOI: 10.3390/ph15091141] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 12/14/2022] Open
Abstract
The therapeutic potential of venom-derived peptides, such as bioactive peptides (BAPs), is determined by specificity, stability, and pharmacokinetics properties. BAPs, including anti-infective or antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs), share several physicochemical characteristics and are potential alternatives to antibiotic-based therapies and drug delivery systems, respectively. This study used in silico methods to predict AMPs and CPPs derived from natterins from the venomous fish Thalassophryne nattereri. Fifty-seven BAPs (19 AMPs, 8 CPPs, and 30 AMPs/CPPs) were identified using the web servers CAMP, AMPA, AmpGram, C2Pred, and CellPPD. The physicochemical properties were analyzed using ProtParam, PepCalc, and DispHred tools. The membrane-binding potential and cellular location of each peptide were analyzed using the Boman index by APD3, and TMHMM web servers. All CPPs and two AMPs showed high membrane-binding potential. Fifty-four peptides were located in the plasma membrane. Peptide immunogenicity, toxicity, allergenicity, and ADMET parameters were evaluated using several web servers. Sixteen antiviral peptides and 37 anticancer peptides were predicted using the web servers Meta-iAVP and ACPred. Secondary structures and helical wheel projections were predicted using the PEP-FOLD3 and Heliquest web servers. Fifteen peptides are potential lead compounds and were selected to be further synthesized and tested experimentally in vitro to validate the in silico screening. The use of computer-aided design for predicting peptide structure and activity is fast and cost-effective and facilitates the design of potent therapeutic peptides. The results demonstrate that toxins form a natural biotechnological platform in drug discovery, and the presence of CPP and AMP sequences in toxin families opens new possibilities in toxin biochemistry research.
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26
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Juretić D. Designed Multifunctional Peptides for Intracellular Targets. Antibiotics (Basel) 2022; 11:antibiotics11091196. [PMID: 36139975 PMCID: PMC9495127 DOI: 10.3390/antibiotics11091196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Nature’s way for bioactive peptides is to provide them with several related functions and the ability to cooperate in performing their job. Natural cell-penetrating peptides (CPP), such as penetratins, inspired the design of multifunctional constructs with CPP ability. This review focuses on known and novel peptides that can easily reach intracellular targets with little or no toxicity to mammalian cells. All peptide candidates were evaluated and ranked according to the predictions of low toxicity to mammalian cells and broad-spectrum activity. The final set of the 20 best peptide candidates contains the peptides optimized for cell-penetrating, antimicrobial, anticancer, antiviral, antifungal, and anti-inflammatory activity. Their predicted features are intrinsic disorder and the ability to acquire an amphipathic structure upon contact with membranes or nucleic acids. In conclusion, the review argues for exploring wide-spectrum multifunctionality for novel nontoxic hybrids with cell-penetrating peptides.
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Affiliation(s)
- Davor Juretić
- Mediterranean Institute for Life Sciences, 21000 Split, Croatia;
- Faculty of Science, University of Split, 21000 Split, Croatia;
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27
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Sidorczuk K, Gagat P, Pietluch F, Kała J, Rafacz D, Bąkała L, Słowik J, Kolenda R, Rödiger S, Fingerhut LCHW, Cooke IR, Mackiewicz P, Burdukiewicz M. Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data. Brief Bioinform 2022; 23:6672903. [PMID: 35988923 PMCID: PMC9487607 DOI: 10.1093/bib/bbac343] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/07/2022] [Accepted: 07/25/2022] [Indexed: 12/29/2022] Open
Abstract
Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.
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Affiliation(s)
| | | | | | - Jakub Kała
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Dominik Rafacz
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Laura Bąkała
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Jadwiga Słowik
- Warsaw University of Technology, Faculty of Mathematics and Information Science, Poland
| | - Rafał Kolenda
- Quadram Institute Biosciences, Norwich Research Park, Norwich, United Kingdom,Wrocław University of Environmental and Life Sciences, Faculty of Veterinary Medicine, Poland
| | - Stefan Rödiger
- Brandenburg University of Technology Cottbus-Senftenberg, Faculty of Natural Sciences, Germany
| | - Legana C H W Fingerhut
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Australia
| | - Ira R Cooke
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Australia
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Lv H, Yan K, Guo Y, Zou Q, Hesham AEL, Liu B. AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning. Comput Biol Med 2022; 146:105577. [PMID: 35576825 DOI: 10.1016/j.compbiomed.2022.105577] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/22/2022] [Accepted: 04/28/2022] [Indexed: 11/15/2022]
Abstract
Antimicrobial peptides (AMPs) are important for the human immune system and are currently applied in clinical trials. AMPs have been received much attention for accurate recognition. Recently, several computational methods for identifying AMPs have been proposed. However, existing methods have difficulty in accurately predicting AMPs. In this paper, we propose a novel AMP prediction method called AMPpred-EL based on an ensemble learning strategy. AMPred-EL is constructed based on ensemble learning combined with LightGBM and logistic regression. Experimental results demonstrate that AMPpred-EL outperforms several state-of-the-art methods on the benchmark datasets and then improves the efficiency performance.
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Affiliation(s)
- Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yichen Guo
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt.
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China; Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.
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29
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Humanizing plant-derived snakins and their encrypted antimicrobial peptides. Biochimie 2022; 199:92-111. [PMID: 35472564 DOI: 10.1016/j.biochi.2022.04.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 04/16/2022] [Accepted: 04/20/2022] [Indexed: 12/11/2022]
Abstract
Due to safety restrictions, plant-derived antimicrobial peptides (AMPs) need optimization to be consumed beyond preservatives. Herein, 175 GASA-domain-containing snakins were analyzed. Factors including charge, hydrophobicity, helicity, hydrophobic moment (μH), folding enthalpy, folding heat capacity, folding free energy, therapeutic index, allergenicity, and bitterness were considered. The most optimal snakins for oral consumption as preservatives were from Cajanus cajan, Cucumis melo, Durio zibethinus, Glycine soja, Herrania umbratica, and Ziziphus jujuba. Virtual digestion of snakins predicted ACE1 and DPPIV inhibitory as dominant effects upon oral use with antihypertensive and antidiabetic properties. To be applied as a therapeutic in parenteral administration, snakins were browsed for short 20-mer encrypted fragments that were non-toxic or with eliminated toxicity using directed mutagenesis yet retaining the AMP property. The most promising 20-mer AMPs were Mr-SNK2-1a in Morella rubra with BBB permeation, Na-SNK2-2a(C18W), and Na-SNK2-2b(C16F) from Nicotiana attenuata. These AMPs were cell-penetrating peptides (CPPs), with a charge of +6, a μH of about 0.40, and a Boman-index higher than 2.48 Kcalmol-1. Na-SNK2-2a(C18W) had putative activity against gram-negative bacteria with MIC lower than 25 μgml-1, and Na-SNK2-2b(C16F) was a potential anti-HIV with an IC50 of 3.04 μM. Other 20-mer AMPs, such as Cc-SNK1-2a from Cajanus cajan displayed an anti-HCV property with an IC50 of 13.91 μM. While Si-SNK2-3a(C17P) from Sesamum indicum was a cationic anti-angiogenic CPP targeting the acidic microenvironment of tumors, Cme-SNK2-1a(C11F) from Cucumis melo was an immunomodulator CPP applicable as a vaccine adjuvant. Because of combined mechanisms, investigating cysteine-rich peptides can nominate effective biotherapeutics.
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30
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Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073631] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.
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31
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Shin MK, Lee B, Kim ST, Yoo JS, Sung JS. Designing a Novel Functional Peptide With Dual Antimicrobial and Anti-inflammatory Activities via in Silico Methods. Front Immunol 2022; 13:821070. [PMID: 35432369 PMCID: PMC9010562 DOI: 10.3389/fimmu.2022.821070] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/10/2022] [Indexed: 01/31/2023] Open
Abstract
As spider venom is composed of various bioactive substances, it can be utilized as a platform for discovering future therapeutics. Host defense peptides are great candidates for developing novel antimicrobial agents due to their multifunctional properties. In this study, novel functional peptides were rationally designed to have dual antibacterial and anti-inflammatory activities with high cytocompatibility. Based on a template sequence from the transcriptome of spider Agelena koreana, a series of via in silico analysis were conducted, incorporating web-based machine learning tools along with the alteration of amino acid residues. Two peptides, Ak-N’ and Ak-N’m, were designed and were subjected to functional validation. The peptides inhibited gram-negative and gram-positive bacteria by disrupting the outer and bacterial cytoplasmic membrane. Moreover, the peptides down-regulated the expression of pro-inflammatory mediators, tumor necrosis factor-α, interleukin (IL)-1β, and IL6. Along with low cytotoxicity, Ak-N’m was shown to interact with macrophage surface receptors, inhibiting both Myeloid differentiation primary response 88-dependent and TIR-domain-containing adapter-inducing interferon-β-dependent pathways of Toll-like receptor 4 signaling on lipopolysaccharide-stimulated THP-1-derived macrophages. Here, we rationally designed functional peptides based on the suggested in silico strategy, demonstrating new insights for utilizing biological resources as well as developing therapeutic agents with enhanced properties.
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Affiliation(s)
- Min Kyoung Shin
- Department of Life Science, Dongguk University-Seoul, Goyang, South Korea
| | - Byungjo Lee
- Department of Life Science, Dongguk University-Seoul, Goyang, South Korea
| | - Seung Tae Kim
- Life and Environment Research Institute, Konkuk University, Seoul, South Korea
| | - Jung Sun Yoo
- Animal Resources Division, National Institute of Biological Resources, Incheon, South Korea
| | - Jung-Suk Sung
- Department of Life Science, Dongguk University-Seoul, Goyang, South Korea
- *Correspondence: Jung-Suk Sung,
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32
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Ramazi S, Mohammadi N, Allahverdi A, Khalili E, Abdolmaleki P. A review on antimicrobial peptides databases and the computational tools. Database (Oxford) 2022; 2022:6550847. [PMID: 35305010 PMCID: PMC9216472 DOI: 10.1093/database/baac011] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 02/04/2022] [Accepted: 02/28/2022] [Indexed: 12/29/2022]
Abstract
Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.
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Affiliation(s)
- Shahin Ramazi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 14115-111, Iran
| | - Neda Mohammadi
- Department of Medical Biotechnology, Faculty of Allied Medical Sciences, Iran University of Medical Sciences, Hemmat Highway, Tehran 1449614535, Iran,Institute of Pharmacology and Toxicology, University of Bonn, Biomedical Center, Venusberg Campus 1, Bonn 53127, Germany
| | - Abdollah Allahverdi
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 14115-111, Iran
| | - Elham Khalili
- Department of Plant Biology, Faculty of Biological Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran 14115-111, Iran
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33
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Singh V, Shrivastava S, Kumar Singh S, Kumar A, Saxena S. Accelerating the discovery of antifungal peptides using deep temporal convolutional networks. Brief Bioinform 2022; 23:6526725. [DOI: 10.1093/bib/bbac008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/27/2021] [Accepted: 01/06/2022] [Indexed: 02/02/2023] Open
Abstract
Abstract
The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism’s intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal–Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India
| | - Sameer Shrivastava
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, 243122, Uttar Pradesh, India
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India
| | - Abhinav Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India
| | - Sonal Saxena
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, 243122, Uttar Pradesh, India
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Abstract
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.
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Affiliation(s)
- Guangshun Wang
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, NE 68198-5900, USA;,Corresponding to: Dr. Monique van Hoek: ; Dr. Iosif Vaisman: ; Dr. Guangshun Wang:
| | - Iosif I. Vaisman
- School of Systems Biology, George Mason University, 10920 George Mason Circle, Manassas, VA, 20110, USA.,Corresponding to: Dr. Monique van Hoek: ; Dr. Iosif Vaisman: ; Dr. Guangshun Wang:
| | - Monique L. van Hoek
- School of Systems Biology, George Mason University, 10920 George Mason Circle, Manassas, VA, 20110, USA.,Corresponding to: Dr. Monique van Hoek: ; Dr. Iosif Vaisman: ; Dr. Guangshun Wang:
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35
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Design and Manufacture of a Low-Cost Microfluidic System for the Synthesis of Giant Liposomes for the Encapsulation of Yeast Homologues: Applications in the Screening of Membrane-Active Peptide Libraries. MICROMACHINES 2021; 12:mi12111377. [PMID: 34832789 PMCID: PMC8619280 DOI: 10.3390/mi12111377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/02/2021] [Accepted: 11/06/2021] [Indexed: 11/24/2022]
Abstract
The discovery of new membrane-active peptides (MAPs) is an area of considerable interest in modern biotechnology considering their ample applicability in several fields ranging from the development of novel delivery vehicles (via cell-penetrating peptides) to responding to the latent threat of antibiotic resistance (via antimicrobial peptides). Different strategies have been devised for such discovery process, however, most of them involve costly, tedious, and low-efficiency methods. We have recently proposed an alternative route based on constructing a non-rationally designed library recombinantly expressed on the yeasts’ surfaces. However, a major challenge is to conduct a robust and high-throughput screening of possible candidates with membrane activity. Here, we addressed this issue by putting forward low-cost microfluidic platforms for both the synthesis of Giant Unilamellar Vesicles (GUVs) as mimicking entities of cell membranes and for providing intimate contact between GUVs and homologues of yeasts expressing MAPs. The homologues were chitosan microparticles functionalized with the membrane translocating peptide Buforin II, while intimate contact was through passive micromixers with different channel geometries. Both microfluidic platforms were evaluated both in silico (via Multiphysics simulations) and in vitro with a high agreement between the two approaches. Large and stable GUVs (5–100 µm) were synthesized effectively, and the mixing processes were comprehensively studied leading to finding the best operating parameters. A serpentine micromixer equipped with circular features showed the highest average encapsulation efficiencies, which was explained by the unique mixing patterns achieved within the device. The microfluidic devices developed here demonstrate high potential as platforms for the discovery of novel MAPs as well as for other applications in the biomedical field such as the encapsulation and controlled delivery of bioactive compounds.
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36
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Bin Hafeez A, Jiang X, Bergen PJ, Zhu Y. Antimicrobial Peptides: An Update on Classifications and Databases. Int J Mol Sci 2021; 22:11691. [PMID: 34769122 PMCID: PMC8583803 DOI: 10.3390/ijms222111691] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/24/2021] [Accepted: 10/25/2021] [Indexed: 02/06/2023] Open
Abstract
Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.
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Affiliation(s)
- Ahmer Bin Hafeez
- Centre of Biotechnology and Microbiology, University of Peshawar, Peshawar 25120, Pakistan;
| | - Xukai Jiang
- Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; (X.J.); (P.J.B.)
- National Glycoengineering Research Center, Shandong University, Qingdao 266237, China
| | - Phillip J. Bergen
- Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; (X.J.); (P.J.B.)
| | - Yan Zhu
- Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; (X.J.); (P.J.B.)
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37
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Is It Possible to Create Antimicrobial Peptides Based on the Amyloidogenic Sequence of Ribosomal S1 Protein of P. aeruginosa? Int J Mol Sci 2021; 22:ijms22189776. [PMID: 34575940 PMCID: PMC8469417 DOI: 10.3390/ijms22189776] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/14/2022] Open
Abstract
The development and testing of new antimicrobial peptides (AMPs) represent an important milestone toward the development of new antimicrobial drugs that can inhibit the growth of pathogens and multidrug-resistant microorganisms such as Pseudomonas aeruginosa, Gram-negative bacteria. Most AMPs achieve these goals through mechanisms that disrupt the normal permeability of the cell membrane, which ultimately leads to the death of the pathogenic cell. Here, we developed a unique combination of a membrane penetrating peptide and peptides prone to amyloidogenesis to create hybrid peptide: "cell penetrating peptide + linker + amyloidogenic peptide". We evaluated the antimicrobial effects of two peptides that were developed from sequences with different propensities for amyloid formation. Among the two hybrid peptides, one was found with antibacterial activity comparable to antibiotic gentamicin sulfate. Our peptides showed no toxicity to eukaryotic cells. In addition, we evaluated the effect on the antimicrobial properties of amino acid substitutions in the non-amyloidogenic region of peptides. We compared the results with data on the predicted secondary structure, hydrophobicity, and antimicrobial properties of the original and modified peptides. In conclusion, our study demonstrates the promise of hybrid peptides based on amyloidogenic regions of the ribosomal S1 protein for the development of new antimicrobial drugs against P. aeruginosa.
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Rádai Z, Kiss J, Nagy NA. Taxonomic bias in AMP prediction of invertebrate peptides. Sci Rep 2021; 11:17924. [PMID: 34504226 PMCID: PMC8429723 DOI: 10.1038/s41598-021-97415-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022] Open
Abstract
Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A yet neglected aspect is taxonomic bias in the performance of these tools. Owing to differences in the prediction algorithms and used training data sets between tools, and phylogenetic differences in sequence diversity, physicochemical properties and evolved biological functions of AMPs between taxa, notable discrepancies may exist in performance between the currently available prediction tools. Here we tested if there is taxonomic bias in the prediction power in 10 tools with a total of 20 prediction algorithms in 19 invertebrate taxa, using a data set containing 1525 AMP and 3050 non-AMP sequences. We found that most of the tools exhibited considerable variation in performance between tested invertebrate groups. Based on the per-taxa performances and on the variation in performances across taxa we provide guidance in choosing the best-performing prediction tool for all assessed taxa, by listing the highest scoring tool for each of them.
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Affiliation(s)
- Zoltán Rádai
- Lendület Seed Ecology Research Group, Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, Hungary.
- Department of Metagenomics, University of Debrecen, Debrecen, Hungary.
| | - Johanna Kiss
- MTA-DE Behavioural Ecology Research Group, Department of Evolutionary Zoology and Human Biology, University of Debrecen, Debrecen, Hungary
| | - Nikoletta A Nagy
- Department of Metagenomics, University of Debrecen, Debrecen, Hungary
- MTA-DE Behavioural Ecology Research Group, Department of Evolutionary Zoology and Human Biology, University of Debrecen, Debrecen, Hungary
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Aronica PGA, Reid LM, Desai N, Li J, Fox SJ, Yadahalli S, Essex JW, Verma CS. Computational Methods and Tools in Antimicrobial Peptide Research. J Chem Inf Model 2021; 61:3172-3196. [PMID: 34165973 DOI: 10.1021/acs.jcim.1c00175] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.
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Affiliation(s)
- Pietro G A Aronica
- Bioinformatics Institute at A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Lauren M Reid
- Bioinformatics Institute at A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671.,School of Chemistry, University of Southampton, Highfield Southampton, Hampshire, U.K. SO17 1BJ.,MedChemica Ltd, Alderley Park, Macclesfield, Cheshire, U.K. SK10 4TG
| | - Nirali Desai
- Bioinformatics Institute at A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671.,Division of Biological and Life Sciences, Ahmedabad University, Central Campus, Ahmedabad, Gujarat, India 380009
| | - Jianguo Li
- Bioinformatics Institute at A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671.,Singapore Eye Research Institute, 20 College Road Discovery Tower, Singapore 169856
| | - Stephen J Fox
- Bioinformatics Institute at A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Shilpa Yadahalli
- Bioinformatics Institute at A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield Southampton, Hampshire, U.K. SO17 1BJ
| | - Chandra S Verma
- Bioinformatics Institute at A*STAR (Agency for Science, Technology and Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671.,Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, 117543 Singapore.,School of Biological Sciences, Nanyang Technological University, 50 Nanyang Drive, 637551 Singapore
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The role of full-length apoE in clearance of Gram-negative bacteria and their endotoxins. J Lipid Res 2021; 62:100086. [PMID: 34019903 PMCID: PMC8225977 DOI: 10.1016/j.jlr.2021.100086] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/20/2021] [Accepted: 05/08/2021] [Indexed: 11/24/2022] Open
Abstract
ApoE is a well-known lipid-binding protein that plays a main role in the metabolism and transport of lipids. More recently, apoE-derived peptides have been shown to exert antimicrobial effects. Here, we investigated the antibacterial activity of apoE using in vitro assays, advanced imaging techniques, and in vivo mouse models. The formation of macromolecular complexes of apoE and endotoxins from Gram-negative bacteria was explored using gel shift assays, transmission electron microscopy, and CD spectroscopy followed by calculation of the α-helical content. The binding affinity of apoE to endotoxins was also confirmed by fluorescent spectroscopy detecting the quenching and shifting of tryptophan intrinsic fluorescence. We showed that apoE exhibits antibacterial activity particularly against Gram-negative bacteria such as Pseudomonas aeruginosa and Escherichia coli. ApoE protein folding was affected by binding of bacterial endotoxin components such as lipopolysaccharide (LPS) and lipid A, yielding similar increases in the apoE α-helical content. Moreover, high-molecular-weight complexes of apoE were formed in the presence of LPS, but not to the same extent as with lipid A. Together, our results demonstrate the ability of apoE to kill Gram-negative bacteria, interact with their endotoxins, which leads to the structural changes in apoE and the formation of aggregate-like complexes.
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Zagui GS, Moreira NC, Santos DV, Darini ALC, Domingo JL, Segura-Muñoz SI, Andrade LN. High occurrence of heavy metal tolerance genes in bacteria isolated from wastewater: A new concern? ENVIRONMENTAL RESEARCH 2021; 196:110352. [PMID: 33098821 DOI: 10.1016/j.envres.2020.110352] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/19/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
Some heavy metals have antimicrobial activity and are considered as potential alternatives to traditional antibiotic therapy. However, heavy metal tolerance genes (HMTG) have been already detected and coding different tolerance mechanisms. Considering that certain metals are promising for antimicrobial therapy, evaluation of HMTG dissemination in bacteria from sewage is essential to understand the evolution of these bacteria and to predict antimicrobial use and control. The present study aimed to evaluate the occurrence of bacteria carrying HMTG in samples of hospital wastewater and from urban wastewater treatment plant (WWTP). The acquired HMTG were investigated by PCR in bacterial collection previously characterized for antibiotic resistant genes (ARGs). HMTG searched include arsB (arsenic efflux pump), czcA (cadmium, zinc and cobalt efflux pump), merA (mercuric reductase), pcoD (copper efflux pump), silA (silver efflux pump) and terF (tellurite resistance protein). Among 45 isolates, 82% of them carried at last one HMTG, in which the silA and pcoD tolerance genes were the most prevalent. A very strong positive correlation was found between these genes (r = 0.91, p < 0.0001). Tolerance genes merA, arsB, czcA and terF were detected in 47%, 13%, 13% and 7% of the isolates, respectively. It was found that 15 isolates co-harbored ARGs (β-lactamase encoding genes). HMTG are probably more dispersed than ARGs in bacteria, representing a new concern for heavy metals use as effective antimicrobials. To the best of our knowledge, this is the first study on the HMTG searched in Hafnia alvei, Serratia fonticola and Serratia liquefaciens. Hospital wastewater treatment implementation and additional technologies for treatment in WWTP can reduce the impacts on water resources and HMTG spread, ensureing the environmental and human health safety.
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Affiliation(s)
| | | | - Danilo Vitorino Santos
- Laboratory of Chemical Residues, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | - José L Domingo
- Laboratory of Toxicology and Environmental Health, School of Medicine, Universitat Rovira i Virgili, 43210, Reus, Catalonia, Spain
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Wesseling CMJ, Wood TM, Slingerland CJ, Bertheussen K, Lok S, Martin NI. Thrombin-Derived Peptides Potentiate the Activity of Gram-Positive-Specific Antibiotics against Gram-Negative Bacteria. Molecules 2021; 26:molecules26071954. [PMID: 33808488 PMCID: PMC8037310 DOI: 10.3390/molecules26071954] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 03/24/2021] [Accepted: 03/26/2021] [Indexed: 12/13/2022] Open
Abstract
The continued rise of antibiotic resistance threatens to undermine the utility of the world’s current antibiotic arsenal. This problem is particularly troubling when it comes to Gram-negative pathogens for which there are inherently fewer antibiotics available. To address this challenge, recent attention has been focused on finding compounds capable of disrupting the Gram-negative outer membrane as a means of potentiating otherwise Gram-positive-specific antibiotics. In this regard, agents capable of binding to the lipopolysaccharide (LPS) present in the Gram-negative outer membrane are of particular interest as synergists. Recently, thrombin-derived C-terminal peptides (TCPs) were reported to exhibit unique LPS-binding properties. We here describe investigations establishing the capacity of TCPs to act as synergists with the antibiotics erythromycin, rifampicin, novobiocin, and vancomycin against multiple Gram-negative strains including polymyxin-resistant clinical isolates. We further assessed the structural features most important for the observed synergy and characterized the outer membrane permeabilizing activity of the most potent synergists. Our investigations highlight the potential for such peptides in expanding the therapeutic range of antibiotics typically only used to treat Gram-positive infections.
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Affiliation(s)
- Charlotte M. J. Wesseling
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2333 Leiden, The Netherlands; (C.M.J.W.); (T.M.W.); (C.J.S.); (K.B.); (S.L.)
| | - Thomas M. Wood
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2333 Leiden, The Netherlands; (C.M.J.W.); (T.M.W.); (C.J.S.); (K.B.); (S.L.)
- Department of Chemical Biology & Drug Discovery, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, 3584 Utrecht, The Netherlands
| | - Cornelis J. Slingerland
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2333 Leiden, The Netherlands; (C.M.J.W.); (T.M.W.); (C.J.S.); (K.B.); (S.L.)
| | - Kristine Bertheussen
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2333 Leiden, The Netherlands; (C.M.J.W.); (T.M.W.); (C.J.S.); (K.B.); (S.L.)
- Bio-Organic Synthesis Group, Leiden Institute of Chemistry, Leiden University, 2333 Leiden, The Netherlands
| | - Samantha Lok
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2333 Leiden, The Netherlands; (C.M.J.W.); (T.M.W.); (C.J.S.); (K.B.); (S.L.)
| | - Nathaniel I. Martin
- Biological Chemistry Group, Institute of Biology Leiden, Leiden University, 2333 Leiden, The Netherlands; (C.M.J.W.); (T.M.W.); (C.J.S.); (K.B.); (S.L.)
- Correspondence:
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Xu J, Li F, Leier A, Xiang D, Shen HH, Marquez Lago TT, Li J, Yu DJ, Song J. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides. Brief Bioinform 2021; 22:6189771. [PMID: 33774670 DOI: 10.1093/bib/bbab083] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/20/2021] [Accepted: 02/22/2021] [Indexed: 12/13/2022] Open
Abstract
Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.
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Affiliation(s)
- Jing Xu
- Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University, Australia
| | - Fuyi Li
- Department of Microbiology and Immunology, the Peter Doherty Institute for Infection and Immunity, the University of Melbourne, Australia
| | - André Leier
- Department of Genetics, UAB School of Medicine, USA
| | - Dongxu Xiang
- Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University, Australia
| | - Hsin-Hui Shen
- Department of Biochemistry & Molecular Biology and Department of Materials Science & Engineering, Monash University, Australia
| | | | - Jian Li
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Australia
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Kurpe SR, Grishin SY, Surin AK, Panfilov AV, Slizen MV, Chowdhury SD, Galzitskaya OV. Antimicrobial and Amyloidogenic Activity of Peptides. Can Antimicrobial Peptides Be Used against SARS-CoV-2? Int J Mol Sci 2020; 21:E9552. [PMID: 33333996 PMCID: PMC7765370 DOI: 10.3390/ijms21249552] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/07/2020] [Accepted: 12/12/2020] [Indexed: 02/07/2023] Open
Abstract
At present, much attention is paid to the use of antimicrobial peptides (AMPs) of natural and artificial origin to combat pathogens. AMPs have several points that determine their biological activity. We analyzed the structural properties of AMPs, as well as described their mechanism of action and impact on pathogenic bacteria and viruses. Recently published data on the development of new AMP drugs based on a combination of molecular design and genetic engineering approaches are presented. In this article, we have focused on information on the amyloidogenic properties of AMP. This review examines AMP development strategies from the perspective of the current high prevalence of antibiotic-resistant bacteria, and the potential prospects and challenges of using AMPs against infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Affiliation(s)
- Stanislav R. Kurpe
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Sergei Yu. Grishin
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Alexey K. Surin
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
- The Branch of the Institute of Bioorganic Chemistry, Russian Academy of Sciences, 142290 Pushchino, Russia
- State Research Center for Applied Microbiology and Biotechnology, 142279 Obolensk, Russia
| | - Alexander V. Panfilov
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Mikhail V. Slizen
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
| | - Saikat D. Chowdhury
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur 741246, West Bengal, India;
| | - Oxana V. Galzitskaya
- Institute of Protein Research, Russian Academy of Sciences, 142290 Pushchino, Russia; (S.R.K.); (S.Y.G.); (A.K.S.); (A.V.P.); (M.V.S.)
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
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Puentes PR, Henao MC, Torres CE, Gómez SC, Gómez LA, Burgos JC, Arbeláez P, Osma JF, Muñoz-Camargo C, Reyes LH, Cruz JC. Design, Screening, and Testing of Non-Rational Peptide Libraries with Antimicrobial Activity: In Silico and Experimental Approaches. Antibiotics (Basel) 2020; 9:E854. [PMID: 33265897 PMCID: PMC7759991 DOI: 10.3390/antibiotics9120854] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/20/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
One of the challenges of modern biotechnology is to find new routes to mitigate the resistance to conventional antibiotics. Antimicrobial peptides (AMPs) are an alternative type of biomolecules, naturally present in a wide variety of organisms, with the capacity to overcome the current microorganism resistance threat. Here, we reviewed our recent efforts to develop a new library of non-rationally produced AMPs that relies on bacterial genome inherent diversity and compared it with rationally designed libraries. Our approach is based on a four-stage workflow process that incorporates the interplay of recent developments in four major emerging technologies: artificial intelligence, molecular dynamics, surface-display in microorganisms, and microfluidics. Implementing this framework is challenging because to obtain reliable results, the in silico algorithms to search for candidate AMPs need to overcome issues of the state-of-the-art approaches that limit the possibilities for multi-space data distribution analyses in extremely large databases. We expect to tackle this challenge by using a recently developed classification algorithm based on deep learning models that rely on convolutional layers and gated recurrent units. This will be complemented by carefully tailored molecular dynamics simulations to elucidate specific interactions with lipid bilayers. Candidate AMPs will be recombinantly-expressed on the surface of microorganisms for further screening via different droplet-based microfluidic-based strategies to identify AMPs with the desired lytic abilities. We believe that the proposed approach opens opportunities for searching and screening bioactive peptides for other applications.
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Affiliation(s)
- Paola Ruiz Puentes
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogota DC 111711, Colombia; (P.R.P.); (P.A.)
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - María C. Henao
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogota DC 111711, Colombia;
| | - Carlos E. Torres
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Saúl C. Gómez
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Laura A. Gómez
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Juan C. Burgos
- Chemical Engineering Program, Universidad de Cartagena, Cartagena 130015, Colombia;
| | - Pablo Arbeláez
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogota DC 111711, Colombia; (P.R.P.); (P.A.)
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Johann F. Osma
- Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogota DC 111711, Colombia;
| | - Carolina Muñoz-Camargo
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Luis H. Reyes
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogota DC 111711, Colombia;
| | - Juan C. Cruz
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide 5005, Australia
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Oberleitner L, Poschmann G, Macorano L, Schott-Verdugo S, Gohlke H, Stühler K, Nowack ECM. The Puzzle of Metabolite Exchange and Identification of Putative Octotrico Peptide Repeat Expression Regulators in the Nascent Photosynthetic Organelles of Paulinella chromatophora. Front Microbiol 2020; 11:607182. [PMID: 33329499 PMCID: PMC7729196 DOI: 10.3389/fmicb.2020.607182] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/05/2020] [Indexed: 12/12/2022] Open
Abstract
The endosymbiotic acquisition of mitochondria and plastids more than one billion years ago was central for the evolution of eukaryotic life. However, owing to their ancient origin, these organelles provide only limited insights into the initial stages of organellogenesis. The cercozoan amoeba Paulinella chromatophora contains photosynthetic organelles-termed chromatophores-that evolved from a cyanobacterium ∼100 million years ago, independently from plastids in plants and algae. Despite the more recent origin of the chromatophore, it shows tight integration into the host cell. It imports hundreds of nucleus-encoded proteins, and diverse metabolites are continuously exchanged across the two chromatophore envelope membranes. However, the limited set of chromatophore-encoded solute transporters appears insufficient for supporting metabolic connectivity or protein import. Furthermore, chromatophore-localized biosynthetic pathways as well as multiprotein complexes include proteins of dual genetic origin, suggesting that mechanisms evolved that coordinate gene expression levels between chromatophore and nucleus. These findings imply that similar to the situation in mitochondria and plastids, also in P. chromatophora nuclear factors evolved that control metabolite exchange and gene expression in the chromatophore. Here we show by mass spectrometric analyses of enriched insoluble protein fractions that, unexpectedly, nucleus-encoded transporters are not inserted into the chromatophore inner envelope membrane. Thus, despite the apparent maintenance of its barrier function, canonical metabolite transporters are missing in this membrane. Instead we identified several expanded groups of short chromatophore-targeted orphan proteins. Members of one of these groups are characterized by a single transmembrane helix, and others contain amphipathic helices. We hypothesize that these proteins are involved in modulating membrane permeability. Thus, the mechanism generating metabolic connectivity of the chromatophore fundamentally differs from the one for mitochondria and plastids, but likely rather resembles the poorly understood mechanism in various bacterial endosymbionts in plants and insects. Furthermore, our mass spectrometric analysis revealed an expanded family of chromatophore-targeted helical repeat proteins. These proteins show similar domain architectures as known organelle-targeted expression regulators of the octotrico peptide repeat type in algae and plants. Apparently these chromatophore-targeted proteins evolved convergently to plastid-targeted expression regulators and are likely involved in gene expression control in the chromatophore.
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Affiliation(s)
- Linda Oberleitner
- Department of Biology, Institute of Microbial Cell Biology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gereon Poschmann
- Medical Faculty, Institute for Molecular Medicine, Proteome Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Luis Macorano
- Department of Biology, Institute of Microbial Cell Biology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Stephan Schott-Verdugo
- Department of Pharmacy, Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Faculty of Engineering, Centro de Bioinformática y Simulación Molecular, Universidad de Talca, Talca, Chile
| | - Holger Gohlke
- Department of Pharmacy, Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Jülich Supercomputing Centre, John von Neumann Institute for Computing, Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Kai Stühler
- Medical Faculty, Institute for Molecular Medicine, Proteome Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Molecular Proteomics Laboratory, Biologisch-Medizinisches Forschungszentrum, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Eva C. M. Nowack
- Department of Biology, Institute of Microbial Cell Biology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Burdukiewicz M, Sidorczuk K, Rafacz D, Pietluch F, Bąkała M, Słowik J, Gagat P. CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides. Pharmaceutics 2020; 12:pharmaceutics12111045. [PMID: 33142753 PMCID: PMC7692641 DOI: 10.3390/pharmaceutics12111045] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 01/10/2023] Open
Abstract
Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules that provide multicellular organisms with protection against microorganisms, and microorganisms with weaponry for competition. Some AMPs can target cancer cells; thus, they are called anticancer peptides (ACPs). Due to their small size, positive charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively charged components of biological membranes. AMPs preferentially permeabilize microbial membranes, but ACPs additionally target mitochondrial and plasma membranes of cancer cells. The preference towards mitochondrial membranes is explained by their membrane potential, membrane composition resulting from α-proteobacterial origin and the fact that mitochondrial targeting signals could have evolved from AMPs. Taking into account the therapeutic potential of ACPs and millions of deaths due to cancer annually, it is of vital importance to find new cationic peptides that selectively destroy cancer cells. Therefore, to reduce the costs of experimental research, we have created a robust computational tool, CancerGram, that uses n-grams and random forests for predicting ACPs. Compared to other ACP classifiers, CancerGram is the first three-class model that effectively classifies peptides into: ACPs, AMPs and non-ACPs/non-AMPs, with AU1U amounting to 0.89 and a Kappa statistic of 0.65. CancerGram is available as a web server and R package on GitHub.
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Affiliation(s)
- Michał Burdukiewicz
- Faculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, 01968 Senftenberg, Germany;
- Why R? Foundation, 03-214 Warsaw, Poland;
| | - Katarzyna Sidorczuk
- Department of Bioinformatics and Genomics, Faculty of Biotechnology, University of Wrocław, 50-383 Wrocław, Poland; (K.S.); (F.P.)
| | - Dominik Rafacz
- Why R? Foundation, 03-214 Warsaw, Poland;
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland; (M.B.); (J.S.)
| | - Filip Pietluch
- Department of Bioinformatics and Genomics, Faculty of Biotechnology, University of Wrocław, 50-383 Wrocław, Poland; (K.S.); (F.P.)
| | - Mateusz Bąkała
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland; (M.B.); (J.S.)
| | - Jadwiga Słowik
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland; (M.B.); (J.S.)
| | - Przemysław Gagat
- Department of Bioinformatics and Genomics, Faculty of Biotechnology, University of Wrocław, 50-383 Wrocław, Poland; (K.S.); (F.P.)
- Correspondence:
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