151
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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: 100] [Impact Index Per Article: 33.3] [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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152
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Jing R, Wen T, Liao C, Xue L, Liu F, Yu L, Luo J. DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework. NAR Genom Bioinform 2021; 3:lqab086. [PMID: 34617013 PMCID: PMC8489581 DOI: 10.1093/nargab/lqab086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 08/12/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022] Open
Abstract
Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs.
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Affiliation(s)
- Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Tingke Wen
- School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Chengxiang Liao
- School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Li Xue
- School of Public Health, Southwest Medical University, Luzhou 646000, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang 550018, China
| | - Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China
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153
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Ren Y, Chakraborty T, Doijad S, Falgenhauer L, Falgenhauer J, Goesmann A, Hauschild AC, Schwengers O, Heider D. Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics 2021; 38:325-334. [PMID: 34613360 PMCID: PMC8722762 DOI: 10.1093/bioinformatics/btab681] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/27/2021] [Accepted: 09/24/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Antimicrobial resistance (AMR) is one of the biggest global problems threatening human and animal health. Rapid and accurate AMR diagnostic methods are thus very urgently needed. However, traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and whole-genome sequencing data without previously known knowledge remains to be done. RESULTS In this study, we evaluated logistic regression (LR), support vector machine (SVM), random forest (RF) and convolutional neural network (CNN) for the prediction of AMR for the antibiotics ciprofloxacin, cefotaxime, ceftazidime and gentamicin. We could demonstrate that these models can effectively predict AMR with label encoding, one-hot encoding and frequency matrix chaos game representation (FCGR encoding) on whole-genome sequencing data. We trained these models on a large AMR dataset and evaluated them on an independent public dataset. Generally, RFs and CNNs perform better than LR and SVM with AUCs up to 0.96. Furthermore, we were able to identify mutations that are associated with AMR for each antibiotic. AVAILABILITY AND IMPLEMENTATION Source code in data preparation and model training are provided at GitHub website (https://github.com/YunxiaoRen/ML-iAMR). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yunxiao Ren
- Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, Marburg 35032, Germany
| | - Trinad Chakraborty
- Institute of Medical Microbiology, Justus Liebig University Giessen, Giessen 35392, Germany,German Center for Infection Research, Partner site Giessen-Marburg-Langen, Giessen 35392, Germany
| | - Swapnil Doijad
- Institute of Medical Microbiology, Justus Liebig University Giessen, Giessen 35392, Germany,German Center for Infection Research, Partner site Giessen-Marburg-Langen, Giessen 35392, Germany
| | - Linda Falgenhauer
- German Center for Infection Research, Partner site Giessen-Marburg-Langen, Giessen 35392, Germany,Institute of Hygiene and Environmental Medicine, Justus Liebig University Giessen, Giessen 35392, Germany,Hessisches universitäres Kompetenzzentrum Krankenhaushygiene, Giessen 35392, Germany
| | - Jane Falgenhauer
- Institute of Medical Microbiology, Justus Liebig University Giessen, Giessen 35392, Germany,German Center for Infection Research, Partner site Giessen-Marburg-Langen, Giessen 35392, Germany
| | - Alexander Goesmann
- German Center for Infection Research, Partner site Giessen-Marburg-Langen, Giessen 35392, Germany,Department of Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
| | - Anne-Christin Hauschild
- Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, Philipps-University of Marburg, Marburg 35032, Germany
| | - Oliver Schwengers
- German Center for Infection Research, Partner site Giessen-Marburg-Langen, Giessen 35392, Germany,Department of Bioinformatics and Systems Biology, Justus Liebig University Giessen, Giessen 35392, Germany
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154
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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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155
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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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156
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Yakimovich A. Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case. Infect Drug Resist 2021; 14:3319-3326. [PMID: 34456575 PMCID: PMC8385421 DOI: 10.2147/idr.s292743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 08/03/2021] [Indexed: 01/27/2023] Open
Abstract
The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.
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157
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Fingerhut LCHW, Miller DJ, Strugnell JM, Daly NL, Cooke IR. ampir: an R package for fast genome-wide prediction of antimicrobial peptides. Bioinformatics 2021; 36:5262-5263. [PMID: 32683445 DOI: 10.1093/bioinformatics/btaa653] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/28/2020] [Accepted: 07/13/2020] [Indexed: 12/21/2022] Open
Abstract
SUMMARY Antimicrobial peptides (AMPs) are the key components of the innate immune system that protect against pathogens, regulate the microbiome and are promising targets for pharmaceutical research. Computational tools based on machine learning have the potential to aid discovery of genes encoding novel AMPs but existing approaches are not designed for genome-wide scans. To facilitate such genome-wide discovery of AMPs we developed a fast and accurate AMP classification framework, ampir. ampir is designed for high throughput, integrates well with existing bioinformatics pipelines, and has much higher classification accuracy than existing methods when applied to whole genome data. AVAILABILITY AND IMPLEMENTATION ampir is implemented primarily in R with core feature calculation methods written in C++. Release versions are available via CRAN and work on all major operating systems. The development version is maintained at https://github.com/legana/ampir. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Legana C H W Fingerhut
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, Townsville, Qld, 4811, Australia.,Department of Molecular and Cell Biology, Townsville, Qld, 4811, Australia.,ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Qld, 4811, Australia
| | - David J Miller
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, Townsville, Qld, 4811, Australia.,Department of Molecular and Cell Biology, Townsville, Qld, 4811, Australia.,ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Qld, 4811, Australia
| | - Jan M Strugnell
- Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, Qld 4811, Australia
| | - Norelle L Daly
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, Townsville, Qld, 4811, Australia.,Centre for Molecular Therapeutics, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD 4870, Australia
| | - Ira R Cooke
- Department of Molecular and Cell Biology, Centre for Tropical Bioinformatics and Molecular Biology, Townsville, Qld, 4811, Australia.,Department of Molecular and Cell Biology, Townsville, Qld, 4811, Australia
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158
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Singh O, Hsu WL, Su ECY. Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features. BMC Bioinformatics 2021; 22:389. [PMID: 34330209 PMCID: PMC8325260 DOI: 10.1186/s12859-021-04305-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/21/2021] [Indexed: 12/24/2022] Open
Abstract
Background Antimicrobial peptides (AMPs) are oligopeptides that act as crucial components of innate immunity, naturally occur in all multicellular organisms, and are involved in the first line of defense function. Recent studies showed that AMPs perpetuate great potential that is not limited to antimicrobial activity. They are also crucial regulators of host immune responses that can modulate a wide range of activities, such as immune regulation, wound healing, and apoptosis. However, a microorganism's ability to adapt and to resist existing antibiotics triggered the scientific community to develop alternatives to conventional antibiotics. Therefore, to address this issue, we proposed Co-AMPpred, an in silico-aided AMP prediction method based on compositional features of amino acid residues to classify AMPs and non-AMPs. Results In our study, we developed a prediction method that incorporates composition-based sequence and physicochemical features into various machine-learning algorithms. Then, the boruta feature-selection algorithm was used to identify discriminative biological features. Furthermore, we only used discriminative biological features to develop our model. Additionally, we performed a stratified tenfold cross-validation technique to validate the predictive performance of our AMP prediction model and evaluated on the independent holdout test dataset. A benchmark dataset was collected from previous studies to evaluate the predictive performance of our model. Conclusions Experimental results show that combining composition-based and physicochemical features outperformed existing methods on both the benchmark training dataset and a reduced training dataset. Finally, our proposed method achieved 80.8% accuracies and 0.871 area under the receiver operating characteristic curve by evaluating on independent test set. Our code and datasets are available at https://github.com/onkarS23/CoAMPpred. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04305-2.
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Affiliation(s)
- Onkar Singh
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan.,Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei, 11031, Taiwan
| | - Wen-Lian Hsu
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan.,Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Emily Chia-Yu Su
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei, 11031, Taiwan. .,Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
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159
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Holch A, Bauer R, Olari LR, Rodriguez AA, Ständker L, Preising N, Karacan M, Wiese S, Walther P, Ruiz-Blanco YB, Sanchez-Garcia E, Schumann C, Münch J, Spellerberg B. Respiratory ß-2-Microglobulin exerts pH dependent antimicrobial activity. Virulence 2021; 11:1402-1414. [PMID: 33092477 PMCID: PMC7588194 DOI: 10.1080/21505594.2020.1831367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The respiratory tract is a major entry site for microbial pathogens. To combat bacterial infections, the immune system has various defense mechanisms at its disposal, including antimicrobial peptides (AMPs). To search for novel AMPs from the respiratory tract, a peptide library from human broncho-alveolar-lavage (BAL) fluid was screened for antimicrobial activity by radial diffusion assays allowing the efficient detection of antibacterial activity within a small sample size. After repeated testing-cycles and subsequent purification, we identified ß-2-microglobulin (B2M) in antibacterially active fractions. B2M belongs to the MHC-1 receptor complex present at the surface of nucleated cells. It is known to inhibit the growth of Listeria monocytogenes and Escherichia coli and to facilitate phagocytosis of Staphylococcus aureus. Using commercially available B2M we confirmed a dose-dependent inhibition of Pseudomonas aeruginosa and L. monocytogenes. To characterize AMP activity within the B2M sequence, peptide fragments of the molecule were tested for antimicrobial activity. Activity could be localized to the C-terminal part of B2M. Investigating pH dependency of the antimicrobial activity of B2M demonstrated an increased activity at pH values of 5.5 and below, a hallmark of infection and inflammation. Sytox green uptake into bacterial cells following the exposure to B2M was determined and revealed a pH-dependent loss of bacterial membrane integrity. TEM analysis showed areas of disrupted bacterial membranes in L. monocytogenes incubated with B2M and high amounts of lysed bacterial cells. In conclusion, B2M as part of a ubiquitous cell surface complex may represent a potent antimicrobial agent by interfering with bacterial membrane integrity.
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Affiliation(s)
- Armin Holch
- Institute of Medical Microbiology and Hygiene, University Hospital , Ulm, Germany
| | - Richard Bauer
- Institute of Medical Microbiology and Hygiene, University Hospital , Ulm, Germany
| | - Lia-Raluca Olari
- Institute of Molecular Virology, University Hospital , Ulm, Germany
| | - Armando A Rodriguez
- Core Facility Functional Peptidomics, Ulm University Medical Center , Ulm, Germany.,Core Unit Mass Spectrometry and Proteomics, Ulm University , Ulm, Germany
| | - Ludger Ständker
- Core Facility Functional Peptidomics, Ulm University Medical Center , Ulm, Germany
| | - Nico Preising
- Core Facility Functional Peptidomics, Ulm University Medical Center , Ulm, Germany
| | - Merve Karacan
- Core Facility Functional Peptidomics, Ulm University Medical Center , Ulm, Germany
| | - Sebastian Wiese
- Core Unit Mass Spectrometry and Proteomics, Ulm University , Ulm, Germany
| | - Paul Walther
- Central Facility for Electron Microscopy, Ulm University Medical Center , Ulm, Germany
| | - Yasser B Ruiz-Blanco
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen , Essen, Germany
| | - Elsa Sanchez-Garcia
- Computational Biochemistry, Center of Medical Biotechnology, University of Duisburg-Essen , Essen, Germany
| | - Christian Schumann
- Pneumology, Thoracic Oncology, Sleep and Respiratory Critical Care Medicine, Clinics Kempten-Allgäu, Kempten and Immenstadt , Germany
| | - Jan Münch
- Institute of Molecular Virology, University Hospital , Ulm, Germany.,Core Facility Functional Peptidomics, Ulm University Medical Center , Ulm, Germany
| | - Barbara Spellerberg
- Institute of Medical Microbiology and Hygiene, University Hospital , Ulm, Germany
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160
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Sharma R, Shrivastava S, Kumar Singh S, Kumar A, Saxena S, Kumar Singh R. AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom. Brief Bioinform 2021; 22:6320952. [PMID: 34259329 DOI: 10.1093/bib/bbab242] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 06/02/2021] [Accepted: 06/21/2021] [Indexed: 12/12/2022] Open
Abstract
With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length $\in $[10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.
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Affiliation(s)
- Ritesh Sharma
- 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
| | - Raj Kumar Singh
- Former Director & Vice Chancellor, ICAR-Indian Veterinary Research Institute, Izatnagar, 243122, Uttar Pradesh, India
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161
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Capecchi A, Cai X, Personne H, Köhler T, van Delden C, Reymond JL. Machine learning designs non-hemolytic antimicrobial peptides. Chem Sci 2021; 12:9221-9232. [PMID: 34349895 PMCID: PMC8285431 DOI: 10.1039/d1sc01713f] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/05/2021] [Indexed: 12/28/2022] Open
Abstract
Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.
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Affiliation(s)
- Alice Capecchi
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Xingguang Cai
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Hippolyte Personne
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Thilo Köhler
- Department of Microbiology and Molecular Medicine, University of Geneva Switzerland
- Service of Infectious Diseases, University Hospital of Geneva Geneva Switzerland
| | - Christian van Delden
- Department of Microbiology and Molecular Medicine, University of Geneva Switzerland
- Service of Infectious Diseases, University Hospital of Geneva Geneva Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
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162
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Beitzinger B, Gerbl F, Vomhof T, Schmid R, Noschka R, Rodriguez A, Wiese S, Weidinger G, Ständker L, Walther P, Michaelis J, Lindén M, Stenger S. Delivery by Dendritic Mesoporous Silica Nanoparticles Enhances the Antimicrobial Activity of a Napsin-Derived Peptide Against Intracellular Mycobacterium tuberculosis. Adv Healthc Mater 2021; 10:e2100453. [PMID: 34142469 DOI: 10.1002/adhm.202100453] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/20/2021] [Indexed: 12/28/2022]
Abstract
Tuberculosis remains a serious global health problem causing 1.3 million deaths annually. The causative pathogen Mycobacterium tuberculosis (Mtb) has developed several mechanisms to evade the immune system and resistances to many conventional antibiotics, so that alternative treatment strategies are urgently needed. By isolation from bronchoalveolar lavage and peptide optimization, a new antimicrobial peptide named NapFab is discovered. While showing robust activity against extracellular Mtb, the activity of NapFab against intracellular bacteria is limited due to low intracellular availability. By loading NapFab onto dendritic mesoporous silica nanoparticles (DMSN) as a carrier system, cellular uptake, and consequently antimycobacterial activity against intracellular Mtb is significantly enhanced. Furthermore, using lattice light-sheet fluorescence microscopy, it can be shown that the peptide is gradually released from the DMSN inside living macrophages over time. By electron microscopy and tomography, it is demonstrated that peptide loaded DMSN are stored in vesicular structures in proximity to mycobacterial phagosomes inside the cells, but the nanoparticles are typically not in direct contact with the bacteria. Based on the combination of functional and live-cell imaging analyses, it is hypothesized that after being released from the DMSN NapFab is able to enter the bacterial phagosome and gain access to the bacilli.
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Affiliation(s)
- Bastian Beitzinger
- Institute of Inorganic Chemistry II Ulm University Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Fabian Gerbl
- Institute of Medical Microbiology and Hygiene Ulm University Hospital Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Thomas Vomhof
- Institute of Biophysics Ulm University Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Roman Schmid
- Institute of Inorganic Chemistry II Ulm University Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Reiner Noschka
- Institute of Medical Microbiology and Hygiene Ulm University Hospital Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Armando Rodriguez
- Core Facility of Functional Peptidomics Ulm University Meyerhofstraße 4 Ulm 89081 Germany
- Core Unit of Mass Spectrometry and Proteomics Ulm University Albert‐Einstein Allee 11 Ulm 89081 Germany
| | - Sebastian Wiese
- Core Unit of Mass Spectrometry and Proteomics Ulm University Albert‐Einstein Allee 11 Ulm 89081 Germany
| | - Gilbert Weidinger
- Institute of Biochemistry and Molecular Biology Ulm University Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Ludger Ständker
- Core Facility of Functional Peptidomics Ulm University Meyerhofstraße 4 Ulm 89081 Germany
| | - Paul Walther
- Central Facility for Electron Microscopy Ulm University Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Jens Michaelis
- Institute of Biophysics Ulm University Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Mika Lindén
- Institute of Inorganic Chemistry II Ulm University Albert‐Einstein‐Allee 11 Ulm 89081 Germany
| | - Steffen Stenger
- Institute of Medical Microbiology and Hygiene Ulm University Hospital Albert‐Einstein‐Allee 11 Ulm 89081 Germany
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163
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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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164
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Marzuoli I, Cruz CHB, Lorenz CD, Fraternali F. Nanocapsule designs for antimicrobial resistance. NANOSCALE 2021; 13:10342-10355. [PMID: 34137751 DOI: 10.1039/d0nr08146a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The pressing need of new antimicrobial products is growing stronger, particularly because of widespread antimicrobial resistance, endangering our ability to treat common infections. The recent coronavirus pandemic has dramatically highlighted the necessity of effective antibacterial and antiviral protection. This work explores at the molecular level the mechanism of action of antibacterial nanocapsules assembled in virus-like particles, their stability and their interaction with mammal and antimicrobial model membranes. We use Molecular Dynamics with force-fields of different granularity and protein design strategies to study the stability, self-assembly and membrane poration properties of these nanocapsules.
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Affiliation(s)
- Irene Marzuoli
- Randall Centre for Cell and Molecular Biology, King's College London, London, UK.
| | - Carlos H B Cruz
- Randall Centre for Cell and Molecular Biology, King's College London, London, UK.
| | | | - Franca Fraternali
- Randall Centre for Cell and Molecular Biology, King's College London, London, UK.
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165
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Xiao X, Shao YT, Cheng X, Stamatovic B. iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types. Brief Bioinform 2021; 22:6291944. [PMID: 34086856 DOI: 10.1093/bib/bbab209] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/07/2021] [Accepted: 05/11/2021] [Indexed: 01/05/2023] Open
Abstract
Predicting antimicrobial peptides (AMPs') function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes. By introducing the 'CNN-BiLSTM-SVM classifier' and 'cellular automata image', a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has ≥90% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http://www.jci-bioinfo.cn/ iAMP-CA2L, and the predictor program has been uploaded to https://github.com/liujin66/iAMP-CA2L.
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Affiliation(s)
- Xuan Xiao
- Jing-De-Zhen Ceramic Institute, China
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166
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Pinacho-Castellanos SA, García-Jacas CR, Gilson MK, Brizuela CA. Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set. J Chem Inf Model 2021; 61:3141-3157. [PMID: 34081438 DOI: 10.1021/acs.jcim.1c00251] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence-activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at https://biocom-ampdiscover.cicese.mx/.
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Affiliation(s)
- Sergio A Pinacho-Castellanos
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México.,Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI), Instituto Politécnico Nacional (IPN), 22435 Tijuana, Baja California, México
| | - César R García-Jacas
- Cátedras CONACYT-Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
| | - Carlos A Brizuela
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
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167
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Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nat Biomed Eng 2021; 5:613-623. [PMID: 33707779 DOI: 10.1038/s41551-021-00689-x] [Citation(s) in RCA: 119] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 01/16/2021] [Indexed: 01/31/2023]
Abstract
The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.
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168
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Zhang Y, Lin J, Zhao L, Zeng X, Liu X. A novel antibacterial peptide recognition algorithm based on BERT. Brief Bioinform 2021; 22:6284370. [PMID: 34037687 DOI: 10.1093/bib/bbab200] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/19/2021] [Accepted: 05/03/2021] [Indexed: 12/31/2022] Open
Abstract
As the best substitute for antibiotics, antimicrobial peptides (AMPs) have important research significance. Due to the high cost and difficulty of experimental methods for identifying AMPs, more and more researches are focused on using computational methods to solve this problem. Most of the existing calculation methods can identify AMPs through the sequence itself, but there is still room for improvement in recognition accuracy, and there is a problem that the constructed model cannot be universal in each dataset. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved gratifying results. It also has great application prospects in the field of AMP recognition and prediction. In this paper, we apply the pre-training strategy to the model training of AMP classifiers and propose a novel recognition algorithm. Our model is constructed based on the BERT model, pre-trained with the protein data from UniProt, and then fine-tuned and evaluated on six AMP datasets with large differences. Our model is superior to the existing methods and achieves the goal of accurate identification of datasets with small sample size. We try different word segmentation methods for peptide chains and prove the influence of pre-training steps and balancing datasets on the recognition effect. We find that pre-training on a large number of diverse AMP data, followed by fine-tuning on new data, is beneficial for capturing both new data's specific features and common features between AMP sequences. Finally, we construct a new AMP dataset, on which we train a general AMP recognition model.
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Affiliation(s)
- Yue Zhang
- Xiamen University, Xiamen 361005, China
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169
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Pirtskhalava M, Vishnepolsky B, Grigolava M, Managadze G. Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane. Pharmaceuticals (Basel) 2021; 14:471. [PMID: 34067510 PMCID: PMC8156082 DOI: 10.3390/ph14050471] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/25/2021] [Accepted: 04/29/2021] [Indexed: 02/06/2023] Open
Abstract
Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.
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Affiliation(s)
- Malak Pirtskhalava
- Ivane Beritashvili Center of Experimental Biomedicine, Tbilisi 0160, Georgia; (B.V.); (M.G.); (G.M.)
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170
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Using an Ensemble to Identify and Classify Macroalgae Antimicrobial Peptides. Interdiscip Sci 2021; 13:321-333. [PMID: 33978916 DOI: 10.1007/s12539-021-00435-6] [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: 11/28/2020] [Revised: 04/27/2021] [Accepted: 04/27/2021] [Indexed: 10/21/2022]
Abstract
The rapid spread of multi-drug resistant microbes has lead researchers to discover natural alternative remedies such as antimicrobial peptides (AMPs). In the first line of defense, AMPs display a broad spectrum of potent activity against multi-resistant pathogenic bacteria, viruses, fungi, and even cancer. AMPs can be further characterised into families according to amino acid composition, secondary structure, and function. However, despite recent advancements in rapid computational methods for AMP prediction from various mammalian, aquatic, and terrestrial species, there is limited information regarding their presence, functional roles, and family type from marine macroalgae. In this paper, we present a promising two-tier ensemble of heterogeneous machine learning models that integrates seven well-known machine learning classifiers to predict AMPs from macroalgae. The first tier of the ensemble consists of a suite of binary classifiers that identify AMPs from protein sequence data which are then forwarded to a second-tier multi-class ensemble to characterise their functional family type. The two-tier ensemble was successfully used to identify 39 putative AMP sequences in 12 macroalgae species from three different phyla groups. The approach we describe is not limited to AMPs and can also be applied to search sequence data for other types of proteins.
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171
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Boone K, Wisdom C, Camarda K, Spencer P, Tamerler C. Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides. BMC Bioinformatics 2021; 22:239. [PMID: 33975547 PMCID: PMC8111958 DOI: 10.1186/s12859-021-04156-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 04/27/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space. METHODS Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process. RESULTS We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis. CONCLUSIONS Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.
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Affiliation(s)
- Kyle Boone
- Bioengineering Program, University of Kansas, Institute of Bioengineering Research, University of Kansas, 1530 W 15th Street, Learned Hall, Room 5109, Lawrence, KS 66045 USA
| | - Cate Wisdom
- Bioengineering Program, University of Kansas, Institute of Bioengineering Research, University of Kansas, 1530 W 15th Street, Learned Hall, Room 5109, Lawrence, KS 66045 USA
| | - Kyle Camarda
- Chemical and Petroleum Engineering Department, University of Kansas, 1530 West 15th Street, Learned Hall, Room 4154, Lawrence, KS 66045 USA
| | - Paulette Spencer
- Mechanical Engineering Department, University of Kansas, 1530 West 15th Street, Learned Hall, Room 3111, Lawrence, KS 66045 USA
- Institute of Bioengineering Research, University of Kansas, 1530 West 15th Street, Learned Hall, Room 3111, Lawrence, KS 66045 USA
| | - Candan Tamerler
- Mechanical Engineering Department, University of Kansas, 1530 W 15th St, Learned Hall, Room 3135A, Lawrence, KS 66045 USA
- Institute of Bioengineering Research, University of Kansas, 1530 W 15th St, Learned Hall, Room 3135A, Lawrence, KS 66045 USA
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172
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Evolutionary algorithm-based generation of optimum peptide sequences with dengue virus inhibitory activity. Future Med Chem 2021; 13:993-1000. [PMID: 33890502 DOI: 10.4155/fmc-2020-0372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: There is currently no effective dengue virus (DENV) therapeutic. We aim to develop a genetic algorithm-based framework for the design of peptides with possible DENV inhibitory activity. Methods & results: A Python-based tool (denominated AutoPepGEN) based on a DENV support vector machine classifier as the objective function was implemented. AutoPepGEN was applied to the design of three- to seven-amino acid sequences and ten peptides were selected. Peptide-protease (DENV) docking and Molecular Mechanics-Generalized Born Surface Area calculations were performed for the selected sequences and favorable binding energies were observed. Conclusion: It is hoped that AutoPepGEN will serve as an in silico alternative to the experimental design of positional scanning combinatorial libraries, known to be prone to a combinatorial explosion. AutoPepGEN is available at: https://github.com/sjbarigye/AutoPepGEN.
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173
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Dong GF, Zheng L, Huang SH, Gao J, Zuo YC. Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities. Front Genet 2021; 12:669328. [PMID: 33959153 PMCID: PMC8093877 DOI: 10.3389/fgene.2021.669328] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/23/2021] [Indexed: 02/03/2023] Open
Abstract
Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu. cn/iampraac.
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Affiliation(s)
- Gai-Fang Dong
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Lei Zheng
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Sheng-Hui Huang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Jing Gao
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Yong-Chun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
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174
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Tang X, Zhang T, Cheng N, Wang H, Zheng CH, Xia J, Zhang T. usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme. Brief Bioinform 2021; 22:6236069. [PMID: 33866367 DOI: 10.1093/bib/bbab123] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/12/2021] [Accepted: 03/15/2021] [Indexed: 12/11/2022] Open
Abstract
Although synonymous mutations do not alter the encoded amino acids, they may impact protein function by interfering with the regulation of RNA splicing or altering transcript splicing. New progress on next-generation sequencing technologies has put the exploration of synonymous mutations at the forefront of precision medicine. Several approaches have been proposed for predicting the deleterious synonymous mutations specifically, but their performance is limited by imbalance of the positive and negative samples. In this study, we firstly expanded the number of samples greatly from various data sources and compared six undersampling strategies to solve the problem of the imbalanced datasets. The results suggested that cluster centroid is the most effective scheme. Secondly, we presented a computational model, undersampling scheme based method for deleterious synonymous mutation (usDSM) prediction, using 14-dimensional biology features and random forest classifier to detect the deleterious synonymous mutation. The results on the test datasets indicated that the proposed usDSM model can attain superior performance in comparison with other state-of-the-art machine learning methods. Lastly, we found that the deep learning model did not play a substantial role in deleterious synonymous mutation prediction through a lot of experiments, although it achieves superior results in other fields. In conclusion, we hope our work will contribute to the future development of computational methods for a more accurate prediction of the deleterious effect of human synonymous mutation. The web server of usDSM is freely accessible at http://usdsm.xialab.info/.
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Affiliation(s)
- Xi Tang
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University and the Institutes of Physical Science and Information Technology, Anhui University, China
| | - Tao Zhang
- School of Computer Science and Technology, Anhui University, China
| | - Na Cheng
- Institutes of Physical Science and Information Technology, Anhui University, China
| | - Huadong Wang
- School of Computer Science and Technology, Anhui University, China
| | - Chun-Hou Zheng
- School of Computer Science and Technology, Anhui University, China
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University, China
| | - Tiejun Zhang
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, China
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175
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Lei M, Jayaraman A, Van Deventer JA, Lee K. Engineering Selectively Targeting Antimicrobial Peptides. Annu Rev Biomed Eng 2021; 23:339-357. [PMID: 33852346 DOI: 10.1146/annurev-bioeng-010220-095711] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The rise of antibiotic-resistant strains of bacterial pathogens has necessitated the development of new therapeutics. Antimicrobial peptides (AMPs) are a class of compounds with potentially attractive therapeutic properties, including the ability to target specific groups of bacteria. In nature, AMPs exhibit remarkable structural and functional diversity, which may be further enhanced through genetic engineering, high-throughput screening, and chemical modification strategies. In this review, we discuss the molecular mechanisms underlying AMP selectivity and highlight recent computational and experimental efforts to design selectively targeting AMPs. While there has been an extensive effort to find broadly active and highly potent AMPs, it remains challenging to design targeting peptides to discriminate between different bacteria on the basis of physicochemical properties. We also review approaches for measuring AMP activity, point out the challenges faced in assaying for selectivity, and discuss the potential for increasing AMP diversity through chemical modifications.
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Affiliation(s)
- Ming Lei
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, USA; , ,
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical Engineering and Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, USA; .,Department of Microbial Pathogenesis and Immunology, College of Medicine, Texas Health Science Center, Texas A&M University, College Station, Texas 77843, USA
| | - James A Van Deventer
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, USA; , , .,Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, USA
| | - Kyongbum Lee
- Department of Chemical and Biological Engineering, Tufts University, Medford, Massachusetts 02155, USA; , ,
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176
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Sharma R, Shrivastava S, Kumar Singh S, Kumar A, Saxena S, Kumar Singh R. Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec. Brief Bioinform 2021; 22:6204762. [PMID: 33784381 DOI: 10.1093/bib/bbab065] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/04/2021] [Indexed: 12/13/2022] Open
Abstract
The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.
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Affiliation(s)
- Ritesh Sharma
- Department of Computer Science and Engineering at IIT (BHU), Varanasi, India
| | | | - Sanjay Kumar Singh
- Department of Computer Science and Engineering at IIT (BHU), Varanasi, India
| | - Abhinav Kumar
- Department of Computer Science and Engineering at IIT (BHU), Varanasi, India
| | - Sonal Saxena
- Division of Veterinary Biotechnology, IVRI, Izatnagar, India
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177
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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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178
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Wang C, Garlick S, Zloh M. Deep Learning for Novel Antimicrobial Peptide Design. Biomolecules 2021; 11:biom11030471. [PMID: 33810011 PMCID: PMC8004669 DOI: 10.3390/biom11030471] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 11/23/2022] Open
Abstract
Antimicrobial resistance is an increasing issue in healthcare as the overuse of antibacterial agents rises during the COVID-19 pandemic. The need for new antibiotics is high, while the arsenal of available agents is decreasing, especially for the treatment of infections by Gram-negative bacteria like Escherichia coli. Antimicrobial peptides (AMPs) are offering a promising route for novel antibiotic development and deep learning techniques can be utilised for successful AMP design. In this study, a long short-term memory (LSTM) generative model and a bidirectional LSTM classification model were constructed to design short novel AMP sequences with potential antibacterial activity against E. coli. Two versions of the generative model and six versions of the classification model were trained and optimised using Bayesian hyperparameter optimisation. These models were used to generate sets of short novel sequences that were classified as antimicrobial or non-antimicrobial. The validation accuracies of the classification models were 81.6–88.9% and the novel AMPs were classified as antimicrobial with accuracies of 70.6–91.7%. Predicted three-dimensional conformations of selected short AMPs exhibited the alpha-helical structure with amphipathic surfaces. This demonstrates that LSTMs are effective tools for generating novel AMPs against targeted bacteria and could be utilised in the search for new antibiotics leads.
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Affiliation(s)
- Christina Wang
- UCL School of Pharmacy, University College London, London WC1N 1AX, UK;
| | - Sam Garlick
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK;
| | - Mire Zloh
- UCL School of Pharmacy, University College London, London WC1N 1AX, UK;
- Faculty of Pharmacy, University Business Academy in Novi Sad, 21000 Novi Sad, Serbia
- Correspondence:
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179
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Pang Y, Wang Z, Jhong JH, Lee TY. Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies. Brief Bioinform 2021; 22:1085-1095. [PMID: 33497434 PMCID: PMC7929366 DOI: 10.1093/bib/bbaa423] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/30/2020] [Accepted: 08/20/2020] [Indexed: 12/16/2022] Open
Abstract
As the current worldwide outbreaks of the SARS-CoV-2, it is urgently needed to develop effective therapeutic agents for inhibiting the pathogens or treating the related diseases. Antimicrobial peptides (AMP) with functional activity against coronavirus could be a considerable solution, yet there is no research for identifying anti-coronavirus (anti-CoV) peptides with the computational approach. In this study, we first investigated the physiochemical and compositional properties of the collected anti-CoV peptides by comparing against three other negative sets: antivirus peptides without anti-CoV function (antivirus), regular AMP without antivirus functions (non-AVP) and peptides without antimicrobial functions (non-AMP). Then, we established classifiers for identifying anti-CoV peptides between different negative sets based on random forest. Imbalanced learning strategies were adopted due to the severe class-imbalance within the datasets. The geometric mean of the sensitivity and specificity (GMean) under the identification from antivirus, non-AVP and non-AMP reaches 83.07%, 85.51% and 98.82%, respectively. Then, to pursue identifying anti-CoV peptides from broad-spectrum peptides, we designed a double-stages classifier based on the collected datasets. In the first stage, the classifier characterizes AMPs from regular peptides. It achieves an area under the receiver operating curve (AUCROC) value of 97.31%. The second stage is to identify the anti-CoV peptides between the combined negatives of other AMPs. Here, the GMean of evaluation on the independent test set is 79.42%. The proposed approach is considered as an applicable scheme for assisting the development of novel anti-CoV peptides. The datasets and source codes used in this study are available at https://github.com/poncey/PreAntiCoV.
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Affiliation(s)
- Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, P.R. China
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, P.R. China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, P.R. China
| | - Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, P.R. China
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, P.R. China
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, P.R. China
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180
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Yusuf SM, Zhang F, Zeng M, Li M. DeepPPF: A deep learning framework for predicting protein family. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.11.062] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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181
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Charoenkwan P, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides. Bioinformatics 2021; 37:2556-2562. [PMID: 33638635 DOI: 10.1093/bioinformatics/btab133] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/08/2021] [Accepted: 02/24/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number of newly available peptide sequences in the post-genomic era, the development of automated computational models for the identification of novel bitter peptides is highly desira-ble. RESULTS In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model for predicting bitter peptides directly from their amino acid sequence without using any structural information. To the best of our knowledge, this is the first time a BERT-based model has been employed to identify bitter peptides. Compared to widely used machine learning models, BERT4Bitter achieved the best performance with accuracy of 0.861 and 0.922 for cross-validation and independent tests, respectively. Furthermore, extensive empirical benchmarking experiments on the independent dataset demonstrated that BERT4Bitter clearly outperformed the existing method with improvements of > 8% accuracy and >16% Matthews coefficient correlation, highlighting the effectiveness and robustness of BERT4Bitter. We believe that the BERT4Bitter method proposed herein will be a useful tool for rapidly screening and identifying novel bitter peptides for drug development and nutritional research. AVAILABILITY The user-friendly web server of the proposed BERT4Bitter is freely accessible at: http://pmlab.pythonanywhere.com/BERT4Bitter. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | | | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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182
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Petruschke H, Schori C, Canzler S, Riesbeck S, Poehlein A, Daniel R, Frei D, Segessemann T, Zimmerman J, Marinos G, Kaleta C, Jehmlich N, Ahrens CH, von Bergen M. Discovery of novel community-relevant small proteins in a simplified human intestinal microbiome. MICROBIOME 2021; 9:55. [PMID: 33622394 PMCID: PMC7903761 DOI: 10.1186/s40168-020-00981-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 12/16/2020] [Indexed: 05/13/2023]
Abstract
BACKGROUND The intestinal microbiota plays a crucial role in protecting the host from pathogenic microbes, modulating immunity and regulating metabolic processes. We studied the simplified human intestinal microbiota (SIHUMIx) consisting of eight bacterial species with a particular focus on the discovery of novel small proteins with less than 100 amino acids (= sProteins), some of which may contribute to shape the simplified human intestinal microbiota. Although sProteins carry out a wide range of important functions, they are still often missed in genome annotations, and little is known about their structure and function in individual microbes and especially in microbial communities. RESULTS We created a multi-species integrated proteogenomics search database (iPtgxDB) to enable a comprehensive identification of novel sProteins. Six of the eight SIHUMIx species, for which no complete genomes were available, were sequenced and de novo assembled. Several proteomics approaches including two earlier optimized sProtein enrichment strategies were applied to specifically increase the chances for novel sProtein discovery. The search of tandem mass spectrometry (MS/MS) data against the multi-species iPtgxDB enabled the identification of 31 novel sProteins, of which the expression of 30 was supported by metatranscriptomics data. Using synthetic peptides, we were able to validate the expression of 25 novel sProteins. The comparison of sProtein expression in each single strain versus a multi-species community cultivation showed that six of these sProteins were only identified in the SIHUMIx community indicating a potentially important role of sProteins in the organization of microbial communities. Two of these novel sProteins have a potential antimicrobial function. Metabolic modelling revealed that a third sProtein is located in a genomic region encoding several enzymes relevant for the community metabolism within SIHUMIx. CONCLUSIONS We outline an integrated experimental and bioinformatics workflow for the discovery of novel sProteins in a simplified intestinal model system that can be generically applied to other microbial communities. The further analysis of novel sProteins uniquely expressed in the SIHUMIx multi-species community is expected to enable new insights into the role of sProteins on the functionality of bacterial communities such as those of the human intestinal tract. Video abstract.
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Affiliation(s)
- Hannes Petruschke
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Christian Schori
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Sebastian Canzler
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Sarah Riesbeck
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Anja Poehlein
- Institute of Microbiology and Genetics, Department of Genomic and Applied Microbiology, Georg-August University of Göttingen, Göttingen, Germany
| | - Rolf Daniel
- Institute of Microbiology and Genetics, Department of Genomic and Applied Microbiology, Georg-August University of Göttingen, Göttingen, Germany
| | - Daniel Frei
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Tina Segessemann
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Johannes Zimmerman
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Georgios Marinos
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Nico Jehmlich
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany
| | - Christian H Ahrens
- Agroscope, Molecular Diagnostics, Genomics & Bioinformatics and SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland.
| | - Martin von Bergen
- Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ GmbH, Leipzig, Germany.
- Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, University of Leipzig, Leipzig, Germany.
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183
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Lv J, Deng S, Zhang L. A review of artificial intelligence applications for antimicrobial resistance. BIOSAFETY AND HEALTH 2021. [DOI: 10.1016/j.bsheal.2020.08.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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184
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Barrett R, White AD. Investigating Active Learning and Meta-Learning for Iterative Peptide Design. J Chem Inf Model 2021; 61:95-105. [PMID: 33350829 PMCID: PMC7842147 DOI: 10.1021/acs.jcim.0c00946] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Indexed: 01/14/2023]
Abstract
Often the development of novel functional peptides is not amenable to high throughput or purely computational screening methods. Peptides must be synthesized one at a time in a process that does not generate large amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both peptide properties and predictive modeling accuracy. Here, we study the effectiveness of active learning, optimizing experiment order, and meta-learning, transferring knowledge between contexts, to reduce the number of experiments necessary to build a predictive model. We present a multitask benchmark database of peptides designed to advance these methods for experimental design. Each task is a binary classification of peptides represented as a sequence string. We find neither active learning method tested to be better than random choice. The meta-learning method Reptile was found to improve the average accuracy across data sets. Combining meta-learning with active learning offers inconsistent benefits.
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Affiliation(s)
- Rainier Barrett
- Department of Chemical Engineering,
University of Rochester, Rochester, New York 14627,
United States
| | - Andrew D. White
- Department of Chemical Engineering,
University of Rochester, Rochester, New York 14627,
United States
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185
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Yu L, Liu F, Li Y, Luo J, Jing R. DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors. Front Microbiol 2021; 12:605782. [PMID: 33552038 PMCID: PMC7858263 DOI: 10.3389/fmicb.2021.605782] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 01/04/2021] [Indexed: 01/17/2023] Open
Abstract
Gram-negative bacteria can deliver secreted proteins (also known as secreted effectors) directly into host cells through type III secretion system (T3SS), type IV secretion system (T4SS), and type VI secretion system (T6SS) and cause various diseases. These secreted effectors are heavily involved in the interactions between bacteria and host cells, so their identification is crucial for the discovery and development of novel anti-bacterial drugs. It is currently challenging to accurately distinguish type III secreted effectors (T3SEs) and type IV secreted effectors (T4SEs) because neither T3SEs nor T4SEs contain N-terminal signal peptides, and some of these effectors have similar evolutionary conserved profiles and sequence motifs. To address this challenge, we develop a deep learning (DL) approach called DeepT3_4 to correctly classify T3SEs and T4SEs. We generate amino-acid character dictionary and sequence-based features extracted from effector proteins and subsequently implement these features into a hybrid model that integrates recurrent neural networks (RNNs) and deep neural networks (DNNs). After training the model, the hybrid neural network classifies secreted effectors into two different classes with an accuracy, F-value, and recall of over 80.0%. Our approach stands for the first DL approach for the classification of T3SEs and T4SEs, providing a promising supplementary tool for further secretome studies.
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Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang, China
| | - Yizhou Li
- College of Cybersecurity, Sichuan University, Chengdu, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, China
| | - Runyu Jing
- College of Cybersecurity, Sichuan University, Chengdu, China
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186
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Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs. Genes (Basel) 2021; 12:genes12020137. [PMID: 33494403 PMCID: PMC7911732 DOI: 10.3390/genes12020137] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/16/2021] [Accepted: 01/18/2021] [Indexed: 01/04/2023] Open
Abstract
Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs.
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187
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Decio P, Ustaoglu P, Derecka K, Hardy ICW, Roat TC, Malaspina O, Mongan N, Stöger R, Soller M. Thiamethoxam exposure deregulates short ORF gene expression in the honey bee and compromises immune response to bacteria. Sci Rep 2021; 11:1489. [PMID: 33452318 PMCID: PMC7811001 DOI: 10.1038/s41598-020-80620-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 12/23/2020] [Indexed: 01/29/2023] Open
Abstract
Maximizing crop yields relies on the use of agrochemicals to control insect pests. One of the most widely used classes of insecticides are neonicotinoids that interfere with signalling of the neurotransmitter acetylcholine, but these can also disrupt crop-pollination services provided by bees. Here, we analysed whether chronic low dose long-term exposure to the neonicotinoid thiamethoxam alters gene expression and alternative splicing in brains of Africanized honey bees, Apis mellifera, as adaptation to altered neuronal signalling. We find differentially regulated genes that show concentration-dependent responses to thiamethoxam, but no changes in alternative splicing. Most differentially expressed genes have no annotated function but encode short Open Reading Frames, a characteristic feature of anti-microbial peptides. As this suggested that immune responses may be compromised by thiamethoxam exposure, we tested the impact of thiamethoxam on bee immunity by injecting bacteria. We show that intrinsically sub-lethal thiamethoxam exposure makes bees more vulnerable to normally non-pathogenic bacteria. Our findings imply a synergistic mechanism for the observed bee population declines that concern agriculturists, conservation ecologists and the public.
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Affiliation(s)
- Pâmela Decio
- grid.410543.70000 0001 2188 478XInstitute of Biosciences, São Paulo State University (Unesp), Rio Claro, Brazil
| | - Pinar Ustaoglu
- grid.6572.60000 0004 1936 7486School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Kamila Derecka
- grid.4563.40000 0004 1936 8868School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, LE12 5RD UK
| | - Ian C. W. Hardy
- grid.4563.40000 0004 1936 8868School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, LE12 5RD UK
| | - Thaisa C. Roat
- grid.410543.70000 0001 2188 478XInstitute of Biosciences, São Paulo State University (Unesp), Rio Claro, Brazil
| | - Osmar Malaspina
- grid.410543.70000 0001 2188 478XInstitute of Biosciences, São Paulo State University (Unesp), Rio Claro, Brazil
| | - Nigel Mongan
- grid.4563.40000 0004 1936 8868School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Loughborough, LE12 5RD UK
| | - Reinhard Stöger
- grid.4563.40000 0004 1936 8868School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, LE12 5RD UK
| | - Matthias Soller
- grid.6572.60000 0004 1936 7486School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
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188
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Machine-learning Applications to Membrane Active Peptides. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11544-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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189
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Yakimovich A. Artificial Intelligence in Infection Biology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_105-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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190
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Zheng D, Pang G, Liu B, Chen L, Yang J. Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors. Bioinformatics 2020; 36:3693-3702. [PMID: 32251507 DOI: 10.1093/bioinformatics/btaa230] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/25/2020] [Accepted: 04/01/2020] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available. RESULTS We first construct a large VF dataset, covering 3446 VF classes with 160 495 sequences, and then propose deep convolutional neural network models for VF classification. We show that (i) for common VF classes with sufficient samples, our models can achieve state-of-the-art performance with an overall accuracy of 0.9831 and an F1-score of 0.9803; (ii) for uncommon VF classes with limited samples, our models can learn transferable features from auxiliary data and achieve good performance with accuracy ranging from 0.9277 to 0.9512 and F1-score ranging from 0.9168 to 0.9446 when combined with different predefined features, outperforming traditional classifiers by 1-13% in accuracy and by 1-16% in F1-score. AVAILABILITY AND IMPLEMENTATION All of our datasets are made publicly available at http://www.mgc.ac.cn/VFNet/, and the source code of our models is publicly available at https://github.com/zhengdd0422/VFNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dandan Zheng
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
| | - Guansong Pang
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Bo Liu
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
| | - Lihong Chen
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
| | - Jian Yang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China
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191
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Santos-Júnior CD, Pan S, Zhao XM, Coelho LP. Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ 2020; 8:e10555. [PMID: 33384902 PMCID: PMC7751412 DOI: 10.7717/peerj.10555] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 11/22/2020] [Indexed: 12/21/2022] Open
Abstract
Motivation Antimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall. Results Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data. Availability Macrel is implemented in Python 3. It is available as open source at https://github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: https://big-data-biology.org/software/macrel.
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Affiliation(s)
- Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Shanghai, China
| | - Shaojun Pan
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Shanghai, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Shanghai, China
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Shanghai, China
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192
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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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193
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Timmons PB, Hewage CM. ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides. Biomed Pharmacother 2020; 133:111051. [PMID: 33254015 DOI: 10.1016/j.biopha.2020.111051] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/08/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
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194
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Scorpion-Venom-Derived Antimicrobial Peptide Css54 Exerts Potent Antimicrobial Activity by Disrupting Bacterial Membrane of Zoonotic Bacteria. Antibiotics (Basel) 2020; 9:antibiotics9110831. [PMID: 33233541 PMCID: PMC7699533 DOI: 10.3390/antibiotics9110831] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/11/2020] [Accepted: 11/18/2020] [Indexed: 01/10/2023] Open
Abstract
Antibiotic resistance is an important issue affecting humans and livestock. Antimicrobial peptides are promising alternatives to antibiotics. In this study, the antimicrobial peptide Css54, isolated from the venom of C. suffuses, was found to exhibit antimicrobial activity against bacteria such as Listeria monocytogenes, Streptococcus suis, Campylobacter jejuni, and Salmonella typhimurium that cause zoonotic diseases. Moreover, the cytotoxicity and hemolytic activity of Css54 was lower than that of melittin isolated from bee venom. Circular dichroism assays showed that Css54 has an α-helix structure in an environment mimicking that of bacterial cell membranes. We examined the effect of Css54 on bacterial membranes using N-phenyl-1-naphthylamine, 3,3'-dipropylthiadicarbbocyanine iodides, SYTOX green, and propidium iodide. Our findings suggest that the Css54 peptide kills bacteria by disrupting the bacterial membrane. Moreover, Css54 exhibited antibiofilm activity against L. monocytogenes. Thus, Css54 may be useful as an alternative to antibiotics in humans and animal husbandry.
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195
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González García M, Rodríguez A, Alba A, Vázquez AA, Morales Vicente FE, Pérez-Erviti J, Spellerberg B, Stenger S, Grieshober M, Conzelmann C, Münch J, Raber H, Kubiczek D, Rosenau F, Wiese S, Ständker L, Otero-González A. New Antibacterial Peptides from the Freshwater Mollusk Pomacea poeyana (Pilsbry, 1927). Biomolecules 2020; 10:biom10111473. [PMID: 33113998 PMCID: PMC7690686 DOI: 10.3390/biom10111473] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 01/04/2023] Open
Abstract
Antimicrobial peptides (AMPs) are biomolecules with antimicrobial activity against a broad group of pathogens. In the past few decades, AMPs have represented an important alternative for the treatment of infectious diseases. Their isolation from natural sources has been widely investigated. In this sense, mollusks are promising organisms for the identification of AMPs given that their immune system mainly relies on innate response. In this report, we characterized the peptide fraction of the Cuban freshwater snail Pomacea poeyana (Pilsbry, 1927) and identified 37 different peptides by nanoLC-ESI-MS-MS technology. From these peptide sequences, using bioinformatic prediction tools, we discovered two potential antimicrobial peptides named Pom-1 (KCAGSIAWAIGSGLFGGAKLIKIKKYIAELGGLQ) and Pom-2 (KEIERAGQRIRDAIISAAPAVETLAQAQKIIKGG). Database search revealed that Pom-1 is a fragment of Closticin 574 previously isolated from the bacteria Clostridium tyrobutyrium, and Pom-2 is a fragment of cecropin D-like peptide first isolated from Galleria mellonella hemolymph. These sequences were chemically synthesized and evaluated against different human pathogens. Interestingly, structural predictions of both peptides in the presence of micelles showed models that comprise two alpha helices joined by a short loop. The CD spectra analysis of Pom-1 and Pom-2 in water showed for both structures a high random coil content, a certain content of α-helix and a low β-sheet content. Like other described AMPs displaying a disordered structure in water, the peptides may adopt a helical conformation in presence of bacterial membranes. In antimicrobial assays, Pom-1 demonstrated high activity against the Gram-negative bacteria Pseudomonas aeruginosa and moderate activity against Klebsiella pneumoniae and Listeria monocytogenes. Neither of the two peptides showed antifungal action. Pom-1 moderately inhibits Zika Virus infection but slightly enhances HIV-1 infectivion in vitro. The evaluation of cell toxicity on primary human macrophages did not show toxicity on THP-1 cells, although slight overall toxicity was observed in high concentrations of Pom-1. We assume that both peptides may play a key role in innate defense of P. poeyana and represent promising antimicrobial candidates for humans.
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Affiliation(s)
- Melaine González García
- Center for Protein Studies, Faculty of Biology, University of Havana, 25 street, 10400 Havana, Cuba; (M.G.G.); (J.P.-E.)
| | - Armando Rodríguez
- Core Facility for Functional Peptidomics, Faculty of Medicine, Ulm University, 89081 Ulm, Germany;
- Core Unit of Mass Spectrometry and Proteomics, Faculty of Medicine, Ulm University, 89081 Ulm, Germany;
| | - Annia Alba
- Reference Center for Research and Diagnosis, Pedro Kourí Institute for Tropical Medicine, 11400 Havana, Cuba; (A.A.); (A.A.V.)
| | - Antonio A. Vázquez
- Reference Center for Research and Diagnosis, Pedro Kourí Institute for Tropical Medicine, 11400 Havana, Cuba; (A.A.); (A.A.V.)
| | - Fidel E. Morales Vicente
- General Chemistry Department, Faculty of Chemistry, University of Havana, Zapata y G, 10400 Havana, Cuba;
- Synthetic Peptides Group, Center for Genetic Engineering and Biotechnology, P.O. Box 6162, 10600 Havana, Cuba
| | - Julio Pérez-Erviti
- Center for Protein Studies, Faculty of Biology, University of Havana, 25 street, 10400 Havana, Cuba; (M.G.G.); (J.P.-E.)
| | - Barbara Spellerberg
- Institute of Medical Microbiology and Hygiene, University Hospital Ulm, 89081 Ulm, Germany; (B.S.); (S.S.); (M.G.)
| | - Steffen Stenger
- Institute of Medical Microbiology and Hygiene, University Hospital Ulm, 89081 Ulm, Germany; (B.S.); (S.S.); (M.G.)
| | - Mark Grieshober
- Institute of Medical Microbiology and Hygiene, University Hospital Ulm, 89081 Ulm, Germany; (B.S.); (S.S.); (M.G.)
| | - Carina Conzelmann
- Institute of Molecular Virology, Ulm University, Meyerhofstrasse 1, 89081 Ulm, Germany; (C.C.); (J.M.)
| | - Jan Münch
- Institute of Molecular Virology, Ulm University, Meyerhofstrasse 1, 89081 Ulm, Germany; (C.C.); (J.M.)
| | - Heinz Raber
- Institute of Pharmaceutical Biotechnology, Ulm University, 89081 Ulm, Germany; (H.R.); (D.K.); (F.R.)
| | - Dennis Kubiczek
- Institute of Pharmaceutical Biotechnology, Ulm University, 89081 Ulm, Germany; (H.R.); (D.K.); (F.R.)
| | - Frank Rosenau
- Institute of Pharmaceutical Biotechnology, Ulm University, 89081 Ulm, Germany; (H.R.); (D.K.); (F.R.)
| | - Sebastian Wiese
- Core Unit of Mass Spectrometry and Proteomics, Faculty of Medicine, Ulm University, 89081 Ulm, Germany;
| | - Ludger Ständker
- Core Facility for Functional Peptidomics, Faculty of Medicine, Ulm University, 89081 Ulm, Germany;
- Correspondence: (L.S.); (A.O.-G.)
| | - Anselmo Otero-González
- Center for Protein Studies, Faculty of Biology, University of Havana, 25 street, 10400 Havana, Cuba; (M.G.G.); (J.P.-E.)
- Correspondence: (L.S.); (A.O.-G.)
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196
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Huan Y, Kong Q, Mou H, Yi H. Antimicrobial Peptides: Classification, Design, Application and Research Progress in Multiple Fields. Front Microbiol 2020; 11:582779. [PMID: 33178164 PMCID: PMC7596191 DOI: 10.3389/fmicb.2020.582779] [Citation(s) in RCA: 593] [Impact Index Per Article: 148.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 09/23/2020] [Indexed: 12/12/2022] Open
Abstract
Antimicrobial peptides (AMPs) are a class of small peptides that widely exist in nature and they are an important part of the innate immune system of different organisms. AMPs have a wide range of inhibitory effects against bacteria, fungi, parasites and viruses. The emergence of antibiotic-resistant microorganisms and the increasing of concerns about the use of antibiotics resulted in the development of AMPs, which have a good application prospect in medicine, food, animal husbandry, agriculture and aquaculture. This review introduces the progress of research on AMPs comprehensively and systematically, including their classification, mechanism of action, design methods, environmental factors affecting their activity, application status, prospects in various fields and problems to be solved. The research progress on antivirus peptides, especially anti-coronavirus (COVID-19) peptides, has been introduced given the COVID-19 pandemic worldwide in 2020.
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Affiliation(s)
| | - Qing Kong
- College of Food Science and Engineering, Ocean University of China, Qingdao, China
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197
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Taguchi AT, Boyd J, Diehnelt CW, Legutki JB, Zhao ZG, Woodbury NW. Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning. ACS COMBINATORIAL SCIENCE 2020; 22:500-508. [PMID: 32786325 DOI: 10.1021/acscombsci.0c00003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computational prescreening. Here, a different approach is considered that uses sparse measurements of library molecules as the input to a machine learning algorithm which generates a comprehensive, quantitative relationship between covalent molecular structure and function that can then be used to predict the function of any molecule in the possible combinatorial space. To test the feasibility of the approach, a defined combinatorial chemical space consisting of ∼1012 possible linear combinations of 16 different amino acids was used. The binding of a very sparse, but nearly random, sampling of this amino acid sequence space to 9 different protein targets is measured and used to generate a general relationship between peptide sequence and binding for each target. Surprisingly, measuring as little as a few hundred to a few thousand of the ∼1012 possible molecules provides sufficient training to be highly predictive of the binding of the remaining molecules in the combinatorial space. Furthermore, measuring only amino acid sequences that bind weakly to a target allows the accurate prediction of which sequences will bind 10-100 times more strongly. Thus, the molecular recognition information contained in a tiny fraction of molecules in this combinatorial space is sufficient to characterize any set of molecules randomly selected from the entire space, a fact that potentially has significant implications for the design of new chemical function using combinatorial chemical libraries.
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Affiliation(s)
| | - James Boyd
- HealthTell, Inc., 145 S 79th Street, Chandler, Arizona 85226, United States
| | - Chris W. Diehnelt
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Joseph B. Legutki
- HealthTell, Inc., 145 S 79th Street, Chandler, Arizona 85226, United States
| | - Zhan-Gong Zhao
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
| | - Neal W. Woodbury
- Center for Innovations in Medicine at the Biodesign Institute, Arizona State University, Tempe, Arizona 85287, United States
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
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198
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Yu L, Jing R, Liu F, Luo J, Li Y. DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm. MOLECULAR THERAPY-NUCLEIC ACIDS 2020; 22:862-870. [PMID: 33230481 PMCID: PMC7658571 DOI: 10.1016/j.omtn.2020.10.005] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/06/2020] [Indexed: 12/24/2022]
Abstract
Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs.
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Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China
- Corresponding author: Lezheng Yu, School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China.
| | - Runyu Jing
- College of Cybersecurity, Sichuan University, Chengdu 610065, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang 550018, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, Sichuan, China
- Corresponding author: Jiesi Luo, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, Sichuan, China.
| | - Yizhou Li
- College of Cybersecurity, Sichuan University, Chengdu 610065, China
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199
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Muschelli J. ROC and AUC with a Binary Predictor: a Potentially Misleading Metric. JOURNAL OF CLASSIFICATION 2020; 37:696-708. [PMID: 33250548 PMCID: PMC7695228 DOI: 10.1007/s00357-019-09345-1] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
In analysis of binary outcomes, the receiver operator characteristic (ROC) curve is heavily used to show the performance of a model or algorithm. The ROC curve is informative about the performance over a series of thresholds and can be summarized by the area under the curve (AUC), a single number. When a predictor is categorical, the ROC curve has one less than number of categories as potential thresholds; when the predictor is binary there is only one threshold. As the AUC may be used in decision-making processes on determining the best model, it important to discuss how it agrees with the intuition from the ROC curve. We discuss how the interpolation of the curve between thresholds with binary predictors can largely change the AUC. Overall, we show using a linear interpolation from the ROC curve with binary predictors corresponds to the estimated AUC, which is most commonly done in software, which we believe can lead to misleading results. We compare R, Python, Stata, and SAS software implementations. We recommend using reporting the interpolation used and discuss the merit of using the step function interpolator, also referred to as the "pessimistic" approach by Fawcett (2006).
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Affiliation(s)
- John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205
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200
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Li J, Pu Y, Tang J, Zou Q, Guo F. DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides. IEEE J Biomed Health Inform 2020; 24:3012-3019. [DOI: 10.1109/jbhi.2020.2977091] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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