1
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Yuan L, Wu S, Tian K, Wang S, Wu H, Qiao J. Nisin-relevant antimicrobial peptides: synthesis strategies and applications. Food Funct 2024. [PMID: 39246095 DOI: 10.1039/d3fo05619h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024]
Abstract
Small pentacyclic peptides, represented by nisin, have been successfully utilized as preservatives in the food industry and have evolved into a paradigm for understanding the genetic structure, expression, and control of genes created by lantibiotics. Due to the ever-increasing antibiotic resistance, nisin-relevant antimicrobial peptides have received much attention, which calls for a summarization of their synthesis, modification and applications. In this review, we first provided a timeline of select highlights in nisin biosynthesis and engineering. Then, we outlined the current developments in nisin synthesis. We also provided an overview of the engineering, screening, and production of nisin-relevant antimicrobial peptides based on enzyme alteration, substrate modification, and sequence mining. Furthermore, an updated summary of applications of nisin-relevant antimicrobial peptides has been developed for food applications. Finally, this study offers insights into emerging technologies, limitations and the future development of nisin-relevant antimicrobial peptides for pathogen inhibition, food preservatives, and improved health.
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Affiliation(s)
- Lin Yuan
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Tianjin Agricultural University, Tianjin 300072, China
| | - Shengbo Wu
- Zhejiang Research Institute of Tianjin University (Shaoxing), Shaoxing, 312300, China.
- School of Life Sciences, Tianjin University, Tianjin 300072, China
| | - Kairen Tian
- Zhejiang Research Institute of Tianjin University (Shaoxing), Shaoxing, 312300, China.
- School of Life Sciences, Tianjin University, Tianjin 300072, China
| | - Shengli Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Hao Wu
- Zhejiang Research Institute of Tianjin University (Shaoxing), Shaoxing, 312300, China.
- School of Life Sciences, Tianjin University, Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Research Institute of Tianjin University (Shaoxing), Shaoxing, 312300, China.
- Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
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2
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Periwal N, Arora P, Thakur A, Agrawal L, Goyal Y, Rathore AS, Anand HS, Kaur B, Sood V. Antiprotozoal peptide prediction using machine learning with effective feature selection techniques. Heliyon 2024; 10:e36163. [PMID: 39247292 PMCID: PMC11380031 DOI: 10.1016/j.heliyon.2024.e36163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/09/2024] [Accepted: 08/11/2024] [Indexed: 09/10/2024] Open
Abstract
Background Protozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens. Objective The primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets. Methods A comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models. Results XGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset. Conclusion The models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.
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Affiliation(s)
- Neha Periwal
- Department of Biochemistry, Jamia Hamdard, India
| | - Pooja Arora
- Department of Zoology, Hansraj College, University of Delhi, India
| | | | | | - Yash Goyal
- Department of Computer Science, Hansraj College, University of Delhi, India
| | - Anand S Rathore
- Department of Zoology, Hansraj College, University of Delhi, India
| | | | - Baljeet Kaur
- Department of Computer Science, Hansraj College, University of Delhi, India
| | - Vikas Sood
- Department of Biochemistry, Jamia Hamdard, India
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3
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Medina-Ortiz D, Contreras S, Fernández D, Soto-García N, Moya I, Cabas-Mora G, Olivera-Nappa Á. Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides. Int J Mol Sci 2024; 25:8851. [PMID: 39201537 DOI: 10.3390/ijms25168851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 08/05/2024] [Accepted: 08/08/2024] [Indexed: 09/02/2024] Open
Abstract
Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.
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Affiliation(s)
- David Medina-Ortiz
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Santiago 8370456, Chile
| | - Seba Contreras
- Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077 Göttingen, Germany
| | - Diego Fernández
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Nicole Soto-García
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Iván Moya
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
- Departamento de Ingeniería Química, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Gabriel Cabas-Mora
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas 6210005, Chile
| | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Santiago 8370456, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Universidad de Chile, Santiago 8370456, Chile
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4
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Li C, Sutherland D, Richter A, Coombe L, Yanai A, Warren RL, Kotkoff M, Hof F, Hoang LMN, Helbing CC, Birol I. De novo synthetic antimicrobial peptide design with a recurrent neural network. Protein Sci 2024; 33:e5088. [PMID: 38988311 PMCID: PMC11237553 DOI: 10.1002/pro.5088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/16/2024] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of host defense peptides, are emerging as promising candidates to address this need. The potential sequence space of amino acids is combinatorially vast, making it possible to extend the current arsenal of antimicrobial agents with a practically infinite number of new peptide-based candidates. However, mining naturally occurring AMPs, whether directly by wet lab screening methods or aided by bioinformatics prediction tools, has its theoretical limit regarding the number of samples or genomic/transcriptomic resources researchers have access to. Further, manually designing novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods are gaining interest as a high-throughput solution to the problem. Here, we introduce AMPd-Up, a recurrent neural network based tool for de novo AMP design, and demonstrate its utility over existing methods. Validation of candidates designed by AMPd-Up through antimicrobial susceptibility testing revealed that 40 of the 58 generated sequences possessed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. These results illustrate that AMPd-Up can be used to design novel synthetic AMPs with potent activities.
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Affiliation(s)
- Chenkai Li
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Bioinformatics Graduate ProgramUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Darcy Sutherland
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
- Department of Pathology and Laboratory MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Amelia Richter
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
| | - Lauren Coombe
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
| | - Anat Yanai
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
| | - René L. Warren
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
| | - Monica Kotkoff
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
| | - Fraser Hof
- Department of Chemistry and the Centre for Advanced Materials and Related TechnologyUniversity of VictoriaVictoriaBritish ColumbiaCanada
| | - Linda M. N. Hoang
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
- Department of Pathology and Laboratory MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Caren C. Helbing
- Department of Biochemistry and MicrobiologyUniversity of VictoriaVictoriaBritish ColumbiaCanada
| | - Inanc Birol
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
- Department of Pathology and Laboratory MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Medical GeneticsUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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5
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Zeng P, Wang H, Zhang P, Leung SSY. Unearthing naturally-occurring cyclic antibacterial peptides and their structural optimization strategies. Biotechnol Adv 2024; 73:108371. [PMID: 38704105 DOI: 10.1016/j.biotechadv.2024.108371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/08/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
Natural products with antibacterial activity are highly desired globally to combat against multidrug-resistant (MDR) bacteria. Antibacterial peptide (ABP), especially cyclic ABP (CABP), is one of the abundant classes. Most of them were isolated from microbes, demonstrating excellent bactericidal effects. With the improved proteolytic stability, CABPs are normally considered to have better druggability than linear peptides. However, most clinically-used CABP-based antibiotics, such as colistin, also face the challenges of drug resistance soon after they reached the market, urgently requiring the development of next-generation succedaneums. We present here a detail review on the novel naturally-occurring CABPs discovered in the past decade and some of them are under clinical trials, exhibiting anticipated application potential. According to their chemical structures, they were broadly classified into five groups, including (i) lactam/lactone-based CABPs, (ii) cyclic lipopeptides, (iii) glycopeptides, (iv) cyclic sulfur-rich peptides and (v) multiple-modified CABPs. Their chemical structures, antibacterial spectrums and proposed mechanisms are discussed. Moreover, engineered analogs of these novel CABPs are also summarized to preliminarily analyze their structure-activity relationship. This review aims to provide a global perspective on research and development of novel CABPs to highlight the effectiveness of derivatives design in identifying promising antibacterial agents. Further research efforts in this area are believed to play important roles in fighting against the multidrug-resistance crisis.
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Affiliation(s)
- Ping Zeng
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Honglan Wang
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Pengfei Zhang
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Sharon Shui Yee Leung
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong.
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6
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Yin K, Xu W, Ren S, Xu Q, Zhang S, Zhang R, Jiang M, Zhang Y, Xu D, Li R. Machine Learning Accelerates De Novo Design of Antimicrobial Peptides. Interdiscip Sci 2024; 16:392-403. [PMID: 38416364 DOI: 10.1007/s12539-024-00612-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/29/2024]
Abstract
Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10 μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.
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Affiliation(s)
- Kedong Yin
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
| | - Wen Xu
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
- Law College, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China.
| | - Shiming Ren
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
| | - Qingpeng Xu
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Shaojie Zhang
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
| | - Ruiling Zhang
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China
- School of Economics and Trade, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Mengwan Jiang
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Yuhong Zhang
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, People's Republic of China
| | - Degang Xu
- College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
| | - Ruifang Li
- Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
- College of Biological Engineering, Henan University of Technology, 100 Lianhua Street, Zhengzhou, 450001, Henan, People's Republic of China.
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7
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Goles M, Daza A, Cabas-Mora G, Sarmiento-Varón L, Sepúlveda-Yañez J, Anvari-Kazemabad H, Davari MD, Uribe-Paredes R, Olivera-Nappa Á, Navarrete MA, Medina-Ortiz D. Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides. Brief Bioinform 2024; 25:bbae275. [PMID: 38856172 PMCID: PMC11163380 DOI: 10.1093/bib/bbae275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/23/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024] Open
Abstract
With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.
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Affiliation(s)
- Montserrat Goles
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Anamaría Daza
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Gabriel Cabas-Mora
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Lindybeth Sarmiento-Varón
- Centro Asistencial de Docencia e Investigación, CADI, Universidad de Magallanes, Av. Los Flamencos 01364, 6210005, Punta Arenas, Chile
| | - Julieta Sepúlveda-Yañez
- Facultad de Ciencias de la Salud, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Hoda Anvari-Kazemabad
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Mehdi D Davari
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
| | - Roberto Uribe-Paredes
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Marcelo A Navarrete
- Centro Asistencial de Docencia e Investigación, CADI, Universidad de Magallanes, Av. Los Flamencos 01364, 6210005, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - David Medina-Ortiz
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
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8
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Zervou MA, Doutsi E, Pantazis Y, Tsakalides P. De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks. Int J Mol Sci 2024; 25:5506. [PMID: 38791544 PMCID: PMC11122239 DOI: 10.3390/ijms25105506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.
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Affiliation(s)
- Michaela Areti Zervou
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Effrosyni Doutsi
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Yannis Pantazis
- Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
| | - Panagiotis Tsakalides
- Department of Computer Science, University of Crete, 700 13 Heraklion, Greece
- Institute of Computer Science, Foundation for Research and Technology-Hellas, 700 13 Heraklion, Greece;
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9
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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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10
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Bui Thi Phuong H, Doan Ngan H, Le Huy B, Vu Dinh H, Luong Xuan H. The amphipathic design in helical antimicrobial peptides. ChemMedChem 2024; 19:e202300480. [PMID: 38408263 DOI: 10.1002/cmdc.202300480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/18/2023] [Indexed: 02/28/2024]
Abstract
Amphipathicity is a critical characteristic of helical antimicrobial peptides (AMPs). The hydrophilic region, primarily composed of cationic residues, plays a pivotal role in the initial binding to negatively charged components on bacterial membranes through electrostatic interactions. Subsequently, the hydrophobic region interacts with hydrophobic components, inducing membrane perturbation, ultimately leading to cell death, or inhibiting intracellular function. Due to the extensive diversity of natural and synthetic AMPs with regard to the design of amphipathicity, it is complicated to study the structure-activity relationships. Therefore, this work aims to categorize the common amphipathic design and investigate their impact on the biological properties of AMPs. Besides, the connection between current structural modification approaches and amphipathic styles was also discussed.
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Affiliation(s)
| | - Hoa Doan Ngan
- Faculty of Medical Technology, PHENIKAA University, Hanoi, 12116, Vietnam
| | - Binh Le Huy
- Center for High Technology Development, Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi, 11307, Vietnam
- School of Chemical Engineering -, Hanọi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, 11615, Vietnam
| | - Hoang Vu Dinh
- School of Chemical Engineering -, Hanọi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, 11615, Vietnam
| | - Huy Luong Xuan
- Faculty of Pharmacy, PHENIKAA University, Hanoi, 12116, Vietnam
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11
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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12
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Dong Q, Wang S, Miao Y, Luo H, Weng Z, Yu L. Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning. Sci Rep 2024; 14:4529. [PMID: 38402320 PMCID: PMC10894229 DOI: 10.1038/s41598-024-55205-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/21/2024] [Indexed: 02/26/2024] Open
Abstract
The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2-4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides.
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Affiliation(s)
- Qichang Dong
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Shaohua Wang
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Ying Miao
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Heng Luo
- Shanghai MetaNovas Biotech Co., Ltd, Shanghai, 200120, China
| | - Zuquan Weng
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Lun Yu
- Metanovas Biotech Inc., Foster City, 94404, USA.
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13
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Wang R, Wang T, Zhuo L, Wei J, Fu X, Zou Q, Yao X. Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization. Brief Bioinform 2024; 25:bbae078. [PMID: 38446739 PMCID: PMC10939340 DOI: 10.1093/bib/bbae078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 03/08/2024] Open
Abstract
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.
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Affiliation(s)
- Rui Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Tao Wang
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Jinhang Wei
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325000 Wenzhou, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012 Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 611730 Chengdu, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078 Macao, China
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14
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Chang L, Mondal A, Singh B, Martínez-Noa Y, Perez A. Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2024; 14:e1693. [PMID: 38680429 PMCID: PMC11052547 DOI: 10.1002/wcms.1693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 09/18/2023] [Indexed: 05/01/2024]
Abstract
Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.
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Affiliation(s)
- Liwei Chang
- Department of Chemistry, University of Florida, Gainesville, FL 32611
| | - Arup Mondal
- Department of Chemistry, University of Florida, Gainesville, FL 32611
| | - Bhumika Singh
- Department of Chemistry, University of Florida, Gainesville, FL 32611
| | | | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL 32611
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15
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Aguilera-Puga MDC, Cancelarich NL, Marani MM, de la Fuente-Nunez C, Plisson F. Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. Methods Mol Biol 2024; 2714:329-352. [PMID: 37676607 DOI: 10.1007/978-1-0716-3441-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico
| | - Natalia L Cancelarich
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Mariela M Marani
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Fabien Plisson
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico.
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico.
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16
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La Paglia L, Vazzana M, Mauro M, Urso A, Arizza V, Vizzini A. Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence. Mar Drugs 2023; 22:6. [PMID: 38276644 PMCID: PMC10817596 DOI: 10.3390/md22010006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.
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Affiliation(s)
- Laura La Paglia
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Mirella Vazzana
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Manuela Mauro
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Alfonso Urso
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Vincenzo Arizza
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Aiti Vizzini
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
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17
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Nath A. Physicochemical and sequence determinants of antiviral peptides. Biol Futur 2023; 74:489-506. [PMID: 37889451 DOI: 10.1007/s42977-023-00188-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 10/06/2023] [Indexed: 10/28/2023]
Abstract
Antiviral peptides (AVPs) open new possibilities as an effective antiviral therapeutic in the current scenario of evolving drug-resistant viruses. Knowledge about the sequence and structure activity relationship in AVPs is still largely unknown. AVPs and antimicrobial peptides (AMPs) share several common features but as they target different life forms (living organisms and viruses), exploring the differential sequence features may facilitate in designing specific AVPs. The current work developed accurate prediction models for discriminating (a) AVPs from AMPs, (b) Coronaviridae AVPs from other virus family specific AVPs and (c) highly active AVPs (HAA) from lowly active AVPs (LAA). Further explainable machine learning methods (using model agnostic global interpretable methods) are utilized for exploring and interpreting the physicochemical spaces of AVPs, Coronaviridae AVPs and highly active AVPs. To further understand the association of physicochemical space distribution with pIC50 values, regression models were developed and analyzed using accumulated local effects and interaction strength analysis. An independent sample t-test is used to filter out the significant compositional differences between the smaller length HAA and longer length HAA groups. AVPs prefer lower charge/length ratio and basic residues in comparison with AMPs. Coronaviridae family-specific AVPs have lower propensities for basic amino acids, charge and preference for aspartic acid. Further there is prevalence for basic residues in lowly active AVPs as compared to highly active AVPs. Sequence order effects captured in terms of average amino acid pair distances proved to be more constructive in deciphering the sequences of AVPs.
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Affiliation(s)
- Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, 492001, India.
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18
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Szymczak P, Szczurek E. Artificial intelligence-driven antimicrobial peptide discovery. Curr Opin Struct Biol 2023; 83:102733. [PMID: 37992451 DOI: 10.1016/j.sbi.2023.102733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/06/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023]
Abstract
Antimicrobial peptides (AMPs) emerge as promising agents against antimicrobial resistance, providing an alternative to conventional antibiotics. Artificial intelligence (AI) revolutionized AMP discovery through both discrimination and generation approaches. The discriminators aid in the identification of promising candidates by predicting key peptide properties such as activity and toxicity, while the generators learn the distribution of peptides and enable sampling novel AMP candidates, either de novo or as analogs of a prototype peptide. Moreover, the controlled generation of AMPs with desired properties is achieved by discriminator-guided filtering, positive-only learning, latent space sampling, as well as conditional and optimized generation. Here we review recent achievements in AI-driven AMP discovery, highlighting the most exciting directions.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland.
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19
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Gallardo-Becerra L, Cervantes-Echeverría M, Cornejo-Granados F, Vazquez-Morado LE, Ochoa-Leyva A. Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota. MICROBIAL ECOLOGY 2023; 87:8. [PMID: 38036921 PMCID: PMC10689560 DOI: 10.1007/s00248-023-02313-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023]
Abstract
Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.
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Affiliation(s)
- Luigui Gallardo-Becerra
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Melany Cervantes-Echeverría
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Fernanda Cornejo-Granados
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Luis E Vazquez-Morado
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico
| | - Adrian Ochoa-Leyva
- Departamento de Microbiologia Molecular, Instituto de Biotecnologia, Universidad Nacional Autonoma de Mexico (UNAM), Avenida Universidad 2001, C.P. 62210, Cuernavaca, Morelos, Mexico.
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20
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Ye Y, Shen Y, Wang J, Li D, Zhu Y, Zhao Z, Pan Y, Wang Y, Liu X, Wan J. SIGANEO: Similarity network with GAN enhancement for immunogenic neoepitope prediction. Comput Struct Biotechnol J 2023; 21:5538-5543. [PMID: 38034402 PMCID: PMC10681954 DOI: 10.1016/j.csbj.2023.10.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Target selection of the personalized cancer neoantigen vaccine, which is highly dependent on computational prediction algorithms, is crucial for its clinical efficacy. Due to the limited number of experimentally validated immunogenic neoepitopes as well as the complexity of neoantigens in eliciting T cell response, the accuracy of neoepitope immunogenicity prediction methods requires persistent efforts for improvement. We present a deep learning framework for neoepitope immunogenicity prediction - SIGANEO by integrating GAN-like network with similarity network to address issues of missing values and limited data concerning neoantigen prediction. This framework exhibits superior performance over competing machine-learning-based neoantigen prediction algorithms over an independent test dataset from TESLA consortium. Particularly for the clinical setting of neoantigen vaccine where only the top 10 and 20 predictions are selected for vaccine production, SIGANEO achieves significantly better accuracy for predicting experimentally validated neoepitopes. Our work demonstrates that deep learning techniques can greatly boost the accuracy of target identification for cancer neoantigen vaccine.
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Affiliation(s)
- Yilin Ye
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Yiming Shen
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Jian Wang
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Dong Li
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Yu Zhu
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Zhao Zhao
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Youdong Pan
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Yi Wang
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
| | - Xing Liu
- The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ji Wan
- Shenzhen Neocura Biotechnology Co. Ltd., Shenzhen 518055, China
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21
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Gasu EN, Mensah JK, Borquaye LS. Computer-aided design of proline-rich antimicrobial peptides based on the chemophysical properties of a peptide isolated from Olivancillaria hiatula. J Biomol Struct Dyn 2023; 41:8254-8275. [PMID: 36218088 DOI: 10.1080/07391102.2022.2131626] [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: 05/30/2022] [Accepted: 09/27/2022] [Indexed: 10/17/2022]
Abstract
The chemophysical properties of a peptide isolated from Olivancillaria hiatula were combined with computational tools to design new antimicrobial peptides (AMPs). The in silico peptide design utilized arbitrary sequence shuffling, AMP sequence prediction and alignments such that putative sequences mimicked those of proline-rich AMPs (PrAMPs) and were potentially active against bacteria. Molecular modelling and docking experiments were used to monitor peptide binding to some intracellular targets like bacteria ribosome, DnaK and LasR. Peptide candidates were tested in vitro for antibacterial and antivirulence activities. Chemophysical studies of peptide extract suggested hydrophobic, acidic and proline-rich peptide properties. The amino acid signature of the extract matched that of AMPs that inhibit intracellular targets. Two of the designed PrAMP peptides (OhPrP-3 and OhPrP-5) had high affinity for the ribosome and DnaK. OhPrP-1, 2 and 4 also had favorable interactions with the biomolecular targets investigated. Peptides had bactericidal activity at the minimum inhibitory concentration against Pseudomonas aeruginosa. The designed peptides docked strongly to LasR suggesting possible interference with quorum sensing, and this was corroborated by in vitro data where sub-inhibitory doses of all peptides reduced pyocyanin and pyoverdine expression. The designed peptides can be further studied for the development of new anti-infective agents.
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Affiliation(s)
- Edward Ntim Gasu
- Department of Chemistry, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Central Laboratory, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - John Kenneth Mensah
- Department of Chemistry, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Lawrence Sheringham Borquaye
- Department of Chemistry, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Central Laboratory, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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22
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Cesaro A, Bagheri M, Torres MDT, Wan F, de la Fuente-Nunez C. Deep learning tools to accelerate antibiotic discovery. Expert Opin Drug Discov 2023; 18:1245-1257. [PMID: 37794737 PMCID: PMC10790350 DOI: 10.1080/17460441.2023.2250721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
INTRODUCTION As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity. AREAS COVERED This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics. EXPERT OPINION Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.
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Affiliation(s)
- Angela Cesaro
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Mojtaba Bagheri
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marcelo D. T. Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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23
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Mousa WK, Ghemrawi R, Abu-Izneid T, Ramadan A, Al-Marzooq F. Discovery of Lactomodulin, a Unique Microbiome-Derived Peptide That Exhibits Dual Anti-Inflammatory and Antimicrobial Activity against Multidrug-Resistant Pathogens. Int J Mol Sci 2023; 24:ijms24086901. [PMID: 37108065 PMCID: PMC10138793 DOI: 10.3390/ijms24086901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
The human body is a superorganism that harbors trillions of microbes, most of which inhabit the gut. To colonize our bodies, these microbes have evolved strategies to regulate the immune system and maintain intestinal immune homeostasis by secreting chemical mediators. There is much interest in deciphering these chemicals and furthering their development as novel therapeutics. In this work, we present a combined experimental and computational approach to identifying functional immunomodulatory molecules from the gut microbiome. Based on this approach, we report the discovery of lactomodulin, a unique peptide from Lactobacillus rhamnosus that exhibits dual anti-inflammatory and antibiotic activities and minimal cytotoxicity in human cell lines. Lactomodulin reduces several secreted proinflammatory cytokines, including IL-8, IL-6, IL-1β, and TNF-α. As an antibiotic, lactomodulin is effective against a range of human pathogens, and is most potent against antibiotic-resistant strains such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). The multifunctional activity of lactomodulin affirms that the microbiome encodes evolved functional molecules with promising therapeutic potential.
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Affiliation(s)
- Walaa K Mousa
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
- College of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Rose Ghemrawi
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Tareq Abu-Izneid
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Azza Ramadan
- College of Pharmacy, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Farah Al-Marzooq
- Department of Medical Microbiology and Immunology, College of Medicine and Health Sciences, UAE University, Al Ain P.O. Box 15551, United Arab Emirates
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24
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Lin TT, Yang LY, Lin CY, Wang CT, Lai CW, Ko CF, Shih YH, Chen SH. Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains. Int J Mol Sci 2023; 24:ijms24076788. [PMID: 37047760 PMCID: PMC10095442 DOI: 10.3390/ijms24076788] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/24/2023] [Accepted: 04/02/2023] [Indexed: 04/09/2023] Open
Abstract
Because of the growing number of clinical antibiotic resistance cases in recent years, novel antimicrobial peptides (AMPs) may be ideal for next-generation antibiotics. This study trained a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based on known AMPs to generate novel AMP candidates. The quality of the GAN-designed peptides was evaluated in silico, and eight of them, named GAN-pep 1–8, were selected by an AMP Artificial Intelligence (AI) classifier and synthesized for further experiments. Disc diffusion testing and minimum inhibitory concentration (MIC) determinations were used to identify the antibacterial effects of the synthesized GAN-designed peptides. Seven of the eight synthesized GAN-designed peptides displayed antibacterial activity. Additionally, GAN-pep 3 and GAN-pep 8 presented a broad spectrum of antibacterial effects and were effective against antibiotic-resistant bacteria strains, such as methicillin-resistant Staphylococcus aureus and carbapenem-resistant Pseudomonas aeruginosa. GAN-pep 3, the most promising GAN-designed peptide candidate, had low MICs against all the tested bacteria. In brief, our approach shows an efficient way to discover AMPs effective against general and antibiotic-resistant bacteria strains. In addition, such a strategy also allows other novel functional peptides to be quickly designed, identified, and synthesized for validation on the wet bench.
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Affiliation(s)
- Tzu-Tang Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Li-Yen Yang
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chung-Yen Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ching-Tien Wang
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chia-Wen Lai
- Department of Agricultural Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Chi-Fong Ko
- Department of Agricultural Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Yang-Hsin Shih
- Department of Agricultural Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Shu-Hwa Chen
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110301, Taiwan
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25
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Szymczak P, Możejko M, Grzegorzek T, Jurczak R, Bauer M, Neubauer D, Sikora K, Michalski M, Sroka J, Setny P, Kamysz W, Szczurek E. Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. Nat Commun 2023; 14:1453. [PMID: 36922490 PMCID: PMC10017685 DOI: 10.1038/s41467-023-36994-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marcin Możejko
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Tomasz Grzegorzek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA, 95051, USA
| | - Radosław Jurczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marta Bauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Damian Neubauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Karol Sikora
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Michał Michalski
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Jacek Sroka
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Piotr Setny
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Wojciech Kamysz
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland.
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26
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Gao B, Huang Y, Peng C, Lin B, Liao Y, Bian C, Yang J, Shi Q. High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development. BIODESIGN RESEARCH 2022; 2022:9895270. [PMID: 37850131 PMCID: PMC10521759 DOI: 10.34133/2022/9895270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/23/2022] [Indexed: 10/19/2023] Open
Abstract
Cone snail venoms have been considered a valuable treasure for international scientists and businessmen, mainly due to their pharmacological applications in development of marine drugs for treatment of various human diseases. To date, around 800 Conus species are recorded, and each of them produces over 1,000 venom peptides (termed as conopeptides or conotoxins). This reflects the high diversity and complexity of cone snails, although most of their venoms are still uncharacterized. Advanced multiomics (such as genomics, transcriptomics, and proteomics) approaches have been recently developed to mine diverse Conus venom samples, with the main aim to predict and identify potentially interesting conopeptides in an efficient way. Some bioinformatics techniques have been applied to predict and design novel conopeptide sequences, related targets, and their binding modes. This review provides an overview of current knowledge on the high diversity of conopeptides and multiomics advances in high-throughput prediction of novel conopeptide sequences, as well as molecular modeling and design of potential drugs based on the predicted or validated interactions between these toxins and their molecular targets.
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Affiliation(s)
- Bingmiao Gao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Pharmacy, Hainan Medical University, Haikou, Hainan 570102, China
| | - Yu Huang
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
| | - Chao Peng
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
- BGI-Marine Research Institute for Biomedical Technology, Shenzhen Huahong Marine Biomedicine Co. Ltd., Shenzhen, Guangdong 518119, China
| | - Bo Lin
- Hainan Provincial Key Laboratory of Carcinogenesis and Intervention, Hainan Medical University, Haikou, Hainan 570102, China
| | - Yanling Liao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Pharmacy, Hainan Medical University, Haikou, Hainan 570102, China
| | - Chao Bian
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
| | - Jiaan Yang
- Research and Development Department, Micro Pharmtech Ltd., Wuhan, Hubei 430075, China
| | - Qiong Shi
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
- BGI-Marine Research Institute for Biomedical Technology, Shenzhen Huahong Marine Biomedicine Co. Ltd., Shenzhen, Guangdong 518119, China
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27
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Yan J, Cai J, Zhang B, Wang Y, Wong DF, Siu SWI. Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning. Antibiotics (Basel) 2022; 11:1451. [PMID: 36290108 PMCID: PMC9598685 DOI: 10.3390/antibiotics11101451] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.
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Affiliation(s)
- Jielu Yan
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Jianxiu Cai
- Faculty of Applied Sciences, Macao Polytechnic University, Macau, China
- Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China
| | - Bob Zhang
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Yapeng Wang
- Faculty of Applied Sciences, Macao Polytechnic University, Macau, China
| | - Derek F. Wong
- NLP2CT Lab, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Shirley W. I. Siu
- Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
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28
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Yang L, Yang G, Bing Z, Tian Y, Huang L, Niu Y, Yang L. Accelerating the discovery of anticancer peptides targeting lung and breast cancers with the Wasserstein autoencoder model and PSO algorithm. Brief Bioinform 2022; 23:6658854. [PMID: 35945135 DOI: 10.1093/bib/bbac320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/14/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
In the development of targeted drugs, anticancer peptides (ACPs) have attracted great attention because of their high selectivity, low toxicity and minimal non-specificity. In this work, we report a framework of ACPs generation, which combines Wasserstein autoencoder (WAE) generative model and Particle Swarm Optimization (PSO) forward search algorithm guided by attribute predictive model to generate ACPs with desired properties. It is well known that generative models based on Variational AutoEncoder (VAE) and Generative Adversarial Networks (GAN) are difficult to be used for de novo design due to the problems of posterior collapse and difficult convergence of training. Our WAE-based generative model trains more successfully (lower perplexity and reconstruction loss) than both VAE and GAN-based generative models, and the semantic connections in the latent space of WAE accelerate the process of forward controlled generation of PSO, while VAE fails to capture this feature. Finally, we validated our pipeline on breast cancer targets (HIF-1) and lung cancer targets (VEGR, ErbB2), respectively. By peptide-protein docking, we found candidate compounds with the same binding sites as the peptides carried in the crystal structure but with higher binding affinity and novel structures, which may be potent antagonists that interfere with these target-mediated signaling.
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Affiliation(s)
- Lijuan Yang
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,School of Physics and Technology, Lanzhou University, Lanzhou 730000, China.,School of Physics, University of Chinese Academy of Science, Beijing 100049, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
| | - Guanghui Yang
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
| | - Zhitong Bing
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
| | - Yuan Tian
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Liang Huang
- School of Physics and Technology, Lanzhou University, Lanzhou 730000, China
| | - Yuzhen Niu
- Shandong Provincial Research Center for Bioinformatic Engineering and Technique, School of Life Sciences, Shandong University of Technology, Zibo 255000, China
| | - Lei Yang
- Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.,Advanced Energy Science and Technology Guangdong Laboratory, Huizhou 516000, China
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29
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Agüero-Chapin G, Galpert-Cañizares D, Domínguez-Pérez D, Marrero-Ponce Y, Pérez-Machado G, Teijeira M, Antunes A. Emerging Computational Approaches for Antimicrobial Peptide Discovery. Antibiotics (Basel) 2022; 11:antibiotics11070936. [PMID: 35884190 PMCID: PMC9311958 DOI: 10.3390/antibiotics11070936] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 02/05/2023] Open
Abstract
In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
| | - Deborah Galpert-Cañizares
- Departamento de Ciencia de la Computación, Universidad Central Marta Abreu de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Dany Domínguez-Pérez
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Proquinorte, Unipessoal, Lda, Avenida 5 de Outubro, 124, 7º Piso, Avenidas Novas, 1050-061 Lisboa, Portugal
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador;
| | - Gisselle Pérez-Machado
- EpiDisease S.L—Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Marta Teijeira
- Departamento de Química Orgánica, Facultade de Química, Universidade de Vigo, 36310 Vigo, Spain;
- Instituto de Investigación Sanitaria Galicia Sur, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
| | - Agostinho Antunes
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
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30
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Otović E, Njirjak M, Kalafatovic D, Mauša G. Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides. J Chem Inf Model 2022; 62:2961-2972. [PMID: 35704881 DOI: 10.1021/acs.jcim.2c00526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme. While the peptide physicochemical and compositional representations fail to distinguish sequence permutations, the amino acid arrangement within the sequence lacks the important information contained in physicochemical, conformational, topological, and geometrical properties. In this paper, we propose a solution to the identified information gap by implementing a hybrid scheme that complements the best traits from both approaches with the aim of predicting antimicrobial and antiviral activities based on experimental data from DRAMP 2.0, AVPdb, and Uniprot data repositories. Using the Friedman test of statistical significance, we compared our hybrid, sequential properties approach to peptide properties, one-hot vector encoding, and word embedding schemes in the 10-fold cross-validation setting, with respect to the F1 score, Matthews correlation coefficient, geometric mean, recall, and precision evaluation metrics. Moreover, the sequence modeling neural network was employed to gain insight into the synergic effect of both properties- and amino acid order-based predictions. The results suggest that sequential properties significantly (P < 0.01) surpasses the aforementioned state-of-the-art representation schemes. This makes it a strong candidate for increasing the predictive power of screening methods based on machine learning, applicable to any category of peptides.
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Affiliation(s)
- Erik Otović
- University of Rijeka, Faculty of Engineering, 51000 Rijeka, Croatia
| | - Marko Njirjak
- University of Rijeka, Faculty of Engineering, 51000 Rijeka, Croatia
| | - Daniela Kalafatovic
- University of Rijeka, Department of Biotechnology, 51000 Rijeka, Croatia.,University of Rijeka, Center for Artificial Intelligence and Cybersecurity, 51000 Rijeka, Croatia
| | - Goran Mauša
- University of Rijeka, Faculty of Engineering, 51000 Rijeka, Croatia.,University of Rijeka, Center for Artificial Intelligence and Cybersecurity, 51000 Rijeka, Croatia
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31
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Wan F, Kontogiorgos-Heintz D, de la Fuente-Nunez C. Deep generative models for peptide design. DIGITAL DISCOVERY 2022; 1:195-208. [PMID: 35769205 PMCID: PMC9189861 DOI: 10.1039/d1dd00024a] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/19/2022] [Indexed: 12/13/2022]
Abstract
Computers can already be programmed for superhuman pattern recognition of images and text. For machines to discover novel molecules, they must first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize functions of interest. Machines need to be able to understand, read, write, and eventually create new molecules. Today, this creative process relies on deep generative models, which have gained popularity since powerful deep neural networks were introduced to generative model frameworks. In recent years, they have demonstrated excellent ability to model complex distribution of real-word data (e.g., images, audio, text, molecules, and biological sequences). Deep generative models can generate data beyond those provided in training samples, thus yielding an efficient and rapid tool for exploring the massive search space of high-dimensional data such as DNA/protein sequences and facilitating the design of biomolecules with desired functions. Here, we review the emerging field of deep generative models applied to peptide science. In particular, we discuss several popular deep generative model frameworks as well as their applications to generate peptides with various kinds of properties (e.g., antimicrobial, anticancer, cell penetration, etc). We conclude our review with a discussion of current limitations and future perspectives in this emerging field.
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Affiliation(s)
- Fangping Wan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Penn Institute for Computational Science, University of Pennsylvania Philadelphia Pennsylvania USA
| | - Daphne Kontogiorgos-Heintz
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Penn Institute for Computational Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania Philadelphia Pennsylvania USA
- Penn Institute for Computational Science, University of Pennsylvania Philadelphia Pennsylvania USA
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32
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Remington JM, Ferrell JB, Schneebeli ST, Li J. Concerted Rolling and Penetration of Peptides during Membrane Binding. J Chem Theory Comput 2022; 18:3921-3929. [PMID: 35507824 DOI: 10.1021/acs.jctc.2c00014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Peptide binding to membranes is common and fundamental in biochemistry and biophysics and critical for applications ranging from drug delivery to the treatment of bacterial infections. However, it is largely unclear, from a theoretical point of view, what peptides of different sequences and structures share in the membrane-binding and insertion process. In this work, we analyze three prototypical membrane-binding peptides (α-helical magainin, PGLa, and β-hairpin tachyplesin) during membrane binding, using molecular details provided by Markov state modeling and microsecond-long molecular dynamics simulations. By leveraging both geometric and data-driven collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions, we reveal how the slowest kinetic process of membrane binding is the dynamic rolling of the peptide from an attached to a fully bound state. These results not only add fundamental knowledge of the theory of how peptides bind to biological membranes but also open new avenues to study general peptides in more complex environments for further applications.
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Affiliation(s)
- Jacob M Remington
- Department of Chemistry, The University of Vermont, Burlington, Vermont 05405, United States
| | - Jonathon B Ferrell
- Department of Chemistry, The University of Vermont, Burlington, Vermont 05405, United States
| | - Severin T Schneebeli
- Department of Chemistry, The University of Vermont, Burlington, Vermont 05405, United States
| | - Jianing Li
- Department of Chemistry, The University of Vermont, Burlington, Vermont 05405, United States
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Jukič M, Bren U. Machine Learning in Antibacterial Drug Design. Front Pharmacol 2022; 13:864412. [PMID: 35592425 PMCID: PMC9110924 DOI: 10.3389/fphar.2022.864412] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/28/2022] [Indexed: 12/17/2022] Open
Abstract
Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.
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Affiliation(s)
- Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
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Chen CH, Bepler T, Pepper K, Fu D, Lu TK. Synthetic molecular evolution of antimicrobial peptides. Curr Opin Biotechnol 2022; 75:102718. [PMID: 35395425 DOI: 10.1016/j.copbio.2022.102718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/14/2022] [Accepted: 03/01/2022] [Indexed: 01/18/2023]
Abstract
As we learn more about how peptide structure and activity are related, we anticipate that antimicrobial peptides will be engineered to have strong potency and distinct functions and that synthetic peptides will have new biomedical applications, such as treatments for emerging infectious diseases. As a result of the enormous number of possible amino acid sequences and the low-throughput nature of antimicrobial peptide assays, computational tools for peptide design and optimization are needed for direct experimentation toward obtaining functional sequences. Recent developments in computational tools have improved peptide design, saving labor, reagents, costs, and time. At the same time, improvements in peptide synthesis and experimental platforms continue to reduce the cost and increase the throughput of peptide-drug screening. In this review, we discuss the current methods of peptide design and engineering, including in silico methods and peptide synthesis and screening, and highlight areas of potential improvement.
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Affiliation(s)
- Charles H Chen
- Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Tristan Bepler
- Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Simons Machine Learning Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Karen Pepper
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Debbie Fu
- Department of Biology, Tufts University, Medford, MA 02155, USA
| | - Timothy K Lu
- Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA; Senti Biosciences, South San Francisco, CA 94080, USA.
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35
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Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat Biotechnol 2022; 40:921-931. [PMID: 35241840 DOI: 10.1038/s41587-022-01226-0] [Citation(s) in RCA: 135] [Impact Index Per Article: 67.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 01/19/2022] [Indexed: 02/07/2023]
Abstract
The human gut microbiome encodes a large variety of antimicrobial peptides (AMPs), but the short lengths of AMPs pose a challenge for computational prediction. Here we combined multiple natural language processing neural network models, including LSTM, Attention and BERT, to form a unified pipeline for candidate AMP identification from human gut microbiome data. Of 2,349 sequences identified as candidate AMPs, 216 were chemically synthesized, with 181 showing antimicrobial activity (a positive rate of >83%). Most of these peptides have less than 40% sequence homology to AMPs in the training set. Further characterization of the 11 most potent AMPs showed high efficacy against antibiotic-resistant, Gram-negative pathogens and demonstrated significant efficacy in lowering bacterial load by more than tenfold against a mouse model of bacterial lung infection. Our study showcases the potential of machine learning approaches for mining functional peptides from metagenome data and accelerating the discovery of promising AMP candidate molecules for in-depth investigations.
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