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Olawade DB, Teke J, Fapohunda O, Weerasinghe K, Usman SO, Ige AO, Clement David-Olawade A. Leveraging artificial intelligence in vaccine development: A narrative review. J Microbiol Methods 2024; 224:106998. [PMID: 39019262 DOI: 10.1016/j.mimet.2024.106998] [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: 06/10/2024] [Revised: 07/12/2024] [Accepted: 07/12/2024] [Indexed: 07/19/2024]
Abstract
Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.
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Affiliation(s)
- David B Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom.
| | - Jennifer Teke
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom
| | | | - Kusal Weerasinghe
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom
| | - Sunday O Usman
- Department of Systems and Industrial Engineering, University of Arizona, USA
| | - Abimbola O Ige
- Department of Chemistry, Faculty of Science, University of Ibadan, Ibadan, Nigeria
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Rahman S, Chiou CC, Ahmad S, Islam ZU, Tanaka T, Alouffi A, Chen CC, Almutairi MM, Ali A. Subtractive Proteomics and Reverse-Vaccinology Approaches for Novel Drug Target Identification and Chimeric Vaccine Development against Bartonella henselae Strain Houston-1. Bioengineering (Basel) 2024; 11:505. [PMID: 38790371 PMCID: PMC11118080 DOI: 10.3390/bioengineering11050505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/03/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Bartonella henselae is a Gram-negative bacterium causing a variety of clinical symptoms, ranging from cat-scratch disease to severe systemic infections, and it is primarily transmitted by infected fleas. Its status as an emerging zoonotic pathogen and its capacity to persist within host erythrocytes and endothelial cells emphasize its clinical significance. Despite progress in understanding its pathogenesis, limited knowledge exists about the virulence factors and regulatory mechanisms specific to the B. henselae strain Houston-1. Exploring these aspects is crucial for targeted therapeutic strategies against this versatile pathogen. Using reverse-vaccinology-based subtractive proteomics, this research aimed to identify the most antigenic proteins for formulating a multi-epitope vaccine against the B. henselae strain Houston-1. One crucial virulent and antigenic protein, the PAS domain-containing sensor histidine kinase protein, was identified. Subsequently, the identification of B-cell and T-cell epitopes for the specified protein was carried out and the evaluated epitopes were checked for their antigenicity, allergenicity, solubility, MHC binding capability, and toxicity. The filtered epitopes were merged using linkers and an adjuvant to create a multi-epitope vaccine construct. The structure was then refined, with 92.3% of amino acids falling within the allowed regions. Docking of the human receptor (TLR4) with the vaccine construct was performed and demonstrated a binding energy of -1047.2 Kcal/mol with more interactions. Molecular dynamic simulations confirmed the stability of this docked complex, emphasizing the conformation and interactions between the molecules. Further experimental validation is necessary to evaluate its effectiveness against B. henselae.
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Affiliation(s)
- Sudais Rahman
- Department of Zoology, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan;
| | - Chien-Chun Chiou
- Department of Dermatology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan;
| | - Shabir Ahmad
- Institute of Chemistry and Center for Computing in Engineering and Sciences, University of Campinas (UNICAMP), Campinas 13084-862, Brazil;
| | - Zia Ul Islam
- Department of Biotechnology, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan
| | - Tetsuya Tanaka
- Laboratory of Infectious Diseases, Joint Faculty of Veterinary Medicine, Kagoshima University, Kagoshima 890-0065, Japan
| | - Abdulaziz Alouffi
- King Abdulaziz City for Science and Technology, Riyadh 12354, Saudi Arabia
| | - Chien-Chin Chen
- Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi 600, Taiwan
- Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan
- Ph.D. Program in Translational Medicine, Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung 402, Taiwan
- Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan 701, Taiwan
| | - Mashal M. Almutairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Abid Ali
- Department of Zoology, Abdul Wali Khan University, Mardan 23200, Khyber Pakhtunkhwa, Pakistan;
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Batra U, Nathany S, Nath SK, Jose JT, Sharma T, P P, Pasricha S, Sharma M, Arambam N, Khanna V, Bansal A, Mehta A, Rawal K. AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data. THE LANCET REGIONAL HEALTH. SOUTHEAST ASIA 2024; 24:100352. [PMID: 38756151 PMCID: PMC11096686 DOI: 10.1016/j.lansea.2024.100352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 12/11/2023] [Accepted: 01/04/2024] [Indexed: 05/18/2024]
Abstract
Background The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy. Methods The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC). Findings AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910. Interpretation The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner. Funding This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.
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Affiliation(s)
- Ullas Batra
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | | | | | - Joslia T. Jose
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Trapti Sharma
- Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India
| | - Preeti P
- Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India
| | - Sunil Pasricha
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Mansi Sharma
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Nevidita Arambam
- Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India
| | - Vrinda Khanna
- Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India
| | - Abhishek Bansal
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Anurag Mehta
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Kamal Rawal
- Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India
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Zhang Y, Huffman A, Johnson J, He Y. Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569096. [PMID: 38076796 PMCID: PMC10705428 DOI: 10.1101/2023.11.29.569096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Reverse vaccinology (RV) provides a systematic approach to identifying potential vaccine candidates based on protein sequences. The integration of machine learning (ML) into this process has greatly enhanced our ability to predict viable vaccine candidates from these sequences. We have previously developed a Vaxign-ML program based on the eXtreme Gradient Boosting (XGBoost). In this study, we further extend our work to develop a Vaxign-DL program based on deep learning techniques. Deep neural networks assemble non-linear models and learn multilevel abstraction of data using hierarchically structured layers, offering a data-driven approach in computational design models. Vaxign-DL uses a three-layer fully connected neural network model. Using the same bacterial vaccine candidate training data as used in Vaxign-ML development, Vaxign-DL was able to achieve an Area Under the Receiver Operating Characteristic of 0.94, specificity of 0.99, sensitivity of 0.74, and accuracy of 0.96. Using the Leave-One-Pathogen-Out Validation (LOPOV) method, Vaxign-DL was able to predict vaccine candidates for 10 pathogens. Our benchmark study shows that Vaxign-DL achieved comparable results with Vaxign-ML in most cases, and our method outperforms Vaxi-DL in the accurate prediction of bacterial protective antigens.
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Affiliation(s)
- Yuhan Zhang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Justin Johnson
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Unit of Laboratory Animal Medicine and Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
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5
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Clímaco MDC, de Figueiredo LA, Lucas RC, Pinheiro GRG, Dias Magalhães LM, Oliveira ALGD, Almeida RM, Barbosa FS, Castanheira Bartholomeu D, Bueno LL, Mendes TA, Zhan B, Jones KM, Hotez P, Bottazzi ME, Oliveira FMS, Fujiwara RT. Development of chimeric protein as a multivalent vaccine for human Kinetoplastid infections: Chagas disease and leishmaniasis. Vaccine 2023; 41:5400-5411. [PMID: 37479612 DOI: 10.1016/j.vaccine.2023.07.020] [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: 09/27/2022] [Revised: 06/23/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023]
Abstract
Leishmania spp. and Trypanosoma cruzi are parasitic kinetoplastids of great medical and epidemiological importance since they are responsible for thousands of deaths and disability-adjusted life-years annually, especially in low- and middle-income countries. Despite efforts to minimize their impact, current prevention measures have failed to fully control their spread. There are still no vaccines available. Taking into account the genetic similarity within the Class Kinetoplastida, we selected CD8+ T cell epitopes preserved among Leishmania spp. and T. cruzi to construct a multivalent and broad-spectrum chimeric polyprotein vaccine. In addition to inducing specific IgG production, immunization with the vaccine was able to significantly reduce parasite burden in the colon, liver and skin lesions from T. cruzi, L. infantum and L. mexicana challenged mice, respectively. These findings were supported by histopathological analysis, which revealed decreased inflammation in the colon, a reduced number of degenerated hepatocytes and an increased proliferation of connective tissue in the skin lesions of the corresponding T. cruzi, L. infantum and L. mexicana vaccinated and challenged mice. Collectively, our results support the protective effect of a polyprotein vaccine approach and further studies will elucidate the immune profile associated with this protection. Noteworthy, our results act as conceptual proof that a single multi-kinetoplastida vaccine can be used effectively to control different infectious etiologies, which in turn can have a profound impact on the development of a new generation of vaccines.
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Affiliation(s)
- Marianna de Carvalho Clímaco
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Luiza Almeida de Figueiredo
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Rayane Cristina Lucas
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Luísa Mourão Dias Magalhães
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ana Laura Grossi de Oliveira
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Raquel Martins Almeida
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | | | - Lilian Lacerda Bueno
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Tiago Antonio Mendes
- Department of Biochemistry and Molecular Biology, Institute of Biotechnology Applied to Agropecuaria, Universidade Federal de Viçosa, Minas Gerais, Brazil
| | - Bin Zhan
- National School of Tropical Medicine, Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA; Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Kathryn Marie Jones
- National School of Tropical Medicine, Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA; Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Peter Hotez
- National School of Tropical Medicine, Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA; Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Maria Elena Bottazzi
- National School of Tropical Medicine, Departments of Pediatrics and Molecular Virology & Microbiology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA; Texas Children's Hospital Center for Vaccine Development, Baylor College of Medicine, Houston, TX, USA
| | - Fabrício Marcus Silva Oliveira
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ricardo Toshio Fujiwara
- Department of Parasitology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
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Nath SK, Pankajakshan P, Sharma T, Kumari P, Shinde S, Garg N, Mathur K, Arambam N, Harjani D, Raj M, Kwatra G, Venkatesh S, Choudhoury A, Bano S, Tayal P, Sharan M, Arora R, Strych U, Hotez PJ, Bottazzi ME, Rawal K. A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets. Vaccines (Basel) 2023; 11:vaccines11020267. [PMID: 36851145 PMCID: PMC9963959 DOI: 10.3390/vaccines11020267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/28/2023] Open
Abstract
Chagas disease (CD) is endemic in large parts of Central and South America, as well as in Texas and the southern regions of the United States. Successful parasites, such as the causative agent of CD, Trypanosoma cruzi have adapted to specific hosts during their phylogenesis. In this work, we have assembled an interactive network of the complex relations that occur between molecules within T. cruzi. An expert curation strategy was combined with a text-mining approach to screen 10,234 full-length research articles and over 200,000 abstracts relevant to T. cruzi. We obtained a scale-free network consisting of 1055 nodes and 874 edges, and composed of 838 proteins, 43 genes, 20 complexes, 9 RNAs, 36 simple molecules, 81 phenotypes, and 37 known pharmaceuticals. Further, we deployed an automated docking pipeline to conduct large-scale docking studies involving several thousand drugs and potential targets to identify network-based binding propensities. These experiments have revealed that the existing FDA-approved drugs benznidazole (Bz) and nifurtimox (Nf) show comparatively high binding energies to the T. cruzi network proteins (e.g., PIF1 helicase-like protein, trans-sialidase), when compared with control datasets consisting of proteins from other pathogens. We envisage this work to be of value to those interested in finding new vaccines for CD, as well as drugs against the T. cruzi parasite.
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Affiliation(s)
- Swarsat Kaushik Nath
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Preeti Pankajakshan
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Trapti Sharma
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Priya Kumari
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Sweety Shinde
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Nikita Garg
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Kartavya Mathur
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Nevidita Arambam
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Divyank Harjani
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Manpriya Raj
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Garwit Kwatra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Sayantan Venkatesh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Alakto Choudhoury
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Saima Bano
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Prashansa Tayal
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Mahek Sharan
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Ruchika Arora
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
| | - Ulrich Strych
- Texas Children’s Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
- National School of Tropical Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Peter J. Hotez
- Texas Children’s Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
- National School of Tropical Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Biology, Baylor University, Waco, TX 76798, USA
| | - Maria Elena Bottazzi
- Texas Children’s Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
- National School of Tropical Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Biology, Baylor University, Waco, TX 76798, USA
| | - Kamal Rawal
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida 201303, Uttar Pradesh, India
- Correspondence:
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7
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Preeti P, Nath SK, Arambam N, Sharma T, Choudhury PR, Choudhury A, Khanna V, Strych U, Hotez PJ, Bottazzi ME, Rawal K. Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development. Methods Mol Biol 2023; 2673:305-316. [PMID: 37258923 DOI: 10.1007/978-1-0716-3239-0_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates. In this chapter, we describe a deep learning-based technique which utilizes 18 biological and 9154 physicochemical properties of proteins for finding potential vaccine candidates. Using this technique, a new web-based system, named Vaxi-DL, was developed which helped in finding new vaccine candidates from bacteria, protozoa, viruses, and fungi. Vaxi-DL is available at: https://vac.kamalrawal.in/vaxidl/ .
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Affiliation(s)
- P Preeti
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Swarsat Kaushik Nath
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Nevidita Arambam
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Trapti Sharma
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Priyanka Ray Choudhury
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Alakto Choudhury
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Vrinda Khanna
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Ulrich Strych
- Department of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital Center for Vaccine Development, Houston, TX, USA
| | - Peter J Hotez
- Department of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital Center for Vaccine Development, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
| | - Maria Elena Bottazzi
- Department of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital Center for Vaccine Development, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
| | - Kamal Rawal
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
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8
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Osamor VC, Ikeakanam E, Bishung JU, Abiodun TN, Ekpo RH. COVID-19 Vaccines: Computational tools and Development. INFORMATICS IN MEDICINE UNLOCKED 2023; 37:101164. [PMID: 36644198 PMCID: PMC9830932 DOI: 10.1016/j.imu.2023.101164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/12/2023] Open
Abstract
The 2019 coronavirus outbreak, also known as COVID-19, poses a serious threat to global health and has already had widespread, devastating effects around the world. Scientists have been working tirelessly to develop vaccines to stop the virus from spreading as much as possible, as its cure has not yet been found. As of December 2022, 651,918,402 cases and 6,656,601 deaths had been reported. Globally, over 13 billion doses of vaccine have been administered, representing 64.45% of the world's population that has received the vaccine. To expedite the vaccine development process, computational tools have been utilized. This paper aims to analyze some computational tools that aid vaccine development by presenting positive evidence for proving the efficacy of these vaccines to suppress the spread of the virus and for the use of computational tools in the development of vaccines for emerging diseases.
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Affiliation(s)
- Victor Chukwudi Osamor
- Department of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE), CUCRID Building, Covenant University, Canaanland, Ota, Ogun State, Nigeria
| | - Excellent Ikeakanam
- Department of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria
| | - Janet U Bishung
- Department of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria
| | - Theresa N Abiodun
- Department of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria
| | - Raphael Henshaw Ekpo
- Department of Computer and Information Sciences, Covenant University, Canaanland, Ota, Ogun State, Nigeria
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9
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Abbasi BA, Saraf D, Sharma T, Sinha R, Singh S, Sood S, Gupta P, Gupta A, Mishra K, Kumari P, Rawal K. Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. PeerJ 2022; 10:e13380. [PMID: 35611169 PMCID: PMC9124463 DOI: 10.7717/peerj.13380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/13/2022] [Indexed: 01/13/2023] Open
Abstract
An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.
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