1
|
Duraisamy N, Khan MY, Shah AU, Elalaoui RN, Cherkaoui M, Hemida MG. Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines. Front Vet Sci 2024; 11:1468890. [PMID: 39415947 PMCID: PMC11479863 DOI: 10.3389/fvets.2024.1468890] [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: 07/22/2024] [Accepted: 09/09/2024] [Indexed: 10/19/2024] Open
Abstract
Introduction BCoV is one of the significant causes of enteritis in young calves; it may also be responsible for many respiratory outbreaks in young calves. BCoV participates in the development of bovine respiratory disease complex in association with other bacterial pathogens. Our study aimed (1) to map the immunogenic epitopes (B and T cells) within the major BCoV structural proteins. These epitopes are believed to induce a robust immune response through the interaction with major histocompatibility complex (MHC class II) molecules (2) to design some novel BCoV multiepitope-based vaccines. Materials and Methods The goal is achieved through several integrated in silico prediction computational tools to map these epitopes within the major BCoV structural proteins. The final vaccine was constructed in conjugation with the Choleratoxin B toxin as an adjuvant. The tertiary structure of each vaccine construct was modeled through the AlphaFold2 tools. The constructed vaccine was linked to some immunostimulants such as Toll-like receptors (TLR2 and TLR4). We also predicted the affinity binding of these vaccines with this targeted protein using molecular docking. The stability and purity of each vaccine construct were assessed using the Ramachandran plot and the Z-score values. We created the in silico cloning vaccine constructs using various expression vectors through vector builder and Snap gene. Results and discussion The average range of major BCoV structural proteins was detected within the range of 0.4 to 0.5, which confirmed their antigen and allergic properties. The binding energy values were detected between -7.9 and -9.4 eV and also confirmed their best interaction between our vaccine construct and Toll-like receptors. Our in silico cloning method expedited the creation of vaccine constructs and established a strong basis for upcoming clinical trials and experimental validations. Conclusion Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high-water solubility. Further studies are highly encouraged to validate the efficacy of these novel BCoV vaccines in the natural host.
Collapse
Affiliation(s)
- Nithyadevi Duraisamy
- College of Science, School of Engineering, Department of Digital Engineering, Computer Science, and Artificial Intelligence, Long Island University, Brooklyn, NY, United States
| | - Mohd Yasir Khan
- College of Science, School of Engineering, Department of Digital Engineering, Computer Science, and Artificial Intelligence, Long Island University, Brooklyn, NY, United States
| | - Abid Ullah Shah
- Department of Veterinary Biomedical Sciences, College of Veterinary Medicine, Long Island University, Brookville, NY, United States
| | - Reda Nacif Elalaoui
- College of Science, School of Engineering, Department of Digital Engineering, Computer Science, and Artificial Intelligence, Long Island University, Brooklyn, NY, United States
| | - Mohammed Cherkaoui
- College of Science, School of Engineering, Department of Digital Engineering, Computer Science, and Artificial Intelligence, Long Island University, Brooklyn, NY, United States
| | - Maged Gomaa Hemida
- Department of Veterinary Biomedical Sciences, College of Veterinary Medicine, Long Island University, Brookville, NY, United States
| |
Collapse
|
2
|
Rocha LGDN, Guimarães PAS, Carvalho MGR, Ruiz JC. Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies. Vaccines (Basel) 2024; 12:836. [PMID: 39203962 PMCID: PMC11360805 DOI: 10.3390/vaccines12080836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 09/03/2024] Open
Abstract
Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.
Collapse
Affiliation(s)
- Luiz Gustavo do Nascimento Rocha
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Paul Anderson Souza Guimarães
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Maria Gabriela Reis Carvalho
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Jeronimo Conceição Ruiz
- Biologia Computacional e Sistemas (BCS), Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz, Rio de Janeiro 21040-900, Brazil; (L.G.d.N.R.); (P.A.S.G.)
- Grupo Informática de Biossistemas e Genômica, Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| |
Collapse
|
3
|
Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
Collapse
Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| |
Collapse
|
4
|
Zakariya F, Salem FK, Alamrain AA, Sanker V, Abdelazeem ZG, Hosameldin M, Tan JK, Howard R, Huang H, Awuah WA. Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence. Eur J Med Res 2024; 29:25. [PMID: 38183141 PMCID: PMC10768232 DOI: 10.1186/s40001-023-01625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/25/2023] [Indexed: 01/07/2024] Open
Abstract
Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.
Collapse
Affiliation(s)
- Farida Zakariya
- Faculty of Pharmaceutical Sciences, Ahmadu Bello University, Zaria, Nigeria
- Division of Experimental Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Fatma K Salem
- Faculty of Veterinary Medicine, South Valley University, Qena, 83523, Egypt
| | | | - Vivek Sanker
- Research Assistant, Dept. Of Neurosurgery, Trivandrum Medical College, Trivandrum, India
| | - Zainab G Abdelazeem
- Division of Molecular Biology, Department of Zoology, Faculty of Science, Alexandria University, Alexandria, Egypt
| | | | | | - Rachel Howard
- School of Clinical Medicine, University of Cambridge, Cambridge, England
| | - Helen Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Wireko Andrew Awuah
- Medical Institute, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine.
| |
Collapse
|
5
|
Tian J, Ma J. The Value of Microbes in Cancer Neoantigen Immunotherapy. Pharmaceutics 2023; 15:2138. [PMID: 37631352 PMCID: PMC10459105 DOI: 10.3390/pharmaceutics15082138] [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: 07/17/2023] [Revised: 08/06/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.
Collapse
Affiliation(s)
- Junrui Tian
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
| | - Jian Ma
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
| |
Collapse
|
6
|
Hartout P, Počuča B, Méndez-García C, Schleberger C. Investigating the human and nonobese diabetic mouse MHC class II immunopeptidome using protein language modeling. Bioinformatics 2023; 39:btad469. [PMID: 37527005 PMCID: PMC10421966 DOI: 10.1093/bioinformatics/btad469] [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: 11/22/2022] [Revised: 06/17/2023] [Accepted: 07/31/2023] [Indexed: 08/03/2023] Open
Abstract
MOTIVATION Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large-scale datasets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in protein language modeling have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. RESULTS Here, we train a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluate its performance on the peptide presentation prediction task. We show that the transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance. We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by nonobese diabetic mice enabled for the first time the accurate in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/Novartis/AEGIS.
Collapse
Affiliation(s)
- Philip Hartout
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Bojana Počuča
- NIBR Research Informatics, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Celia Méndez-García
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Christian Schleberger
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| |
Collapse
|
7
|
Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, van der Burg SH, Ott PA, Bogaert C. Challenges in neoantigen-directed therapeutics. Cancer Cell 2023; 41:15-40. [PMID: 36368320 DOI: 10.1016/j.ccell.2022.10.013] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.
Collapse
Affiliation(s)
| | | | | | - Evelien Smits
- Center for Oncological Research, University of Antwerp, 2610 Wilrijk, Belgium
| | - Bruno De Geest
- Department of Pharmaceutics, Ghent University, 9000 Ghent, Belgium
| | - Karine Breckpot
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Steven A Feldman
- Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Wim van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kris Thielemans
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sjoerd H van der Burg
- Medical Oncology, Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | | |
Collapse
|