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Wang X, Gao X, Fan X, Huai Z, Zhang G, Yao M, Wang T, Huang X, Lai L. WUREN: Whole-modal union representation for epitope prediction. Comput Struct Biotechnol J 2024; 23:2122-2131. [PMID: 38817963 PMCID: PMC11137340 DOI: 10.1016/j.csbj.2024.05.023] [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: 01/26/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.
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Affiliation(s)
| | | | - Xuezhe Fan
- XtalPi Innovation Center, Beijing, China
| | - Zhe Huai
- XtalPi Innovation Center, Beijing, China
| | | | | | | | | | - Lipeng Lai
- XtalPi Innovation Center, Beijing, China
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2
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Ananya, Panchariya DC, Karthic A, Singh SP, Mani A, Chawade A, Kushwaha S. Vaccine design and development: Exploring the interface with computational biology and AI. Int Rev Immunol 2024:1-20. [PMID: 38982912 DOI: 10.1080/08830185.2024.2374546] [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: 03/22/2024] [Accepted: 06/26/2024] [Indexed: 07/11/2024]
Abstract
Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.
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Affiliation(s)
- Ananya
- National Institute of Animal Biotechnology, Hyderabad, India
| | | | | | | | - Ashutosh Mani
- Motilal Nehru National Institute of Technology, Prayagraj, India
| | - Aakash Chawade
- Swedish University of Agricultural Sciences, Alnarp, Sweden
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3
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Yang Y, He X, Li F, He S, Liu M, Li M, Xia F, Su W, Liu G. Animal-derived food allergen: A review on the available crystal structure and new insights into structural epitope. Compr Rev Food Sci Food Saf 2024; 23:e13340. [PMID: 38778570 DOI: 10.1111/1541-4337.13340] [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: 08/19/2023] [Accepted: 03/19/2024] [Indexed: 05/25/2024]
Abstract
Immunoglobulin E (IgE)-mediated food allergy is a rapidly growing public health problem. The interaction between allergens and IgE is at the core of the allergic response. One of the best ways to understand this interaction is through structural characterization. This review focuses on animal-derived food allergens, overviews allergen structures determined by X-ray crystallography, presents an update on IgE conformational epitopes, and explores the structural features of these epitopes. The structural determinants of allergenicity and cross-reactivity are also discussed. Animal-derived food allergens are classified into limited protein families according to structural features, with the calcium-binding protein and actin-binding protein families dominating. Progress in epitope characterization has provided useful information on the structural properties of the IgE recognition region. The data reveals that epitopes are located in relatively protruding areas with negative surface electrostatic potential. Ligand binding and disulfide bonds are two intrinsic characteristics that influence protein structure and impact allergenicity. Shared structures, local motifs, and shared epitopes are factors that lead to cross-reactivity. The structural properties of epitope regions and structural determinants of allergenicity and cross-reactivity may provide directions for the prevention, diagnosis, and treatment of food allergies. Experimentally determined structure, especially that of antigen-antibody complexes, remains limited, and the identification of epitopes continues to be a bottleneck in the study of animal-derived food allergens. A combination of traditional immunological techniques and emerging bioinformatics technology will revolutionize how protein interactions are characterized.
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Affiliation(s)
- Yang Yang
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
- College of Environment and Public Health, Xiamen Huaxia University, Xiamen, Fujian, China
| | - Xinrong He
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
| | - Fajie Li
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
| | - Shaogui He
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Xiamen, Fujian, China
| | - Meng Liu
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
- College of Marine Biology, Xiamen Ocean Vocational College, Xiamen, Fujian, China
| | - Mengsi Li
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
- School of Food Engineering, Zhangzhou Institute of Technology, Zhangzhou, Fujian, China
| | - Fei Xia
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
| | - Wenjin Su
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
| | - Guangming Liu
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, Fujian, China
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4
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Wang H, Hao X, He Y, Fan L. AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features. PLoS One 2024; 19:e0296737. [PMID: 38394128 PMCID: PMC10889861 DOI: 10.1371/journal.pone.0296737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 12/18/2023] [Indexed: 02/25/2024] Open
Abstract
Due to the unnecessary immune responses induced by therapeutic antibodies in clinical applications, immunogenicity is an important factor to be considered in the development of antibody therapeutics. To a certain extent, there is a lag in using wet-lab experiments to test the immunogenicity in the development process of antibody therapeutics. Developing a computational method to predict the immunogenicity at once the antibody sequence is designed, is of great significance for the screening in the early stage and reducing the risk of antibody therapeutics development. In this study, a computational immunogenicity prediction method was proposed on the basis of AntiBERTy-based features of amino sequences in the antibody variable region. The AntiBERTy-based sequence features were first calculated using the AntiBERTy pre-trained model. Principal component analysis (PCA) was then applied to reduce the extracted feature to two dimensions to obtain the final features. AutoGluon was then used to train multiple machine learning models and the best one, the weighted ensemble model, was obtained through 5-fold cross-validation on the collected data. The data contains 199 commercial therapeutic antibodies, of which 177 samples were used for model training and 5-fold cross-validation, and the remaining 22 samples were used as an independent test dataset to evaluate the performance of the constructed model and compare it with other prediction methods. Test results show that the proposed method outperforms the comparison method with 0.7273 accuracy on the independent test dataset, which is 9.09% higher than the comparison method. The corresponding web server is available through the official website of GenScript Co., Ltd., https://www.genscript.com/tools/antibody-immunogenicity.
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Affiliation(s)
- Hong Wang
- Production and R&D Center I of Life Science Services, GenScript Biotech Corporation, Nanjing, China
| | - Xiaohu Hao
- Production and R&D Center I of Life Science Services, GenScript Biotech Corporation, Nanjing, China
| | - Yuzhuo He
- Production and R&D Center I of Life Science Services, GenScript Biotech Corporation, Nanjing, China
| | - Long Fan
- Production and R&D Center I of Life Science Services, GenScript Biotech Corporation, Nanjing, China
- Production and R&D Center I of Life Science Services, GenScript (Shanghai) Biotech Co., Ltd., Shanghai, China
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5
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Xu Y, Zhang F, Mu G, Zhu X. Effect of lactic acid bacteria fermentation on cow milk allergenicity and antigenicity: A review. Compr Rev Food Sci Food Saf 2024; 23:e13257. [PMID: 38284611 DOI: 10.1111/1541-4337.13257] [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: 03/02/2023] [Revised: 09/22/2023] [Accepted: 10/02/2023] [Indexed: 01/30/2024]
Abstract
Cow milk is a major allergenic food. The potential prevention and treatment effects of lactic acid bacteria (LAB)-fermented dairy products on allergic symptoms have garnered considerable attention. Cow milk allergy (CMA) is mainly attributed to extracellular and/or cell envelope proteolytic enzymes with hydrolysis specificity. Numerous studies have demonstrated that LAB prevents the risk of allergies by modulating the development and regulation of the host immune system. Specifically, LAB and its effectors can enhance intestinal barrier function and affect immune cells by interfering with humoral and cellular immunity. Fermentation hydrolysis of allergenic epitopes is considered the main mechanism of reducing CMA. This article reviews the linear epitopes of allergens in cow milk and the effect of LAB on these allergens and provides insight into the means of predicting allergenic epitopes by conventional laboratory analysis methods combined with molecular simulation. Although LAB can reduce CMA in several ways, the mechanism of action remains partially clarified. Therefore, this review additionally attempts to summarize the main mechanism of LAB fermentation to provide guidance for establishing an effective preventive and treatment method for CMA and serve as a reference for the screening, research, and application of LAB-based intervention.
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Affiliation(s)
- Yunpeng Xu
- School of Food Science and Technology, Dalian Polytechnic University, Dalian, Liaoning, P. R. China
| | - Feifei Zhang
- Liaoning Ocean and Fisheries Science Research Institute, Dalian, Liaoning, P. R. China
| | - Guangqing Mu
- Dalian Key Laboratory of Functional Probiotics, Dalian, Liaoning, P. R. China
| | - Xuemei Zhu
- School of Food Science and Technology, Dalian Polytechnic University, Dalian, Liaoning, P. R. China
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6
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Quadrini M, Ferrari C. Exploiting the Role of Features for Antigens-Antibodies Interaction Site Prediction. Methods Mol Biol 2024; 2780:303-325. [PMID: 38987475 DOI: 10.1007/978-1-0716-3985-6_16] [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] [Indexed: 07/12/2024]
Abstract
Antibodies are a class of proteins that recognize and neutralize pathogens by binding to their antigens. They are the most significant category of biopharmaceuticals for both diagnostic and therapeutic applications. Understanding how antibodies interact with their antigens plays a fundamental role in drug and vaccine design and helps to comprise the complex antigen binding mechanisms. Computational methods for predicting interaction sites of antibody-antigen are of great value due to the overall cost of experimental methods. Machine learning methods and deep learning techniques obtained promising results.In this work, we predict antibody interaction interface sites by applying HSS-PPI, a hybrid method defined to predict the interface sites of general proteins. The approach abstracts the proteins in terms of hierarchical representation and uses a graph convolutional network to classify the amino acids between interface and non-interface. Moreover, we also equipped the amino acids with different sets of physicochemical features together with structural ones to describe the residues. Analyzing the results, we observe that the structural features play a fundamental role in the amino acid descriptions. We compare the obtained performances, evaluated using standard metrics, with the ones obtained with SVM with 3D Zernike descriptors, Parapred, Paratome, and Antibody i-Patch.
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Affiliation(s)
- Michela Quadrini
- School of Science and Technology, University of Camerino, Camerino, Italy.
| | - Carlo Ferrari
- Department of Information Engineering, University of Padua, Padua, Italy
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7
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Razali SA, Shamsir MS, Ishak NF, Low CF, Azemin WA. Riding the wave of innovation: immunoinformatics in fish disease control. PeerJ 2023; 11:e16419. [PMID: 38089909 PMCID: PMC10712311 DOI: 10.7717/peerj.16419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/17/2023] [Indexed: 12/18/2023] Open
Abstract
The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.
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Affiliation(s)
- Siti Aisyah Razali
- Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
- Biological Security and Sustainability Research Interest Group (BIOSES), Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Mohd Shahir Shamsir
- Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
| | - Nur Farahin Ishak
- Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Chen-Fei Low
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Wan-Atirah Azemin
- School of Biological Sciences, Universiti Sains Malaysia, Minden, Pulau Pinang, Malaysia
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8
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Kumar N, Bajiya N, Patiyal S, Raghava GPS. Multi-perspectives and challenges in identifying B-cell epitopes. Protein Sci 2023; 32:e4785. [PMID: 37733481 PMCID: PMC10578127 DOI: 10.1002/pro.4785] [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: 06/26/2023] [Revised: 09/11/2023] [Accepted: 09/16/2023] [Indexed: 09/23/2023]
Abstract
The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).
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Affiliation(s)
- Nishant Kumar
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Nisha Bajiya
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Sumeet Patiyal
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Gajendra P. S. Raghava
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
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9
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Bai G, Sun C, Guo Z, Wang Y, Zeng X, Su Y, Zhao Q, Ma B. Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects. Semin Cancer Biol 2023; 95:13-24. [PMID: 37355214 DOI: 10.1016/j.semcancer.2023.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
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Affiliation(s)
- Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziang Guo
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Yangjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuhong Su
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Digiwiser BioTechnolgy, Limited, Shanghai 201203, China.
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10
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Angaitkar P, Janghel RR, Sahu TP. DL-TCNN: Deep Learning-based Temporal Convolutional Neural Network for prediction of conformational B-cell epitopes. 3 Biotech 2023; 13:297. [PMID: 37575599 PMCID: PMC10412510 DOI: 10.1007/s13205-023-03716-7] [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: 06/11/2023] [Accepted: 07/24/2023] [Indexed: 08/15/2023] Open
Abstract
Prediction of conformational B-cell epitopes (CBCE) is an essential phase for vaccine design, drug invention, and accurate disease diagnosis. Many laboratorial and computational approaches have been developed to predict CBCE. However, laboratorial experiments are costly and time consuming, leading to the popularity of Machine Learning (ML)-based computational methods. Although ML methods have succeeded in many domains, achieving higher accuracy in CBCE prediction remains a challenge. To overcome this drawback and consider the limitations of ML methods, this paper proposes a novel DL-based framework for CBCE prediction, leveraging the capabilities of deep learning in the medical domain. The proposed model is named Deep Learning-based Temporal Convolutional Neural Network (DL-TCNN), which hybridizes empirical hyper-tuned 1D-CNN and TCN. TCN is an architecture that employs causal convolutions and dilations, adapting well to sequential input with extensive receptive fields. To train the proposed model, physicochemical features are firstly extracted from antigen sequences. Next, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. Finally, the proposed DL-TCNN is employed for the prediction of CBCE. The model's performance is evaluated and validated on a benchmark antigen-antibody dataset. The DL-TCNN achieves 94.44% accuracy, and 0.989 AUC score for the training dataset, 78.53% accuracy, and 0.661 AUC score for the validation dataset; and 85.10% accuracy, 0.855 AUC score for the testing dataset. The proposed model outperforms all the existing CBCE methods.
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Affiliation(s)
- Pratik Angaitkar
- Department of Information Technology, National Institute of Technology, Raipur, G.E. Road, Raipur, C.G. 492010 India
| | - Rekh Ram Janghel
- Department of Information Technology, National Institute of Technology, Raipur, G.E. Road, Raipur, C.G. 492010 India
| | - Tirath Prasad Sahu
- Department of Information Technology, National Institute of Technology, Raipur, G.E. Road, Raipur, C.G. 492010 India
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11
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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12
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Zeng X, Bai G, Sun C, Ma B. Recent Progress in Antibody Epitope Prediction. Antibodies (Basel) 2023; 12:52. [PMID: 37606436 PMCID: PMC10443277 DOI: 10.3390/antib12030052] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/23/2023] Open
Abstract
Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.
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Affiliation(s)
- Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
- Shanghai Digiwiser Biological, Inc., Shanghai 200131, China
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13
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Beglov D, Vajda S, Kozakov D. The ClusPro AbEMap web server for the prediction of antibody epitopes. Nat Protoc 2023; 18:1814-1840. [PMID: 37188806 PMCID: PMC10898366 DOI: 10.1038/s41596-023-00826-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/19/2023] [Indexed: 05/17/2023]
Abstract
Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.
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Affiliation(s)
- Israel T Desta
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | | | - Daron M Standley
- Department of Genome Informatics, Osaka University, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
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14
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Inácio MM, Moreira ALE, Cruz-Leite VRM, Mattos K, Silva LOS, Venturini J, Ruiz OH, Ribeiro-Dias F, Weber SS, Soares CMDA, Borges CL. Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics. J Fungi (Basel) 2023; 9:633. [PMID: 37367569 DOI: 10.3390/jof9060633] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023] Open
Abstract
Fungal infections represent a serious global health problem, causing damage to health and the economy on the scale of millions. Although vaccines are the most effective therapeutic approach used to combat infectious agents, at the moment, no fungal vaccine has been approved for use in humans. However, the scientific community has been working hard to overcome this challenge. In this sense, we aim to describe here an update on the development of fungal vaccines and the progress of methodological and experimental immunotherapies against fungal infections. In addition, advances in immunoinformatic tools are described as an important aid by which to overcome the difficulty of achieving success in fungal vaccine development. In silico approaches are great options for the most important and difficult questions regarding the attainment of an efficient fungal vaccine. Here, we suggest how bioinformatic tools could contribute, considering the main challenges, to an effective fungal vaccine.
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Affiliation(s)
- Moisés Morais Inácio
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia 74605-170, Brazil
- Estácio de Goiás University Center, Goiânia 74063-010, Brazil
| | - André Luís Elias Moreira
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia 74605-170, Brazil
| | | | - Karine Mattos
- Faculty of Medicine, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
| | - Lana O'Hara Souza Silva
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia 74605-170, Brazil
| | - James Venturini
- Faculty of Medicine, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
| | - Orville Hernandez Ruiz
- MICROBA Research Group-Cellular and Molecular Biology Unit-CIB, School of Microbiology, University of Antioquia, Medellín 050010, Colombia
| | - Fátima Ribeiro-Dias
- Laboratório de Imunidade Natural (LIN), Instituto de Patologia Tropical e Saúde Pública, Federal University of Goiás, Goiânia 74001-970, Brazil
| | - Simone Schneider Weber
- Bioscience Laboratory, Faculty of Pharmaceutical Sciences, Food and Nutrition, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
| | - Célia Maria de Almeida Soares
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia 74605-170, Brazil
| | - Clayton Luiz Borges
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia 74605-170, Brazil
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15
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Rhee JH, Khim K, Puth S, Choi Y, Lee SE. Deimmunization of flagellin adjuvant for clinical application. Curr Opin Virol 2023; 60:101330. [PMID: 37084463 DOI: 10.1016/j.coviro.2023.101330] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 04/23/2023]
Abstract
Flagellin is the cognate ligand for host pattern recognition receptors, toll-like receptor 5 (TLR5) in the cell surface, and NAIP5/NLRC4 inflammasome in the cytosol. TLR5-binding domain is located in D1 domain, where crucial amino acid sequences are conserved among diverse bacteria. The highly conserved C-terminal 35 amino acids of flagellin were proved to be responsible for the inflammasome activation by binding to NAIP5. D2/D3 domains, located in the central region and exposed to the outside surface of flagellar filament, are heterogeneous across bacterial species and highly immunogenic. Taking advantage of TLR5- and NLRC4-stimulating activities, flagellin has been actively developed as a vaccine adjuvant and immunotherapeutic. Because of its immunogenicity, there exist worries concerning diminished efficacy and possible reactogenicity after repeated administration. Deimmunization of flagellin derivatives while preserving the TLR5/NLRC4-mediated immunomodulatory activity should be the most reasonable option for clinical application. This review describes strategies and current achievements in flagellin deimmunization.
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Affiliation(s)
- Joon Haeng Rhee
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea; Department of Microbiology, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea.
| | - Koemchhoy Khim
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea
| | - Sao Puth
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea
| | - Yoonjoo Choi
- Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea; Department of Microbiology, Chonnam National University Medical School, Hwasun-gun, Jeonnam, Republic of Korea
| | - Shee Eun Lee
- Clinical Vaccine R&D Center, Chonnam National University, Hwasun-gun, Jeonnam, Republic of Korea; Immunotherapy Innovation Center, Hwasun-gun, Jeonnam, Republic of Korea
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16
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Harvey WT, Davies V, Daniels RS, Whittaker L, Gregory V, Hay AJ, Husmeier D, McCauley JW, Reeve R. A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. PLoS Comput Biol 2023; 19:e1010885. [PMID: 36972311 PMCID: PMC10079231 DOI: 10.1371/journal.pcbi.1010885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/06/2023] [Accepted: 01/20/2023] [Indexed: 03/29/2023] Open
Abstract
Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development, though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharacterised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.
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Affiliation(s)
- William T. Harvey
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- * E-mail: (WTH); (RR)
| | - Vinny Davies
- School of Computing, College of Science and Engineering, University of Glasgow, Glasgow, United Kingdom
- School of Mathematics and Statistics, College of Science and Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Rodney S. Daniels
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Lynne Whittaker
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Victoria Gregory
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Alan J. Hay
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Dirk Husmeier
- School of Mathematics and Statistics, College of Science and Engineering, University of Glasgow, Glasgow, United Kingdom
| | - John W. McCauley
- Worldwide Influenza Centre, The Francis Crick Institute, London, United Kingdom
| | - Richard Reeve
- Boyd Orr Centre for Population and Ecosystem Health, School of Biodiversity, One Health and Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- * E-mail: (WTH); (RR)
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17
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Cia G, Pucci F, Rooman M. Critical review of conformational B-cell epitope prediction methods. Brief Bioinform 2023; 24:6972295. [PMID: 36611255 DOI: 10.1093/bib/bbac567] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 01/09/2023] Open
Abstract
Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.
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Affiliation(s)
- Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
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18
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Kuri P, Goswami P. Current Update on Rotavirus in-Silico Multiepitope Vaccine Design. ACS OMEGA 2023; 8:190-207. [PMID: 36643547 PMCID: PMC9835168 DOI: 10.1021/acsomega.2c07213] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/14/2022] [Indexed: 06/06/2023]
Abstract
Rotavirus gastroenteritis is one of the leading causes of pediatric morbidity and mortality worldwide in infants and under-five populations. The World Health Organization (WHO) recommended global incorporation of the rotavirus vaccine in national immunization programs to alleviate the burden of the disease. Implementation of the rotavirus vaccination in certain regions of the world brought about a significant and consistent reduction of rotavirus-associated hospitalizations. However, the efficacy of licensed vaccines remains suboptimal in low-income countries where the incidences of rotavirus gastroenteritis continue to happen unabated. The problem of low efficacy of currently licensed oral rotavirus vaccines in low-income countries necessitates continuous exploration, design, and development of new rotavirus vaccines. Traditional vaccine development is a complex, expensive, labor-intensive, and time-consuming process. Reverse vaccinology essentially utilizes the genome and proteome information on pathogens and has opened new avenues for in-silico multiepitope vaccine design for a plethora of pathogens, promising time reduction in the complete vaccine development pipeline by complementing the traditional vaccinology approach. A substantial number of reviews on licensed rotavirus vaccines and those under evaluation are already available in the literature. However, a collective account of rotavirus in-silico vaccines is lacking in the literature, and such an account may further fuel the interest of researchers to use reverse vaccinology to expedite the vaccine development process. Therefore, the main focus of this review is to summarize the research endeavors undertaken for the design and development of rotavirus vaccines by the reverse vaccinology approach utilizing the tools of immunoinformatics.
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19
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Biner DW, Grosch JS, Ortoleva PJ. B-cell epitope discovery: The first protein flexibility-based algorithm-Zika virus conserved epitope demonstration. PLoS One 2023; 18:e0262321. [PMID: 36920995 PMCID: PMC10016673 DOI: 10.1371/journal.pone.0262321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 12/22/2021] [Indexed: 03/16/2023] Open
Abstract
Antibody-antigen interaction-at antigenic local environments called B-cell epitopes-is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-nine enveloped virus-antibody structures), 2) two structurally characterized non-enveloped virus epitopes which evolved slowly (out of eight non-enveloped virus-antibody structures), and 3) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of five Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.
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Affiliation(s)
- Daniel W. Biner
- Department of Chemistry, Indiana University, Bloomington, Indiana, United States of America
| | - Jason S. Grosch
- Department of Chemistry, Indiana University, Bloomington, Indiana, United States of America
| | - Peter J. Ortoleva
- Department of Chemistry, Indiana University, Bloomington, Indiana, United States of America
- * E-mail:
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20
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Zheng D, Liang S, Zhang C. B-Cell Epitope Predictions Using Computational Methods. Methods Mol Biol 2023; 2552:239-254. [PMID: 36346595 DOI: 10.1007/978-1-0716-2609-2_12] [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] [Indexed: 06/16/2023]
Abstract
Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .
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Affiliation(s)
- Dandan Zheng
- Department of Radiation Oncology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Shide Liang
- Department of Research and Development, Bio-Thera Solutions, Guangzhou, China.
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
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21
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Vij S, Thakur R, Rishi P. Reverse engineering approach: a step towards a new era of vaccinology with special reference to Salmonella. Expert Rev Vaccines 2022; 21:1763-1785. [PMID: 36408592 DOI: 10.1080/14760584.2022.2148661] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Salmonella is responsible for causing enteric fever, septicemia, and gastroenteritis in humans. Due to high disease burden and emergence of multi- and extensively drug-resistant Salmonella strains, it is becoming difficult to treat the infection with existing battery of antibiotics as we are not able to discover newer antibiotics at the same pace at which the pathogens are acquiring resistance. Though vaccines against Salmonella are available commercially, they have limited efficacy. Advancements in genome sequencing technologies and immunoinformatics approaches have solved the problem significantly by giving rise to a new era of vaccine designing, i.e. 'Reverse engineering.' Reverse engineering/vaccinology has expedited the vaccine identification process. Using this approach, multiple potential proteins/epitopes can be identified and constructed as a single entity to tackle enteric fever. AREAS COVERED This review provides details of reverse engineering approach and discusses various protein and epitope-based vaccine candidates identified using this approach against typhoidal Salmonella. EXPERT OPINION Reverse engineering approach holds great promise for developing strategies to tackle the pathogen(s) by overcoming the limitations posed by existing vaccines. Progressive advancements in the arena of reverse vaccinology, structural biology, and systems biology combined with an improved understanding of host-pathogen interactions are essential components to design new-generation vaccines.
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Affiliation(s)
- Shania Vij
- Department of Microbiology, Panjab University, Chandigarh, India
| | - Reena Thakur
- Department of Microbiology, Panjab University, Chandigarh, India
| | - Praveen Rishi
- Department of Microbiology, Panjab University, Chandigarh, India
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22
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Chen Y, Wu Q, Li G, Li H, Li W, Li H, Qin L, Zheng H, Liu C, Hou M, Liu L. Identification and genetic characterization of a minor norovirus genotype, GIX.1[GII.P15], from China. BMC Genom Data 2022; 23:50. [PMID: 35794533 PMCID: PMC9261040 DOI: 10.1186/s12863-022-01066-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/28/2022] [Indexed: 11/17/2022] Open
Abstract
Background Human noroviruses, single-stranded RNA viruses in the family Caliciviridae, are a leading cause of nonbacterial acute gastroenteritis in people of all ages worldwide. Despite three decades of genomic sequencing and epidemiological norovirus studies, full-length genome analyses of the non-epidemic or minor norovirus genotypes are rare and genomic regions other than ORF2 and 3′-end of ORF1 have been largely understudied, which hampers a better understanding of the evolutionary mechanisms of emergence of new strains. In this study, we detected a rare norovirus genotype, GIX.1[GII.P15], in a vomit sample of a 60 year old woman with acute gastroenteritis using Raji cells and sequenced the complete genome. Results Using electron microscopy, a morphology of spherical and lace-like appearance of norovirus virus particles with a diameter of approximately 30 nm were observed. Phylogenetic analysis of VP1 and the RdRp region indicated that the KMN1 strain could be genotyped as GIX.1[GII.P15]. In addition, the VP1 region of KMN1 strain had 94.15% ± 3.54% percent nucleotide identity (PNI) compared to 26 genomic sequences available in GenBank, indicating a higher degree similarity between KMN1 and other GIX.1[GII.P15] strains. Further analysis of the full genome sequence of KMN1 strain showed that a total of 96 nucleotide substitutions (63 in ORF1, 25 in ORF2, and 8 in ORF3) were found across the genome compared with the consensus sequence of GIX.1[GII.P15] genome, and 6 substitutions caused amino acid changes (4 in ORF1, 1 in ORF2, and 1 in ORF3). However, only one nucleotide substitution results in the amino acid change (P302S) in the VP1 protein and the site was located near one of the predicted conformational B epitopes on the dimer structure. Conclusions The genomic information of the new GIX.1[GII.P15] strain KMN1, which was identified in Kunming, China could provide helpful insights for the study of the genetic evolution of the virus. Supplementary Information The online version contains supplementary material available at 10.1186/s12863-022-01066-6.
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23
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Rashid S, Ng TA, Kwoh CK. Jupytope: computational extraction of structural properties of viral epitopes. Brief Bioinform 2022; 23:6696137. [PMID: 36094101 DOI: 10.1093/bib/bbac362] [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: 03/16/2022] [Revised: 07/29/2022] [Accepted: 08/02/2022] [Indexed: 12/14/2022] Open
Abstract
Epitope residues located on viral surface proteins are of immense interest in immunology and related applications such as vaccine development, disease diagnosis and drug design. Most tools rely on sequence-based statistical comparisons, such as information entropy of residue positions in aligned columns to infer location and properties of epitope sites. To facilitate cross-structural comparisons of epitopes on viral surface proteins, a python-based extraction tool implemented with Jupyter notebook is presented (Jupytope). Given a viral antigen structure of interest, a list of known epitope sites and a reference structure, the corresponding epitope structural properties can quickly be obtained. The tool integrates biopython modules for commonly used software such as NACCESS, DSSP as well as residue depth and outputs a list of structure-derived properties such as dihedral angles, solvent accessibility, residue depth and secondary structure that can be saved in several convenient data formats. To ensure correct spatial alignment, Jupytope takes a list of given epitope sites and their corresponding reference structure and aligns them before extracting the desired properties. Examples are demonstrated for epitopes of Influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) viral strains. The extracted properties assist detection of two Influenza subtypes and show potential in distinguishing between four major clades of SARS-CoV2, as compared with randomized labels. The tool will facilitate analytical and predictive works on viral epitopes through the extracted structural information. Jupytope and extracted datasets are available at https://github.com/shamimarashid/Jupytope.
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Affiliation(s)
- Shamima Rashid
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
| | - Teng Ann Ng
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
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24
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Xu Z, Ismanto HS, Zhou H, Saputri DS, Sugihara F, Standley DM. Advances in antibody discovery from human BCR repertoires. FRONTIERS IN BIOINFORMATICS 2022; 2:1044975. [PMID: 36338807 PMCID: PMC9631452 DOI: 10.3389/fbinf.2022.1044975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.
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Affiliation(s)
- Zichang Xu
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hao Zhou
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Fuminori Sugihara
- Core Instrumentation Facility, Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Daron M. Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Department Systems Immunology, Immunology Frontier Research Center, Osaka University, Suita, Japan
- *Correspondence: Daron M. Standley,
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25
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Shashkova TI, Umerenkov D, Salnikov M, Strashnov PV, Konstantinova AV, Lebed I, Shcherbinin DN, Asatryan MN, Kardymon OL, Ivanisenko NV. SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning. Front Immunol 2022; 13:960985. [PMID: 36189325 PMCID: PMC9523212 DOI: 10.3389/fimmu.2022.960985] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
One of the primary tasks in vaccine design and development of immunotherapeutic drugs is to predict conformational B-cell epitopes corresponding to primary antibody binding sites within the antigen tertiary structure. To date, multiple approaches have been developed to address this issue. However, for a wide range of antigens their accuracy is limited. In this paper, we applied the transfer learning approach using pretrained deep learning models to develop a model that predicts conformational B-cell epitopes based on the primary antigen sequence and tertiary structure. A pretrained protein language model, ESM-1v, and an inverse folding model, ESM-IF1, were fine-tuned to quantitatively predict antibody-antigen interaction features and distinguish between epitope and non-epitope residues. The resulting model called SEMA demonstrated the best performance on an independent test set with ROC AUC of 0.76 compared to peer-reviewed tools. We show that SEMA can quantitatively rank the immunodominant regions within the SARS-CoV-2 RBD domain. SEMA is available at https://github.com/AIRI-Institute/SEMAi and the web-interface http://sema.airi.net.
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Affiliation(s)
| | | | | | | | | | - Ivan Lebed
- AI Center Block Services, Sber, Moscow, Russia
| | - Dmitriy N. Shcherbinin
- Federal Research Centre of Epidemiology and Microbiology named after Honorary Academician N. F. Gamaleya, Ministry of Health, Moscow, Russia
| | - Marina N. Asatryan
- Federal Research Centre of Epidemiology and Microbiology named after Honorary Academician N. F. Gamaleya, Ministry of Health, Moscow, Russia
| | | | - Nikita V. Ivanisenko
- Artificial Intelligence Research Institute, Moscow, Russia
- Laboratory of Computational Proteomics, Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- *Correspondence: Nikita V. Ivanisenko,
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26
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Cano-Garrido O, Serna N, Unzueta U, Parladé E, Mangues R, Villaverde A, Vázquez E. Protein scaffolds in human clinics. Biotechnol Adv 2022; 61:108032. [PMID: 36089254 DOI: 10.1016/j.biotechadv.2022.108032] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/30/2022] [Accepted: 09/03/2022] [Indexed: 11/02/2022]
Abstract
Fundamental clinical areas such as drug delivery and regenerative medicine require biocompatible materials as mechanically stable scaffolds or as nanoscale drug carriers. Among the wide set of emerging biomaterials, polypeptides offer enticing properties over alternative polymers, including full biocompatibility, biodegradability, precise interactivity, structural stability and conformational and functional versatility, all of them tunable by conventional protein engineering. However, proteins from non-human sources elicit immunotoxicities that might bottleneck further development and narrow their clinical applicability. In this context, selecting human proteins or developing humanized protein versions as building blocks is a strict demand to design non-immunogenic protein materials. We review here the expanding catalogue of human or humanized proteins tailored to execute different levels of scaffolding functions and how they can be engineered as self-assembling materials in form of oligomers, polymers or complex networks. In particular, we emphasize those that are under clinical development, revising their fields of applicability and how they have been adapted to offer, apart from mere mechanical support, highly refined functions and precise molecular interactions.
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Affiliation(s)
- Olivia Cano-Garrido
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain
| | - Naroa Serna
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain
| | - Ugutz Unzueta
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain; Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; Biomedical Research Institute Sant Pau (IIB Sant Pau), 08025 Barcelona, Spain; Josep Carreras Leukaemia Research Institute, 08916 Badalona (Barcelona), Spain
| | - Eloi Parladé
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain
| | - Ramón Mangues
- Biomedical Research Institute Sant Pau (IIB Sant Pau), 08025 Barcelona, Spain; Josep Carreras Leukaemia Research Institute, 08916 Badalona (Barcelona), Spain
| | - Antonio Villaverde
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain; Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain.
| | - Esther Vázquez
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, 08193 Cerdanyola del Vallès (Barcelona), Spain; Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès (Barcelona), Spain.
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27
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Lou H, Cao X. Antibody variable region engineering for improving cancer immunotherapy. Cancer Commun (Lond) 2022; 42:804-827. [PMID: 35822503 PMCID: PMC9456695 DOI: 10.1002/cac2.12330] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/25/2022] [Accepted: 06/22/2022] [Indexed: 04/09/2023] Open
Abstract
The efficacy and specificity of conventional monoclonal antibody (mAb) drugs in the clinic require further improvement. Currently, the development and application of novel antibody formats for improving cancer immunotherapy have attracted much attention. Variable region-retaining antibody fragments, such as antigen-binding fragment (Fab), single-chain variable fragment (scFv), bispecific antibody, and bi/trispecific cell engagers, are engineered with humanization, multivalent antibody construction, affinity optimization and antibody masking for targeting tumor cells and killer cells to improve antibody-based therapy potency, efficacy and specificity. In this review, we summarize the application of antibody variable region engineering and discuss the future direction of antibody engineering for improving cancer therapies.
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Affiliation(s)
- Hantao Lou
- Ludwig Institute of Cancer ResearchUniversity of OxfordOxfordOX3 7DRUK
- Chinese Academy for Medical Sciences Oxford InstituteNuffield Department of MedicineUniversity of OxfordOxfordOX3 7FZUK
| | - Xuetao Cao
- Chinese Academy for Medical Sciences Oxford InstituteNuffield Department of MedicineUniversity of OxfordOxfordOX3 7FZUK
- Department of ImmunologyCentre for Immunotherapy, Institute of Basic Medical SciencesChinese Academy of Medical SciencesBeijing100005P. R. China
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28
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Dhankhar R, Kawatra A, Gupta V, Mohanty A, Gulati P. In silico and in vitro analysis of arginine deiminase from Pseudomonas furukawaii as a potential anticancer enzyme. 3 Biotech 2022; 12:220. [PMID: 35971334 PMCID: PMC9374873 DOI: 10.1007/s13205-022-03292-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 07/30/2022] [Indexed: 11/24/2022] Open
Abstract
Arginine deiminase (ADI), a promising anticancer enzyme from Mycoplasma hominis, is currently in phase III of clinical trials for the treatment of arginine auxotrophic tumors. However, it has been associated with several drawbacks in terms of low stability at human physiological conditions, high immunogenicity, hypersensitivity and systemic toxicity. In our previous work, Pseudomonas furukawaii 24 was identified as a potent producer of ADI with optimum activity under physiological conditions. In the present study, phylogenetic analysis of microbial ADIs indicated P. furukawaii ADI (PfADI) to be closely related to experimentally characterized ADIs of Pseudomonas sp. with proven anticancer activity. Immunoinformatics analysis was performed indicating lower immunogenicity of PfADI than MhADI (M. hominis ADI) both in terms of number of linear and conformational B-cell epitopes and T-cell epitope density. Overall antigenicity and allergenicity of PfADI was also lower as compared to MhADI, suggesting the applicability of PfADI as an alternative anticancer biotherapeutic. Hence, in vitro experiments were performed in which the ADI coding arcA gene of P. furukawaii was cloned and expressed in E. coli BL21. Recombinant ADI of P. furukawaii was purified, characterized and its anticancer activity was assessed. The enzyme was stable at human physiological conditions (pH 7 and 37 °C) with Km of 1.90 mM. PfADI was found to effectively inhibit the HepG2 cells with an IC50 value of 0.1950 IU/ml. Therefore, the current in silico and in vitro studies establish PfADI as a potential anticancer drug candidate with improved efficacy and low immunogenicity. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-022-03292-2.
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Affiliation(s)
- Rakhi Dhankhar
- Medical Microbiology and Bioprocess Technology Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana India
| | - Anubhuti Kawatra
- Medical Microbiology and Bioprocess Technology Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana India
| | - Vatika Gupta
- Medical Microbiology and Bioprocess Technology Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana India
- Molecular Biology and Genetic Engineering Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Aparajita Mohanty
- Bioinformatics Infrastructure Facility, Gargi College, University of Delhi, New Delhi, India
| | - Pooja Gulati
- Medical Microbiology and Bioprocess Technology Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana India
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29
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Willett BJ, Grove J, MacLean OA, Wilkie C, De Lorenzo G, Furnon W, Cantoni D, Scott S, Logan N, Ashraf S, Manali M, Szemiel A, Cowton V, Vink E, Harvey WT, Davis C, Asamaphan P, Smollett K, Tong L, Orton R, Hughes J, Holland P, Silva V, Pascall DJ, Puxty K, da Silva Filipe A, Yebra G, Shaaban S, Holden MTG, Pinto RM, Gunson R, Templeton K, Murcia PR, Patel AH, Klenerman P, Dunachie S, Haughney J, Robertson DL, Palmarini M, Ray S, Thomson EC. SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway. Nat Microbiol 2022; 7:1161-1179. [PMID: 35798890 PMCID: PMC9352574 DOI: 10.1038/s41564-022-01143-7] [Citation(s) in RCA: 309] [Impact Index Per Article: 154.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022]
Abstract
Vaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the receptor-binding domain. Here we demonstrate substantial evasion of neutralization by Omicron BA.1 and BA.2 variants in vitro using sera from individuals vaccinated with ChAdOx1, BNT162b2 and mRNA-1273. These data were mirrored by a substantial reduction in real-world vaccine effectiveness that was partially restored by booster vaccination. The Omicron variants BA.1 and BA.2 did not induce cell syncytia in vitro and favoured a TMPRSS2-independent endosomal entry pathway, these phenotypes mapping to distinct regions of the spike protein. Impaired cell fusion was determined by the receptor-binding domain, while endosomal entry mapped to the S2 domain. Such marked changes in antigenicity and replicative biology may underlie the rapid global spread and altered pathogenicity of the Omicron variant.
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Affiliation(s)
- Brian J Willett
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK.
| | - Joe Grove
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK.
| | - Oscar A MacLean
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Craig Wilkie
- School of Mathematics & Statistics, University of Glasgow, Glasgow, UK
| | - Giuditta De Lorenzo
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Wilhelm Furnon
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Diego Cantoni
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Sam Scott
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Nicola Logan
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Shirin Ashraf
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Maria Manali
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Agnieszka Szemiel
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Vanessa Cowton
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Elen Vink
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - William T Harvey
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Chris Davis
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Patawee Asamaphan
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Katherine Smollett
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Lily Tong
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Richard Orton
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | | | | | - David J Pascall
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | | | - Ana da Silva Filipe
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | | | | | - Matthew T G Holden
- Public Health Scotland, Glasgow, UK
- School of Medicine, University of St Andrews, St Andrews, UK
| | - Rute Maria Pinto
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | | | | | - Pablo R Murcia
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Arvind H Patel
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | | | | | | | - David L Robertson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Massimo Palmarini
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Surajit Ray
- School of Mathematics & Statistics, University of Glasgow, Glasgow, UK
| | - Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK.
- NHS Greater Glasgow & Clyde, Glasgow, UK.
- London School of Hygiene and Tropical Medicine, London, UK.
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30
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Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Młokosiewicz J, Rouyan A, Satława T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief Bioinform 2022; 23:6643456. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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31
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Tubiana J, Schneidman-Duhovny D, Wolfson HJ. ScanNet: A web server for structure-based prediction of protein binding sites with geometric deep learning. J Mol Biol 2022; 434:167758. [DOI: 10.1016/j.jmb.2022.167758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022]
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32
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Comprehensive Linear Epitope Prediction System for Host Specificity in Nodaviridae. Viruses 2022; 14:v14071357. [PMID: 35891339 PMCID: PMC9319239 DOI: 10.3390/v14071357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/15/2022] [Accepted: 06/20/2022] [Indexed: 02/01/2023] Open
Abstract
Background: Nodaviridae infection is one of the leading causes of death in commercial fish. Although many vaccines against this virus family have been developed, their efficacies are relatively low. Nodaviridae are categorized into three subfamilies: alphanodavirus (infects insects), betanodavirus (infects fish), and gammanodavirus (infects prawns). These three subfamilies possess host-specific characteristics that could be used to identify effective linear epitopes (LEs). Methodology: A multi-expert system using five existing LE prediction servers was established to obtain initial LE candidates. Based on the different clustered pathogen groups, both conserved and exclusive LEs among the Nodaviridae family could be identified. The advantages of undocumented cross infection among the different host species for the Nodaviridae family were applied to re-evaluate the impact of LE prediction. The surface structural characteristics of the identified conserved and unique LEs were confirmed through 3D structural analysis, and concepts of surface patches to analyze the spatial characteristics and physicochemical propensities of the predicted segments were proposed. In addition, an intelligent classifier based on the Immune Epitope Database (IEDB) dataset was utilized to review the predicted segments, and enzyme-linked immunosorbent assays (ELISAs) were performed to identify host-specific LEs. Principal findings: We predicted 29 LEs for Nodaviridae. The analysis of the surface patches showed common tendencies regarding shape, curvedness, and PH features for the predicted LEs. Among them, five predicted exclusive LEs for fish species were selected and synthesized, and the corresponding ELISAs for antigenic feature analysis were examined. Conclusion: Five identified LEs possessed antigenicity and host specificity for grouper fish. We demonstrate that the proposed method provides an effective approach for in silico LE prediction prior to vaccine development and is especially powerful for analyzing antigen sequences with exclusive features among clustered antigen groups.
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33
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Shen X, Lin Q, Liang Z, Wang J, Yang X, Liang Y, Liang H, Pan H, Yang J, Zhu Y, Li M, Xiang W, Zhu H. Reduction of Pre-Existing Adaptive Immune Responses Against SaCas9 in Humans Using Epitope Mapping and Identification. CRISPR J 2022; 5:445-456. [PMID: 35686980 DOI: 10.1089/crispr.2021.0142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The CRISPR-Cas9 system is increasingly being used as a gene editing therapeutic technique in complex diseases but concerns remain regarding the clinical risks of Cas9 immunogenicity. In this study, we detected antibodies against Staphylococcus aureus Cas9 (SaCas9) and anti-SaCas9 T cells in 4.8% and 70% of Chinese donors, respectively. We predicted 135 SaCas9-derived B cell epitopes and 50 SaCas9-derived CD8+ T cell epitopes for HLA-A*24:02, HLA-A*11:01, and HLA-A*02:01. We identified R338 as an immunodominant SaCas9 B cell epitope and SaCas9_200-208 as an immunodominant CD8+ T cell epitope for the three human leukocyte antigen allotypes through immunological assays using sera positive for SaCas9-specific antibodies and peripheral blood mononuclear cells positive for SaCas9-reactive T cells, respectively. We also demonstrated that an SaCas9 variant bearing an R338G substitution reduces B cell immunogenicity and retains its gene-editing function. Our study highlights the immunological risks of the CRISPR-Cas9 system and provides a solution to mitigate pre-existing adaptive immune responses against Cas9 in humans.
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Affiliation(s)
- Xiaoting Shen
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Qinru Lin
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Zhiming Liang
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Jing Wang
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Xinyi Yang
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yue Liang
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Huitong Liang
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Hanyu Pan
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Jinlong Yang
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yuqi Zhu
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Min Li
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Weirong Xiang
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Huanzhang Zhu
- State Key Laboratory of Genetic Engineering and Engineering Research Center of Gene Technology, Ministry of Education, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
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34
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ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction. Nat Methods 2022; 19:730-739. [DOI: 10.1038/s41592-022-01490-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 04/12/2022] [Indexed: 11/08/2022]
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35
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Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets. Pathogens 2022; 11:pathogens11050569. [PMID: 35631090 PMCID: PMC9143782 DOI: 10.3390/pathogens11050569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/18/2022] [Accepted: 04/21/2022] [Indexed: 12/04/2022] Open
Abstract
Systemic mycoses have been viewed as neglected diseases and they are responsible for deaths and disabilities around the world. Rapid, low-cost, simple, highly-specific and sensitive diagnostic tests are critical components of patient care, disease control and active surveillance. However, the diagnosis of fungal infections represents a great challenge because of the decline in the expertise needed for identifying fungi, and a reduced number of instruments and assays specific to fungal identification. Unfortunately, time of diagnosis is one of the most important risk factors for mortality rates from many of the systemic mycoses. In addition, phenotypic and biochemical identification methods are often time-consuming, which has created an increasing demand for new methods of fungal identification. In this review, we discuss the current context of the diagnosis of the main systemic mycoses and propose alternative approaches for the identification of new targets for fungal pathogens, which can help in the development of new diagnostic tests.
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36
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Gong W, Pan C, Cheng P, Wang J, Zhao G, Wu X. Peptide-Based Vaccines for Tuberculosis. Front Immunol 2022; 13:830497. [PMID: 35173740 PMCID: PMC8841753 DOI: 10.3389/fimmu.2022.830497] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/10/2022] [Indexed: 12/12/2022] Open
Abstract
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO’s End TB Strategy.
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Affiliation(s)
- Wenping Gong
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Chao Pan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, Beijing, China
| | - Peng Cheng
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
- Hebei North University, Zhangjiakou City, China
| | - Jie Wang
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Guangyu Zhao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
- *Correspondence: Xueqiong Wu, ; Guangyu Zhao,
| | - Xueqiong Wu
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
- *Correspondence: Xueqiong Wu, ; Guangyu Zhao,
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37
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Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J. Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review. Pathogens 2022; 11:pathogens11020146. [PMID: 35215090 PMCID: PMC8879824 DOI: 10.3390/pathogens11020146] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 02/01/2023] Open
Abstract
The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety—they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.
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Affiliation(s)
- Syed Nisar Hussain Bukhari
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
- Correspondence:
| | - Amit Jain
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Ehtishamul Haq
- Department of Biotechnology, University of Kashmir, Srinagar 190006, India;
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City 20185145, Kuwait;
| | - Julian Webber
- Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan;
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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40
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Kumar P, Lata S, Shankar UN, Akif M. Immunoinformatics-Based Designing of a Multi-Epitope Chimeric Vaccine From Multi-Domain Outer Surface Antigens of Leptospira. Front Immunol 2021; 12:735373. [PMID: 34917072 PMCID: PMC8670241 DOI: 10.3389/fimmu.2021.735373] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/08/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate information on antigenic epitopes within a multi-domain antigen would provide insights into vaccine design and immunotherapy. The multi-domain outer surface Leptospira immunoglobulin-like (Lig) proteins LigA and LigB, consisting of 12–13 homologous bacterial Ig (Big)-like domains, are potential antigens of Leptospira interrogans. Currently, no effective vaccine is available against pathogenic Leptospira. Both the humoral immunity and cell-mediated immunity of the host play critical roles in defending against Leptospira infection. Here, we used immunoinformatics approaches to evaluate antigenic B-cell lymphocyte (BCL) and cytotoxic T-lymphocyte (CTL) epitopes from Lig proteins. Based on certain crucial parameters, potential epitopes that can stimulate both types of adaptive immune responses were selected to design a chimeric vaccine construct. Additionally, an adjuvant, the mycobacterial heparin-binding hemagglutinin adhesin (HBHA), was incorporated into the final multi-epitope vaccine construct with a suitable linker. The final construct was further scored for its antigenicity, allergenicity, and physicochemical parameters. A three-dimensional (3D) modeled construct of the vaccine was implied to interact with Toll-like receptor 4 (TLR4) using molecular docking. The stability of the vaccine construct with TLR4 was predicted with molecular dynamics simulation. Our results demonstrate the application of immunoinformatics and structure biology strategies to develop an epitope-specific chimeric vaccine from multi-domain proteins. The current findings will be useful for future experimental validation to ratify the immunogenicity of the chimera.
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Affiliation(s)
- Pankaj Kumar
- Laboratory of Structural Biology, Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Surabhi Lata
- Laboratory of Structural Biology, Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Umate Nachiket Shankar
- Laboratory of Structural Biology, Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Mohd Akif
- Laboratory of Structural Biology, Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
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Saito M, Tsukagoshi H, Sada M, Sunagawa S, Shirai T, Okayama K, Sugai T, Tsugawa T, Hayashi Y, Ryo A, Takeda M, Kawashima H, Saruki N, Kimura H. Detailed Evolutionary Analyses of the F Gene in the Respiratory Syncytial Virus Subgroup A. Viruses 2021; 13:v13122525. [PMID: 34960794 PMCID: PMC8706373 DOI: 10.3390/v13122525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/06/2021] [Accepted: 12/13/2021] [Indexed: 11/20/2022] Open
Abstract
We performed evolution, phylodynamics, and reinfection-related antigenicity analyses of respiratory syncytial virus subgroup A (RSV-A) fusion (F) gene in globally collected strains (1465 strains) using authentic bioinformatics methods. The time-scaled evolutionary tree using the Bayesian Markov chain Monte Carlo method estimated that a common ancestor of the RSV-A, RSV-B, and bovine-RSV diverged at around 450 years ago, and RSV-A and RSV-B diverged around 250 years ago. Finally, the RSV-A F gene formed eight genotypes (GA1-GA7 and NA1) over the last 80 years. Phylodynamics of RSV-A F gene, including all genotype strains, increased twice in the 1990s and 2010s, while patterns of each RSV-A genotype were different. Phylogenetic distance analysis suggested that the genetic distances of the strains were relatively short (less than 0.05). No positive selection sites were estimated, while many negative selection sites were found. Moreover, the F protein 3D structure mapping and conformational epitope analysis implied that the conformational epitopes did not correspond to the neutralizing antibody binding sites of the F protein. These results suggested that the RSV-A F gene is relatively conserved, and mismatches between conformational epitopes and neutralizing antibody binding sites of the F protein are responsible for the virus reinfection.
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Affiliation(s)
- Mariko Saito
- Gunma Prefectural Institute of Public Health and Environmental Sciences, Maebashi-shi 371-0052, Japan; (M.S.); (H.T.); (N.S.)
| | - Hiroyuki Tsukagoshi
- Gunma Prefectural Institute of Public Health and Environmental Sciences, Maebashi-shi 371-0052, Japan; (M.S.); (H.T.); (N.S.)
| | - Mitsuru Sada
- Department of Health Science, Gunma Paz University Graduate School, Takasaki-shi 370-0006, Japan; (M.S.); (S.S.); (K.O.); (Y.H.)
| | - Soyoka Sunagawa
- Department of Health Science, Gunma Paz University Graduate School, Takasaki-shi 370-0006, Japan; (M.S.); (S.S.); (K.O.); (Y.H.)
| | - Tatsuya Shirai
- Department of Respiratory Medicine, Kyorin University School of Medicine, Mitaka-shi 181-8611, Japan;
| | - Kaori Okayama
- Department of Health Science, Gunma Paz University Graduate School, Takasaki-shi 370-0006, Japan; (M.S.); (S.S.); (K.O.); (Y.H.)
| | - Toshiyuki Sugai
- Division of Nursing Science, Hiroshima University, Hiroshima-shi 734-8551, Japan;
| | - Takeshi Tsugawa
- Department of Pediatrics, Sapporo Medical University School of Medicine, Sapporo-shi 060-8543, Japan;
| | - Yuriko Hayashi
- Department of Health Science, Gunma Paz University Graduate School, Takasaki-shi 370-0006, Japan; (M.S.); (S.S.); (K.O.); (Y.H.)
| | - Akihide Ryo
- Department of Microbiology, Yokohama City University School of Medicine, Yokohama-shi 236-0004, Japan;
| | - Makoto Takeda
- Department of Virology, National Institute of Infectious Diseases, Musashimurayama-shi 208-0011, Japan;
| | - Hisashi Kawashima
- Department of Pediatrics, Tokyo Medical University, Shinjuku-ku 160-0023, Japan;
| | - Nobuhiro Saruki
- Gunma Prefectural Institute of Public Health and Environmental Sciences, Maebashi-shi 371-0052, Japan; (M.S.); (H.T.); (N.S.)
| | - Hirokazu Kimura
- Department of Health Science, Gunma Paz University Graduate School, Takasaki-shi 370-0006, Japan; (M.S.); (S.S.); (K.O.); (Y.H.)
- Correspondence: ; Tel.: +81-27-388-0336
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42
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da Silva BM, Myung Y, Ascher DB, Pires DEV. epitope3D: a machine learning method for conformational B-cell epitope prediction. Brief Bioinform 2021; 23:6407730. [PMID: 34676398 DOI: 10.1093/bib/bbab423] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/25/2021] [Accepted: 09/14/2021] [Indexed: 11/13/2022] Open
Abstract
The ability to identify antigenic determinants of pathogens, or epitopes, is fundamental to guide rational vaccine development and immunotherapies, which are particularly relevant for rapid pandemic response. A range of computational tools has been developed over the past two decades to assist in epitope prediction; however, they have presented limited performance and generalization, particularly for the identification of conformational B-cell epitopes. Here, we present epitope3D, a novel scalable machine learning method capable of accurately identifying conformational epitopes trained and evaluated on the largest curated epitope data set to date. Our method uses the concept of graph-based signatures to model epitope and non-epitope regions as graphs and extract distance patterns that are used as evidence to train and test predictive models. We show epitope3D outperforms available alternative approaches, achieving Mathew's Correlation Coefficient and F1-scores of 0.55 and 0.57 on cross-validation and 0.45 and 0.36 during independent blind tests, respectively.
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Affiliation(s)
- Bruna Moreira da Silva
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - YooChan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia.,Department of Biochemistry, University of Cambridge, 80 Tennis Ct Rd, Cambridge CB2 1GA, UK
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
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43
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Pascarella S, Ciccozzi M, Bianchi M, Benvenuto D, Giovanetti M, Cauda R, Cassone A. Shortening Epitopes to Survive: The Case of SARS-CoV-2 Lambda Variant. Biomolecules 2021; 11:1494. [PMID: 34680128 PMCID: PMC8533401 DOI: 10.3390/biom11101494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/06/2021] [Accepted: 10/07/2021] [Indexed: 12/23/2022] Open
Abstract
Among the more recently identified SARS-CoV-2 Variants of Interest (VOI) is the Lambda variant, which emerged in Peru and has rapidly spread to South American regions and the US. This variant remains poorly investigated, particularly regarding the effects of mutations on the thermodynamic parameters affecting the stability of the Spike protein and its Receptor Binding Domain. We report here an in silico study on the potential impact of the Spike protein mutations on the immuno-escape ability of the Lambda variant. Bioinformatics analysis suggests that a combination of shortening the immunogenic epitope loops and the generation of potential N-glycosylation sites may be a viable adaptation strategy, potentially allowing this emerging viral variant to escape from host immunity.
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Affiliation(s)
- Stefano Pascarella
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza Università di Roma, 00185 Rome, Italy;
| | - Massimo Ciccozzi
- Medical Statistic and Molecular Epidemiology Unit, University of Biomedical Campus, 00128 Rome, Italy;
| | - Martina Bianchi
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza Università di Roma, 00185 Rome, Italy;
| | - Domenico Benvenuto
- Medical Statistic and Molecular Epidemiology Unit, University of Biomedical Campus, 00128 Rome, Italy;
| | - Marta Giovanetti
- Flavivirus Laboratory, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil;
| | - Roberto Cauda
- Dipartimento di Sicurezza e Bioetica, Sezione Malattie Infettive, Università Cattolica S. Cuore, 00168 Rome, Italy;
| | - Antonio Cassone
- Center of Genomics, Genetics and Biology, 53100 Siena, Italy;
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44
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Cai X, Li JJ, Liu T, Brian O, Li J. Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design. Brief Funct Genomics 2021; 20:289-303. [PMID: 34089044 PMCID: PMC8194884 DOI: 10.1093/bfgp/elab027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 12/15/2022] Open
Abstract
Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.
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Affiliation(s)
- Xinhui Cai
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Jiao Jiao Li
- School of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Tao Liu
- School of Life Sciences, Faculty of Science, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
| | - Oliver Brian
- Children’s Cancer Institute Australia, University of New South Wales Sydney, Children’s Cancer Institute Australia, Randwick, Sydney, 2031, New South Wales, Australia
| | - Jinyan Li
- Data Science Institute, Faculty of Engineering & IT, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia
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45
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Rawal K, Sinha R, Abbasi BA, Chaudhary A, Nath SK, Kumari P, Preeti P, Saraf D, Singh S, Mishra K, Gupta P, Mishra A, Sharma T, Gupta S, Singh P, Sood S, Subramani P, Dubey AK, Strych U, Hotez PJ, Bottazzi ME. Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Sci Rep 2021; 11:17626. [PMID: 34475453 PMCID: PMC8413327 DOI: 10.1038/s41598-021-96863-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.
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Affiliation(s)
- Kamal Rawal
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India.
| | - Robin Sinha
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Bilal Ahmed Abbasi
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Amit Chaudhary
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Swarsat Kaushik Nath
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Priya Kumari
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - P Preeti
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Devansh Saraf
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Shachee Singh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Kartik Mishra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Pranjay Gupta
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Astha Mishra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Trapti Sharma
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Srijanee Gupta
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Prashant Singh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Shriya Sood
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Preeti Subramani
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Aman Kumar Dubey
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Ulrich Strych
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Peter J Hotez
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
| | - Maria Elena Bottazzi
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
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Ostuni A, Monné M, Crudele MA, Cristinziano PL, Cecchini S, Amati M, De Vendel J, Raimondi P, Chassalevris T, Dovas CI, Bavoso A. Design and structural bioinformatic analysis of polypeptide antigens useful for the SRLV serodiagnosis. J Virol Methods 2021; 297:114266. [PMID: 34454989 DOI: 10.1016/j.jviromet.2021.114266] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 06/30/2021] [Accepted: 08/18/2021] [Indexed: 10/20/2022]
Abstract
Due to their intrinsic genetic, structural and phenotypic variability the Lentiviruses, and specifically small ruminant lentiviruses (SRLV), are considered viral quasispecies with a population structure that consists of extremely large numbers of variant genomes, termed mutant spectra or mutant cloud. Immunoenzymatic tests for SRLVs are available but the dynamic heterogeneity of the virus makes the development of a diagnostic "golden standard" extremely difficult. The ELISA reported in the literature have been obtained using proteins derived from a single strain or they are multi-strain based assay that may increase the sensitivity of the serological diagnosis. Hundreds of SRLV protein sequences derived from different viral strains are deposited in GenBank. The aim of this study is to verify if the database can be exploited with the help of bioinformatics in order to have a more systematic approach in the design of a set of representative protein antigens useful in the SRLV serodiagnosis. Clustering, molecular modelling, molecular dynamics, epitope predictions and aggregative/solubility predictions were the main bioinformatic tools used. This approach led to the design of SRLV antigenic proteins that were expressed by recombinant DNA technology using synthetic genes, analyzed by CD spectroscopy, tested by ELISA and preliminarily compared to currently commercially available detection kits.
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Affiliation(s)
- Angela Ostuni
- Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy.
| | - Magnus Monné
- Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy
| | | | - Pier Luigi Cristinziano
- Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy
| | - Stefano Cecchini
- Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy
| | - Mario Amati
- Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy
| | | | | | - Taxiarchis Chassalevris
- Diagnostic Laboratory, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 11 Stavrou Voutyra Str., 54627, Thessaloniki, Greece
| | - Chrysostomos I Dovas
- Diagnostic Laboratory, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 11 Stavrou Voutyra Str., 54627, Thessaloniki, Greece
| | - Alfonso Bavoso
- Department of Sciences, University of Basilicata, viale Ateneo Lucano 10, 85100, Potenza, Italy
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Tahir S, Bourquard T, Musnier A, Jullian Y, Corde Y, Omahdi Z, Mathias L, Reiter E, Crépieux P, Bruneau G, Poupon A. Accurate determination of epitope for antibodies with unknown 3D structures. MAbs 2021; 13:1961349. [PMID: 34432559 PMCID: PMC8405158 DOI: 10.1080/19420862.2021.1961349] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
MAbTope is a docking-based method for the determination of epitopes. It has been used to successfully determine the epitopes of antibodies with known 3D structures. However, during the antibody discovery process, this structural information is rarely available. Although we already have evidence that homology models of antibodies could be used instead of their 3D structure, the choice of the template, the methodology for homology modeling and the resulting performance still have to be clarified. Here, we show that MAbTope has the same performance when working with homology models of the antibodies as compared to crystallographic structures. Moreover, we show that even low-quality models can be used. We applied MAbTope to determine the epitope of dupilumab, an anti- interleukin 4 receptor alpha subunit therapeutic antibody of unknown 3D structure, that we validated experimentally. Finally, we show how the MAbTope-determined epitopes for a series of antibodies targeting the same protein can be used to predict competitions, and demonstrate the accuracy with an experimentally validated example. 3D: three-dimensionalRMSD: root mean square deviationCDR: complementary-determining regionCPU: central processing unitsVH: heavy chain variable regionVL: light chain variable regionscFv: single-chain variable fragmentsVHH: single-chain antibody variable regionIL4Rα: Interleukin 4 receptor alpha chainSPR: surface plasmon resonancePDB: protein data bankHEK293: Human embryonic kidney 293 cellsEDTA: Ethylenediaminetetraacetic acidFBS: Fetal bovine serumANOVA: Analysis of varianceEGFR: Epidermal growth factor receptorPE: PhycoerythrinAPC: AllophycocyaninFSC: forward scatterSSC: side scatterWT: wild type Keywords: MAbTope, Epitope Mapping, Molecular docking, Antibody modeling, Antibody-antigen docking
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Affiliation(s)
- Shifa Tahir
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France
| | - Thomas Bourquard
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,MAbSilico SAS, 1 Impasse Du Palais
| | - Astrid Musnier
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,MAbSilico SAS, 1 Impasse Du Palais
| | - Yann Jullian
- MAbSilico SAS, 1 Impasse Du Palais.,CaSciModOT, UFR De Sciences Et Techniques, Université De Tours
| | | | | | | | - Eric Reiter
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,France Inria, Inria Saclay-Île-de-France, Palaiseau, France.,Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | - Pascale Crépieux
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,France Inria, Inria Saclay-Île-de-France, Palaiseau, France.,Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | | | - Anne Poupon
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,MAbSilico SAS, 1 Impasse Du Palais.,France Inria, Inria Saclay-Île-de-France, Palaiseau, France.,Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
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Harvey WT, Carabelli AM, Jackson B, Gupta RK, Thomson EC, Harrison EM, Ludden C, Reeve R, Rambaut A, Peacock SJ, Robertson DL. SARS-CoV-2 variants, spike mutations and immune escape. Nat Rev Microbiol 2021; 19:409-424. [PMID: 34075212 PMCID: PMC8167834 DOI: 10.1038/s41579-021-00573-0] [Citation(s) in RCA: 2100] [Impact Index Per Article: 700.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2021] [Indexed: 02/07/2023]
Abstract
Although most mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome are expected to be either deleterious and swiftly purged or relatively neutral, a small proportion will affect functional properties and may alter infectivity, disease severity or interactions with host immunity. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis lasting about 11 months. Since late 2020, however, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations, in the context of 'variants of concern', that impact virus characteristics, including transmissibility and antigenicity, probably in response to the changing immune profile of the human population. There is emerging evidence of reduced neutralization of some SARS-CoV-2 variants by postvaccination serum; however, a greater understanding of correlates of protection is required to evaluate how this may impact vaccine effectiveness. Nonetheless, manufacturers are preparing platforms for a possible update of vaccine sequences, and it is crucial that surveillance of genetic and antigenic changes in the global virus population is done alongside experiments to elucidate the phenotypic impacts of mutations. In this Review, we summarize the literature on mutations of the SARS-CoV-2 spike protein, the primary antigen, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets.
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Affiliation(s)
- William T Harvey
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | | | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Ravindra K Gupta
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
| | - Emma C Thomson
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Ewan M Harrison
- Department of Medicine, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
| | | | - Richard Reeve
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
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49
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Conformational epitope matching and prediction based on protein surface spiral features. BMC Genomics 2021; 22:116. [PMID: 34058977 PMCID: PMC8165135 DOI: 10.1186/s12864-020-07303-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 01/20/2023] Open
Abstract
Background A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. Results We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. Conclusions The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.
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50
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Hwang W, Lei W, Katritsis NM, MacMahon M, Chapman K, Han N. Current and prospective computational approaches and challenges for developing COVID-19 vaccines. Adv Drug Deliv Rev 2021; 172:249-274. [PMID: 33561453 PMCID: PMC7871111 DOI: 10.1016/j.addr.2021.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.
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Affiliation(s)
- Woochang Hwang
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Winnie Lei
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Surgery, University of Cambridge, Cambridge, UK
| | - Nicholas M Katritsis
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Méabh MacMahon
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Centre for Therapeutics Discovery, LifeArc, Stevenage, UK
| | - Kathryn Chapman
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Namshik Han
- Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
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