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Ras-Carmona A, Reche PA. Analysis of Virus-Specific B Cell Epitopes Reveals Extensive Antigen Degradation Prior to Recognition. Cells 2024; 13:1076. [PMID: 38994930 PMCID: PMC11240346 DOI: 10.3390/cells13131076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/14/2024] [Accepted: 06/19/2024] [Indexed: 07/13/2024] Open
Abstract
B cell epitopes must be visible for recognition by cognate B cells and/or antibodies. Here, we studied that premise for known linear B cell epitopes that were collected from the Immune Epitope Database as being recognized by humans during microbial infections. We found that the majority of such known B cell epitopes are virus-specific linear B cell epitopes (87.96%), and most are located in antigens that remain enclosed in host cells and/or virus particles, preventing antibody recognition (18,832 out of 29,225 epitopes). Moreover, we estimated that only a minority (32.72%) of the virus-specific linear B cell epitopes that are found in exposed viral regions (e.g., the ectodomains of envelope proteins) are solvent accessible on intact antigens. Hence, we conclude that ample degradation/processing of viral particles and/or infected cells must occur prior to B cell recognition, thus shaping the B cell epitope repertoire.
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Affiliation(s)
- Alvaro Ras-Carmona
- Laboratory of Immunomedicine, Department of Immunology & O2, Faculty of Medicine, University Complutense of Madrid, Pza Ramon y Cajal S/N, 28040 Madrid, Spain
| | - Pedro A Reche
- Laboratory of Immunomedicine, Department of Immunology & O2, Faculty of Medicine, University Complutense of Madrid, Pza Ramon y Cajal S/N, 28040 Madrid, Spain
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2
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Elrashedy A, Nayel M, Salama A, Salama MM, Hasan ME. Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31. Sci Rep 2024; 14:11951. [PMID: 38789443 PMCID: PMC11126717 DOI: 10.1038/s41598-024-61991-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.
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Affiliation(s)
- Alyaa Elrashedy
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt.
| | - Mohamed Nayel
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Akram Salama
- Department of Animal Medicine and Infectious Diseases (Infectious Diseases), Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt
| | - Mohammed M Salama
- Physics Department, Medical Biophysics Division, Faculty of Science, Helwan University, Cairo, Egypt
| | - Mohamed E Hasan
- Bioinformatics Department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City, Egypt
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3
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Kumar N, Tripathi S, Sharma N, Patiyal S, Devi NL, Raghava GPS. A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence. Comput Biol Med 2024; 170:108083. [PMID: 38295479 DOI: 10.1016/j.compbiomed.2024.108083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 12/26/2023] [Accepted: 01/27/2024] [Indexed: 02/02/2024]
Abstract
B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, and Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUROC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we developed a hybrid model that combines alignment-free (dipeptide based random forest model) and alignment-based (BLAST-based similarity) models. Our hybrid model attained a maximum AUROC of 0.83 with an MCC of 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models were trained and tested on 80 % of the data using a cross-validation technique, and the final model was evaluated on 20 % of the data, called an independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence available at (https://webs.iiitd.edu.in/raghava/clbtope/).
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Affiliation(s)
- Nishant Kumar
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Sadhana Tripathi
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Naorem Leimarembi Devi
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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Arshad SF, Rehana R, Saleem MA, Usman M, Arshad HJ, Rizwana R, Shakeela S, Rukh AS, Khan IA, Hayssam MA, Anwar M. Multi-epitopes vaccine design for surface glycoprotein against SARS-CoV-2 using immunoinformatic approach. Heliyon 2024; 10:e24186. [PMID: 38298616 PMCID: PMC10827691 DOI: 10.1016/j.heliyon.2024.e24186] [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: 10/24/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
Background The recent COVID vaccinations have successfully reduced death and severity but did not stop the transmission of viruses by the emerging SARS-CoV-2 strain. There is a need for better and long-lasting dynamic vaccines for numerous prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of broad-spectrum strains being used to stop infection by reducing the spread rate and re-infection. The spike (S) glycoprotein is one of the proteins expressed commonly in the early phases of SARS-CoV-2 infection. It has been identified as the most immunogenic protein of SARS-CoV-2. Methods In this study, advanced bioinformatics techniques have been exploited to design the novel multi-epitope vaccine using conserved S protein portions from widespread strains of SARS-CoV-2 to predict B cell and T cell epitopes. These epitopes were selected based on toxicity, antigenicity score and immunogenicity. Epitope combinations were used to construct the maximum potent multi-epitope construct with potential immunogenic features. EAAAK, AAY, and GPGPG were used as linkers to construct epitopes. Results The developed vaccine has shown positive results. After the chimeric vaccine construct was cloned into the PET28a (+) vector for expression screening in Escherichia coli, the potential expression of the construct was identified. Conclusion The construct vaccine performed well in computer-based immune response simulation and covered a variety of allelic populations. These computational results are more helpful for further analysis of our contract vaccine, which can finally help control and prevent SARS-CoV-2 infections worldwide.
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Affiliation(s)
- Sarmad Frogh Arshad
- Department of Biochemistry and Biotechnology, Muhammad Nawaz Shareef University of Agriculture, Multan, 66000, Pakistan
| | - Rehana Rehana
- Institute of Plant Breeding & Biotechnology (IPBB), Muhammad Nawaz Shareef University of Agriculture, Multan, 66000, Pakistan
| | - Muhammad Asif Saleem
- Department of Plant Breeding and Genetics, Bahauddin Zakaria University, Multan, 60800, Pakistan
| | - Muhammad Usman
- Department of Biochemistry and Biotechnology, Muhammad Nawaz Shareef University of Agriculture, Multan, 66000, Pakistan
| | - Hasan Junaid Arshad
- Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad, 38000, Pakistan
| | - Rizwana Rizwana
- Department of Biochemistry, Bahauddin Zakaria University, Multan, 60800, Pakistan
| | | | - Asma Shah Rukh
- Department of Pharmacy, College of Pharmacy Punjab University, Lahore, 54590, Pakistan
| | - Imran Ahmad Khan
- Department of Pharmacy, MNS University of Agriculture, Multan, 54590, Pakistan
| | - M. Ali Hayssam
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, 1145, Saudi Arabia
| | - Muhammad Anwar
- School of Tropical Agriculture and Forestry, Hainan University, Haikou, PR China
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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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Takahashi T, Akagawa M, Kimura R, Sada M, Shirai T, Okayama K, Hayashi Y, Kondo M, Takeda M, Ryo A, Kimura H. Molecular evolutionary analyses of the fusion protein gene in human respirovirus 1. Virus Res 2023; 333:199142. [PMID: 37270034 PMCID: PMC10352714 DOI: 10.1016/j.virusres.2023.199142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/26/2023] [Accepted: 05/31/2023] [Indexed: 06/05/2023]
Abstract
Few evolutionary studies of the human respiratory virus (HRV) have been conducted, but most of them have focused on HRV3. In this study, the full-length fusion (F) genes in HRV1 strains collected from various countries were subjected to time-scaled phylogenetic, genome population size, and selective pressure analyses. Antigenicity analysis was performed on the F protein. The time-scaled phylogenetic tree using the Bayesian Markov Chain Monte Carlo method estimated that the common ancestor of the HRV1 F gene diverged in 1957 and eventually formed three lineages. Phylodynamic analyses showed that the genome population size of the F gene has doubled over approximately 80 years. Phylogenetic distances between the strains were short (< 0.02). No positive selection sites were detected for the F protein, whereas many negative selection sites were identified. Almost all conformational epitopes of the F protein, except one in each monomer, did not correspond to the neutralising antibody (NT-Ab) binding sites. These results suggest that the HRV1 F gene has constantly evolved over many years, infecting humans, while the gene may be relatively conserved. Mismatches between computationally predicted epitopes and NT-Ab binding sites may be partially responsible for HRV1 reinfection and other viruses such as HRV3 and respiratory syncytial virus.
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Affiliation(s)
- Tomoko Takahashi
- Iwate Prefectural Research Institute for Environmental Science and Public Health, Morioka-shi, Iwate 020-0857, Japan
| | - Mao Akagawa
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki-shi, Gunma 370-0006, Japan
| | - Ryusuke Kimura
- Advanced Medical Science Research Center, Gunma Paz University Research Institute, Shibukawa-shi, Gunma 377-0008, Japan; Department of Bacteriology, Gunma University Graduate School of Medicine, Maebashi-shi, Gunma 371-8514, Japan
| | - Mitsuru Sada
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki-shi, Gunma 370-0006, Japan; Advanced Medical Science Research Center, Gunma Paz University Research Institute, Shibukawa-shi, Gunma 377-0008, Japan
| | - Tatsuya Shirai
- Advanced Medical Science Research Center, Gunma Paz University Research Institute, Shibukawa-shi, Gunma 377-0008, Japan
| | - Kaori Okayama
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki-shi, Gunma 370-0006, Japan
| | - Yuriko Hayashi
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki-shi, Gunma 370-0006, Japan
| | - Mayumi Kondo
- Department of Clinical Engineering, Faculty of Medical Technology, Gunma Paz University, Takasaki-shi, Gunma 370-0006, Japan
| | - Makoto Takeda
- Department of Microbiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Akihide Ryo
- Department of Microbiology, Yokohama City University School of Medicine, Yokohama-shi, Kanagawa 236-0004, Japan
| | - Hirokazu Kimura
- Department of Health Science, Gunma Paz University Graduate School of Health Sciences, Takasaki-shi, Gunma 370-0006, Japan; Advanced Medical Science Research Center, Gunma Paz University Research Institute, Shibukawa-shi, Gunma 377-0008, Japan.
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7
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Shams M, Heydaryan S, Bashi MC, Gorgani BN, Ghasemi E, Majidiani H, Nazari N, Irannejad H. In silico design of a novel peptide-based vaccine against the ubiquitous apicomplexan Toxoplasma gondii using surface antigens. In Silico Pharmacol 2023; 11:5. [PMID: 36960094 PMCID: PMC10027966 DOI: 10.1007/s40203-023-00140-w] [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: 09/09/2022] [Accepted: 01/23/2023] [Indexed: 03/25/2023] Open
Abstract
Human toxoplasmosis is a global public health concern and a commercial vaccine is still lacking. The present in silico study was done to design a novel vaccine candidate using tachyzoite-specific SAG1-realted sequence (SRS) proteins. Overlapping B-cell and strictly-chosen human MHC-I binding epitopes were predicted and connected together using appropriate spacers. Moreover, a TLR4 agonist, human high mobility group box protein 1 (HMGB1), and His-tag were added to the N- and C-terminus of the vaccine sequence. The final vaccine had 442 residues and a molecular weight of 47.71 kDa. Physico-chemical evaluation showed a soluble, highly antigenic and non-allergen protein, with coils and helices as secondary structures. The vaccine 3D model was predicted by ITASSER server, subsequently refined and was shown to possess significant interactions with human TLR4. As well, potent stimulation of cellular and humoral immunity was demonstrated upon chimeric vaccine injection. Finally, the outputs showed that this vaccine model possesses top antigenicity, which could provoke significant cell-mediated immune profile including IFN-γ, and can be utilized towards prophylactic purposes. Supplementary Information The online version contains supplementary material available at 10.1007/s40203-023-00140-w.
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Affiliation(s)
- Morteza Shams
- Zoonotic Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran
| | - Saeed Heydaryan
- Department of Internal Medicine, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Mehdi Cheraghchi Bashi
- Department of Avian Diseases, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | | | - Ezatollah Ghasemi
- Department of Medical Parasitology, School of Medicine, Dezful University of Medical Sciences, Dezful, Iran
| | - Hamidreza Majidiani
- Department of Basic Medical Sciences, Neyshabur University of Medical Sciences, Neyshabur, Iran
| | - Naser Nazari
- Department of Parasitology and Mycology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Hamid Irannejad
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
- Pharmaceutical Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran
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Qi Y, Zheng P, Huang G. DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes. Front Microbiol 2023; 14:1117027. [PMID: 36910218 PMCID: PMC9992402 DOI: 10.3389/fmicb.2023.1117027] [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: 12/06/2022] [Accepted: 01/17/2023] [Indexed: 02/24/2023] Open
Abstract
The epitope is the site where antigens and antibodies interact and is vital to understanding the immune system. Experimental identification of linear B-cell epitopes (BCEs) is expensive, is labor-consuming, and has a low throughput. Although a few computational methods have been proposed to address this challenge, there is still a long way to go for practical applications. We proposed a deep learning method called DeepLBCEPred for predicting linear BCEs, which consists of bi-directional long short-term memory (Bi-LSTM), feed-forward attention, and multi-scale convolutional neural networks (CNNs). We extensively tested the performance of DeepLBCEPred through cross-validation and independent tests on training and two testing datasets. The empirical results showed that the DeepLBCEPred obtained state-of-the-art performance. We also investigated the contribution of different deep learning elements to recognize linear BCEs. In addition, we have developed a user-friendly web application for linear BCEs prediction, which is freely available for all scientific researchers at: http://www.biolscience.cn/DeepLBCEPred/.
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Affiliation(s)
- Yue Qi
- School of Information Engineering, Shaoyang University, Shaoyang, Hunan, China
| | - Peijie Zheng
- School of Information Engineering, Shaoyang University, Shaoyang, Hunan, China
| | - Guohua Huang
- School of Information Engineering, Shaoyang University, Shaoyang, Hunan, China
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9
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de Los Toyos JR. Prediction of Linear B Cell Epitopes in Proteins. Methods Mol Biol 2023; 2673:189-196. [PMID: 37258915 DOI: 10.1007/978-1-0716-3239-0_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The accurate prediction of B cell epitopes is crucial for the design and development of vaccines, especially of those preventive for emerging pathogenic diseases. Preventive vaccines are mainly based on the induction of highly specific neutralizing antibodies. This chapter deals with some prediction methods, which are currently available as user-friendly online servers, to predict B cell epitopes in proteins. A final assessment to validate these predictions is done by recurring to the Immune Epitope Database (IEDB).
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Affiliation(s)
- Juan R de Los Toyos
- Área de Inmunología, Facultad de Medicina y Ciencias de la Salud, Universidad de Oviedo, Oviedo, Spain.
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10
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Genome-Based Multi-Antigenic Epitopes Vaccine Construct Designing against Staphylococcus hominis Using Reverse Vaccinology and Biophysical Approaches. Vaccines (Basel) 2022; 10:vaccines10101729. [PMID: 36298594 PMCID: PMC9611379 DOI: 10.3390/vaccines10101729] [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: 09/01/2022] [Revised: 10/11/2022] [Accepted: 10/14/2022] [Indexed: 11/07/2022] Open
Abstract
Staphylococcus hominis is a Gram-positive bacterium from the staphylococcus genus; it is also a member of coagulase-negative staphylococci because of its opportunistic nature and ability to cause life-threatening bloodstream infections in immunocompromised patients. Gram-positive and opportunistic bacteria have become a major concern for the medical community. It has also drawn the attention of scientists due to the evaluation of immune evasion tactics and the development of multidrug-resistant strains. This prompted the need to explore novel therapeutic approaches as an alternative to antibiotics. The current study aimed to develop a broad-spectrum, multi-epitope vaccine to control bacterial infections and reduce the burden on healthcare systems. A computational framework was designed to filter the immunogenic potent vaccine candidate. This framework consists of pan-genomics, subtractive proteomics, and immunoinformatics approaches to prioritize vaccine candidates. A total of 12,285 core proteins were obtained using a pan-genome analysis of all strains. The screening of the core proteins resulted in the selection of only two proteins for the next epitope prediction phase. Eleven B-cell derived T-cell epitopes were selected that met the criteria of different immunoinformatics approaches such as allergenicity, antigenicity, immunogenicity, and toxicity. A vaccine construct was formulated using EAAAK and GPGPG linkers and a cholera toxin B subunit. This formulated vaccine construct was further used for downward analysis. The vaccine was loop refined and improved for structure stability through disulfide engineering. For an efficient expression, the codons were optimized as per the usage pattern of the E coli (K12) expression system. The top three refined docked complexes of the vaccine that docked with the MHC-I, MHC-II, and TLR-4 receptors were selected, which proved the best binding potential of the vaccine with immune receptors; this was followed by molecular dynamic simulations. The results indicate the best intermolecular bonding between immune receptors and vaccine epitopes and that they are exposed to the host’s immune system. Finally, the binding energies were calculated to confirm the binding stability of the docked complexes. This work aimed to provide a manageable list of immunogenic and antigenic epitopes that could be used as potent vaccine candidates for experimental in vivo and in vitro studies.
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11
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Xu H, Zhao Z. NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:1002-1012. [PMID: 36526218 PMCID: PMC10025766 DOI: 10.1016/j.gpb.2022.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 10/27/2022] [Accepted: 11/11/2022] [Indexed: 12/15/2022]
Abstract
Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.
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Affiliation(s)
- Haodong Xu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.
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12
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Prediction of B cell epitopes in proteins using a novel sequence similarity-based method. Sci Rep 2022; 12:13739. [PMID: 35962028 PMCID: PMC9374694 DOI: 10.1038/s41598-022-18021-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/03/2022] [Indexed: 11/29/2022] Open
Abstract
Prediction of B cell epitopes that can replace the antigen for antibody production and detection is of great interest for research and the biotech industry. Here, we developed a novel BLAST-based method to predict linear B cell epitopes. To that end, we generated a BLAST-formatted database upon a dataset of 62,730 known linear B cell epitope sequences and considered as a B cell epitope any peptide sequence producing ungapped BLAST hits to this database with identity ≥ 80% and length ≥ 8. We examined B cell epitope predictions by this method in tenfold cross-validations in which we considered various types of non-B cell epitopes, including 62,730 peptide sequences with verified negative B cell assays. As a result, we obtained values of accuracy, specificity and sensitivity of 72.54 ± 0.27%, 81.59 ± 0.37% and 63.49 ± 0.43%, respectively. In an independent dataset incorporating 503 B cell epitopes, this method reached accuracy, specificity and sensitivity of 74.85%, 99.20% and 50.50%, respectively, outperforming state-of-the-art methods to predict linear B cell epitopes. We implemented this BLAST-based approach to predict B cell epitopes at http://imath.med.ucm.es/bepiblast.
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Wang A, Tian Y, Liu H, Ding P, Chen Y, Liang C, Du Y, Jiang D, Zhu X, Yin J, Zhang G. Identification of three conserved linear B cell epitopes on the SARS-CoV-2 spike protein. Emerg Microbes Infect 2022; 11:2120-2131. [PMID: 35916768 PMCID: PMC9487943 DOI: 10.1080/22221751.2022.2109515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Spike (S) glycoproteins is the most significant structural protein of SARS-CoV-2 and a key target for neutralizing antibodies. In light of the ongoing SARS-CoV-2 pandemic, identification and screening of epitopes of spike glycoproteins will provide vital progress in the development of sensitive and specific diagnostic tools. In the present study, NTD, RBD and S2 gene were inserted to the pcDNA3.1(+) vector and designed with N-terminal 6×His-tag for fusion expression in HEK293F cells by transient transfection. Six monoclonal antibodies (4G, 9E, 4B, 7D, 8F, 3D) were prepared using the expressed proteins by cell fusion technique. The characterization of mAbs were performed by indirect -ELISA, western blot and IFA. We designed 49 overlapping synthetized peptides cover the extracellular region of S protein which 6 amino acid residues were offset between adjacent (S1-S49). Peptides S12, S19 and S49 were identified as the immunodominant epitopes regions by the mAbs. These regions were further truncated and the peptides S12.2 286TDAVDCALDPLS297, S19.2 464FERDISTEIYQA475 and S49.4 1202ELGKYEQYIKWP1213 were identified as B- cell linear epitopes for the first time. Alanine scans showed that, the D467, I468, E471, Q474, A475 of the epitope S19.2 and K1205, Q1208, Y1209 of the epitope S49.4 were the core sites involved in the mAbs binding. Multiple sequence alignment analysis showed that these three epitopes were highly conserved among the variants of concern (VOCs) and variants of interest (VOIs). Taken together, the findings provide a potential material for rapid diagnosis methods of COVID-19.
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Affiliation(s)
- Aiping Wang
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Yuanyuan Tian
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Hongliang Liu
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Peiyang Ding
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Yumei Chen
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Chao Liang
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Yongkun Du
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Dawei Jiang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Xifang Zhu
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Jiajia Yin
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China
| | - Gaiping Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, P.R. China.,Longhu laboratory of advanced immunology, Zhengzhou 450002, P.R. China.,Henan Key Laboratory of Immunobiology, Zhengzhou 450001, P.R. China.,College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, P.R. China
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Caoili SEC. Comprehending B-Cell Epitope Prediction to Develop Vaccines and Immunodiagnostics. Front Immunol 2022; 13:908459. [PMID: 35874755 PMCID: PMC9300992 DOI: 10.3389/fimmu.2022.908459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022] Open
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15
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Quantitative In Silico Evaluation of Allergenic Proteins from Anacardium occidentale, Carya illinoinensis, Juglans regia and Pistacia vera and Their Epitopes as Precursors of Bioactive Peptides. Curr Issues Mol Biol 2022; 44:3100-3117. [PMID: 35877438 PMCID: PMC9317212 DOI: 10.3390/cimb44070214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of the study presented here was to determine if there is a correlation between the presence of specific protein domains within tree nut allergens or tree nut allergen epitopes and the frequency of bioactive fragments and the predicted susceptibility to enzymatic digestion in allergenic proteins from tree nuts of cashew (Anacardium occidentale), pecan (Carya illinoinensis), English walnut (Juglans regia) and pistachio (Pistacia vera) plants. These bioactive peptides are distributed along the length of the protein and are not enriched in IgE epitope sequences. Classification of proteins as bioactive peptide precursors based on the presence of specific protein domains may be a promising approach. Proteins possessing a vicilin, N-terminal family domain, or napin domain contain a relatively low occurrence of bioactive fragments. In contrast, proteins possessing the cupin 1 domain without the vicilin N-terminal family domain contain a relatively high total frequency of bioactive fragments and predicted release of bioactive fragments by the joint action of pepsin, trypsin, and chymotrypsin. This approach could be utilized in food science to simplify the selection of protein domains enriched for bioactive peptides.
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Maleki A, Russo G, Parasiliti Palumbo GA, Pappalardo F. In silico design of recombinant multi-epitope vaccine against influenza A virus. BMC Bioinformatics 2022; 22:617. [PMID: 35109785 PMCID: PMC8808469 DOI: 10.1186/s12859-022-04581-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 01/20/2022] [Indexed: 11/10/2022] Open
Abstract
Background Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated.
Results This study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a+ vector. Conclusions The proposed “Recombinant multi-epitope vaccine” was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04581-6.
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Affiliation(s)
- Avisa Maleki
- Department of Mathematics and Computer Science, University of Catania, 95125, Catania, Italy
| | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, 95125, Catania, Italy
| | | | - Francesco Pappalardo
- Department of Drug and Health Sciences, University of Catania, 95125, Catania, Italy.
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Pritam M, Singh G, Kumar R, Singh SP. Screening of potential antigens from whole proteome and development of multi-epitope vaccine against Rhizopus delemar using immunoinformatics approaches. J Biomol Struct Dyn 2022; 41:2118-2145. [PMID: 35067195 DOI: 10.1080/07391102.2022.2028676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Mucormycosis is a deadly fungal disease mainly caused by Rhizopus oryzae (strain 99-880), also known as Rhizopus delemar. Previously, mucormycosis occurs in immunocompromised patients of diabetes mellitus, cancer, organ transplant, etc. But there was a drastic increase in mucormycosis cases in the ongoing COVID-19 pandemic. Despite several available therapies and antifungal treatments, the mortality rate of mucormycosis is about more than 50%. Currently, there is no vaccine available in the market for mucormycosis that urgently needs to develop a potential vaccine against mucormycosis with high efficacy. In the present study, we have screened 4 genome-derived predicted antigens (GDPA) through sequential filtration of the whole proteome of R. delemar using different benchmarked bioinformatics tools. These 4 GDPA along with 4 randomly selected experimentally reported antigens (ERA) were sourced for prediction of B- and T- cell epitopes and utilized in designing of two potential multi-epitope vaccine candidates which can induce both innate and adaptive immunity against R. delemar. Besides these, comparative immune simulation studies and in silico cloning were performed using L. lactis as an expression system for their possible uses as oral vaccines. This is the first multi-epitope vaccine designed against R. delemar through systematic pipelined reverse vaccinology and immunoinformatic approaches. Although the wet-lab based experimental validation of designed vaccines is required before testing in the preclinical model, the current study will significantly help in reducing the cost of experimentation as well as improving the efficacy of vaccine therapy against mucormycosis and other pathogenic diseases.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Manisha Pritam
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Garima Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
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