1
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Vardaxis I, Simovski B, Anzar I, Stratford R, Clancy T. Deep learning of antibody epitopes using positional permutation vectors. Comput Struct Biotechnol J 2024; 23:2695-2707. [PMID: 39035832 PMCID: PMC11260035 DOI: 10.1016/j.csbj.2024.06.005] [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: 04/02/2024] [Revised: 06/04/2024] [Accepted: 06/04/2024] [Indexed: 07/23/2024] Open
Abstract
Background The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies. Results Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training. Conclusions With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.
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Affiliation(s)
- Ioannis Vardaxis
- NEC OncoImmunity AS, Oslo Cancer Cluster, Ullernchausseen 64/66, Oslo 0379, Norway
| | - Boris Simovski
- NEC OncoImmunity AS, Oslo Cancer Cluster, Ullernchausseen 64/66, Oslo 0379, Norway
| | - Irantzu Anzar
- NEC OncoImmunity AS, Oslo Cancer Cluster, Ullernchausseen 64/66, Oslo 0379, Norway
| | - Richard Stratford
- NEC OncoImmunity AS, Oslo Cancer Cluster, Ullernchausseen 64/66, Oslo 0379, Norway
| | - Trevor Clancy
- NEC OncoImmunity AS, Oslo Cancer Cluster, Ullernchausseen 64/66, Oslo 0379, Norway
- Department of Vaccine Informatics, Institute for Tropical Medicine, Nagasaki University, Japan
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2
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Nagm AM, Moussa MM, Shoitan R, Ali A, Mashhour M, Salama AS, AbdulWakel HI. Detecting image manipulation with ELA-CNN integration: a powerful framework for authenticity verification. PeerJ Comput Sci 2024; 10:e2205. [PMID: 39145198 PMCID: PMC11323046 DOI: 10.7717/peerj-cs.2205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/26/2024] [Indexed: 08/16/2024]
Abstract
The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision.
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Affiliation(s)
- Ahmad M. Nagm
- Department of Computer Engineering and Electronics, Cairo Higher Institute for Engineering, Computer Science and Management, Cairo, Egypt
| | - Mona M. Moussa
- Computer and Systems Department, Electronics Research Institute, Cairo, Egypt
| | - Rasha Shoitan
- Computer and Systems Department, Electronics Research Institute, Cairo, Egypt
| | - Ahmed Ali
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Computer Science, Higher Future Institute for Specialized Technological Studies, Cairo, Egypt
| | - Mohamed Mashhour
- Department of Computer Engineering and Electronics, Cairo Higher Institute for Engineering, Computer Science and Management, Cairo, Egypt
- Computer Science Department, Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Ahmed S. Salama
- Department of Computer Engineering and Electronics, Cairo Higher Institute for Engineering, Computer Science and Management, Cairo, Egypt
- Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, Egypt
| | - Hamada I. AbdulWakel
- Computer Science Department, Faculty of Computers and Information, Minia University, Minia, Egypt
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3
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Oladipo EK, Ojo TO, Elegbeleye OE, Bolaji OQ, Oyewole MP, Ogunlana AT, Olalekan EO, Abiodun B, Adediran DA, Obideyi OA, Olufemi SE, Salamatullah AM, Bourhia M, Younous YA, Adelusi TI. Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus. Sci Rep 2024; 14:16798. [PMID: 39039173 PMCID: PMC11263613 DOI: 10.1038/s41598-024-66828-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 07/04/2024] [Indexed: 07/24/2024] Open
Abstract
The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6-16 mers linear B-cell epitopes, 4-9 mer CTL epitopes, and 8-15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3-which has been reported to induce the same TLRs as EBV-as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY - 0.710; and a ProsaWeb Z score of - 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases-which include various malignancies, tumors, and cancers-preventively.
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Affiliation(s)
- Elijah Kolawole Oladipo
- Division of Vaccine Design and Development, Helix Biogen Institute, Ogbomoso, 210214, Nigeria
- Department of Microbiology, Laboratory of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, 232104, Nigeria
| | - Taiwo Ooreoluwa Ojo
- Division of Vaccine Design and Development, Helix Biogen Institute, Ogbomoso, 210214, Nigeria
- Computational Biology and Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, (LAUTECH), Ogbomoso, 210214, Nigeria
| | - Oluwabamise Emmanuel Elegbeleye
- Computational Biology and Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, (LAUTECH), Ogbomoso, 210214, Nigeria
| | - Olawale Quadri Bolaji
- Computational Biology and Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, (LAUTECH), Ogbomoso, 210214, Nigeria
| | - Moyosoluwa Precious Oyewole
- Division of Vaccine Design and Development, Helix Biogen Institute, Ogbomoso, 210214, Nigeria
- Department of Biochemistry, Bowen University, Iwo, 232101, Nigeria
| | - Abdeen Tunde Ogunlana
- Institute of Advanced Medical Research and Training (IAMRAT), College of Medicine, University of Ibadan, Ibadan, 200005, Nigeria
| | - Emmanuel Obanijesu Olalekan
- Computational Biology and Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, (LAUTECH), Ogbomoso, 210214, Nigeria
| | - Bamidele Abiodun
- Computational Biology and Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, (LAUTECH), Ogbomoso, 210214, Nigeria
| | - Daniel Adewole Adediran
- Division of Vaccine Design and Development, Helix Biogen Institute, Ogbomoso, 210214, Nigeria
| | | | - Seun Elijah Olufemi
- Division of Vaccine Design and Development, Helix Biogen Institute, Ogbomoso, 210214, Nigeria
| | - Ahmad Mohammad Salamatullah
- Department of Food Science and Nutrition, College of Food and Agricultural Sciences, King Saud University, 11, P.O. Box 2460, 11451, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Laboratory of Therapeutic and Organic Chemistry, Faculty of Pharmacy, University of Montpellier, Montpellier, 34000, France
| | | | - Temitope Isaac Adelusi
- Computational Biology and Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, (LAUTECH), Ogbomoso, 210214, Nigeria.
- Department of Surgery, School of Medicine, University of Connecticut Health, Farmington Ave, Farmington, CT, 06030, USA.
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4
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Fu Y, Guo L, Huang F. A lightweight CNN model for pepper leaf disease recognition in a human palm background. Heliyon 2024; 10:e33447. [PMID: 39027426 PMCID: PMC11254718 DOI: 10.1016/j.heliyon.2024.e33447] [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: 12/26/2023] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/20/2024] Open
Abstract
The identification of pepper leaf diseases is crucial for ensuring the safety and quality of pepper yield. However, existing methods heavily rely on manual diagnosis, resulting in inefficiencies and inaccuracies. In this study, we propose a lightweight convolutional neural network (CNN) model for recognizing pepper leaf diseases and subsequently develop an application based on this model. To begin with, we acquired various images depicting healthy leaves as well as leaves affected by viral diseases, brown spots, and leaf mold. It is noteworthy that these images were captured against a background of human palms, which is commonly encountered in field conditions. The proposed CNN model adopts the GGM-VGG16 architecture, incorporating Ghost modules, global average pooling, and multi-scale convolution. Following training with the collected image dataset, the model was deployed on a mobile terminal, where an application for pepper leaf disease recognition was developed using Android Studio. Experimental results indicate that the proposed model achieved 100 % accuracy on images with a human palm background, while also demonstrating satisfactory performance on images with other backgrounds, achieving an accuracy of 87.38 %. Furthermore, the developed application has a compact size of only 12.84 MB and exhibits robust performance in recognizing pepper leaf diseases.
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Affiliation(s)
- Youyao Fu
- School of Electronic & Information Engineering, Taizhou University, Taizhou, 318000, China
- Nuclear Technology Application Engineering Research Center of the Ministry of Education, Nanchang, 330013, China
| | - Linsheng Guo
- School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, 330013, China
| | - Fang Huang
- School of Electronic & Information Engineering, Taizhou University, Taizhou, 318000, China
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5
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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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6
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Alganmi N, Bashanfar A, Alotaibi R, Banjar H, Karim S, Mirza Z, Abusamra H, Al-Attas M, Turkistany S, Abuzenadah A. Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population. PeerJ Comput Sci 2024; 10:e1942. [PMID: 38660159 PMCID: PMC11042021 DOI: 10.7717/peerj-cs.1942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024]
Abstract
Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding regions of an individual's genome, providing a comprehensive analysis that surpasses traditional gene-by-gene sequencing methods. This technology offers efficiency, cost-effectiveness and the potential to identify new genetic variants contributing to the susceptibility to the diseases. Interpreting WES data for disease-causing variants is challenging due to its complex nature. Machine learning techniques can uncover hidden genetic-variant patterns associated with cancer susceptibility. In this study, we used the extreme gradient boosting (XGBoost) and random forest (RF) algorithms to identify BRCA-related cancer high-risk genes specifically in the Saudi population. The experimental results exposed that the RF method scored superior performance with an accuracy of 88.16% and an area under the receiver-operator characteristic curve of 0.95. Using bioinformatics analysis tools, we explored the top features of the high-accuracy machine learning model that we built to enhance our knowledge of genetic interactions and find complex genetic patterns connected to the development of BRCA-related cancers. We were able to identify the significance of HLA gene variations in these WES datasets for BRCA-related patients. We find that immune response mechanisms play a major role in the development of BRCA-related cancer. It specifically highlights genes associated with antigen processing and presentation, such as HLA-B, HLA-A and HLA-DRB1 and their possible effects on tumour progression and immune evasion. In summary, by utilizing machine learning approaches, we have the potential to aid in the development of precision medicine approaches for early detection and personalized treatment strategies.
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Affiliation(s)
- Nofe Alganmi
- Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Arwa Bashanfar
- Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Reem Alotaibi
- Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sajjad Karim
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Zeenat Mirza
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- King Fahd Medical Research Center, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Heba Abusamra
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Manal Al-Attas
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Shereen Turkistany
- Center of Innovation Personalized Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Adel Abuzenadah
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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7
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Ozsahin DU, Ameen ZS, Hassan AS, Mubarak AS. Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis. Sci Rep 2024; 14:6737. [PMID: 38509174 PMCID: PMC10954760 DOI: 10.1038/s41598-024-55762-7] [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: 08/05/2023] [Accepted: 02/27/2024] [Indexed: 03/22/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, UAE
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, UAE
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
| | - Zubaida Said Ameen
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
- Department of Biochemistry, Yusuf Maitama Sule University, Kano, Nigeria
| | - Abdurrahman Shuaibu Hassan
- Department of Electrical Electronics and Automation Systems Engineering, Kampala International University, Kampala, Uganda.
| | - Auwalu Saleh Mubarak
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey
- Department of Electrical Engineering, Aliko Dangote University of Science and Technology, Wudil, Kano, Nigeria
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8
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Kim DN, McNaughton AD, Kumar N. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 2024; 11:185. [PMID: 38391671 PMCID: PMC10886287 DOI: 10.3390/bioengineering11020185] [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: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
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Affiliation(s)
- Doo Nam Kim
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Andrew D McNaughton
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
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9
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Liu F, Yuan C, Chen H, Yang F. Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings. Sci Rep 2024; 14:2464. [PMID: 38291341 PMCID: PMC10828400 DOI: 10.1038/s41598-024-53028-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/26/2024] [Indexed: 02/01/2024] Open
Abstract
Linear B-cell epitopes (BCEs) play a key role in the development of peptide vaccines and immunodiagnostic reagents. Therefore, the accurate identification of linear BCEs is of great importance in the prevention of infectious diseases and the diagnosis of related diseases. The experimental methods used to identify BCEs are both expensive and time-consuming and they do not meet the demand for identification of large-scale protein sequence data. As a result, there is a need to develop an efficient and accurate computational method to rapidly identify linear BCE sequences. In this work, we developed the new linear BCE prediction method LBCE-BERT. This method is based on peptide chain sequence information and natural language model BERT embedding information, using an XGBoost classifier. The models were trained on three benchmark datasets. The model was training on three benchmark datasets for hyperparameter selection and was subsequently evaluated on several test datasets. The result indicate that our proposed method outperforms others in terms of AUROC and accuracy. The LBCE-BERT model is publicly available at: https://github.com/Lfang111/LBCE-BERT .
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Affiliation(s)
- Fang Liu
- School of Humanistic Medicine, Anhui Medical University, Hefei, 230032, Anhui, China
| | - ChengCheng Yuan
- School of Biomedical Engineering, Anhui Medical University, Hefei, 230030, Anhui, China
| | - Haoqiang Chen
- School of Humanistic Medicine, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Fei Yang
- School of Biomedical Engineering, Anhui Medical University, Hefei, 230030, Anhui, China.
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10
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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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11
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Wen Y, Chen R, Yang J, Yu E, Liu W, Liao Y, Wen Y, Wu R, Zhao Q, Du S, Yan Q, Han X, Cao S, Huang X. Identification of potential SLA-I-specific T-cell epitopes within the structural proteins of porcine deltacoronavirus. Int J Biol Macromol 2023; 251:126327. [PMID: 37579907 DOI: 10.1016/j.ijbiomac.2023.126327] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/09/2023] [Accepted: 08/11/2023] [Indexed: 08/16/2023]
Abstract
Porcine deltacoronavirus (PDCoV) is an emerging swine enteropathogenic coronavirus that mainly threatens newborn piglets and poses a potential broad cross-species transmission risk. The antigenic epitopes of PDCoV are currently unidentified, and no information about T cell epitopes is available. Here, T-cell epitopes of PDCoV structural proteins were predicted using computational methods. 17 epitope peptides were synthesized and then screened using ELIspot, intracellular cytokine staining (ICS), and RT-qPCR detection of IFN-γ mRNA to evaluate their ability to elicit interferon-gamma (IFN-γ) responses in peripheral blood mononuclear cells (PBMCs) from PDCoV-challenged pigs. Five peptides (M1, M2, M3, N6, and S4) elicited high levels of IFN-γ and were investigated further as potential T-cell epitope candidates. All five peptides were cytotoxic T lymphocyte (CTL) epitopes, and two peptides (M3, N6) were recognized simultaneously by CD8 + and CD4 + T cells. A multi-epitope peptide combining the five epitopes (designated "5T") was synthesized and its immune response and protection efficacy was evaluated in a piglet model. ELISpot assay results indicated that 5T induces robust epitope-specific cellular immune responses. Four epitopes (M1, M2, N6, S4) elicited IFN-γ responses in 5T-vaccinated piglets. No obvious protection efficacy was detected in piglets vaccinated with 5T alone. Our results provide valuable information concerning PDCoV-related antigenic epitopes and will be useful in the design of epitope-based vaccines.
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Affiliation(s)
- Yimin Wen
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Rui Chen
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Junpeng Yang
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Enbo Yu
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Weizhe Liu
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Yijie Liao
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Yiping Wen
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Rui Wu
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Qin Zhao
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Senyan Du
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Qigui Yan
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China
| | - Xinfeng Han
- Sichuan Science-Observation Experimental Station for Veterinary Drugs and Veterinary Diagnostic Technology, Ministry of Agriculture, Chengdu 611130, China
| | - Sanjie Cao
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; Sichuan Science-Observation Experimental Station for Veterinary Drugs and Veterinary Diagnostic Technology, Ministry of Agriculture, Chengdu 611130, China
| | - Xiaobo Huang
- Research Center for Swine Diseases, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu 611130, China; Sichuan Science-Observation Experimental Station for Veterinary Drugs and Veterinary Diagnostic Technology, Ministry of Agriculture, Chengdu 611130, China.
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12
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Elgebaly SA, Peacock WF, Christenson RH, Kreutzer DL, Faraag AHI, Sarguos AMM, El-Khazragy N. Integrated Bioinformatics Analysis Confirms the Diagnostic Value of Nourin-Dependent miR-137 and miR-106b in Unstable Angina Patients. Int J Mol Sci 2023; 24:14783. [PMID: 37834231 PMCID: PMC10573268 DOI: 10.3390/ijms241914783] [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: 08/27/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
The challenge of rapidly diagnosing myocardial ischemia in unstable angina (UA) patients presenting to the Emergency Department (ED) is due to a lack of sensitive blood biomarkers. This has prompted an investigation into microRNAs (miRNAs) related to cardiac-derived Nourin for potential diagnostic application. The Nourin protein is rapidly expressed in patients with acute coronary syndrome (ACS) (UA and acute myocardial infarction (AMI)). MicroRNAs regulate gene expression through mRNA binding and, thus, may represent potential biomarkers. We initially identified miR-137 and miR-106b and conducted a clinical validation, which demonstrated that they were highly upregulated in ACS patients, but not in healthy subjects and non-ACS controls. Using integrated comprehensive bioinformatics analysis, the present study confirms that the Nourin protein targets miR-137 and miR-106b, which are linked to myocardial ischemia and inflammation associated with ACS. Molecular docking demonstrated robust interactions between the Nourin protein and miR137/hsa-miR-106b, involving hydrogen bonds and hydrophobic interactions, with -10 kcal/mol binding energy. I-TASSER generated Nourin analogs, with the top 10 chosen for structural insights. Antigenic regions and MHCII epitopes within the Nourin SPGADGNGGEAMPGG sequence showed strong binding to HLA-DR/DQ alleles. The Cytoscape network revealed interactions of -miR137/hsa-miR--106b and Phosphatase and tensin homolog (PTEN) in myocardial ischemia. RNA Composer predicted the secondary structure of miR-106b. Schrödinger software identified key Nourin-RNA interactions critical for complex stability. The study identifies miR-137 and miR-106b as potential ACS diagnostic and therapeutic targets. This research underscores the potential of miRNAs targeting Nourin for precision ACS intervention. The analysis leverages RNA Composer, Schrödinger, and I-TASSER tools to explore interactions and structural insights. Robust Nourin-miRNA interactions are established, bolstering the case for miRNA-based interventions in ischemic injury. In conclusion, the study contributes to UA and AMI diagnosis strategies through bioinformatics-guided exploration of Nourin-targeting miRNAs. Supported by comprehensive molecular analysis, the hypoxia-induced miR-137 for cell apoptosis (a marker of cell damage) and the inflammation-induced miR-106b (a marker of inflammation) confirmed their potential clinical use as diagnostic biomarkers. This research reinforces the growing role of miR-137/hsa-miR-106b in the early diagnosis of myocardial ischemia in unstable angina patients.
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Affiliation(s)
- Salwa A. Elgebaly
- Research & Development, Nour Heart, Inc., Vienna, VA 22180, USA
- Department of Surgery, University of Connecticut School of Medicine, Farmington, CT 06032, USA;
| | - W. Frank Peacock
- Department of Emergency Medicine, Baylor College of Medicine, Houston, TX 77057, USA;
| | - Robert H. Christenson
- Department of Pathology, School of Medicine, University of Maryland, Baltimore, MD 2120, USA;
| | - Donald L. Kreutzer
- Department of Surgery, University of Connecticut School of Medicine, Farmington, CT 06032, USA;
| | - Ahmed Hassan Ibrahim Faraag
- Department of Botany and Microbiology, Faculty of Science Helwan University, Cairo 11795, Egypt;
- School of Biotechnology, Badr University, Cairo 11829, Egypt
| | | | - Nashwa El-Khazragy
- Department of Clinical Pathology-Hematology, Ain Shams Medical Research Institute (MASRI), Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt;
- Department of Genetics and Molecular Biology, Egypt Center for Research and Regenerative Medicine (ECRRM), Cairo11599, Egypt
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13
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Charoenkwan P, Waramit S, Chumnanpuen P, Schaduangrat N, Shoombuatong W. TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus. PLoS One 2023; 18:e0290538. [PMID: 37624802 PMCID: PMC10456195 DOI: 10.1371/journal.pone.0290538] [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: 06/07/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand
| | - Sajee Waramit
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
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14
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Shukla N, Srivastava N, Gupta R, Srivastava P, Narayan J. COVID Variants, Villain and Victory: A Bioinformatics Perspective. Microorganisms 2023; 11:2039. [PMID: 37630599 PMCID: PMC10459809 DOI: 10.3390/microorganisms11082039] [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: 06/21/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 08/27/2023] Open
Abstract
The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.
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Affiliation(s)
- Nityendra Shukla
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
| | - Neha Srivastava
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow 226010, India; (N.S.); (P.S.)
| | - Rohit Gupta
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
| | - Prachi Srivastava
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow 226010, India; (N.S.); (P.S.)
| | - Jitendra Narayan
- CSIR Institute of Genomics and Integrative Biology, Mall Road, Delhi 110007, India; (N.S.); (R.G.)
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15
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Feng H, Wang F, Li N, Xu Q, Zheng G, Sun X, Hu M, Xing G, Zhang G. A Random Forest Model for Peptide Classification Based on Virtual Docking Data. Int J Mol Sci 2023; 24:11409. [PMID: 37511165 PMCID: PMC10380188 DOI: 10.3390/ijms241411409] [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: 06/06/2023] [Revised: 06/25/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.
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Affiliation(s)
- Hua Feng
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Fangyu Wang
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Ning Li
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Qian Xu
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Guanming Zheng
- Public Health and Preventive Medicine Teaching and Research Center, Henan University of Chinese Medicine, Zhengzhou 450046, China
| | - Xuefeng Sun
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Man Hu
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Guangxu Xing
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Gaiping Zhang
- Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
- Longhu Modern Immunology Laboratory, Zhengzhou 450002, China
- School of Advanced Agricultural Sciences, Peking University, Beijing 100871, China
- Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou 225009, China
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16
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Długosz E, Wesołowska A. Immune Response of the Host and Vaccine Development. Pathogens 2023; 12:pathogens12050637. [PMID: 37242307 DOI: 10.3390/pathogens12050637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
Vaccines are one of the greatest achievements of modern medicine, offering an effective way to fight and control infectious diseases [...].
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Affiliation(s)
- Ewa Długosz
- Division of Parasitology and Invasive Diseases, Department of Preclinical Sciences, Institute of Veterinary Medicine, Warsaw University of Life Sciences (SGGW), 02-787 Warsaw, Poland
| | - Agnieszka Wesołowska
- Museum and Institute of Zoology, Polish Academy of Sciences, Wilcza 64, 00-679 Warsaw, Poland
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17
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State of the art in epitope mapping and opportunities in COVID-19. Future Sci OA 2023; 16:FSO832. [PMID: 36897962 PMCID: PMC9987558 DOI: 10.2144/fsoa-2022-0048] [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: 07/29/2022] [Accepted: 02/15/2023] [Indexed: 03/08/2023] Open
Abstract
The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.
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18
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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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19
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Rawat SS, Keshri AK, Kaur R, Prasad A. Immunoinformatics Approaches for Vaccine Design: A Fast and Secure Strategy for Successful Vaccine Development. Vaccines (Basel) 2023; 11:vaccines11020221. [PMID: 36851099 PMCID: PMC9959071 DOI: 10.3390/vaccines11020221] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 01/11/2023] [Indexed: 01/20/2023] Open
Abstract
Vaccines are major contributors to the cost-effective interventions in major infectious diseases in the global public health space [...].
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20
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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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21
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Bahadori Z, Shafaghi M, Madanchi H, Ranjbar MM, Shabani AA, Mousavi SF. In silico designing of a novel epitope-based candidate vaccine against Streptococcus pneumoniae with introduction of a new domain of PepO as adjuvant. J Transl Med 2022; 20:389. [PMID: 36059030 PMCID: PMC9440865 DOI: 10.1186/s12967-022-03590-6] [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: 02/14/2022] [Accepted: 08/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Streptococcus pneumoniae is the leading reason for invasive diseases including pneumonia and meningitis, and also secondary infections following viral respiratory diseases such as flu and COVID-19. Currently, serotype-dependent vaccines, which have several insufficiency and limitations, are the only way to prevent pneumococcal infections. Hence, it is plain to need an alternative effective strategy for prevention of this organism. Protein-based vaccine involving conserved pneumococcal protein antigens with different roles in virulence could provide an eligible alternative to existing vaccines. METHODS In this study, PspC, PhtD and PsaA antigens from pneumococcus were taken to account to predict B-cell and helper T-cell epitopes, and epitope-rich regions were chosen to build the construct. To enhance the immunogenicity of the epitope-based vaccine, a truncated N-terminal fragment of pneumococcal endopeptidase O (PepO) was used as a potential TLR2/4 agonist which was identified by molecular docking studies. The ultimate construct was consisted of the chosen epitope-rich regions, along with the adjuvant role (truncated N-PepO) and suitable linkers. RESULTS The epitope-based vaccine was assessed as regards physicochemical properties, allergenicity, antigenicity, and toxicity. The 3D structure of the engineered construct was modeled, refined, and validated. Molecular docking and simulation of molecular dynamics (MD) indicated the proper and stable interactions between the vaccine and TLR2/4 throughout the simulation periods. CONCLUSIONS For the first time this work presents a novel vaccine consisting of epitopes of PspC, PhtD, and PsaA antigens which is adjuvanted with a new truncated domain of PepO. The computational outcomes revealed that the suggested vaccine could be deemed an efficient therapeutic vaccine for S. pneumoniae; nevertheless, in vitro and in vivo examinations should be performed to prove the potency of the candidate vaccine.
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Affiliation(s)
- Zohreh Bahadori
- Department of Medical Biotechnology, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran.,Research Center of Biotechnology, Semnan University of Medical Sciences, Semnan, Iran.,Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran
| | - Mona Shafaghi
- Department of Medical Biotechnology, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran.,Research Center of Biotechnology, Semnan University of Medical Sciences, Semnan, Iran.,Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran
| | - Hamid Madanchi
- Department of Medical Biotechnology, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran.,Research Center of Biotechnology, Semnan University of Medical Sciences, Semnan, Iran.,Drug Design and Bioinformatics Unit, Department of Medical Biotechnology, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Mohammad Mehdi Ranjbar
- Agricultural Research, Education, and Extension Organization (AREEO), Razi Vaccine and Serum Research Institute, Karaj, Iran
| | - Ali Akbar Shabani
- Department of Medical Biotechnology, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran. .,Research Center of Biotechnology, Semnan University of Medical Sciences, Semnan, Iran.
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22
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Bukhari SNH, Webber J, Mehbodniya A. Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates. Sci Rep 2022; 12:7810. [PMID: 35552469 PMCID: PMC9096330 DOI: 10.1038/s41598-022-11731-6] [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: 11/29/2021] [Accepted: 04/25/2022] [Indexed: 12/30/2022] Open
Abstract
Zika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen’s selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew’s correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.
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Affiliation(s)
- Syed Nisar Hussain Bukhari
- National Institute of Electronics and Information Technology (NIELIT), Ministry of Electronics and Information Technology (MeitY), Govt. of India, Srinagar, J&K, 191132, India
| | - Julian Webber
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha Area, Kuwait
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha Area, Kuwait.
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23
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Biotechnological Perspectives to Combat the COVID-19 Pandemic: Precise Diagnostics and Inevitable Vaccine Paradigms. Cells 2022; 11:cells11071182. [PMID: 35406746 PMCID: PMC8997755 DOI: 10.3390/cells11071182] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 01/27/2023] Open
Abstract
The outbreak of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause for the ongoing global public health emergency. It is more commonly known as coronavirus disease 2019 (COVID-19); the pandemic threat continues to spread aroundthe world with the fluctuating emergence of its new variants. The severity of COVID-19 ranges from asymptomatic to serious acute respiratory distress syndrome (ARDS), which has led to a high human mortality rate and disruption of socioeconomic well-being. For the restoration of pre-pandemic normalcy, the international scientific community has been conducting research on a war footing to limit extremely pathogenic COVID-19 through diagnosis, treatment, and immunization. Since the first report of COVID-19 viral infection, an array of laboratory-based and point-of-care (POC) approaches have emerged for diagnosing and understanding its status of outbreak. The RT-PCR-based viral nucleic acid test (NAT) is one of the rapidly developed and most used COVID-19 detection approaches. Notably, the current forbidding status of COVID-19 requires the development of safe, targeted vaccines/vaccine injections (shots) that can reduce its associated morbidity and mortality. Massive and accelerated vaccination campaigns would be the most effective and ultimate hope to end the COVID-19 pandemic. Since the SARS-CoV-2 virus outbreak, emerging biotechnologies and their multidisciplinary approaches have accelerated the understanding of molecular details as well as the development of a wide range of diagnostics and potential vaccine candidates, which are indispensable to combating the highly contagious COVID-19. Several vaccine candidates have completed phase III clinical studies and are reported to be effective in immunizing against COVID-19 after their rollout via emergency use authorization (EUA). However, optimizing the type of vaccine candidates and its route of delivery that works best to control viral spread is crucial to face the threatening variants expected to emerge over time. In conclusion, the insights of this review would facilitate the development of more likely diagnostics and ideal vaccines for the global control of COVID-19.
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