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Hao Q, Long Y, Yang Y, Deng Y, Ding Z, Yang L, Shu Y, Xu H. Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens. Vaccines (Basel) 2024; 12:717. [PMID: 39066355 PMCID: PMC11281709 DOI: 10.3390/vaccines12070717] [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: 05/29/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024] Open
Abstract
Neoantigens, presented as peptides on the surfaces of cancer cells, have recently been proposed as optimal targets for immunotherapy in clinical practice. The promising outcomes of neoantigen-based cancer vaccines have inspired enthusiasm for their broader clinical applications. However, the individualized tumor-specific antigens (TSA) entail considerable costs and time due to the variable immunogenicity and response rates of these neoantigens-based vaccines, influenced by factors such as neoantigen response, vaccine types, and combination therapy. Given the crucial role of neoantigen efficacy, a number of bioinformatics algorithms and pipelines have been developed to improve the accuracy rate of prediction through considering a series of factors involving in HLA-peptide-TCR complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. On the other hand, shared neoantigens, originating from driver mutations at hot mutation spots (e.g., KRASG12D), offer a promising and ideal target for the development of therapeutic cancer vaccines. A series of clinical practices have established the efficacy of these vaccines in patients with distinct HLA haplotypes. Moreover, increasing evidence demonstrated that a combination of tumor associated antigens (TAAs) and neoantigens can also improve the prognosis, thus expand the repertoire of shared neoantigens for cancer vaccines. In this review, we provide an overview of the complex process involved in identifying personalized neoantigens, their clinical applications, advances in vaccine technology, and explore the therapeutic potential of shared neoantigen strategies.
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Affiliation(s)
- Qing Hao
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yuhang Long
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yi Yang
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yiqi Deng
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Colorectal Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhenyu Ding
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Li Yang
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yang Shu
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Institute of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Heng Xu
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Institute of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Center of Clinical Laboratory Medicine, Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
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2
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Zhang L, Song W, Zhu T, Liu Y, Chen W, Cao Y. ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model. Brief Bioinform 2024; 25:bbae133. [PMID: 38561979 PMCID: PMC10985285 DOI: 10.1093/bib/bbae133] [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: 11/14/2023] [Revised: 02/11/2024] [Accepted: 03/02/2024] [Indexed: 04/04/2024] Open
Abstract
Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Wenkai Song
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Tinghao Zhu
- College of Computer Science, Sichuan University, Chengdu 610065, China
- Nuclear Power Institute of China, Chengdu 610213, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, No. 29 Wangjiang Road, Chengdu 610065, China
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, No. 29 Wangjiang Road, Chengdu 610065, China
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3
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Yang Y, Wei Z, Cia G, Song X, Pucci F, Rooman M, Xue F, Hou Q. MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods. Front Immunol 2024; 15:1293706. [PMID: 38646540 PMCID: PMC11027168 DOI: 10.3389/fimmu.2024.1293706] [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: 09/13/2023] [Accepted: 02/19/2024] [Indexed: 04/23/2024] Open
Abstract
Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.
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Affiliation(s)
- Yaqing Yang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Zhonghui Wei
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Xixi Song
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
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4
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [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] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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5
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Wang X, Wu T, Jiang Y, Chen T, Pan D, Jin Z, Xie J, Quan L, Lyu Q. RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding. Bioinformatics 2024; 40:btad785. [PMID: 38175759 PMCID: PMC10796178 DOI: 10.1093/bioinformatics/btad785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/06/2024] Open
Abstract
MOTIVATION Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. RESULTS In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. AVAILABILITY The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.
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Affiliation(s)
- Xuejiao Wang
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Tingfang Wu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
| | - Yelu Jiang
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Taoning Chen
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Deng Pan
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Zhi Jin
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Jingxin Xie
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Lijun Quan
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
| | - Qiang Lyu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
- Province Key Lab for Information Processing Technologies, Soochow University, Suzhou, Jiangsu 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, Jiangsu 210000, China
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6
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Huang G, Tang X, Zheng P. DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction. BMC Genomics 2023; 24:706. [PMID: 37993812 PMCID: PMC10666343 DOI: 10.1186/s12864-023-09796-2] [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: 06/20/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023] Open
Abstract
Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred .
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Affiliation(s)
- Guohua Huang
- School of Information Technology and Administration, Hunan University of Finance and Economics, Changsha, Hunan, 410215, China.
- College of Information Science and Engineering, Shaoyang University, Shaoyang, Hunan, 422000, China.
| | - Xingyu Tang
- College of Information Science and Engineering, Shaoyang University, Shaoyang, Hunan, 422000, China
| | - Peijie Zheng
- College of Information Science and Engineering, Shaoyang University, Shaoyang, Hunan, 422000, China
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7
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Qu W, You R, Mamitsuka H, Zhu S. DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction. Bioinformatics 2023; 39:btad551. [PMID: 37669154 PMCID: PMC10516514 DOI: 10.1093/bioinformatics/btad551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/06/2023] [Accepted: 09/04/2023] [Indexed: 09/07/2023] Open
Abstract
MOTIVATION Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels. RESULTS The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction. AVAILABILITY AND IMPLEMENTATION DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.
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Affiliation(s)
- Wei Qu
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Ronghui You
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto Prefecture 611-0011, Japan
- Department of Computer Science, Aalto University, 00076 Espoo, Finland
| | - Shanfeng Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200030, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
- Shanghai Key Lab of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm, Fudan University, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 200433, China
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8
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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9
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Fonseca AF, Antunes DA. CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases. Front Immunol 2023; 14:1142573. [PMID: 37377956 PMCID: PMC10291144 DOI: 10.3389/fimmu.2023.1142573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.
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Affiliation(s)
| | - Dinler A. Antunes
- Antunes Lab, Center for Nuclear Receptors and Cell Signaling (CNRCS), Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
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10
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Advancing our knowledge of antigen processing with computational modelling, structural biology, and immunology. Biochem Soc Trans 2023; 51:275-285. [PMID: 36645000 DOI: 10.1042/bst20220782] [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: 09/19/2022] [Revised: 12/09/2022] [Accepted: 01/03/2023] [Indexed: 01/17/2023]
Abstract
Antigen processing is an immunological mechanism by which intracellular peptides are transported to the cell surface while bound to Major Histocompatibility Complex molecules, where they can be surveyed by circulating CD8+ or CD4+ T-cells, potentially triggering an immunological response. The antigen processing pathway is a complex multistage filter that refines a huge pool of potential peptide ligands derived from protein degradation into a smaller ensemble for surface presentation. Each stage presents unique challenges due to the number of ligands, the polymorphic nature of MHC and other protein constituents of the pathway and the nature of the interactions between them. Predicting the ensemble of displayed peptide antigens, as well as their immunogenicity, is critical for improving T cell vaccines against pathogens and cancer. Our predictive abilities have always been hindered by an incomplete empirical understanding of the antigen processing pathway. In this review, we highlight the role of computational and structural approaches in improving our understanding of antigen processing, including structural biology, computer simulation, and machine learning techniques, with a particular focus on the MHC-I pathway.
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11
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Cai Y, Chen R, Gao S, Li W, Liu Y, Su G, Song M, Jiang M, Jiang C, Zhang X. Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. Front Oncol 2023; 12:1054231. [PMID: 36698417 PMCID: PMC9868469 DOI: 10.3389/fonc.2022.1054231] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023] Open
Abstract
The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.
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Affiliation(s)
- Yu Cai
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Rui Chen
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Shenghan Gao
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Wenqing Li
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Yuru Liu
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Guodong Su
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mingming Song
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mengju Jiang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Chao Jiang
- Department of Neurology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
| | - Xi Zhang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
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12
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Immunoinformatics-Aided Design of a Peptide Based Multiepitope Vaccine Targeting Glycoproteins and Membrane Proteins against Monkeypox Virus. Viruses 2022; 14:v14112374. [PMID: 36366472 PMCID: PMC9693848 DOI: 10.3390/v14112374] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 01/31/2023] Open
Abstract
Monkeypox is a self-limiting zoonotic viral disease and causes smallpox-like symptoms. The disease has a case fatality ratio of 3-6% and, recently, a multi-country outbreak of the disease has occurred. The currently available vaccines that have provided immunization against monkeypox are classified as live attenuated vaccinia virus-based vaccines, which pose challenges of safety and efficacy in chronic infections. In this study, we have used an immunoinformatics-aided design of a multi-epitope vaccine (MEV) candidate by targeting monkeypox virus (MPXV) glycoproteins and membrane proteins. From these proteins, seven epitopes (two T-helper cell epitopes, four T-cytotoxic cell epitopes and one linear B cell epitopes) were finally selected and predicted as antigenic, non-allergic, interferon-γ activating and non-toxic. These epitopes were linked to adjuvants to design a non-allergic and antigenic candidate MPXV-MEV. Further, molecular docking and molecular dynamics simulations predicted stable interactions between predicted MEV and human receptor TLR5. Finally, the immune-simulation analysis showed that the candidate MPXV-MEV could elicit a human immune response. The results obtained from these in silico experiments are promising but require further validation through additional in vivo experiments.
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13
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Design of a multi-epitope vaccine against the pathogenic fungi Candida tropicalis using an in silico approach. J Genet Eng Biotechnol 2022; 20:140. [PMID: 36175808 PMCID: PMC9521867 DOI: 10.1186/s43141-022-00415-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/25/2022] [Indexed: 12/02/2022]
Abstract
Background Candida tropicalis causes tropical invasive fungal infections, with a high mortality. This fungus has been found to be resistant to antifungal classes such as azoles, echinocandins, and polyenes in several studies. As a result, it is vital to identify novel approaches to prevent and treat C. tropicalis infections. In this study, an in silico technique was utilized to deduce and evaluate a powerful multivalent epitope-based vaccine against C. tropicalis, which targets the secreted aspartic protease 2 (SAP2) protein. This protein is implicated in virulence and host invasion. Results By focusing on the Sap2 protein, 11 highly antigenic, non-allergic, non-toxic, and conserved epitopes were identified. These were subsequently paired with RS09 and flagellin adjuvants, as well as a pan HLA DR-binding epitope (PADRE) sequence to create a vaccine candidate that elicited both cell-mediated and humoral immune responses. It was projected that the vaccine design would be soluble, stable, antigenic, and non-allergic. Ramachandran plot analysis was applied to validate the vaccine construct’s 3-dimensional model. The vaccine construct was tested (at 100 ns) using molecular docking and molecular dynamics simulations, which demonstrated that it can stably connect with MHC-I and Toll-like receptor molecules. Based on in silico studies, we have shown that the vaccine construct can be expressed in E. coli. We surmise that the vaccine design is unrelated to any human proteins, indicating that it is safe to use. Conclusions The vaccine design looks to be an effective option for preventing C. tropicalis infections, based on the outcomes of the studies. A fungal vaccine can be proposed as prophylactic medicine and could provide initial protection as sometimes diagnosis of infection could be challenging. However, more in vitro and in vivo research is needed to prove the efficacy and safety of the proposed vaccine design.
Supplementary Information The online version contains supplementary material available at 10.1186/s43141-022-00415-3.
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14
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Pissarra J, Dorkeld F, Loire E, Bonhomme V, Sereno D, Lemesre JL, Holzmuller P. SILVI, an open-source pipeline for T-cell epitope selection. PLoS One 2022; 17:e0273494. [PMID: 36070252 PMCID: PMC9451077 DOI: 10.1371/journal.pone.0273494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 08/09/2022] [Indexed: 11/18/2022] Open
Abstract
High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909.
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Affiliation(s)
- Joana Pissarra
- UMR INTERTRYP, IRD, CIRAD, University of Montpellier (I-MUSE), Montpellier, France
- * E-mail:
| | - Franck Dorkeld
- UMR CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, University of Montpellier (I-MUSE), Montpellier, France
| | - Etienne Loire
- UMR ASTRE, CIRAD, INRAE, University of Montpellier (I-MUSE), Montpellier, France
| | - Vincent Bonhomme
- ISEM, CNRS, EPHE, IRD, University of Montpellier (I-MUSE), Montpellier, France
| | - Denis Sereno
- UMR INTERTRYP, IRD, CIRAD, University of Montpellier (I-MUSE), Montpellier, France
| | - Jean-Loup Lemesre
- UMR INTERTRYP, IRD, CIRAD, University of Montpellier (I-MUSE), Montpellier, France
| | - Philippe Holzmuller
- UMR ASTRE, CIRAD, INRAE, University of Montpellier (I-MUSE), Montpellier, France
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15
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Hu RS, Wu J, Zhang L, Zhou X, Zhang Y. CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach. Front Genet 2022; 13:935989. [PMID: 35937988 PMCID: PMC9354802 DOI: 10.3389/fgene.2022.935989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 05/24/2022] [Indexed: 12/02/2022] Open
Abstract
Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8+ T-cell-related host adaptive immune response and with the available positive and negative CD8+ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8+ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8+ TCEs from non-CD8+ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8+ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).
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Affiliation(s)
- Rui-Si Hu
- Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou, China
| | - Jin Wu
- School of Management, Shenzhen Polytechnic, Shenzhen, China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Xun Zhou
- Beidahuang Industry Group General Hospital, Harbin, China
- *Correspondence: Xun Zhou, ; Ying Zhang,
| | - Ying Zhang
- Department of Anesthesiology, Hospital (T.C.M) Affiliated of Southwest Medical University, Luzhou, China
- *Correspondence: Xun Zhou, ; Ying Zhang,
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16
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Ragone C, Manolio C, Mauriello A, Cavalluzzo B, Buonaguro FM, Tornesello ML, Tagliamonte M, Buonaguro L. Molecular mimicry between tumor associated antigens and microbiota-derived epitopes. Lab Invest 2022; 20:316. [PMID: 35836198 PMCID: PMC9281086 DOI: 10.1186/s12967-022-03512-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 06/28/2022] [Indexed: 12/12/2022]
Abstract
Background The gut microbiota profile is unique for each individual and are composed by different bacteria species according to individual birth-to-infant transitions. In the last years, the local and systemic effects of microbiota on cancer onset, progression and response to treatments, such as immunotherapies, has been extensively described. Here we offer a new perspective, proposing a role for the microbiota based on the molecular mimicry of tumor associated antigens by microbiome-associated antigens. Methods In the present study we looked for homology between published TAAs and non-self microbiota-derived epitopes. Blast search for sequence homology was combined with extensive bioinformatics analyses. Results Several evidences for homology between TAAs and microbiota-derived antigens have been found. Strikingly, three cases of 100% homology between the paired sequences has been identified. The predicted average affinity to HLA molecules of microbiota-derived antigens is very high (< 100 nM). The structural conformation of the microbiota-derived epitopes is, in general, highly similar to the corresponding TAA. In some cases, it is identical and contact areas with both HLA and TCR chains are indistinguishable. Moreover, the spatial conformation of TCR-facing residues can be identical in paired TAA and microbiota-derived epitopes, with exactly the same values of planar as well as dihedral angles. Conclusions The data reported in the present study show for the first time the high homology in the linear sequence as well as in structure and conformation between TAAs and peptides derived from microbiota species of the Firmicutes and the Bacteroidetes phyla, which together account for 90% of gut microbiota. Cross-reacting CD8+ T cell responses are very likely induced. Therefore, the anti-microbiota T cell memory may turn out to be an anti-cancer T cell memory, able to control the growth of a cancer developed during the lifetime if the expressed TAA is similar to the microbiota epitope. This may ultimately represent a relevant selective advantage for cancer patients and may lead to a novel preventive anti-cancer vaccine strategy. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03512-6.
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Affiliation(s)
- Concetta Ragone
- Lab of Innovative Immunological Models, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS, "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Carmen Manolio
- Lab of Innovative Immunological Models, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS, "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Angela Mauriello
- Lab of Innovative Immunological Models, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS, "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Beatrice Cavalluzzo
- Lab of Innovative Immunological Models, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS, "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Franco M Buonaguro
- Molecular Biology and Viral Oncogenesis Unit, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS "Fond G. Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Maria Lina Tornesello
- Molecular Biology and Viral Oncogenesis Unit, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS "Fond G. Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Maria Tagliamonte
- Lab of Innovative Immunological Models, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS, "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy.
| | - Luigi Buonaguro
- Lab of Innovative Immunological Models, Istituto Nazionale per lo Studio e la Cura dei Tumori - IRCCS, "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy.
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17
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Liu Z, Jin J, Cui Y, Xiong Z, Nasiri A, Zhao Y, Hu J. DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2188-2196. [PMID: 33886473 DOI: 10.1109/tcbb.2021.3074927] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed and demonstrated with their excellent performances for peptide-HLA class I binding prediction. However, there is still a lack of effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. We present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction in this work. Our model is an end-to-end neural network model without the need for pre-or post-processing on input samples compared with existing pan-specific models. Besides state-of-the-art performance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide by its attention mechanism-based binding core prediction capability. The leave-one-allele-out cross-validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII.
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18
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A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00459-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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19
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Zhou F, He S, Zhang Y, Wang Y, Sun H, Liu Q. Prediction and characterization of the T cell epitopes for the major soybean protein allergens using bioinformatics approaches. Proteins 2022; 90:418-434. [PMID: 34486167 DOI: 10.1002/prot.26233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/14/2021] [Accepted: 08/30/2021] [Indexed: 12/19/2022]
Abstract
Protein allergens is a health risk for consumption of soybeans. To understand allerginicity mechanism, T cell epitopes of 7 soybean allergens were predicted and screened by abilities to induce cytokine interleukin (IL) 4. The relationships among amino acid composition, properties, allergenicity, and pepsin hydrolysis sites were analyzed. Among the 138 T cell epitopes identified, YIKDVFRVIPSEVLS, KDVFRVIPSEVLSNS, DVFRVIPSEVLSNSY of Gly m 6.0501 (P04347), and AKADALFKAIEAYLL, ADALFKAIEAYLLAH of Gly m 4.0101 (P26987) were the most possible epitope candidates. In T cell epitopes pattern, the frequencies of amino acids Q, D, E, P, and G decreased, while F, I, N, V, K, H, A, L, and S increased. Hydrophobic residues at positions p1 and p2 and positively charged residues in positions p13 might contribute to allergenicity. Most of epitopes could be hydrolyzed by pepsin into small polypeptides within 12 residues length, and the anti-digestive epitope regions contained I, V, S, N, and Q residues. T cell epitopes EEQRQQEGVIVELSK from Gly m 5.03 (P25974) showed resistance to pepsin hydrolysis and would cause a higher Th2 cell response. This research provides basis for the development of hypoallergenic soybean products in the soybean industry as well as for the immunotherapy design for protein allergy.
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Affiliation(s)
- Fanlin Zhou
- Engineering Research Center of Bio-process of Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Shudong He
- Engineering Research Center of Bio-process of Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Yi Zhang
- IPREM, E2S UPPA, CNRS, Université de Pau et des Pays de l'Adour, Pau, France
| | - Yongfei Wang
- Engineering Research Center of Bio-process of Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Hanju Sun
- Engineering Research Center of Bio-process of Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Qian Liu
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang, China
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20
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Gong W, Pan C, Cheng P, Wang J, Zhao G, Wu X. Peptide-Based Vaccines for Tuberculosis. Front Immunol 2022; 13:830497. [PMID: 35173740 PMCID: PMC8841753 DOI: 10.3389/fimmu.2022.830497] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/10/2022] [Indexed: 12/12/2022] Open
Abstract
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO's End TB Strategy.
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Affiliation(s)
- Wenping Gong
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Chao Pan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, Beijing, China
| | - Peng Cheng
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
- Hebei North University, Zhangjiakou City, China
| | - Jie Wang
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Guangyu Zhao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xueqiong Wu
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
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21
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Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J. Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review. Pathogens 2022; 11:146. [PMID: 35215090 PMCID: PMC8879824 DOI: 10.3390/pathogens11020146] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 02/01/2023] Open
Abstract
The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.
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Affiliation(s)
- Syed Nisar Hussain Bukhari
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Amit Jain
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Ehtishamul Haq
- Department of Biotechnology, University of Kashmir, Srinagar 190006, India;
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City 20185145, Kuwait;
| | - Julian Webber
- Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan;
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22
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Michalik M, Djahanschiri B, Leo JC, Linke D. An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2412:45-71. [PMID: 34918241 DOI: 10.1007/978-1-0716-1892-9_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles-mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.
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Affiliation(s)
| | - Bardya Djahanschiri
- Institute of Cell Biology and Neuroscience, Goethe University, Frankfurt, Germany
| | - Jack C Leo
- Department of Biosciences, Nottingham Trent University, Nottingham, UK
| | - Dirk Linke
- Department of Biosciences, University of Oslo, Oslo, Norway.
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23
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Basu A, Albert GK, Awshah S, Datta J, Kodumudi KN, Gallen C, Beyer A, Smalley KS, Rodriguez PC, Duckett DR, Forsyth PA, Soyano A, Koski GK, Lima Barros Costa R, Han H, Soliman H, Lee MC, Kalinski P, Czerniecki BJ. Identification of Immunogenic MHC Class II Human HER3 Peptides that Mediate Anti-HER3 CD4 + Th1 Responses and Potential Use as a Cancer Vaccine. Cancer Immunol Res 2022; 10:108-125. [PMID: 34785506 PMCID: PMC9414303 DOI: 10.1158/2326-6066.cir-21-0454] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/01/2021] [Accepted: 11/16/2021] [Indexed: 01/11/2023]
Abstract
The HER3/ERBB3 receptor is an oncogenic receptor tyrosine kinase that forms heterodimers with EGFR family members and is overexpressed in numerous cancers. HER3 overexpression associates with reduced survival and acquired resistance to targeted therapies, making it a potential therapeutic target in multiple cancer types. Here, we report on immunogenic, promiscuous MHC class II-binding HER3 peptides, which can generate HER3-specific CD4+ Th1 antitumor immune responses. Using an overlapping peptide screening methodology, we identified nine MHC class II-binding HER3 epitopes that elicited specific Th1 immune response in both healthy donors and breast cancer patients. Most of these peptides were not identified by current binding algorithms. Homology assessment of amino acid sequence BLAST showed >90% sequence similarity between human and murine HER3/ERBB3 peptide sequences. HER3 peptide-pulsed dendritic cell vaccination resulted in anti-HER3 CD4+ Th1 responses that prevented tumor development, significantly delayed tumor growth in prevention models, and caused regression in multiple therapeutic models of HER3-expressing murine tumors, including mammary carcinoma and melanoma. Tumors were robustly infiltrated with CD4+ T cells, suggesting their key role in tumor rejection. Our data demonstrate that class II HER3 promiscuous peptides are effective at inducing HER3-specific CD4+ Th1 responses and suggest their applicability in immunotherapies for human HER3-overexpressing tumors.
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Affiliation(s)
- Amrita Basu
- Clinical Science Division, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Gabriella K. Albert
- Clinical Science Division, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Sabrina Awshah
- Clinical Science Division, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Jashodeep Datta
- Department of Surgery, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, Florida
| | - Krithika N. Kodumudi
- Clinical Science Division, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Department of Oncological Sciences, University of South Florida, Tampa, Florida
| | - Corey Gallen
- Clinical Science Division, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Amber Beyer
- Clinical Science Division, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Keiran S.M. Smalley
- Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Paulo C. Rodriguez
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Derek R. Duckett
- Department of Drug Discovery, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Peter A. Forsyth
- Department of NeuroOncology and the NeuroOncology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Aixa Soyano
- Department of Breast Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Gary K. Koski
- Department of Biological Sciences, Kent State University, Kent, Ohio
| | | | - Heather Han
- Department of Breast Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Hatem Soliman
- Department of Breast Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Marie Catherine Lee
- Department of Breast Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Pawel Kalinski
- Department of Immunology, Roswell Park Comprehensive Cancer Center, New York, New York
| | - Brian J. Czerniecki
- Clinical Science Division, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Department of Breast Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Corresponding Author: Brian J. Czerniecki, Department of Breast Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612. E-mail:
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Abstract
Motivation Computationally predicting major histocompatibility complex (MHC)-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with a binding interaction convolution layer, which allows to integrate all potential binding cores (in a given peptide) with the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels. Results Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as 5-fold cross-validation, leave one molecule out, validation with independent testing sets and binding core prediction. All these results and visualization of the predicted binding cores indicate the effectiveness of our model, DeepMHCII, and the importance of properly modeling biological facts in deep learning for high predictive performance and efficient knowledge discovery. Availability and implementation DeepMHCII is publicly available at https://github.com/yourh/DeepMHCII. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ronghui You
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Sciences, Fudan University, Shanghai 200433, China
| | - Wei Qu
- Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Sciences, Fudan University, Shanghai 200433, China
- Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto Prefecture, Japan
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Shanfeng Zhu
- To whom correspondence should be addressed. E-mail:
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25
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Mauriello A, Cavalluzzo B, Manolio C, Ragone C, Luciano A, Barbieri A, Tornesello ML, Buonaguro FM, Tagliamonte M, Buonaguro L. Long-term memory T cells as preventive anticancer immunity elicited by TuA-derived heteroclitic peptides. J Transl Med 2021; 19:526. [PMID: 34952611 PMCID: PMC8709997 DOI: 10.1186/s12967-021-03194-6] [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: 11/08/2021] [Accepted: 12/11/2021] [Indexed: 11/10/2022] Open
Abstract
The host's immune system may be primed against antigens during the lifetime (e.g. microorganisms antigens-MoAs), and swiftly recalled upon growth of a tumor expressing antigens similar in sequence and structure. C57BL/6 mice were immunized in a preventive setting with tumor antigens (TuAs) or corresponding heteroclitic peptides specific for TC-1 and B16 cell lines. Immediately or 2-months after the end of the vaccination protocol, animals were implanted with cell lines. The specific anti-vaccine immune response as well as tumor growth were regularly evaluated for 2 months post-implantation. The preventive vaccination with TuA or their heteroclitic peptides (hPep) was able to delay (B16) or completely suppress (TC-1) tumor growth when cancer cells were implanted immediately after the end of the vaccination. More importantly, TC-1 tumor growth was significantly delayed, and suppressed in 6/8 animals, also when cells were implanted 2-months after the end of the vaccination. The vaccine-specific T cell response provided a strong immune correlate to the pattern of tumor growth. A preventive immunization with heteroclitic peptides resembling a TuA is able to strongly delay or even suppress tumor growth in a mouse model. More importantly, the same effect is observed also when tumor cells are implanted 2 months after the end of vaccination, which corresponds to 8 - 10 years in human life. The observed potent tumor control indicates that a memory T cell immunity elicited during the lifetime by a antigens similar to a TuA, i.e. viral antigens, may ultimately represent a great advantage for cancer patients and may lead to a novel preventive anti-cancer vaccine strategy.
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Affiliation(s)
- Angela Mauriello
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Beatrice Cavalluzzo
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Carmen Manolio
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Concetta Ragone
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Antonio Luciano
- Animal Facility, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Naples, Italy
| | - Antonio Barbieri
- Animal Facility, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Naples, Italy
| | - Maria Lina Tornesello
- Mol Biol and Viral Oncogenesis, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Naples, Italy
| | - Franco M Buonaguro
- Mol Biol and Viral Oncogenesis, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Naples, Italy
| | - Maria Tagliamonte
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Luigi Buonaguro
- Lab of Innovative Immunological Models, Istituto Nazionale Tumori - IRCCS "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy.
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26
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Fotakis G, Trajanoski Z, Rieder D. Computational cancer neoantigen prediction: current status and recent advances. IMMUNO-ONCOLOGY TECHNOLOGY 2021; 12:100052. [PMID: 35755950 PMCID: PMC9216660 DOI: 10.1016/j.iotech.2021.100052] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them. Tumors have the ability to acquire immune escape mechanisms. Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system. The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research. A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.
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Affiliation(s)
- G Fotakis
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Z Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - D Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
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27
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Belkahia H, Ben Abdallah M, Andolsi R, Selmi R, Zamiti S, Kratou M, Mhadhbi M, Darghouth MA, Messadi L, Ben Said M. Screening and Analysis of Anaplasma marginale Tunisian Isolates Reveal the Diversity of lipA Phylogeographic Marker and the Conservation of OmpA Protein Vaccine Candidate. Front Vet Sci 2021; 8:731200. [PMID: 34746278 PMCID: PMC8566978 DOI: 10.3389/fvets.2021.731200] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/16/2021] [Indexed: 01/18/2023] Open
Abstract
Bovine anaplasmosis caused by Anaplasma marginale is a disease responsible for serious animal health problems and great economic losses all over the world. Thereby, the identification of A. marginale isolates from various bioclimatic areas in each country, the phylogeographic analysis of these isolates based on the most informative markers, and the evaluation of the most promising candidate antigens are crucial steps in developing effective vaccines against a wide range of A. marginale strains. In order to contribute to this challenge, a total of 791 bovine samples from various bioclimatic areas of Tunisia were tested for the occurrence of A. marginale DNA through msp4 gene fragment amplification. Phylogeographic analysis was performed by using lipA and sucB gene analyses, and the genetic relationship with previously characterized A. marginale isolates and strains was analyzed by applying similarity comparison and phylogenetic analysis. To evaluate the conservation of OmpA protein vaccine candidate, almost complete ompA nucleotide sequences were also obtained from Tunisian isolates, and various bioinformatics software were used in order to analyze the physicochemical properties and the secondary and tertiary structures of their deduced proteins and to predict their immunodominant epitopes of B and T cells. A. marginale DNA was detected in 19 bovine samples (2.4%). Risk factor analysis shows that cattle derived from subhumid bioclimatic area were more infected than those that originated from other areas. The analysis of lipA phylogeographic marker indicated a higher diversity of Tunisian A. marginale isolates compared with other available worldwide isolates and strains. Molecular, phylogenetic, and immuno-informatics analyses of the vaccine candidate OmpA protein demonstrated that this antigen and its predicted immunodominant epitopes of B and T cells appear to be highly conserved between Tunisian isolates and compared with isolates from other countries, suggesting that the minimal intraspecific modifications will not affect the potential cross-protective capacity of humoral and cell-mediated immune responses against multiple A. marginale worldwide strains.
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Affiliation(s)
- Hanène Belkahia
- Service de Microbiologie et Immunologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Meriem Ben Abdallah
- Service de Microbiologie et Immunologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Rihab Andolsi
- Service de Microbiologie et Immunologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Rachid Selmi
- Service de Microbiologie et Immunologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia.,Ministère de la Défense Nationale, Direction Générale de la Santé Militaire, Service Vétérinaire, Tunis, Tunisia
| | - Sayed Zamiti
- Service de Parasitologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Myriam Kratou
- Service de Microbiologie et Immunologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Moez Mhadhbi
- Service de Parasitologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Mohamed Aziz Darghouth
- Service de Parasitologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Lilia Messadi
- Service de Microbiologie et Immunologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia
| | - Mourad Ben Said
- Service de Microbiologie et Immunologie, Ecole Nationale de Médecine Vétérinaire, University of Manouba, Sidi Thabet, Tunisia.,Département des Sciences Fondamentales, Institut Supérieur de Biotechnologie de Sidi Thabet, University of Manouba, Sidi Thabet, Tunisia
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28
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Connecting MHC-I-binding motifs with HLA alleles via deep learning. Commun Biol 2021; 4:1194. [PMID: 34663927 PMCID: PMC8523706 DOI: 10.1038/s42003-021-02716-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 09/24/2021] [Indexed: 12/17/2022] Open
Abstract
The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned. Ko-Han Lee et al. develop MHCfovea, a machine-learning method for predicting peptide-binding by MHC molecules and inferring peptide motifs and MHC allele signatures. They demonstrate that MHCfovea is capable of detecting meaningful hyper-motifs and allele signatures, making it a useful resource for the community.
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29
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Pearngam P, Sriswasdi S, Pisitkun T, Jones AR. MHCVision: estimation of global and local false discovery rate for MHC class I peptide binding prediction. Bioinformatics 2021; 37:3830-3838. [PMID: 34196671 PMCID: PMC8570816 DOI: 10.1093/bioinformatics/btab479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 06/11/2021] [Accepted: 06/30/2021] [Indexed: 11/29/2022] Open
Abstract
Motivation MHC-peptide binding prediction has been widely used for understanding the immune response of individuals or populations, each carrying different MHC molecules as well as for the development of immunotherapeutics. The results from MHC-peptide binding prediction tools are mostly reported as a predicted binding affinity (IC50) and the percentile rank score, and global thresholds e.g. IC50 value < 500 nM or percentile rank < 2% are generally recommended for distinguishing binding peptides from non-binding peptides. However, it is difficult to evaluate statistically the probability of an individual peptide binding prediction to be true or false solely considering predicted scores. Therefore, statistics describing the overall global false discovery rate (FDR) and local FDR, also called posterior error probability (PEP) are required to give statistical context to the natively produced scores. Result We have developed an algorithm and code implementation, called MHCVision, for estimation of FDR and PEP values for the predicted results of MHC-peptide binding prediction from the NetMHCpan tool. MHCVision performs parameter estimation using a modified expectation maximization framework for a two-component beta mixture model, representing the distribution of true and false scores of the predicted dataset. We can then estimate the PEP of an individual peptide’s predicted score, and conversely the probability that it is true. We demonstrate that the use of global FDR and PEP estimation can provide a better trade-off between sensitivity and precision over using currently recommended thresholds from tools. Availability and implementation https://github.com/PGB-LIV/MHCVision. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Phorutai Pearngam
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Sira Sriswasdi
- Research Affairs, Faculty of Medicine, Chulalongkorn, University, Bangkok, Thailand.,Computational Molecular Biology Group, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Trairak Pisitkun
- Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Andrew R Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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30
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Bingöl EN, Serçinoğlu O, Ozbek P. Unraveling the Allosteric Communication Mechanisms in T-Cell Receptor-Peptide-Loaded Major Histocompatibility Complex Dynamics Using Molecular Dynamics Simulations: An Approach Based on Dynamic Cross Correlation Maps and Residue Interaction Energy Calculations. J Chem Inf Model 2021; 61:2444-2453. [PMID: 33930270 DOI: 10.1021/acs.jcim.1c00338] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antigen presentation by major histocompatibility complex (MHC) proteins to T-cell receptors (TCRs) plays a crucial role in triggering the adaptive immune response. Most of our knowledge on TCR-peptide-loaded major histocompatibility complex (pMHC) interaction stemmed from experiments yielding static structures, yet the dynamic aspects of this molecular interaction are equally important to understand the underlying molecular mechanisms and to develop treatment strategies against diseases such as cancer and autoimmune diseases. To this end, computational biophysics studies including all-atom molecular dynamics simulations have provided useful insights; however, we still lack a basic understanding of an overall allosteric mechanism that results in conformational changes in the TCR and subsequent T-cell activation. Previous hydrogen-deuterium exchange and nuclear magnetic resonance studies provided clues regarding these molecular mechanisms, including global rigidification and allosteric effects on the constant domain of TCRs away from the pMHC interaction site. Here, we show that molecular dynamics simulations can be used to identify how this overall rigidification may be related to the allosteric communication within TCRs upon pMHC interaction via essential dynamics and nonbonded residue-residue interaction energy analyses. The residues taking part in the rigidification effect are highlighted with an intricate analysis on residue interaction changes, which lead to a detailed outline of the complex formation event. Our results indicate that residues of the Cβ domain of TCRs show significant differences in their nonbonded interactions upon complex formation. Moreover, the dynamic cross correlations between these residues are also increased, in line with their nonbonded interaction energy changes. Altogether, our approach may be valuable for elucidating intramolecular allosteric changes in the TCR structure upon pMHC interaction in molecular dynamics simulations.
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Affiliation(s)
- Elif Naz Bingöl
- Department of Bioengineering, Institute of Pure and Applied Sciences, Marmara University, 34722 Istanbul, Turkey
| | - Onur Serçinoğlu
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Kocaeli 41400, Turkey
| | - Pemra Ozbek
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul 34722, Turkey
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31
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Zhuang S, Tang L, Dai Y, Feng X, Fang Y, Tang H, Jiang P, Wu X, Fang H, Chen H. Bioinformatic prediction of immunodominant regions in spike protein for early diagnosis of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). PeerJ 2021; 9:e11232. [PMID: 33889450 PMCID: PMC8038641 DOI: 10.7717/peerj.11232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/16/2021] [Indexed: 01/06/2023] Open
Abstract
Background To contain the pandemics caused by SARS-CoV-2, early detection approaches with high accuracy and accessibility are critical. Generating an antigen-capture based detection system would be an ideal strategy complementing the current methods based on nucleic acids and antibody detection. The spike protein is found on the outside of virus particles and appropriate for antigen detection. Methods In this study, we utilized bioinformatics approaches to explore the immunodominant fragments on spike protein of SARS-CoV-2. Results The S1 subunit of spike protein was identified with higher sequence specificity. Three immunodominant fragments, Spike56-94, Spike199-264, and Spike577-612, located at the S1 subunit were finally selected via bioinformatics analysis. The glycosylation sites and high-frequency mutation sites on spike protein were circumvented in the antigen design. All the identified fragments present qualified antigenicity, hydrophilicity, and surface accessibility. A recombinant antigen with a length of 194 amino acids (aa) consisting of the selected immunodominant fragments as well as a universal Th epitope was finally constructed. Conclusion The recombinant peptide encoded by the construct contains multiple immunodominant epitopes, which is expected to stimulate a strong immune response in mice and generate qualified antibodies for SARS-CoV-2 detection.
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Affiliation(s)
- Siqi Zhuang
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lingli Tang
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yufeng Dai
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiaojing Feng
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yiyuan Fang
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Haoneng Tang
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ping Jiang
- Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiang Wu
- Department of Parasitology, Xiangya School of Basic Medicine, Central South University, Changsha, Hunan, China
| | - Hezhi Fang
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, College of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Hongzhi Chen
- National Clinical Research Center for Metabolic Disease, Key Laboratory of Diabetes Immunology, Ministry of Education, Metabolic Syndrome Research Center, and Department of Metabolism & Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
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32
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Oyarzun P, Kashyap M, Fica V, Salas-Burgos A, Gonzalez-Galarza FF, McCabe A, Jones AR, Middleton D, Kobe B. A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach. Front Immunol 2021; 12:598778. [PMID: 33717077 PMCID: PMC7952308 DOI: 10.3389/fimmu.2021.598778] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/11/2021] [Indexed: 01/06/2023] Open
Abstract
Emerging infectious diseases (EIDs) caused by viruses are increasing in frequency, causing a high disease burden and mortality world-wide. The COVID-19 pandemic caused by the novel SARS-like coronavirus (SARS-CoV-2) underscores the need to innovate and accelerate the development of effective vaccination strategies against EIDs. Human leukocyte antigen (HLA) molecules play a central role in the immune system by determining the peptide repertoire displayed to the T-cell compartment. Genetic polymorphisms of the HLA system thus confer a strong variability in vaccine-induced immune responses and may complicate the selection of vaccine candidates, because the distribution and frequencies of HLA alleles are highly variable among different ethnic groups. Herein, we build on the emerging paradigm of rational epitope-based vaccine design, by describing an immunoinformatics tool (Predivac-3.0) for proteome-wide T-cell epitope discovery that accounts for ethnic-level variations in immune responsiveness. Predivac-3.0 implements both CD8+ and CD4+ T-cell epitope predictions based on HLA allele frequencies retrieved from the Allele Frequency Net Database. The tool was thoroughly assessed, proving comparable performances (AUC ~0.9) against four state-of-the-art pan-specific immunoinformatics methods capable of population-level analysis (NetMHCPan-4.0, Pickpocket, PSSMHCPan and SMM), as well as a strong accuracy on proteome-wide T-cell epitope predictions for HIV-specific immune responses in the Japanese population. The utility of the method was investigated for the COVID-19 pandemic, by performing in silico T-cell epitope mapping of the SARS-CoV-2 spike glycoprotein according to the ethnic context of the countries where the ChAdOx1 vaccine is currently initiating phase III clinical trials. Potentially immunodominant CD8+ and CD4+ T-cell epitopes and population coverages were predicted for each population (the Epitope Discovery mode), along with optimized sets of broadly recognized (promiscuous) T-cell epitopes maximizing coverage in the target populations (the Epitope Optimization mode). Population-specific epitope-rich regions (T-cell epitope clusters) were further predicted in protein antigens based on combined criteria of epitope density and population coverage. Overall, we conclude that Predivac-3.0 holds potential to contribute in the understanding of ethnic-level variations of vaccine-induced immune responsiveness and to guide the development of epitope-based next-generation vaccines against emerging pathogens, whose geographic distributions and populations in need of vaccinations are often well-defined for regional epidemics.
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Affiliation(s)
- Patricio Oyarzun
- Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Sede Concepción, Concepción, Chile
| | - Manju Kashyap
- Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Sede Concepción, Concepción, Chile
| | - Victor Fica
- Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Sede Concepción, Concepción, Chile
| | | | - Faviel F Gonzalez-Galarza
- Center for Biomedical Research, Faculty of Medicine, Autonomous University of Coahuila, Torreon, Mexico
| | - Antony McCabe
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Andrew R Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Derek Middleton
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Bostjan Kobe
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD, Australia
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33
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Mei S, Li F, Xiang D, Ayala R, Faridi P, Webb GI, Illing PT, Rossjohn J, Akutsu T, Croft NP, Purcell AW, Song J. Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. Brief Bioinform 2021; 22:6102669. [PMID: 33454737 DOI: 10.1093/bib/bbaa415] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/29/2020] [Accepted: 12/16/2020] [Indexed: 12/17/2022] Open
Abstract
Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.
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Affiliation(s)
- Shutao Mei
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Australia
| | - Dongxu Xiang
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Rochelle Ayala
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Pouya Faridi
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | | | - Patricia T Illing
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Jamie Rossjohn
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan
| | - Nathan P Croft
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Anthony W Purcell
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Biochemistry and Molecular Biology, Monash University, Australia
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Affiliation(s)
- Luigi Buonaguro
- Innovative Immunological Models, Istituto Nazionale per lo Studio e la Cura dei Tumori, "Fondazione Pascale"-IRCCS, Via Mariano Semmola, 52, 80131 Naples, Italy.
| | - Vincenzo Cerullo
- Drug Research Program ImmunoViroTherapy Lab (IVTLAb), Faculty of Pharmacy, University of Helsinki, Helsinki, Finland; iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland; Translational Immunology Program (TRIMM), University of Helsinki, Helsinki, Finland; Department of Molecular Medicine and Medical Biotechnology, Naples University "Federico II," S. Pansini 5, Italy.
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An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-CoV-2. ACTA ACUST UNITED AC 2020; 2020:7079356. [PMID: 33299503 PMCID: PMC7686850 DOI: 10.1155/2020/7079356] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 09/13/2020] [Accepted: 11/04/2020] [Indexed: 01/05/2023]
Abstract
COVID-19 is a new viral emergent disease caused by a novel strain of coronavirus. This virus has caused a huge problem in the world as millions of people are affected by this disease. We aimed at designing a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system. The envelope protein sequence of SARS-CoV-2 has been retrieved from the NCBI database. The bioinformatics analysis was carried out by using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. The predicted HTL and CTL epitopes were docked with HLA alleles and binding energies were evaluated. The allergenicity of predicted epitopes was analyzed, the conservancy analysis was performed, and the population coverage was determined throughout the world. Some overlapped CTL, HTL, and B-cell epitopes were suggested to become a universal candidate for peptide-based vaccine against COVID-19. This vaccine peptide could simultaneously elicit humoral and cell-mediated immune responses. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate.
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Elucidating the Efficacy of Vaccination against Vibriosis in Lates calcarifer Using Two Recombinant Protein Vaccines Containing the Outer Membrane Protein K (r-OmpK) of Vibrio alginolyticus and the DNA Chaperone J (r-DnaJ) of Vibrio harveyi. Vaccines (Basel) 2020; 8:vaccines8040660. [PMID: 33171991 PMCID: PMC7711666 DOI: 10.3390/vaccines8040660] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/30/2020] [Accepted: 10/08/2020] [Indexed: 12/23/2022] Open
Abstract
Recombinant cell vaccines expressing the OmpK and DnaJ of Vibrio were developed and subsequently, a vaccination efficacy trial was carried out on juvenile seabass (~5 cm; ~20 g). The fish were divided into 5 groups of 50 fish per group, kept in triplicate. Groups 1 and 2 were injected with 107 CFU/mL of the inactivated recombinant cells vaccines, the pET-32/LIC-OmpK and pET-32/LIC-DnaJ, respectively. Group 3 was similarly injected with 107 CFU/mL of inactivated E. coli BL21 (DE3), Group 4 with 107 CFU/mL of formalin killed whole cells V. harveyi, and Group 5 with PBS solution. Serum, mucus, and gut lavage were used to determine the antibody levels before all fish were challenged with V. harveyi, V. alginolyticus, and V. parahemolyticus, respectively on day 15 post-vaccination. There was significant increase in the serum and gut lavage antibody titers in the juvenile seabass vaccinated with r-OmpK vaccine. In addition, there was an up-regulation for TLR2, MyD88, and MHCI genes in the kidney and intestinal tissues of r-OmpK vaccinated fish. At the same time, r-OmpK triggered higher expression level of interleukin IL-10, IL-8, IL-1ß in the spleen, intestine, and kidney compared to r-DnaJ. Overall, r-OmpK and r-DnaJ triggered protection by curbing inflammation and strengthening the adaptive immune response. Vaccinated fish also demonstrated strong cross protection against heterologous of Vibrio isolates, the V. harveyi, V. alginolyticus, and V. parahaemolyticus. The fish vaccinated with r-OmpK protein were completely protected with a relative per cent of survival (RPS) of 90 percent against V. harveyi and 100 percent against V. alginolyticus and V. parahaemolyticus. A semi-quantitative PCR detection of Vibrio spp. from the seawater containing the seabass also revealed that vaccination resulted in reduction of pathogen shedding. In conclusion, our results suggest r-OmpK as a candidate vaccine molecule against multiple Vibrio strain to prevent vibriosis in marine fish.
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Keating JE, Chung C, Chai S, Prins JF, Vincent BG, Hunsucker SA, Armistead PM, Glish GL. Alkali Metal Cationization of Tumor-associated Antigen Peptides for Improved Dissociation and Measurement by Differential Ion Mobility-Mass Spectrometry. J Proteome Res 2020; 19:3176-3183. [PMID: 32627559 PMCID: PMC9260395 DOI: 10.1021/acs.jproteome.0c00157] [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] [Indexed: 11/29/2022]
Abstract
Tandem mass spectrometry (MS/MS) is a highly sensitive and selective method for the detection of tumor-associated peptide antigens. These short, nontryptic sequences may lack basic residues, resulting in the formation of predominantly [peptide + H]+ ions in electrospray. These singly charged ions tend to undergo inefficient dissociation, leading to issues in sequence determination. Addition of alkali metal salts to the electrospray solvent can drive the formation of [peptide + H + metal]2+ ions that have enhanced dissociation characteristics relative to [peptide + H]+ ions. Both previously identified tumor-associated antigens and predicted neoantigen sequences were investigated. The previously reported rearrangement mechanism in MS/MS of sodium-cationized peptides is applied here to demonstrate complete C-terminal sequencing of tumor-associated peptide antigens. Differential ion mobility spectrometry (DIMS) is shown to selectively enrich [peptide + H + metal]2+ species by filtering out singly charged interferences at relatively low field strengths, offsetting the decrease in signal intensity associated with the use of alkali metal cations.
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Affiliation(s)
- James E. Keating
- Department of Chemistry, University of North Carolina at Chapel Hill, NC
| | - Chris Chung
- Department of Chemistry, University of North Carolina at Chapel Hill, NC
| | - Shengjie Chai
- Curriculum in Genetic & Molecular Biology, University of North Carolina at Chapel Hill, NC
| | - Jans F. Prins
- Computer Science, University of North Carolina at Chapel Hill, NC
| | - Benjamin G. Vincent
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, NC
| | - Sally A. Hunsucker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, NC
| | - Paul M. Armistead
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, NC
| | - Gary L. Glish
- Department of Chemistry, University of North Carolina at Chapel Hill, NC
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Abstract
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.
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Affiliation(s)
- Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Massimo Andreatta
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA
- Department of Medicine, University of California, San Diego, La Jolla, California 92093, USA
| | - Søren Buus
- Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
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Jakhar R, Kaushik S, Gakhar SK. 3CL hydrolase-based multiepitope peptide vaccine against SARS-CoV-2 using immunoinformatics. J Med Virol 2020; 92:2114-2123. [PMID: 32379348 DOI: 10.1002/jmv.25993] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/05/2020] [Indexed: 12/21/2022]
Abstract
The present study provides the first multiepitope vaccine construct using the 3CL hydrolase protein of SARS-CoV-2. The coronavirus 3CL hydrolase (Mpro) enzyme is essential for proteolytic maturation of the virus. This study was based on immunoinformatics and structural vaccinology strategies. The design of the multiepitope vaccine was built using helper T-cell and cytotoxic T-cell epitopes from the 3CL hydrolase protein along with an adjuvant to enhance immune response; these are joined to each other by short peptide linkers. The vaccine also carries potential B-cell linear epitope regions, B-cell discontinuous epitopes, and interferon-γ-inducing epitopes. Epitopes of the constructed multiepitope vaccine were found to be antigenic, nonallergic, nontoxic, and covering large human populations worldwide. The vaccine construct was modeled, validated, and refined by different programs to achieve a high-quality three-dimensional structure. The resulting high-quality model was applied for conformational B-cell epitope selection and docking analyses with toll-like receptor-3 for understanding the capability of the vaccine to elicit an immune response. In silico cloning and codon adaptation were also performed with the pET-19b plasmid vector. The designed multiepitope peptide vaccine may prompt the development of a vaccine to control SARS-CoV-2 infection.
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Affiliation(s)
- Renu Jakhar
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Samander Kaushik
- Centre for Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Surendra K Gakhar
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, India
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40
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Mei S, Li F, Leier A, Marquez-Lago TT, Giam K, Croft NP, Akutsu T, Smith AI, Li J, Rossjohn J, Purcell AW, Song J. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief Bioinform 2020; 21:1119-1135. [PMID: 31204427 DOI: 10.1093/bib/bbz051] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 04/02/2019] [Accepted: 04/03/2019] [Indexed: 12/13/2022] Open
Abstract
Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.
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Affiliation(s)
- Shutao Mei
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - André Leier
- Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Kailin Giam
- Department of Immunology, King's College London, London, UK
| | - Nathan P Croft
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Tatsuya Akutsu
- Bioinformatics Centre, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - A Ian Smith
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Jian Li
- Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia
| | - Jamie Rossjohn
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Anthony W Purcell
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia.,Monash Centre for Data Science, Monash University, Melbourne, VIC, Australia
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Abstract
Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.
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Affiliation(s)
- Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark;
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, B1650 Buenos Aires, Argentina
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
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42
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Jakhar R, Kumar P, Sehrawat N, Gakhar SK. A comprehensive analysis of amino-peptidase N1 protein (APN) from Anopheles culicifacies for epitope design using Immuno-informatics models. Bioinformation 2019; 15:600-612. [PMID: 31719771 PMCID: PMC6822521 DOI: 10.6026/97320630015600] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 09/10/2019] [Indexed: 11/23/2022] Open
Abstract
Analysis of the Amino-peptidase N (APN) protein from Anopheles culicifacies as a vector based Transmission Blocking Vaccines (TBV) target has been considered for malaria vaccine development. Short peptides as potential epitopes for B cells and cytotoxic T cells and/or helper T cells were identified using prediction models provided by NetCTL and IEDB servers. Antigenicity determination, allergenicity, immunogenicity, epitope conservancy analysis, atomic interaction with HLA allele specific structure models and population coverage were investigated in this study. The analysis of the target protein helped to identify conserved regions as potential epitopes of APN in various Anopheles species. The T cell epitopes like peptides were further analyzed by using molecular docking to check interactions against the allele specific HLA models. Thus, we report the predicted B cell (VDERYRL) and T cell (RRYLATTQF for HLA class I and LKATFTVSI for HLA class II) epitopes like peptides from APN protein of Anopheles culicifacies (Diptera: Culicidae) for further consideration as vaccine candidates subsequent to in vitro and in vivo analysis.
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Affiliation(s)
- Renu Jakhar
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak - 124001, Haryana
| | - Pawan Kumar
- Centre for Bioinformatics, Maharshi Dayanand University, Rohtak - 124001, Haryana
| | - Neelam Sehrawat
- Department of Genetics, Maharshi Dayanand University, Rohtak - 124001, Haryana
| | - Surendra Kumar Gakhar
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak - 124001, Haryana
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43
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Singh R, Lanchantin J, Robins G, Qi Y. Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1524-1536. [PMID: 27654939 DOI: 10.1109/tcbb.2016.2609918] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context. We, therefore, propose a method called "Transfer String Kernel" (TSK) that achieves improved prediction of transcription factor binding site (TFBS) using knowledge transfer via cross-context sample adaptation. TSK maps sequence segments to a high-dimensional feature space using a discriminative mismatch string kernel framework. In this high-dimensional space, labeled examples of the source context are re-weighted so that the revised sample distribution matches the target context more closely. We have experimentally verified TSK for TFBS identifications on 14 different TFs under a cross-organism setting. We find that TSK consistently outperforms the state-of-the-art TFBS tools, especially when working with TFs whose binding sequences are not conserved across contexts. We also demonstrate the generalizability of TSK by showing its cutting-edge performance on a different set of cross-context tasks for the MHC peptide binding predictions.
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Sharma M, Krammer F, García-Sastre A, Tripathi S. Moving from Empirical to Rational Vaccine Design in the 'Omics' Era. Vaccines (Basel) 2019; 7:vaccines7030089. [PMID: 31416125 PMCID: PMC6789792 DOI: 10.3390/vaccines7030089] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/16/2019] [Accepted: 07/17/2019] [Indexed: 12/11/2022] Open
Abstract
An ideal vaccine provides long lasting protection against a pathogen by eliciting a well-rounded immune response which engages both innate and adaptive immunity. However, we have a limited understanding of how components of innate immunity, antibody and cell-mediated adaptive immunity interact and function together at a systems level. With advances in high-throughput ‘Omics’ methodologies it has become possible to capture global changes in the host, at a cellular and molecular level, that are induced by vaccination and infection. Analysis of these datasets has shown the promise of discovering mechanisms behind vaccine mediated protection, immunological memory, adverse effects as well as development of more efficient antigens and adjuvants. In this review, we will discuss how systems vaccinology takes advantage of new technology platforms and big data analysis, to enable the rational development of better vaccines.
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Affiliation(s)
- Mansi Sharma
- Department of Microbiology & Cell Biology, Indian Institute of Science, Bengaluru 560012, India
- Centre for Infectious Disease Research, Indian Institute of Science, Bengaluru 560012, India
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Shashank Tripathi
- Department of Microbiology & Cell Biology, Indian Institute of Science, Bengaluru 560012, India.
- Centre for Infectious Disease Research, Indian Institute of Science, Bengaluru 560012, India.
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45
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Ning L, He B, Zhou P, Derda R, Huang J. Molecular Design of Peptide-Fc Fusion Drugs. Curr Drug Metab 2019; 20:203-208. [DOI: 10.2174/1389200219666180821095355] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 01/18/2018] [Accepted: 05/29/2018] [Indexed: 12/11/2022]
Abstract
Background:Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed.Methods:We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc.Results:The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools.Conclusion:Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.
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Affiliation(s)
- Lin Ning
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bifang He
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ratmir Derda
- Department of Chemistry, University of Alberta, Alberta, Canada
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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46
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In silico prediction of nonpermissive HLA-DPB1 mismatches in unrelated HCT by functional distance. Blood Adv 2019; 2:1773-1783. [PMID: 30042143 DOI: 10.1182/bloodadvances.2018019620] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 06/15/2018] [Indexed: 01/01/2023] Open
Abstract
In silico prediction of high-risk donor-recipient HLA mismatches after unrelated donor (UD) hematopoietic cell transplantation (HCT) is an attractive, yet elusive, objective. Nonpermissive T-cell epitope (TCE) group mismatches were defined by alloreactive T-cell cross-reactivity for 52/80 HLA-DPB1 alleles (TCE-X). More recently, a numerical functional distance (FD) scoring system for in silico prediction of TCE groups based on the median impact of exon 2-encoded amino acid polymorphism on T-cell alloreactivity was developed for all DPB1 alleles (TCE-FD), including the 28/80 common alleles not assigned by TCE-X. We compared clinical outcome associations of nonpermissive DPB1 mismatches defined by TCE-X or TCE-FD in 8/8 HLA-matched UD-HCT for acute leukemia, myelodysplastic syndrome, and chronic myelogenous leukemia between 1999 and 2011 (N = 2730). Concordance between the 2 models was 92.3%, with most differences arising from DPB1*06:01 and DPB1*19:01 being differently assigned by TCE-X and TCE-FD. In both models, nonpermissive mismatches were associated with reduced overall survival (hazard ratio [HR], 1.15, P < .006 and HR, 1.12, P < .03), increased transplant-related mortality (HR, 1.31, P < .001 and HR, 1.26, P < .001) as well as acute (HR, 1.16, P < .02 and HR, 1.22, P < .001) and chronic (HR, 1.20, P < .003 and HR, 1.22, P < .001) graft-versus-host disease (GVHD). We show that in silico prediction of nonpermissive DPB1 mismatches significantly associated with major transplant outcomes is feasible for any DPB1 allele with known exon 2 sequence based on experimentally elaborated FD scores. This proof-of-principle observation opens new avenues for developing HLA risk-prediction models in HCT and has practical implications for UD searches.
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Liu Z, Cui Y, Xiong Z, Nasiri A, Zhang A, Hu J. DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction. Sci Rep 2019; 9:794. [PMID: 30692623 PMCID: PMC6349913 DOI: 10.1038/s41598-018-37214-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 12/04/2018] [Indexed: 11/09/2022] Open
Abstract
Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in the human immune system. Accurate computational prediction of HLA-binding peptides can be used for peptide drug discovery. Currently, the best prediction algorithms are neural network-based pan-specific models, which take advantage of the large amount of data across HLA alleles. However, current pan-specific models are all based on the pseudo sequence encoding for modeling the binding context, which is based on 34 positions identified from the HLA protein-peptide bound structures in early works. In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding prediction, in which the encoding of the HLA sequence and the binding context are both learned by the network itself without requiring the HLA-peptide bound structure information. Our DCNN model is also characterized by its binding context extraction layer and dual outputs with both binding affinity output and binding probability outputs. Evaluation on public benchmark datasets shows that our DeepSeqPan model without HLA structural information in training achieves state-of-the-art performance on a large number of HLA alleles with good generalization capability. Since our model only needs raw sequences from the HLA-peptide binding pairs, it can be applied to binding predictions of HLAs without structure information and can also be applied to other protein binding problems such as protein-DNA and protein-RNA bindings. The implementation code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPan .
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Affiliation(s)
- Zhonghao Liu
- Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, United States
| | - Yuxin Cui
- Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, United States
| | - Zheng Xiong
- Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, United States
| | - Alierza Nasiri
- Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, United States
| | - Ansi Zhang
- School of Mechanical Engineering, Guizhou University, 50033, Guiyang, Guizhou, China
| | - Jianjun Hu
- Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, United States.
- School of Mechanical Engineering, Guizhou University, 50033, Guiyang, Guizhou, China.
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Shen WJ, Zhang X, Zhang S, Liu C, Cui W. The Utility of Supertype Clustering in Prediction for Class II MHC-Peptide Binding. Molecules 2018; 23:molecules23113034. [PMID: 30463372 PMCID: PMC6278554 DOI: 10.3390/molecules23113034] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 11/14/2018] [Accepted: 11/16/2018] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION Extensive efforts have been devoted to understanding the antigenic peptides binding to MHC class I and II molecules since they play a fundamental role in controlling immune responses and due their involvement in vaccination, transplantation, and autoimmunity. The genes coding for the MHC molecules are highly polymorphic, and it is difficult to build computational models for MHC molecules with few know binders. On the other hand, previous studies demonstrated that some MHC molecules share overlapping peptide binding repertoires and attempted to group them into supertypes. Herein, we present a framework of the utility of supertype clustering to gain more information about the data to improve the prediction accuracy of class II MHC-peptide binding. RESULTS We developed a new method, called superMHC, for class II MHC-peptide binding prediction, including three MHC isotypes of HLA-DR, HLA-DP, and HLA-DQ, by using supertype clustering in conjunction with RLS regression. The supertypes were identified by using a novel repertoire dissimilarity index to quantify the difference in MHC binding specificities. The superMHC method achieves the state-of-the-art performance and is demonstrated to predict binding affinities to a series of MHC molecules with few binders accurately. These results have implications for understanding receptor-ligand interactions involved in MHC-peptide binding.
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Affiliation(s)
- Wen-Jun Shen
- Department of Bioinformatics, Shantou University Medical College, Shantou 515000, China.
| | - Xun Zhang
- Department of Bioinformatics, Shantou University Medical College, Shantou 515000, China.
| | - Shaohong Zhang
- Department of Computer Science, Guangzhou University, Guangzhou 510000, China.
| | - Cheng Liu
- Department of Bioinformatics, Shantou University Medical College, Shantou 515000, China.
| | - Wenjuan Cui
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China.
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Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, Nielsen M. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 2018; 154:394-406. [PMID: 29315598 PMCID: PMC6002223 DOI: 10.1111/imm.12889] [Citation(s) in RCA: 475] [Impact Index Per Article: 79.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 12/19/2017] [Accepted: 12/22/2017] [Indexed: 02/06/2023] Open
Abstract
Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.
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Affiliation(s)
| | - Massimo Andreatta
- Instituto de Investigaciones BiotecnológicasUniversidad Nacional de San MartínBuenos AiresArgentina
| | - Paolo Marcatili
- Department of Bio and Health InformaticsTechnical University of DenmarkLyngbyDenmark
| | - Søren Buus
- Department of Immunology and MicrobiologyFaculty of Health SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Jason A. Greenbaum
- Bioinformatics Core FacilityLa Jolla Institute for Allergy and ImmunologyLa JollaCAUSA
| | - Zhen Yan
- Bioinformatics Core FacilityLa Jolla Institute for Allergy and ImmunologyLa JollaCAUSA
| | - Alessandro Sette
- Division of Vaccine DiscoveryLa Jolla Institute for Allergy and ImmunologyLa JollaCAUSA
- Department of MedicineUniversity of California San DiegoLa JollaCAUSA
| | - Bjoern Peters
- Division of Vaccine DiscoveryLa Jolla Institute for Allergy and ImmunologyLa JollaCAUSA
- Department of MedicineUniversity of California San DiegoLa JollaCAUSA
| | - Morten Nielsen
- Department of Bio and Health InformaticsTechnical University of DenmarkLyngbyDenmark
- Instituto de Investigaciones BiotecnológicasUniversidad Nacional de San MartínBuenos AiresArgentina
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Degoot AM, Chirove F, Ndifon W. Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions. Front Immunol 2018; 9:1410. [PMID: 29988560 PMCID: PMC6026802 DOI: 10.3389/fimmu.2018.01410] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 06/06/2018] [Indexed: 12/30/2022] Open
Abstract
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4+ T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide–MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide–MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket P5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide–MHC interactions.
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Affiliation(s)
- Abdoelnaser M Degoot
- African Institute of Mathematical Sciences (AIMS), Muizenberg, South Africa.,School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.,DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Gauteng, South Africa
| | - Faraimunashe Chirove
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Wilfred Ndifon
- African Institute of Mathematical Sciences (AIMS), Muizenberg, South Africa
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