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Zhang X, Wu J, Baeza J, Gu K, Zheng Y, Chen S, Zhou Z. DeepTAP: An RNN-based method of TAP-binding peptide prediction in the selection of tumor neoantigens. Comput Biol Med 2023; 164:107247. [PMID: 37454505 DOI: 10.1016/j.compbiomed.2023.107247] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/31/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
The transport of peptides from the cytoplasm to the endoplasmic reticulum (ER) by transporters associated with antigen processing (TAP) is a critical step in the intracellular presentation of cytotoxic T lymphocyte (CTL) epitopes. The development and application of computational methods, especially deep learning methods and new neural network strategies that can automatically learn feature representations with limited knowledge, provide an opportunity to develop fast and efficient methods to identify TAP-binding peptides. Herein, this study presents a comprehensive analysis of TAP-binding peptide sequences to derive TAP-binding motifs and preferences for N-terminal and C-terminal amino acids. A novel recurrent neural network (RNN)-based method called DeepTAP, using bidirectional gated recurrent unit (BiGRU), was developed for the accurate prediction of TAP-binding peptides. Our results demonstrated that DeepTAP achieves an optimal balance between prediction precision and false positives, outperforming other baseline models. Furthermore, DeepTAP significantly improves the prediction accuracy of high-confidence neoantigens, especially the top-ranked ones, making it a valuable tool for researchers studying antigen presentation processes and T-cell epitope screening. DeepTAP is freely available at https://github.com/zjupgx/deeptap and https://pgx.zju.edu.cn/deeptap.
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Affiliation(s)
- Xue Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Jingcheng Wu
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Joseph Baeza
- Biology Program, Iowa State University, Ames, IA, 50011, USA
| | - Katie Gu
- Biology Program, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Yichun Zheng
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China.
| | - Shuqing Chen
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Zhan Zhou
- National Key Laboratory of Advanced Drug Delivery and Release Systems & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, 322000, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, 310018, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
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2
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Schmalen A, Kammerl IE, Meiners S, Noessner E, Deeg CA, Hauck SM. A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability. Biomolecules 2023; 13:1300. [PMID: 37759700 PMCID: PMC10527444 DOI: 10.3390/biom13091300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
The majority of peptides presented by MHC class I result from proteasomal protein turnover. The specialized immunoproteasome, which is induced during inflammation, plays a major role in antigenic peptide generation. However, other cellular proteases can, either alone or together with the proteasome, contribute peptides to MHC class I loading non-canonically. We used an immunopeptidomics workflow combined with prediction software for proteasomal cleavage probabilities to analyze how inflammatory conditions affect the proteasomal processing of immune epitopes presented by A549 cells. The treatment of A549 cells with IFNγ enhanced the proteasomal cleavage probability of MHC class I ligands for both the constitutive proteasome and the immunoproteasome. Furthermore, IFNγ alters the contribution of the different HLA allotypes to the immunopeptidome. When we calculated the HLA allotype-specific proteasomal cleavage probabilities for MHC class I ligands, the peptides presented by HLA-A*30:01 showed characteristics hinting at a reduced C-terminal proteasomal cleavage probability independently of the type of proteasome. This was confirmed by HLA-A*30:01 ligands from the immune epitope database, which also showed this effect. Furthermore, two additional HLA allotypes, namely, HLA-A*03:01 and HLA-A*11:01, presented peptides with a markedly reduced C-terminal proteasomal cleavage probability. The peptides eluted from all three HLA allotypes shared a peptide binding motif with a C-terminal lysine residue, suggesting that this lysine residue impairs proteasome-dependent HLA ligand production and might, in turn, favor peptide generation by other cellular proteases.
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Affiliation(s)
- Adrian Schmalen
- Chair of Physiology, Department of Veterinary Sciences, LMU Munich, Martinsried, 82152 Planegg, Germany
- Core Facility—Metabolomics and Proteomics Core, Helmholtz Center Munich, German Research Center for Environmental Health (GmbH), 80939 Munich, Germany
| | - Ilona E. Kammerl
- Comprehensive Pneumology Center (CPC), University Hospital, Ludwig-Maximilians-University, Helmholtz Center Munich, Member of the German Center for Lung Research (DZL), 81377 Munich, Germany
| | - Silke Meiners
- Research Center Borstel, Leibniz Lung Center, Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), 23845 Borstel, Germany
- Institute of Experimental Medicine, Christian-Albrechts University Kiel, 24118 Kiel, Germany
| | - Elfriede Noessner
- Immunoanalytics Research Group—Tissue Control of Immunocytes, Helmholtz Center Munich, 81377 Munich, Germany
| | - Cornelia A. Deeg
- Chair of Physiology, Department of Veterinary Sciences, LMU Munich, Martinsried, 82152 Planegg, Germany
| | - Stefanie M. Hauck
- Core Facility—Metabolomics and Proteomics Core, Helmholtz Center Munich, German Research Center for Environmental Health (GmbH), 80939 Munich, Germany
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<i>In silico</i> Research at the Stages of Designing Modern Means for Prevention of Plague (by the Example of Subunit Vaccines). PROBLEMS OF PARTICULARLY DANGEROUS INFECTIONS 2022. [DOI: 10.21055/0370-1069-2022-3-6-13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The purpose of this review was to analyze the findings of domestic and foreign researchers on the development of modern drugs for the specific prevention of plague and to illustrate the possibilities of using bioinformatics analysis at the design stages to create an effective and safe vaccine. Work on the creation of an effective new-generation plague vaccine is hampered by several factors associated primarily with the presence of mechanisms of evasion from the immune system of the macroorganism, as well as a large number of pathogenicity determinants in the plague agent. Due to the development of approaches that are based on in silico studies, there is a progressive development of vaccine technologies oriented primarily to the use of the most important immunogens of the plague microbe (F1 and V antigen). Studies aimed at improving the antigenic properties of F1 and LcrV, as well as work on bioinformatic search and analysis of additional promising components to be included in the composition of subunit vaccines are considered as topical applications of bioinformatics data analysis in developing the tools for enhancing the effectiveness of protection through vaccination with subunit preparations.
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5
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Dhall A, Jain S, Sharma N, Naorem LD, Kaur D, Patiyal S, Raghava GPS. In silico tools and databases for designing cancer immunotherapy. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 129:1-50. [PMID: 35305716 DOI: 10.1016/bs.apcsb.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Leimarembi Devi Naorem
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India.
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6
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Weeder BR, Wood MA, Li E, Nellore A, Thompson RF. pepsickle rapidly and accurately predicts proteasomal cleavage sites for improved neoantigen identification. Bioinformatics 2021; 37:3723-3733. [PMID: 34478497 DOI: 10.1093/bioinformatics/btab628] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/21/2021] [Accepted: 08/31/2021] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION Proteasomal cleavage is a key component in protein turnover, as well as antigen processing and presentation. Although tools for proteasomal cleavage prediction are available, they vary widely in their performance, options, and availability. RESULTS Herein we present pepsickle, an open-source tool for proteasomal cleavage prediction with better in vivo prediction performance (AUC) and computational speed than current models available in the field and with the ability to predict sites based on both constitutive and immunoproteasome profiles. Post-hoc filtering of predicted patient neoepitopes using pepsickle significantly enriches for immune-responsive epitopes and may improve current epitope prediction and vaccine development pipelines. AVAILABILITY pepsickle is open source and available at https://github.com/pdxgx/pepsickle. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Benjamin R Weeder
- Computational Biology Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Ellysia Li
- Pacific University, Forest Grove, OR, USA
| | - Abhinav Nellore
- Computational Biology Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.,Department of Surgery, Oregon Health & Science University, Portland, Oregon, USA
| | - Reid F Thompson
- Computational Biology Program, Oregon Health & Science University, Portland, Oregon, USA.,Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA.,Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon, USA.,Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.,Division of Hospital and Specialty Medicine, VA Portland Healthcare System, Portland, Oregon, USA
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7
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Megna R, Petretta M, Alfano B, Cantoni V, Green R, Daniele S, Acampa W, Nappi C, Gaudieri V, Assante R, Zampella E, Mazziotti E, Mannarino T, Buongiorno P, Cuocolo A. A New Relational Database Including Clinical Data and Myocardial Perfusion Imaging Findings in Coronary Artery Disease. Curr Med Imaging 2020; 15:661-671. [PMID: 32008514 DOI: 10.2174/1573405614666180807110829] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 06/28/2018] [Accepted: 07/12/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND The aim of this study was to test a relational database including clinical data and imaging findings in a large cohort of subjects with suspected or known Coronary Artery Disease (CAD) undergoing stress single-photon emission computed tomography (SPECT) myocardial perfusion imaging. METHODS We developed a relational database including clinical and imaging data of 7995 subjects with suspected or known CAD. The software system was implemented by PostgreSQL 9.2, an open source object-relational database, and managed from remote by pgAdmin III. Data were arranged according to a logic of aggregation and stored in a schema with twelve tables. Statistical software was connected to the database directly downloading data from server to local personal computer. RESULTS There was no problem or anomaly for database implementation and user connections to the database. The epidemiological analysis performed on data stored in the database demonstrated abnormal SPECT findings in 46% of male subjects and 19% of female subjects. Imaging findings suggest that the use of SPECT imaging in our laboratory is appropriate. CONCLUSION The development of a relational database provides a free software tool for the storage and management of data in line with the current standard.
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Affiliation(s)
- Rosario Megna
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | - Mario Petretta
- Department of Translational Medical Sciences, University of Naples Federico II, Naples, Italy
| | - Bruno Alfano
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | - Valeria Cantoni
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Roberta Green
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Stefania Daniele
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | - Wanda Acampa
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Carmela Nappi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Valeria Gaudieri
- Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy
| | - Roberta Assante
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Emilia Zampella
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Emanuela Mazziotti
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Teresa Mannarino
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Pietro Buongiorno
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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8
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Bahrami AA, Payandeh Z, Khalili S, Zakeri A, Bandehpour M. Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein. Int Rev Immunol 2019; 38:307-322. [PMID: 31478759 DOI: 10.1080/08830185.2019.1657426] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Immunoinformatics is a new critical field with several tools and databases that conduct the eyesight of experimental selection and facilitate analysis of the great amount of immunologic data obtained from experimental researches and helps to design and introducing new hypothesis. Given these visages, immunoinformatics seems to be the way that develop and progress the immunological research. Bioinformatics methods and applications are successfully employed in vaccine informatics to assist different sites of the preclinical, clinical, and post-licensure vaccine enterprises. On the other hand, the progression of molecular biology and immunology caused epitope vaccines have become the focus of research on molecular vaccines. Moreover, reverse vaccinology could improve vaccine production and vaccination protocols by in silico prediction of protein-vaccine candidates from genome sequences. B- and T-cell immune epitopes could be predicted by immunoinformatics algorithms and computational methods to improve the vaccine design, protective immunity analysis, assessment of vaccine safety and efficacy, and immunization modeling. This review aims to discuss the power of computational approaches in vaccine design and their relevance to the development of effective vaccines. Furthermore, the various divisions of this field and available tools in each item are introduced and reviewed.
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Affiliation(s)
- Armina Alagheband Bahrami
- Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Payandeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Alireza Zakeri
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Mojgan Bandehpour
- Department of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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9
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Potocnakova L, Bhide M, Pulzova LB. An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction. J Immunol Res 2016; 2016:6760830. [PMID: 28127568 PMCID: PMC5227168 DOI: 10.1155/2016/6760830] [Citation(s) in RCA: 198] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 11/21/2016] [Accepted: 12/13/2016] [Indexed: 01/09/2023] Open
Abstract
Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.
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Affiliation(s)
- Lenka Potocnakova
- Laboratory of Biomedical Microbiology and Immunology, Department of Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, 041 81 Kosice, Slovakia
| | - Mangesh Bhide
- Laboratory of Biomedical Microbiology and Immunology, Department of Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, 041 81 Kosice, Slovakia
- Institute of Neuroimmunology of Slovak Academy of Sciences, 845 10 Bratislava, Slovakia
| | - Lucia Borszekova Pulzova
- Laboratory of Biomedical Microbiology and Immunology, Department of Microbiology and Immunology, The University of Veterinary Medicine and Pharmacy in Kosice, 041 81 Kosice, Slovakia
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10
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Fuchs JE, Wellenzohn B, Weskamp N, Liedl KR. Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications. J Chem Inf Model 2015; 55:2315-23. [PMID: 26501781 PMCID: PMC4658635 DOI: 10.1021/acs.jcim.5b00476] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Biopharmaceuticals hold great promise
for the future of drug discovery.
Nevertheless, rational drug design strategies are mainly focused on
the discovery of small synthetic molecules. Herein we present matched
peptides, an innovative analysis technique for biological data related
to peptide and protein sequences. It represents an extension of matched
molecular pair analysis toward macromolecular sequence data and allows
quantitative predictions of the effect of single amino acid substitutions
on the basis of statistical data on known transformations. We demonstrate
the application of matched peptides to a data set of major histocompatibility
complex class II peptide ligands and discuss the trends captured with
respect to classical quantitative structure–activity relationship
approaches as well as structural aspects of the investigated protein–peptide
interface. We expect our novel readily interpretable tool at the interface
of cheminformatics and bioinformatics to support the rational design
of biopharmaceuticals and give directions for further development
of the presented methodology.
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Affiliation(s)
- Julian E Fuchs
- Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck , Innrain 82, 6020 Innsbruck, Austria
| | - Bernd Wellenzohn
- Research Germany/Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG , Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Nils Weskamp
- Research Germany/Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG , Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
| | - Klaus R Liedl
- Theoretical Chemistry, Faculty of Chemistry and Pharmacy, University of Innsbruck , Innrain 82, 6020 Innsbruck, Austria
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Abstract
Modem immunology and vaccinology incorporate immunoinformatics techniques to give insights into immune systems and accelerate vaccine design. Databases managing epitope data in a structured form with immune-related annotations including sequences, alleles, source organisms, structures, and diseases could be the most crucial part of immunoinformatics offering data sources for the analysis of immune systems and development of prediction methods. This chapter provides an overview of publicly available databases of T-cell epitopes including general databases, pathogen- and tumor-specific databases, and 3D structure databases.
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Bunkute E, Cummins C, Crofts FJ, Bunce G, Nabney IT, Flower DR. PIP-DB: the Protein Isoelectric Point database. Bioinformatics 2014; 31:295-6. [PMID: 25252779 DOI: 10.1093/bioinformatics/btu637] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
UNLABELLED A protein's isoelectric point or pI corresponds to the solution pH at which its net surface charge is zero. Since the early days of solution biochemistry, the pI has been recorded and reported, and thus literature reports of pI abound. The Protein Isoelectric Point database (PIP-DB) has collected and collated these data to provide an increasingly comprehensive database for comparison and benchmarking purposes. A web application has been developed to warehouse this database and provide public access to this unique resource. PIP-DB is a web-enabled SQL database with an HTML GUI front-end. PIP-DB is fully searchable across a range of properties. AVAILABILITY AND IMPLEMENTATION The PIP-DB database and documentation are available at http://www.pip-db.org.
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Affiliation(s)
- Egle Bunkute
- School of Life and Health Sciences and School of Engineering and Applied Science, University of Aston, Aston Triangle, Birmingham B4 7ET, UK
| | - Christopher Cummins
- School of Life and Health Sciences and School of Engineering and Applied Science, University of Aston, Aston Triangle, Birmingham B4 7ET, UK
| | - Fraser J Crofts
- School of Life and Health Sciences and School of Engineering and Applied Science, University of Aston, Aston Triangle, Birmingham B4 7ET, UK
| | - Gareth Bunce
- School of Life and Health Sciences and School of Engineering and Applied Science, University of Aston, Aston Triangle, Birmingham B4 7ET, UK
| | - Ian T Nabney
- School of Life and Health Sciences and School of Engineering and Applied Science, University of Aston, Aston Triangle, Birmingham B4 7ET, UK
| | - Darren R Flower
- School of Life and Health Sciences and School of Engineering and Applied Science, University of Aston, Aston Triangle, Birmingham B4 7ET, UK
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13
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Khalili S, Jahangiri A, Borna H, Ahmadi Zanoos K, Amani J. Computational vaccinology and epitope vaccine design by immunoinformatics. Acta Microbiol Immunol Hung 2014; 61:285-307. [PMID: 25261943 DOI: 10.1556/amicr.61.2014.3.4] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Human immune system includes variety of different cells and molecules correlating with other body systems. These instances complicate the analysis of the system; particularly in postgenomic era by introducing more amount of data, the complexity is increased and necessity of using computational approaches to process and interpret them is more tangible.Immunoinformatics as a subset of bioinformatics is a new approach with variety of tools and databases that facilitate analysis of enormous amount of immunologic data obtained from experimental researches. In addition to directing the insight regarding experiment selections, it helps new thesis design which was not feasible with conventional methods due to the complexity of data. Considering this features immunoinformatics appears to be one of the fields that accelerate the immunological research progression.In this study we discuss advances in genomics and vaccine design and their relevance to the development of effective vaccines furthermore several division of this field and available tools in each item are introduced.
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Affiliation(s)
- Saeed Khalili
- 1 Tarbiat Modares University Department of Medical Biotechnology Tehran Iran
| | - Abolfazl Jahangiri
- 2 Baqiyatallah University of Medical Sciences Applied Microbiology Research Center Tehran Iran
| | - Hojat Borna
- 3 Baqiyatallah Medical Science University Chemical Injuries Research Center Tehran Iran
| | | | - Jafar Amani
- 2 Baqiyatallah University of Medical Sciences Applied Microbiology Research Center Tehran Iran
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14
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Abstract
Computational identification of B-cell epitopes from antigen chains is a difficult and actively pursued research topic. Efforts towards the development of method for the prediction of linear epitopes span over the last three decades, while only recently several predictors of conformational epitopes were released. We review a comprehensive set of 13 recent approaches that predict linear and 4 methods that predict conformational B-cell epitopes from the antigen sequences. We introduce several databases of B-cell epitopes, since the availability of the corresponding data is at the heart of the development and validation of computational predictors. We also offer practical insights concerning the use and availability of these B-cell epitope predictors, and motivate and discuss feature research in this area.
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Affiliation(s)
- Jianzhao Gao
- School of Mathematical Sciences, Nankai University, Tianjin, People's Republic of China
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15
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Flower DR, Perrie Y. Identification of Candidate Vaccine Antigens In Silico. IMMUNOMIC DISCOVERY OF ADJUVANTS AND CANDIDATE SUBUNIT VACCINES 2013. [PMCID: PMC7120937 DOI: 10.1007/978-1-4614-5070-2_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.
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Affiliation(s)
- Darren R. Flower
- Aston Pharmacy School, School of Life and Health Sciences, University of Aston, Aston Triangle, Birmingham, B4 7ET United Kingdom
| | - Yvonne Perrie
- Aston Pharmacy School, School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET United Kingdom
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Liao WWP, Arthur JW. Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function. PLoS One 2011; 6:e25055. [PMID: 21966412 PMCID: PMC3178607 DOI: 10.1371/journal.pone.0025055] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2011] [Accepted: 08/23/2011] [Indexed: 12/19/2022] Open
Abstract
The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.
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Affiliation(s)
- Webber W. P. Liao
- Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Jonathan W. Arthur
- Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
- Children's Medical Research Institute, Sydney, New South Wales, Australia
- * E-mail:
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17
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Wang Y, Wu W, Negre NN, White KP, Li C, Shah PK. Determinants of antigenicity and specificity in immune response for protein sequences. BMC Bioinformatics 2011; 12:251. [PMID: 21693021 PMCID: PMC3133554 DOI: 10.1186/1471-2105-12-251] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2010] [Accepted: 06/21/2011] [Indexed: 11/22/2022] Open
Abstract
Background Target specific antibodies are pivotal for the design of vaccines, immunodiagnostic tests, studies on proteomics for cancer biomarker discovery, identification of protein-DNA and other interactions, and small and large biochemical assays. Therefore, it is important to understand the properties of protein sequences that are important for antigenicity and to identify small peptide epitopes and large regions in the linear sequence of the proteins whose utilization result in specific antibodies. Results Our analysis using protein properties suggested that sequence composition combined with evolutionary information and predicted secondary structure, as well as solvent accessibility is sufficient to predict successful peptide epitopes. The antigenicity and the specificity in immune response were also found to depend on the epitope length. We trained the B-Cell Epitope Oracle (BEOracle), a support vector machine (SVM) classifier, for the identification of continuous B-Cell epitopes with these protein properties as learning features. The BEOracle achieved an F1-measure of 81.37% on a large validation set. The BEOracle classifier outperformed the classical methods based on propensity and sophisticated methods like BCPred and Bepipred for B-Cell epitope prediction. The BEOracle classifier also identified peptides for the ChIP-grade antibodies from the modENCODE/ENCODE projects with 96.88% accuracy. High BEOracle score for peptides showed some correlation with the antibody intensity on Immunofluorescence studies done on fly embryos. Finally, a second SVM classifier, the B-Cell Region Oracle (BROracle) was trained with the BEOracle scores as features to predict the performance of antibodies generated with large protein regions with high accuracy. The BROracle classifier achieved accuracies of 75.26-63.88% on a validation set with immunofluorescence, immunohistochemistry, protein arrays and western blot results from Protein Atlas database. Conclusions Together our results suggest that antigenicity is a local property of the protein sequences and that protein sequence properties of composition, secondary structure, solvent accessibility and evolutionary conservation are the determinants of antigenicity and specificity in immune response. Moreover, specificity in immune response could also be accurately predicted for large protein regions without the knowledge of the protein tertiary structure or the presence of discontinuous epitopes. The dataset prepared in this work and the classifier models are available for download at https://sites.google.com/site/oracleclassifiers/.
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Affiliation(s)
- Yulong Wang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute & Harvard School of Public Health, Boston 02115 MA, USA.
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18
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Flower DR, Macdonald IK, Ramakrishnan K, Davies MN, Doytchinova IA. Computer aided selection of candidate vaccine antigens. Immunome Res 2010; 6 Suppl 2:S1. [PMID: 21067543 PMCID: PMC2981880 DOI: 10.1186/1745-7580-6-s2-s1] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.
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Affiliation(s)
- Darren R Flower
- School of Life and Health Sciences, University of Aston, Aston Triangle, Birmingham, B4 7ET, UK.
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19
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Bremel RD, Homan EJ. An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics. Immunome Res 2010; 6:8. [PMID: 21044290 PMCID: PMC2991286 DOI: 10.1186/1745-7580-6-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2010] [Accepted: 11/02/2010] [Indexed: 11/25/2022] Open
Abstract
Background Improving our understanding of the immune response is fundamental to developing strategies to combat a wide range of diseases. We describe an integrated epitope analysis system which is based on principal component analysis of sequences of amino acids, using a multilayer perceptron neural net to conduct QSAR regression predictions for peptide binding affinities to 35 MHC-I and 14 MHC-II alleles. Results The approach described allows rapid processing of single proteins, entire proteomes or subsets thereof, as well as multiple strains of the same organism. It enables consideration of the interface of diversity of both microorganisms and of host immunogenetics. Patterns of binding affinity are linked to topological features, such as extracellular or intramembrane location, and integrated into a graphical display which facilitates conceptual understanding of the interplay of B-cell and T-cell mediated immunity. Patterns which emerge from application of this approach include the correlations between peptides showing high affinity binding to MHC-I and to MHC-II, and also with predicted B-cell epitopes. These are characterized as coincident epitope groups (CEGs). Also evident are long range patterns across proteins which identify regions of high affinity binding for a permuted population of diverse and heterozygous HLA alleles, as well as subtle differences in reactions with MHCs of individual HLA alleles, which may be important in disease susceptibility, and in vaccine and clinical trial design. Comparisons are shown of predicted epitope mapping derived from application of the QSAR approach with experimentally derived epitope maps from a diverse multi-species dataset, from Staphylococcus aureus, and from vaccinia virus. Conclusions A desktop application with interactive graphic capability is shown to be a useful platform for development of prediction and visualization tools for epitope mapping at scales ranging from individual proteins to proteomes from multiple strains of an organism. The possible functional implications of the patterns of peptide epitopes observed are discussed, including their implications for B-cell and T-cell cooperation and cross presentation.
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Affiliation(s)
- Robert D Bremel
- 1ioGenetics LLC, 3591 Anderson Street, Madison, WI 53704, USA.
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20
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Abstract
The immune system plays an important role in the development of personalized medicine for a variety of diseases including cancer, autoimmune diseases, and infectious diseases. Immunoinformatics, or computational immunology, is an emerging area that provides fundamental methodologies in the study of immunomics, that is, immune-related genomics and proteomics. The integration of immunoinformatics with systems biology approaches may lead to a better understanding of immune-related diseases at various systems levels. Such methods can contribute to translational studies that bring scientific discoveries of the immune system into better clinical practice. One of the most intensely studied areas of the immune system is immune epitopes. Epitopes are important for disease understanding, host-pathogen interaction analyses, antimicrobial target discovery, and vaccine design. The information about genetic diversity of the immune system may help define patient subgroups for individualized vaccine or drug development. Cellular pathways and host immune-pathogen interactions have a crucial impact on disease pathogenesis and immunogen design. Epigenetic studies may help understand how environmental changes influence complex immune diseases such as allergy. High-throughput technologies enable the measurements and catalogs of genes, proteins, interactions, and behavior. Such perception may contribute to the understanding of the interaction network among humans, vaccines, and drugs, to enable new insights of diseases and therapeutic responses. The integration of immunomics information may ultimately lead to the development of optimized vaccines and drugs tailored to personalized prevention and treatment. An immunoinformatics portal containing relevant resources is available at http://immune.pharmtao.com.
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21
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Walshe VA, Hattotuwagama CK, Doytchinova IA, Wong M, Macdonald IK, Mulder A, Claas FHJ, Pellegrino P, Turner J, Williams I, Turnbull EL, Borrow P, Flower DR. Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes. PLoS One 2009; 4:e8095. [PMID: 19956609 PMCID: PMC2779488 DOI: 10.1371/journal.pone.0008095] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2009] [Accepted: 11/03/2009] [Indexed: 11/24/2022] Open
Abstract
Background Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102. Methodology/Findings Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR) binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1. Conclusions/Significance A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.
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Affiliation(s)
- Valerie A. Walshe
- The Jenner Institute, University of Oxford, Compton, Berkshire, United Kingdom
| | | | | | - MaiLee Wong
- The Jenner Institute, University of Oxford, Compton, Berkshire, United Kingdom
| | - Isabel K. Macdonald
- The Jenner Institute, University of Oxford, Compton, Berkshire, United Kingdom
| | - Arend Mulder
- Department of Immunohaematology and Blood Transfusion, Leiden University Medical Centre, Leiden, The Netherlands
| | - Frans H. J. Claas
- Department of Immunohaematology and Blood Transfusion, Leiden University Medical Centre, Leiden, The Netherlands
| | - Pierre Pellegrino
- Centre for Sexual Health and HIV Research, Royal Free and University College London Medical School and Camden Primary Care Trust, London, United Kingdom
| | - Jo Turner
- Centre for Sexual Health and HIV Research, Royal Free and University College London Medical School and Camden Primary Care Trust, London, United Kingdom
| | - Ian Williams
- Centre for Sexual Health and HIV Research, Royal Free and University College London Medical School and Camden Primary Care Trust, London, United Kingdom
| | - Emma L. Turnbull
- The Jenner Institute, University of Oxford, Compton, Berkshire, United Kingdom
| | - Persephone Borrow
- The Jenner Institute, University of Oxford, Compton, Berkshire, United Kingdom
| | - Darren R. Flower
- The Jenner Institute, University of Oxford, Compton, Berkshire, United Kingdom
- * E-mail:
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22
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Vider-Shalit T, Sarid R, Maman K, Tsaban L, Levi R, Louzoun Y. Viruses selectively mutate their CD8+ T-cell epitopes--a large-scale immunomic analysis. Bioinformatics 2009; 25:i39-44. [PMID: 19478014 PMCID: PMC2687975 DOI: 10.1093/bioinformatics/btp221] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Motivation: Viruses employ various means to evade immune detection. One common evasion strategy is the removal of CD8+cytotoxic T-lymphocyte epitopes. We here use a combination of multiple bioinformatic tools and large amount of genomic data to compute the epitope repertoire presented by over 1300 viruses in many HLA alleles. We define the ‘Size of Immune Repertoire score’, which represents the ratio between the epitope density within a protein and the expected density. This score is used to study viral immune evasion. Results: We show that viral proteins in general have a higher epitope density than human proteins. This difference is due to a good fit of the human MHC molecules to the typical amino-acid usage of viruses. Among different viruses, viruses infecting humans present less epitopes than non-human viruses. This selection is not at the amino-acid usage level, but through the removal of specific epitopes. Within a single virus, not all proteins express the same epitopes density. Proteins expressed early in the viral life cycle have a lower epitope density than late proteins. Such a difference is not observed in non-human viruses. The removal of early epitopes and the targeting of the cellular immune response to late viral proteins, allow the virus a time interval to propagate before its host cells are destroyed by T cells. Contact:louzouy@math.biu.ac.il
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Affiliation(s)
- Tal Vider-Shalit
- Department of Mathematics and Gonda Brain Research Center, Bar Ilan University, Ramat Gan, Israel
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23
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Lata S, Bhasin M, Raghava GPS. MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes. BMC Res Notes 2009; 2:61. [PMID: 19379493 PMCID: PMC2679046 DOI: 10.1186/1756-0500-2-61] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2008] [Accepted: 04/20/2009] [Indexed: 11/23/2022] Open
Abstract
Background Many databases housing the information about MHC binders and non-binders have been developed in the past to help the scientific community working in the field of immunology, immune-informatics or vaccine design. As the information about these MHC binding and non-binding peptides continues to grow with the time and there is a need to keep the databases updated. So, in order to provide the immunological fraternity with the most recent information we need to maintain and update our database regularly. In this paper, we describe the updated version of 4.0 of the database MHCBN. Findings MHCBN is a comprehensive database comprising over 25,857 peptide sequences (1053 TAP binding peptides), whose binding affinity with either MHC or TAP molecules has been assayed experimentally. It is a manually curated database where entries are collected & compiled from published literature and existing immunological public databases. MHCBN has a number of web-based tools for the analysis and retrieval of information like mapping of antigenic regions, creation of allele specific dataset, BLAST search, various diseases associated with MHC alleles etc. Further, all entries are hyper linked to major databases like SWISS-PROT, PDB etc. to provide the information beyond the scope of MHCBN. The latest version 4.0 of MHCBN has 6080 more entries than previously published version 1.1. Conclusion MHCBN database updating is meant to facilitate immunologist in understanding the immune system and provide them the latest information. We feel that our database will complement the existing databases in serving scientific community.
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Affiliation(s)
- Sneh Lata
- Bioinformatics Center, Institute of Microbial Technology, Sector 39A, Chandigarh, India.
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24
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25
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Abstract
The prediction of B-cell epitopes is desirable for designing peptide-based vaccines, or generating antibodies especially if the purified protein is difficult to obtain and immunization has to be performed with protein-derived synthetic peptides. A number of freely available tools predict epitopes from protein sequence or structural information. The handling of these tools is described and the predictive power is assessed using test data based on the proteome of HIV, where comprehensive epitope mapping data are available.
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Affiliation(s)
- Ulf Reimer
- Computational Chemistry Department, Jerini AG, Invalidenstr. 130, D-10115 Berlin, Germany
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26
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Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships. Methods Mol Biol 2008. [PMID: 18450004 DOI: 10.1007/978-1-60327-118-9_16] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.
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27
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Davies MN, Flower DR. Static energy analysis of MHC class I and class II peptide-binding affinity. Methods Mol Biol 2008; 409:309-20. [PMID: 18450011 DOI: 10.1007/978-1-60327-118-9_23] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Abstract
Antigenic peptide is presented to a T-cell receptor (TCR) through the formation of a stable complex with a major histocompatibility complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. A novel predictive technique is described, which uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC class II-peptide complex. The structures are remodeled, energy minimized, and annealed before the energetic interaction is calculated.
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28
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Lata S, Raghava GPS. PRRDB: a comprehensive database of pattern-recognition receptors and their ligands. BMC Genomics 2008; 9:180. [PMID: 18423032 PMCID: PMC2346480 DOI: 10.1186/1471-2164-9-180] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2007] [Accepted: 04/18/2008] [Indexed: 11/23/2022] Open
Abstract
Background Recently in a number of studies, it has been demonstrated that the innate immune system doesn't merely acts as the first line of defense but provides critical signals for the development of specific adaptive immune response. Innate immune system employs a set of receptors called pattern recognition receptors (PRRs) that recognize evolutionarily conserved patterns from pathogens called pathogen associated molecular patterns (PAMPs). In order to assist scientific community, a database PRRDB has been developed that provides extensive information about pattern recognition receptors and their ligands. Results The current version of database contains around 500 patterns recognizing receptors from 77 distinct organisms ranging from insects to human. This includes 177 Toll-like receptors, 124 are Scavenger receptors and 67 are Nucleotide Binding Site-Leucine repeats rich receptors. The database also provides information about 266 ligands that includes carbohydrates, proteins, nucleic acids, glycolipids, glycoproteins, lipopeptides. A number of web tools have been integrated in PRRDB in order to provide following services: i) searching on any field; ii) database browsing; and iii) BLAST search against the pattern-recognition receptors. PRRDB also provides external links to standard databases like Swiss-Prot and Pubmed. Conclusion PRRDB is a unique database of its kind, which provides comprehensive information about innate immunity. This database will be very useful in designing effective adjuvant for subunit vaccine and in understanding role of innate immunity. The database is available from the URL's in the Availabiltiy and requirements section.
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Affiliation(s)
- Sneh Lata
- Institute of Microbial Technology, Sector39A, Chandigarh, India.
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29
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Vider-Shalit T, Fishbain V, Raffaeli S, Louzoun Y. Phase-dependent immune evasion of herpesviruses. J Virol 2007; 81:9536-45. [PMID: 17609281 PMCID: PMC1951411 DOI: 10.1128/jvi.02636-06] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2006] [Accepted: 06/22/2007] [Indexed: 12/14/2022] Open
Abstract
Viruses employ various modes to evade immune detection. Two possible evasion modes are a reduction of the number of epitopes presented and the mimicry of host epitopes. The immune evasion efforts are not uniform among viral proteins. The number of epitopes in a given viral protein and the similarity of the epitopes to host peptides can be used as a measure of the viral attempts to hide this protein. Using bioinformatics tools, we here present a genomic analysis of the attempts of four human herpesviruses (herpes simplex virus type 1-human herpesvirus 1, Epstein-Barr virus-human herpesvirus 4, human cytomegalovirus-human herpesvirus 5, and Kaposi's sarcoma-associated herpesvirus-human herpesvirus 8) and one murine herpesvirus (murine herpesvirus 68) to escape from immune detection. We determined the full repertoire of CD8 T-lymphocyte epitopes presented by each viral protein and show that herpesvirus proteins present many fewer epitopes than expected. Furthermore, the epitopes that are presented are more similar to host epitopes than are random viral epitopes, minimizing the immune response. We defined a score for the size of the immune repertoire (the SIR score) based on the number of epitopes in a protein. The numbers of epitopes in proteins expressed in the latent and early phases of infection were significantly smaller than those in proteins expressed in the lytic phase in all tested viruses. The latent and immediate-early epitopes were also more similar to host epitopes than were lytic epitopes. A clear trend emerged from the analysis. In general, herpesviruses demonstrated an effort to evade immune detection. However, within a given herpesvirus, proteins expressed in phases critical to the fate of infection (e.g., early lytic and latent) evaded immune detection more than all others. The application of the SIR score to specific proteins allows us to quantify the importance of immune evasion and to detect optimal targets for immunotherapy and vaccine development.
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Abstract
With the burgeoning immunological data in the scientific literature, scientists must increasingly rely on Internet resources to inform and enhance their work. Here we provide a brief overview of the adaptive immune response and summaries of immunoinformatics resources, emphasizing those with Web interfaces. These resources include searchable databases of epitopes and immune-related molecules, and analysis tools for T cell and B cell epitope prediction, vaccine design, and protein structure comparisons. There is an agreeable synergy between the growing collections in immune-related databases and the growing sophistication of analysis software; the databases provide the foundation for developing predictive computational tools, which in turn enable more rapid identification of immune responses to populate the databases. Collectively, these resources contribute to improved understanding of immune responses and escape, and evolution of pathogens under immune pressure. The public health implications are vast, including designing vaccines, understanding autoimmune diseases, and defining the correlates of immune protection.
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Affiliation(s)
- Bette Korber
- Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.
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31
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Rajapakse M, Zhang GL, Srinivasan KN, Schmidt B, Petrovsky N, Brusic V. PREDNOD, a prediction server for peptide binding to the H-2g7 haplotype of the non-obese diabetic mouse. Autoimmunity 2007; 39:645-50. [PMID: 17178561 DOI: 10.1080/08916930601062494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The non-obese diabetic (NOD) mouse is a widely used animal model for study of autoimmune diseases, in particular human type 1 diabetes mellitus (T1DM). Identification of the subset of peptides that bind MHC molecules comprising the H-2g7 haplotype of NOD mouse and thereby representing potential NOD T-cell epitopes is important for research into the pathogenesis and immunotherapy of T1DM. The H-2g7 haplotype comprises the MHC class-I molecules Kd and Db and a single class-II molecule I-Ag7. We have developed a prediction system, PREDNOD, for accurate identification of peptides that bind the MHC molecules constituting the H-2g7 haplotype. PREDNOD is accessible at http://antigen.i2r.a-star.edu.sg/Ag7.
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Affiliation(s)
- Menaka Rajapakse
- Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore, Singapore, 119613
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Abstract
Immunoinformatics is the application of informatics techniques to molecules of the immune system. One of its principal goals is the effective prediction of immunogenicity, be that at the level of epitope, subunit vaccine, or attenuated pathogen. Immunogenicity is the ability of a pathogen or component thereof to induce a specific immune response when first exposed to surveillance by the immune system, whereas antigenicity is the capacity for recognition by the extant machinery of the adaptive immune response in a recall response. In thisbook, we introduce these subjects and explore the current state of play in immunoinformatics and the in silico prediction of immunogenicity.
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Srivastava S, Singh MK, Raghava GPS, Varshney GC. Searching haptens, carrier proteins, and anti-hapten antibodies. Methods Mol Biol 2007; 409:125-139. [PMID: 18449996 DOI: 10.1007/978-1-60327-118-9_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Haptens are small molecules that are usually nonimmunogenic unless coupled to some carrier proteins. The generation of anti-hapten antibodies is important for the development of immunodiagnostics and therapeutics. Recently, our group has developed a database called HaptenDB, which provides comprehensive information about 1,087 haptens. In this chapter, we describe following web tools integrated in HaptenDB: (i) keyword search facility allows search on major fields, (ii) browsing service, to display all haptens, carrier proteins and antibodies, and (iii) structure similarity search, which allows the users to search their structure against hapten structures.
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34
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Mallios RR. An iterative approach to class II predictions. Methods Mol Biol 2007; 409:341-353. [PMID: 18450013 DOI: 10.1007/978-1-60327-118-9_25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
An iterative approach to resolving protein-peptide binding motifs is appropriate when the length of the binding protein is variable and a variety of amino acid residues may successfully occupy multiple positions. This chapter describes an iterative algorithm that first aligns binding peptides of variable lengths and then extracts a quantitative motif from the resulting alignment. Numerous examples are presented to illustrate the utility of the iterative process.
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Günther S, Hempel D, Dunkel M, Rother K, Preissner R. SuperHapten: a comprehensive database for small immunogenic compounds. Nucleic Acids Res 2006; 35:D906-10. [PMID: 17090587 PMCID: PMC1669746 DOI: 10.1093/nar/gkl849] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The immune system protects organisms from foreign proteins, peptide epitopes and a multitude of chemical compounds. Among these, haptens are small molecules, eliciting an immune response when conjugated with carrier molecules. Known haptens are xenobiotics or natural compounds, which can induce a number of autoimmune diseases like contact dermatitis or asthma. Furthermore, haptens are utilized in the development of biosensors, immunomodulators and new vaccines. Although hapten-induced allergies account for 6–10% of all adverse drug effects, the understanding of the correlation between structural and haptenic properties is rather fragmentary. We have developed a manually curated hapten database, SuperHapten, integrating information from literature and web resources. The current version of the database compiles 2D/3D structures, physicochemical properties and references for about 7500 haptens and 25,000 synonyms. The commercial availability is documented for about 6300 haptens and 450 related antibodies, enabling experimental approaches on cross-reactivity. The haptens are classified regarding their origin: pesticides, herbicides, insecticides, drugs, natural compounds, etc. Queries allow identification of haptens and associated antibodies according to functional class, carrier protein, chemical scaffold, composition or structural similarity. SuperHapten is available online at .
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Affiliation(s)
- Stefan Günther
- Institute of Molecular Biology and Bioinformatics, Charité-University Medicine Berlin, Arnimallee 22, 14195 Berlin, Germany.
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Vider-Shalit T, Raffaeli S, Louzoun Y. Virus-epitope vaccine design: informatic matching the HLA-I polymorphism to the virus genome. Mol Immunol 2006; 44:1253-61. [PMID: 16930710 DOI: 10.1016/j.molimm.2006.06.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2006] [Revised: 06/07/2006] [Accepted: 06/08/2006] [Indexed: 12/01/2022]
Abstract
Attempts to develop peptide vaccines, based on a limited number of peptides face two problems: HLA polymorphism and the high mutation rate of viral epitopes. We have developed a new genomic method that ensures maximal coverage and thus maximal applicability of the peptide vaccine. The same method also promises a large number of epitopes per HLA to prevent escape via mutations. Our design can be applied swiftly in order to face rapidly emerging viral diseases. We use a genomic scan of all candidate peptides and join them optimally. For a given virus, we use algorithms computing: peptide cleavage probability, transfer through TAP and MHC binding for a large number of HLA alleles. The resulting peptide libraries are pruned for peptides that are not conserved or are too similar to self peptides. We then use a genetic algorithm to produce an optimal protein composed of peptides from this list properly ordered for cleavage. The selected peptides represent an optimal combination to cover all HLA alleles and all viral proteins. We have applied this method to HCV and found that some HCV proteins (mainly envelope proteins) represent much less peptide than expected. A more detailed analysis of the peptide variability shows a balance between the attempts of the immune system to detect less mutating peptides, and the attempts of viruses to mutate peptides and avoid detection by the immune system. In order to show the applicability of our method, we have further used it on HIV-I, Influenza H3N2 and the Avian Flu Viruses.
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Hattotuwagama CK, Toseland CP, Guan P, Taylor DJ, Hemsley SL, Doytchinova IA, Flower DR. Toward prediction of class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique. J Chem Inf Model 2006; 46:1491-502. [PMID: 16711768 DOI: 10.1021/ci050380d] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide-MHC binding affinity. The ISC-PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide-MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms--q2, SEP, and NC--ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).
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Larsen JEP, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res 2006; 2:2. [PMID: 16635264 PMCID: PMC1479323 DOI: 10.1186/1745-7580-2-2] [Citation(s) in RCA: 827] [Impact Index Per Article: 45.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2006] [Accepted: 04/24/2006] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system. Knowledge of B-cell epitopes may be used in the design of vaccines and diagnostics tests. It is therefore of interest to develop improved methods for predicting B-cell epitopes. In this paper, we describe an improved method for predicting linear B-cell epitopes. RESULTS In order to do this, three data sets of linear B-cell epitope annotated proteins were constructed. A data set was collected from the literature, another data set was extracted from the AntiJen database and a data sets of epitopes in the proteins of HIV was collected from the Los Alamos HIV database. An unbiased validation of the methods was made by testing on data sets on which they were neither trained nor optimized on. We have measured the performance in a non-parametric way by constructing ROC-curves. CONCLUSION The best single method for predicting linear B-cell epitopes is the hidden Markov model. Combining the hidden Markov model with one of the best propensity scale methods, we obtained the BepiPred method. When tested on the validation data set this method performs significantly better than any of the other methods tested. The server and data sets are publicly available at http://www.cbs.dtu.dk/services/BepiPred.
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Affiliation(s)
- Jens Erik Pontoppidan Larsen
- Center for Biological Sequence Analysis, BioCentrum-DTU, Building 208, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Ole Lund
- Center for Biological Sequence Analysis, BioCentrum-DTU, Building 208, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Morten Nielsen
- Center for Biological Sequence Analysis, BioCentrum-DTU, Building 208, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
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Davies MN, Hattotuwagama CK, Moss DS, Drew MGB, Flower DR. Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity. BMC STRUCTURAL BIOLOGY 2006; 6:5. [PMID: 16549002 PMCID: PMC1435758 DOI: 10.1186/1472-6807-6-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2005] [Accepted: 03/20/2006] [Indexed: 11/27/2022]
Abstract
Background MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.
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Affiliation(s)
- Matthew N Davies
- Edward Jenner Institute for Vaccine Research, Compton, Newbury, RG20 7NN, UK
| | | | - David S Moss
- School of Crystallography, Birkbeck College, London WC1E 7HX, UK
| | - Michael GB Drew
- Structural and Computational Chemistry Group, University of Reading, Reading RG6 6AH, UK
| | - Darren R Flower
- Edward Jenner Institute for Vaccine Research, Compton, Newbury, RG20 7NN, UK
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Doytchinova IA, Flower DR. Class I T-cell epitope prediction: improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding. Mol Immunol 2006; 43:2037-44. [PMID: 16524630 DOI: 10.1016/j.molimm.2005.12.013] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2005] [Revised: 11/03/2005] [Accepted: 12/23/2005] [Indexed: 01/03/2023]
Abstract
Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.
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Schlessinger A, Ofran Y, Yachdav G, Rost B. Epitome: database of structure-inferred antigenic epitopes. Nucleic Acids Res 2006; 34:D777-80. [PMID: 16381978 PMCID: PMC1347416 DOI: 10.1093/nar/gkj053] [Citation(s) in RCA: 64] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Immunoglobulin molecules specifically recognize particular areas on the surface of proteins. These areas are commonly dubbed B-cell epitopes. The identification of epitopes in proteins is important both for the design of experiments and vaccines. Additionally, the interactions between epitopes and antibodies have often served as a model for protein-protein interactions. One of the main obstacles in creating a database of antigen-antibody interactions is the difficulty in distinguishing between antigenic and non-antigenic interactions. Antigenic interactions involve specific recognition sites on the antibody's surface, while non-antigenic interactions are between a protein and any other site on the antibody. To solve this problem, we performed a comparative analysis of all protein-antibody complexes for which structures have been experimentally determined. Additionally, we developed a semi-automated tool that identified the antigenic interactions within the known antigen-antibody complex structures. We compiled those interactions into Epitome, a database of structure-inferred antigenic residues in proteins. Epitome consists of all known antigen/antibody complex structures, a detailed description of the residues that are involved in the interactions, and their sequence/structure environments. Interactions can be visualized using an interface to Jmol. The database is available at http://www.rostlab.org/services/epitome/.
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Affiliation(s)
- Avner Schlessinger
- CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 1130 St Nicholas Avenue, room 804, New York, NY 10032, USA.
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Singh MK, Srivastava S, Raghava GPS, Varshney GC. HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies. Bioinformatics 2006; 22:253-5. [PMID: 16443637 DOI: 10.1093/bioinformatics/bti692] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED The key requirement for successful immunochemical assay is the availability of antibodies with high specificity and desired affinity. Small molecules, when used as haptens, are not immunogenic. However, on conjugating with carrier molecule they elicit antibody response. The production of anti-hapten antibodies of desired specificity largely depends on the hapten design (preserving greatly the chemical structure and spatial conformation of target compound), selection of the appropriate carrier protein and the conjugation method. This manuscript describes a curated database HaptenDB, where information is collected from published literature and web resources. The current version of the database has 2021 entries for 1087 haptens and 25 carrier proteins, where each entry provides comprehensive details about (1) nature of the hapten, (2) 2D and 3D structures of haptens, (3) carrier proteins, (4) coupling method, (5) method of anti-hapten antibody production, (6) assay method (used for characterization) and (7) specificities of antibodies. The current version of HaptenDB covers a wide array of haptens including pesticides, herbicides, insecticides, drugs, vitamins, steroids, hormones, toxins, dyes, explosives, etc. It provides internal and external links to various databases/resources to obtain further information about the nature of haptens, carriers and respective antibodies. For structure similarity comparison of haptens, the database also integrates tools like JME Editor and JMOL for sketching, displaying and manipulating hapten 2D/3D structures online. So the database would be of great help in identifying functional group(s) in smaller molecules using antibodies as well as for the development of immunodiagnostics/therapeutics by providing data and procedures available so far for the generation of specific or cross-reactive antibodies. AVAILABILITY HaptenDB is available on http://www.imtech.res.in/raghava/haptendb/ and http://bioinformatics.uams.edu/raghava/haptendb/ (Mirror site).
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Guan P, Doytchinova IA, Walshe VA, Borrow P, Flower DR. Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for human histocompatibility complex HLA-A0201. J Med Chem 2006; 48:7418-25. [PMID: 16279801 DOI: 10.1021/jm0505258] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Amino acid descriptors are often used in quantitative structure-activity relationship (QSAR) analysis of proteins and peptides. In the present study, descriptors were used to characterize peptides binding to the human MHC allele HLA-A0201. Two sets of amino acid descriptors were chosen: 93 descriptors taken from the amino acid descriptor database AAindex and the z descriptors defined by Wold and Sandberg. Variable selection techniques (SIMCA, genetic algorithm, and GOLPE) were applied to remove redundant descriptors. Our results indicate that QSAR models generated using five z descriptors had the highest predictivity and explained variance (q2 between 0.6 and 0.7 and r2 between 0.6 and 0.9). Further to the QSAR analysis, 15 peptides were synthesized and tested using a T2 stabilization assay. All peptides bound to HLA-A0201 well, and four peptides were identified as high-affinity binders.
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Affiliation(s)
- Pingping Guan
- Edward Jenner Institute for Vaccine Research, Compton, Berkshire RG20 7NN, UK.
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44
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Hattotuwagama CK, Doytchinova IA, Flower DR. In silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: a comparative molecular similarity index analysis (CoMSIA) study. J Chem Inf Model 2005; 45:1415-23. [PMID: 16180918 DOI: 10.1021/ci049667l] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Current methods for the in silico identification of T cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate prediction of peptide-major histocompatibility complex (MHC) affinity. A three-dimensional quantitative structure-activity relationship (3D-QSAR) for the prediction of peptide binding to class I MHC molecules was established using the comparative molecular similarity index analysis (CoMSIA) method. Three MHC alleles were studied: H2-D(b), H2-K(b), and H2-K(k). Models were produced for each allele. Each model consisted of five physicochemical descriptors-steric bulk, electrostatic potentials, hydrophobic interactions, and hydrogen-bond donor and hydrogen-bond acceptor abilities. The models have an acceptable level of predictivity: cross-validation leave-one-out statistical terms q2 and SEP (standard error of prediction) ranged between 0.490 and 0.679 and between 0.525 and 0.889, respectively. The non-cross-validated statistical terms r2 and SEE (standard error of estimate) ranged between 0.913 and 0.979 and between 0.167 and 0.248, respectively. The use of coefficient contour maps, which indicate favored and disfavored areas for each position of the MHC-bound peptides, allowed the binding specificity of each allele to be identified, visualized, and understood. The present study demonstrates the effectiveness of CoMSIA as a method for studying peptide-MHC interactions. The peptides used in this study are available on the Internet (http://www.jenner.ac.uk/AntiJen). The partial least-squares method is available commercially in the SYBYL molecular modeling software package.
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Toseland CP, Clayton DJ, McSparron H, Hemsley SL, Blythe MJ, Paine K, Doytchinova IA, Guan P, Hattotuwagama CK, Flower DR. AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res 2005; 1:4. [PMID: 16305757 PMCID: PMC1289288 DOI: 10.1186/1745-7580-1-4] [Citation(s) in RCA: 141] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2005] [Accepted: 10/06/2005] [Indexed: 11/30/2022] Open
Abstract
AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries. AntiJen provides the most complete and up-to-date dataset of its kind. While AntiJen v2.0 retains a focus on both T cell and B cell epitopes, its greatest novelty is the archiving of continuous quantitative data on a variety of immunological molecular interactions. This includes thermodynamic and kinetic measures of peptide binding to TAP and the Major Histocompatibility Complex (MHC), peptide-MHC complexes binding to T cell receptors, antibodies binding to protein antigens and general immunological protein-protein interactions. The database also contains quantitative specificity data from position-specific peptide libraries and biophysical data, in the form of diffusion co-efficients and cell surface copy numbers, on MHCs and other immunological molecules. The uses of AntiJen include the design of vaccines and diagnostics, such as tetramers, and other laboratory reagents, as well as helping parameterize the bioinformatic or mathematical in silico modeling of the immune system. The database is accessible from the URL: .
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Affiliation(s)
- Christopher P Toseland
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Debra J Clayton
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Helen McSparron
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Shelley L Hemsley
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Martin J Blythe
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Kelly Paine
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Irini A Doytchinova
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Pingping Guan
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Channa K Hattotuwagama
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
| | - Darren R Flower
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK
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Zhang GL, Srinivasan KN, Veeramani A, August JT, Brusic V. PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse. Nucleic Acids Res 2005; 33:W180-3. [PMID: 15980450 PMCID: PMC1160239 DOI: 10.1093/nar/gki479] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
PREDBALB/c is a computational system that predicts peptides binding to the major histocompatibility complex-2 (H2d) of the BALB/c mouse, an important laboratory model organism. The predictions include the complete set of H2d class I (H2-Kd, H2-Ld and H2-Dd) and class II (I-Ed and I-Ad) molecules. The prediction system utilizes quantitative matrices, which were rigorously validated using experimentally determined binders and non-binders and also by in vivo studies using viral proteins. The prediction performance of PREDBALB/c is of very high accuracy. To our knowledge, this is the first online server for the prediction of peptides binding to a complete set of major histocompatibility complex molecules in a model organism (H2d haplotype). PREDBALB/c is available at .
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Affiliation(s)
- Guang Lan Zhang
- Institute for Infocomm Research21 Heng Mui Keng Terrace, Singapore 119613
- School of Computer Engineering, Nanyang Technological UniversitySingapore 6397984
| | - Kellathur N. Srinivasan
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of MedicineBaltimore, MD 21205, USA
- Division of Biomedical Sciences, Johns Hopkins in Singapore#02–01 The Nanos, 31 Biopolis Way, Singapore 138669
| | - Anitha Veeramani
- Institute for Infocomm Research21 Heng Mui Keng Terrace, Singapore 119613
| | - J. Thomas August
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of MedicineBaltimore, MD 21205, USA
| | - Vladimir Brusic
- Institute for Infocomm Research21 Heng Mui Keng Terrace, Singapore 119613
- School of Land and Food Sciences and the Institute for Molecular Bioscience, University of QueenslandBrisbane QLD 4072, Australia
- To whom correspondence should be addressed: Tel: +65 96 212 415; Fax: +65 6774 8056;
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Abstract
Background Bcipep is a database of experimentally determined linear B-cell epitopes of varying immunogenicity collected from literature and other publicly available databases. Results The current version of Bcipep database contains 3031 entries that include 763 immunodominant, 1797 immunogenic and 471 null-immunogenic epitopes. It covers a wide range of pathogenic organisms like viruses, bacteria, protozoa, and fungi. The database provides a set of tools for the analysis and extraction of data that includes keyword search, peptide mapping and BLAST search. It also provides hyperlinks to various databases such as GenBank, PDB, SWISS-PROT and MHCBN. Conclusion A comprehensive database of B-cell epitopes called Bcipep has been developed that covers information on epitopes from a wide range of pathogens. The Bcipep will be source of information for investigators involved in peptide-based vaccine design, disease diagnosis and research in allergy. It should also be a promising data source for the development and evaluation of methods for prediction of B-cell epitopes. The database is available at .
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Abstract
gp96 is a 96-kDa glycoprotein of the endoplasmic reticulum that is believed to be involved in antigen processing as an intermediate carrier of peptides for presentation by major histocompatibility complex (MHC) class I molecules. This function implies that gp96 carries a large array of different peptides that represent the antigenicity of the cell and can serve all MHC class I molecules. So far, the evidence regarding these peptides is largely indirect and based on experiments where mice immunized with gp96 from tumor or virus-infected cells developed T cellular immune responses with the corresponding specificities. We analyzed by mass spectrometry peptides isolated from gp96 and found a number of different peptides derived from the proteins of different cellular compartments but mostly cytoplasm and nucleus. The sequences of these peptides provide information on the specificity of antigen processing and reveal structural requirements for binding to gp96 that only partially correspond to those of peptides presented by MHC class I molecules. The yield of peptides extracted from gp96 was far substoichiometric with an estimated occupancy of this chaperone of between 0.1% and 0.4%. These results strongly argue against a regular role for gp96 as a peptide chaperone in antigen processing.
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Affiliation(s)
- Rodion Demine
- Charité-University Medicine Berlin, Humboldt University, Clinical Research Group Tumor Immunology, Department of Dermatology and Allergy, D-10098 Berlin, Germany
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Blythe MJ, Flower DR. Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Sci 2004; 14:246-8. [PMID: 15576553 PMCID: PMC2253337 DOI: 10.1110/ps.041059505] [Citation(s) in RCA: 177] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Sequence profiling is used routinely to predict the location of B-cell epitopes. In the postgenomic era, the need for reliable epitope prediction is clear. We assessed 484 amino acid propensity scales in combination with ranges of plotting parameters to examine exhaustively the correlation of peaks and epitope location within 50 proteins mapped for polyclonal responses. After examining more than 10(6) combinations, we found that even the best set of scales and parameters performed only marginally better than random. Our results confirm the null hypothesis: Single-scale amino acid propensity profiles cannot be used to predict epitope location reliably. The implication for studies using such methods is obvious.
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Affiliation(s)
- Martin J Blythe
- Edward Jenner Institute for Vaccine Research, High Street, Compton, Berkshire, RG20 7NN, UK.
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50
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Doytchinova I, Hemsley S, Flower DR. Transporter Associated with Antigen Processing Preselection of Peptides Binding to the MHC: A Bioinformatic Evaluation. THE JOURNAL OF IMMUNOLOGY 2004; 173:6813-9. [PMID: 15557175 DOI: 10.4049/jimmunol.173.11.6813] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.
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Affiliation(s)
- Irini Doytchinova
- Edward Jenner Institute for Vaccine Research, Compton, Berkshire, United Kingdom
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