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Meng Q, Wu Y, Sui X, Meng J, Wang T, Lin Y, Wang Z, Zhou X, Qi Y, Du J, Gao Y. POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction. Front Immunol 2020; 11:02193. [PMID: 33133063 PMCID: PMC7579403 DOI: 10.3389/fimmu.2020.02193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 08/11/2020] [Indexed: 12/23/2022] Open
Abstract
Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.
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Affiliation(s)
- Qingqing Meng
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Yahong Wu
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Xinghua Sui
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
| | - Jingjie Meng
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Tingting Wang
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Yan Lin
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhiwei Wang
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Xiuman Zhou
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Yuanming Qi
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Jiangfeng Du
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Yanfeng Gao
- School of Life Sciences, Zhengzhou University, Zhengzhou, China.,School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Shenzhen, China
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2
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Mei S, Li F, Leier A, Marquez-Lago TT, Giam K, Croft NP, Akutsu T, Smith AI, Li J, Rossjohn J, Purcell AW, Song J. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief Bioinform 2020; 21:1119-1135. [PMID: 31204427 DOI: 10.1093/bib/bbz051] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 04/02/2019] [Accepted: 04/03/2019] [Indexed: 12/13/2022] Open
Abstract
Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.
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Affiliation(s)
- Shutao Mei
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - André Leier
- Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics and Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Kailin Giam
- Department of Immunology, King's College London, London, UK
| | - Nathan P Croft
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Tatsuya Akutsu
- Bioinformatics Centre, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - A Ian Smith
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Jian Li
- Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia
| | - Jamie Rossjohn
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia
| | - Anthony W Purcell
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry & Molecular Biology, Monash University, Melbourne, VIC, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia.,Monash Centre for Data Science, Monash University, Melbourne, VIC, Australia
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3
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Besser H, Yunger S, Merhavi-Shoham E, Cohen CJ, Louzoun Y. Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides. J Immunother Cancer 2019; 7:135. [PMID: 31118084 PMCID: PMC6532181 DOI: 10.1186/s40425-019-0595-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 04/22/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target effects. However, most peptides predicted using MHC binding prediction algorithms do not induce a CD8 + T cell response, and there is a crucial need to refine the predictions to readily identify the best antigens that could mediate T-cell responses. Such a response requires a high enough number of epitopes bound to the target MHC. This number is correlated with both the peptide-MHC binding affinity and the number of peptides reaching the ER. Beyond this, the response may be affected by the properties of the neo-epitope mutated residues. METHODS Herein, we analyzed several experimental datasets from cancer patients to elaborate better predictive algorithms for T-cell reactivity to neo-epitopes. RESULTS Indeed, potent classifiers for epitopes derived from neo-antigens in melanoma and other tumors can be developed based on biochemical properties of the mutated residue, the antigen expression level and the peptide processing stage. Among MHC binding peptides, the present classifiers can remove half of the peptides falsely predicted to activate T cells while maintaining the absolute majority of reactive peptides. CONCLUSIONS The classifier properties further highlight the contribution of the quantity of peptides reaching the ER and the mutation type to CD8 + T cell responses. These classifiers were then validated on neo-antigens obtained from other datasets, confirming the validity of our prediction. Algorithm Availability: http://peptibase.cs.biu.ac.il/Tcell_predictor/ or by request from the authors as a standalone code.
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Affiliation(s)
- Hanan Besser
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, 5290002, Ramat Gan, Israel
- Department of Mathematics, Bar-Ilan University, 5290002, Ramat Gan, Israel
| | - Sharon Yunger
- Ella Lemelbaum Institute for Immuno Oncology, Sheba Medical Center, Ramat Gan, Israel
| | - Efrat Merhavi-Shoham
- Ella Lemelbaum Institute for Immuno Oncology, Sheba Medical Center, Ramat Gan, Israel
| | - Cyrille J Cohen
- Laboratory of Tumor Immunology and Immunotherapy, Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.
| | - Yoram Louzoun
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, 5290002, Ramat Gan, Israel.
- Department of Mathematics, Bar-Ilan University, 5290002, Ramat Gan, Israel.
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Computational Prediction of the Epitopes of HA1 Protein of Influenza Viruses to its Neutralizing Antibodies. Antibodies (Basel) 2018; 8:antib8010002. [PMID: 31544808 PMCID: PMC6640696 DOI: 10.3390/antib8010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 12/11/2018] [Accepted: 12/19/2018] [Indexed: 11/17/2022] Open
Abstract
In this work, we have used a new method to predict the epitopes of HA1 protein of influenza virus to several antibodies HC19, CR9114, BH151 and 4F5. While our results reproduced the binding epitopes of H3N2 or H5N1 for the neutralizing antibodies HC19, CR9114, and BH151 as revealed from the available crystal structures, additional epitopes for these antibodies were also suggested. Moreover, the predicted epitopes of H5N1 HA1 for the newly developed antibody 4F5 are located at the receptor binding domain, while previous study identified a region 76-WLLGNP-81 as the epitope. The possibility of antibody recognition of influenza virus via different mechanism by binding to different epitopes of an antigen is also discussed.
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5
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Tan X, Liu N, Legge FS, Yang M, Zeng J. Computational identification of antibody epitopes of human papillomavirus 16 (HPV16) L1 proteins. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2018. [DOI: 10.1142/s0219633618500177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Previously, we developed a method to predict epitopes on a protein recognized by specific antibodies. In this study, we have applied this method to identify the epitopes of the human papillomavirus 16 (HPV16) L1 capsomer that is bound by monoclonal antibodies U4, AE3 and AG7. Initially, the method was validated by the identification of epitopes of HPV16 L1 capsomer that bind to antibody U4. Our predicted epitopes were in agreement with the cryto-electron microscopy (cryto-EM) structure of the complex. The method was then used to predict the epitopes of HPV16 L1 binding of antibodies AE3 and AG7. Our calculations indicated that antibody AE3 binds to the HPV16 L1 capsomer at two different regions. Firstly, the region recognized by antibody U4 and secondly, the region recognized by antibody V5, which have been shown in the cryto-EM structure of the V5 and HPV16 L1 complex. In comparison, the antibody AG7 binds to the capsomer only at the epitopes bound by antibody U4. Therefore, antibody AE3 is predicted to have higher affinity than antibody AG7 and could be used for developing highly efficient anti-HPV monoclonal antibodies in the clinical treatment of HPV infections.
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Affiliation(s)
- Xin Tan
- Department of Obstetrics and Gynecology, West China Second University Hospital, Chengdu 610041, P. R. China
- Key Laboratory of Birth Defects and Related, Diseases of Women and Children, Sichuan University Ministry of Education, Chengdu 610041, P. R. China
| | - Na Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Fiona S. Legge
- MedChemSoft Solutions Wheelers Hill, VIC 3150, Australia
| | - Minghui Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China
| | - Jun Zeng
- MedChemSoft Solutions Wheelers Hill, VIC 3150, Australia
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6
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Tan X, Liu N, Yang M, Duan M, Zeng J. Design of peptide inhibitors of human papillomavirus 16 (HPV16) transcriptional regulator E1–E2 formation. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2017. [DOI: 10.1142/s0219633617500262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Here, we have proposed a new scheme of the computational combinatorial design approach to identify potential inhibitor peptides. It consists of four steps: (i) using “multiple copy simultaneous search” (MCSS) procedure to locate specific functional groups on the protein surface; (ii) the peptide main chain is constructed based on the location of favored N-methylacetamide (NMA) groups; (iii) molecular dynamics simulations of the complex formed between the constructed peptides with the target protein in explicit water molecules are carried to select the peptides with strong binding to the protein and (iv) the sequences of the stable peptides selected from (iii) are aligned and the frequencies of the amino acids at each position of peptide are calculated. Sequence patterns of potential inhibitors are determined based on the frequency of amino acids at each position. It was applied to design peptide inhibitors that bind to the E2 protein of HPV16 so as to disrupt its transcriptional regulator of E1–E2 complex formation. The sequence pattern of these potential inhibitors is in agreement with known inhibitors obtained from phage display, and the MCSS calculations indicate that a hydrophobic pocket on HPV16 E2 plays a significant role in E1–E2 formation and inhibitor-E2 binding.
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Affiliation(s)
- Xin Tan
- Department of Obstetrics and Gynaecology, West China Second University Hospital, No. 20, the Third Part Renmin South Road, Chengdu 610041, P. R. China
- Key Laboratory of Birth Defects and Related Diseases of Woman and Children, Sichuan University, Ministry of Education, No. 20, the Third Part Renmin South Road, Chengdu 610041, P. R. China
| | - Na Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Min Yang
- School of Laboratory Medicine, Chengdu Medical College, Chengdu, Sichuan 610500, P. R. China
| | - Mojie Duan
- Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, P. R. China
| | - Jun Zeng
- School of Medical Sciences, Royal Melbourne Institute of Technology, Plenty Road, Bundoora, VIC 3083, Australia
- MedChemSoft Solutions, Level 3, 2 Brandon Park Drive, Wheelers Hill, VIC 3150, Australia
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7
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Jones ML, Legge FS, Lebani K, Mahler SM, Young PR, Watterson D, Treutlein HR, Zeng J. Computational Identification of Antibody Epitopes on the Dengue Virus NS1 Protein. Molecules 2017; 22:E607. [PMID: 28394300 PMCID: PMC6154621 DOI: 10.3390/molecules22040607] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 04/05/2017] [Accepted: 04/06/2017] [Indexed: 12/04/2022] Open
Abstract
We have previously described a method to predict antigenic epitopes on proteins recognized by specific antibodies. Here we have applied this method to identify epitopes on the NS1 proteins of the four Dengue virus serotypes (DENV1-4) that are bound by a small panel of monoclonal antibodies 1H7.4, 1G5.3 and Gus2. Several epitope regions were predicted for these antibodies and these were found to reflect the experimentally observed reactivities. The known binding epitopes on DENV2 for the antibodies 1H7.4 and 1G5.3 were identified, revealing the reasons for the serotype specificity of 1H7.4 and 1G5.3, and the non-selectivity of Gus2. As DENV NS1 is critical for virus replication and a key vaccine candidate, epitope prediction will be valuable in designing appropriate vaccine control strategies. The ability to predict potential epitopes by computational methods significantly reduces the amount of experimental work required to screen peptide libraries for epitope mapping.
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Affiliation(s)
- Martina L Jones
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, QLD 4072, Australia.
- ARC Training Centre for Biopharmaceutical Innovation, The University of Queensland, St. Lucia, QLD 4072, Australia.
| | - Fiona S Legge
- Computist Bio-Nanotech, 1 Dalmore Drive, Scoresby, VIC 3179, Australia.
| | - Kebaneilwe Lebani
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, QLD 4072, Australia.
| | - Stephen M Mahler
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, QLD 4072, Australia.
- ARC Training Centre for Biopharmaceutical Innovation, The University of Queensland, St. Lucia, QLD 4072, Australia.
| | - Paul R Young
- ARC Training Centre for Biopharmaceutical Innovation, The University of Queensland, St. Lucia, QLD 4072, Australia.
- School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, QLD 4072, Australia.
- Australian Infectious Diseases Research Centre, The University of Queensland, St. Lucia, QLD 4072, Australia.
| | - Daniel Watterson
- School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, QLD 4072, Australia.
- Australian Infectious Diseases Research Centre, The University of Queensland, St. Lucia, QLD 4072, Australia.
| | - Herbert R Treutlein
- Computist Bio-Nanotech, 1 Dalmore Drive, Scoresby, VIC 3179, Australia.
- School of Medical Sciences, RMIT University, P.O. Box 71, Bundoora, VIC 3083, Australia.
| | - Jun Zeng
- Computist Bio-Nanotech, 1 Dalmore Drive, Scoresby, VIC 3179, Australia.
- School of Medical Sciences, RMIT University, P.O. Box 71, Bundoora, VIC 3083, Australia.
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Vukmanović S, Sadrieh N. Skin sensitizers in cosmetics and beyond: potential multiple mechanisms of action and importance of T-cell assays for in vitro screening. Crit Rev Toxicol 2017; 47:415-432. [DOI: 10.1080/10408444.2017.1288025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Stanislav Vukmanović
- Cosmetics Division, Office of Cosmetics and Colors (OCAC), Center for Food Safety and Applied Nutrition (CFSAN), Food and Drug Administration (FDA), MD, USA
| | - Nakissa Sadrieh
- Cosmetics Division, Office of Cosmetics and Colors (OCAC), Center for Food Safety and Applied Nutrition (CFSAN), Food and Drug Administration (FDA), MD, USA
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9
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Mahendran R, Jeyabaskar S, Sitharaman G, Michael RD, Paul AV. Computer-aided vaccine designing approach against fish pathogens Edwardsiella tarda and Flavobacterium columnare using bioinformatics softwares. Drug Des Devel Ther 2016; 10:1703-14. [PMID: 27284239 PMCID: PMC4883809 DOI: 10.2147/dddt.s95691] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Edwardsiella tarda and Flavobacterium columnare are two important intracellular pathogenic bacteria that cause the infectious diseases edwardsiellosis and columnaris in wild and cultured fish. Prediction of major histocompatibility complex (MHC) binding is an important issue in T-cell epitope prediction. In a healthy immune system, the T-cells must recognize epitopes and induce the immune response. In this study, T-cell epitopes were predicted by using in silico immunoinformatics approach with the help of bioinformatics tools that are less expensive and are not time consuming. Such identification of binding interaction between peptides and MHC alleles aids in the discovery of new peptide vaccines. We have reported the potential peptides chosen from the outer membrane proteins (OMPs) of E. tarda and F. columnare, which interact well with MHC class I alleles. OMPs from E. tarda and F. columnare were selected and analyzed based on their antigenic and immunogenic properties. The OMPs of the genes TolC and FCOL_04620, respectively, from E. tarda and F. columnare were taken for study. Finally, two epitopes from the OMP of E. tarda exhibited excellent protein-peptide interaction when docked with MHC class I alleles. Five epitopes from the OMP of F. columnare had good protein-peptide interaction when docked with MHC class I alleles. Further in vitro studies can aid in the development of potential peptide vaccines using the predicted peptides.
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Affiliation(s)
- Radha Mahendran
- Department of Bioinformatics, School of Life Sciences, Vels University, Pallavaram, Chennai, Tamil Nadu, India
| | - Suganya Jeyabaskar
- Department of Bioinformatics, School of Life Sciences, Vels University, Pallavaram, Chennai, Tamil Nadu, India
| | - Gayathri Sitharaman
- Department of Bioinformatics, School of Life Sciences, Vels University, Pallavaram, Chennai, Tamil Nadu, India
| | - Rajamani Dinakaran Michael
- Centre for Fish Immunology, School of Life Sciences, Vels University, Pallavaram, Chennai, Tamil Nadu, India
| | - Agnal Vincent Paul
- Department of Bioinformatics, School of Life Sciences, Vels University, Pallavaram, Chennai, Tamil Nadu, India
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Jiao Y, Legge FS, Zeng X, Treutlein HR, Zeng J. Antibody recognition of Shiga toxins (Stxs): computational identification of the epitopes of Stx2 subunit A to the antibodies 11E10 and S2C4. PLoS One 2014; 9:e88191. [PMID: 24516609 PMCID: PMC3917601 DOI: 10.1371/journal.pone.0088191] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 01/04/2014] [Indexed: 11/18/2022] Open
Abstract
We have recently developed a new method to predict the epitopes of the antigens that are
recognized by a specific antibody. In this work, we applied the method to identify the epitopes of
the Shiga toxin (Stx2 subunit A) that were bound by two specific antibodies 11E10 and S2C4. The
predicted epitopes of Stx2 binding to the antibody 11E10 resembles the recognition surface
constructed by the regions of Stx2 identified experimentally. For the S2C4, our results indicate
that the antibody recognizes the Stx2 at two different regions on the protein surface. The first
region (residues 246-254: ARSVRAVNE) is similar to the recognition region of the 11E10, while the
second region is formed by two epitopes. The second region is particularly significant because it
includes the amino acid sequence region that is diverse between Stx2 and other Stx (residues
176-188: QREFRQALSETAPV). This new recognition region is believed to play an important role in the
experimentally observed selectivity of S2C4 to the Stx2.
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Affiliation(s)
- Yongjun Jiao
- Institute of Pathogenic Microbiology, Jiangsu Provincial
Center for Disease Prevention and Control, Key Laboratory of Enteric Pathogenic Microbiology,
Ministry Health, Nanjing, China
| | - Fiona S. Legge
- Computist Bio-Nanotech, Small Technology Clusters,
Scoresby, Victoria, Australia
| | - Xiaoyan Zeng
- Institute of Pathogenic Microbiology, Jiangsu Provincial
Center for Disease Prevention and Control, Key Laboratory of Enteric Pathogenic Microbiology,
Ministry Health, Nanjing, China
| | - Herbert R. Treutlein
- Monash Institute of Pharmaceutical Sciences, Monash
University, Parkville, Victoria, Australia
- Computist Bio-Nanotech, Small Technology Clusters,
Scoresby, Victoria, Australia
- * E-mail: (HRT); (JZ)
| | - Jun Zeng
- Monash Institute of Pharmaceutical Sciences, Monash
University, Parkville, Victoria, Australia
- Computist Bio-Nanotech, Small Technology Clusters,
Scoresby, Victoria, Australia
- * E-mail: (HRT); (JZ)
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Zhang W, Zeng X, Zhang L, Peng H, Jiao Y, Zeng J, Treutlein HR. Computational identification of epitopes in the glycoproteins of novel bunyavirus (SFTS virus) recognized by a human monoclonal antibody (MAb 4-5). J Comput Aided Mol Des 2013; 27:539-50. [PMID: 23838839 DOI: 10.1007/s10822-013-9661-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Accepted: 06/24/2013] [Indexed: 12/12/2022]
Abstract
In this work, we have developed a new approach to predict the epitopes of antigens that are recognized by a specific antibody. Our method is based on the "multiple copy simultaneous search" (MCSS) approach which identifies optimal locations of small chemical functional groups on the surfaces of the antibody, and identifying sequence patterns of peptides that can bind to the surface of the antibody. The identified sequence patterns are then used to search the amino-acid sequence of the antigen protein. The approach was validated by reproducing the binding epitope of HIV gp120 envelop glycoprotein for the human neutralizing antibody as revealed in the available crystal structure. Our method was then applied to predict the epitopes of two glycoproteins of a newly discovered bunyavirus recognized by an antibody named MAb 4-5. These predicted epitopes can be verified by experimental methods. We also discuss the involvement of different amino acids in the antigen-antibody recognition based on the distributions of MCSS minima of different functional groups.
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Affiliation(s)
- Wenshuai Zhang
- Key Laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Disease Prevention and Control, Institute of Pathogenic Microbiology, Ministry Health, Nanjing 210009, China
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12
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Ivanov S, Dimitrov I, Doytchinova I. Quantitative prediction of peptide binding to HLA-DP1 protein. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:811-815. [PMID: 24091413 DOI: 10.1109/tcbb.2013.78] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The MHC binding is considered as the crucial prerequisite for T-cell recognition. Only peptides able to form stable complexes with the MHC proteins are recognized by the T-cells. These peptides are known as T-cell epitopes. All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. The T-cell epitope prediction is one of the main priorities of immunoinformatics. In the present study, three chemometric techniques are combined to derive a model for in silico prediction of peptide binding to the human MHC class II protein HLA-DP1. The structures of a set of known peptide binders are described by amino acid z-descriptors. Data are processed by an iterative self-consisted algorithm using the method of partial least squares, and a quantitative matrix (QM) for peptide binding prediction to HLA-DP1 is derived. The QM is validated by two sets of proteins and showed an average accuracy of 86 percent.
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Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol 2013; 3:120139. [PMID: 23303307 PMCID: PMC3603454 DOI: 10.1098/rsob.120139] [Citation(s) in RCA: 255] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2012] [Accepted: 12/11/2012] [Indexed: 01/08/2023] Open
Abstract
Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated ('attenuated') forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.
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Affiliation(s)
| | - Irini Doytchinova
- Department of Chemistry, Faculty of Pharmacy, Medical University of Sofia, Sofia, Bulgaria
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Yang X, Yu X. An introduction to epitope prediction methods and software. Rev Med Virol 2009; 19:77-96. [PMID: 19101924 DOI: 10.1002/rmv.602] [Citation(s) in RCA: 127] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, current prediction methods and algorithms for both T- and B cell epitopes are reviewed, and a comprehensive summary of epitope prediction software and databases currently available online is also provided. This review can offer researchers in this field a sense of direction and insights for future work. However, our main purpose is to introduce clinical and basic biomedical researchers who are not familiar with these biological analysis tools and databases to these online resources and to provide guidance on how to use them effectively.
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Affiliation(s)
- Xingdong Yang
- Department of Veterinary Medicine, Hunan Agricultural University, Changsha, Hunan, P. R. China
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Kangueane P, Sakharkar MK. HLA-peptide binding prediction using structural and modeling principles. Methods Mol Biol 2007; 409:293-299. [PMID: 18450009 DOI: 10.1007/978-1-60327-118-9_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Short peptides binding to specific human leukocyte antigen (HLA) alleles elicit immune response. These candidate peptides have potential utility in peptide vaccine design and development. The binding of peptides to allele-specific HLA molecule is estimated using competitive binding assay and biochemical binding constants. Application of this method for proteome-wide screening in parasites, viruses, and virulent bacterial strains is laborious and expensive. However, short listing of candidate peptides using prediction approaches have been realized lately. Prediction of peptide binding to HLA alleles using structural and modeling principles has gained momentum in recent years. Here, we discuss the current status of such prediction.
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Rapid Generation of C 2 Continuous Blending Surfaces. LECTURE NOTES IN COMPUTER SCIENCE 2002. [DOI: 10.1007/3-540-46080-2_10] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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