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Hashempour A, Khodadad N, Akbarinia S, Ghasabi F, Ghasemi Y, Nazar MMKA, Falahi S. Reverse vaccinology approaches to design a potent multiepitope vaccine against the HIV whole genome: immunoinformatic, bioinformatics, and molecular dynamics approaches. BMC Infect Dis 2024; 24:873. [PMID: 39198721 PMCID: PMC11360854 DOI: 10.1186/s12879-024-09775-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 08/20/2024] [Indexed: 09/01/2024] Open
Abstract
Substantial advances have been made in the development of promising HIV vaccines to eliminate HIV-1 infection. For the first time, one hundred of the most submitted HIV subtypes and CRFs were retrieved from the LANL database, and the consensus sequences of the eleven HIV proteins were obtained to design vaccines for human and mouse hosts. By using various servers and filters, highly qualified B-cell epitopes, as well as HTL and CD8 + epitopes that were common between mouse and human alleles and were also located in the conserved domains of HIV proteins, were considered in the vaccine constructs. With 90% coverage worldwide, the human vaccine model covers a diverse allelic population, making it widely available. Codon optimization and in silico cloning in prokaryotic and eukaryotic vectors guarantee high expression of the vaccine models in human and E. coli hosts. Molecular dynamics confirmed the stable interaction of the vaccine constructs with TLR3, TLR4, and TLR9, leading to a substantial immunogenic response to the designed vaccine. Vaccine models effectively target the humoral and cellular immune systems in humans and mice; however, experimental validation is needed to confirm these findings in silico.
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Affiliation(s)
- Ava Hashempour
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nastaran Khodadad
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Shokufeh Akbarinia
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Farzane Ghasabi
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Younes Ghasemi
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Shahab Falahi
- HIV/AIDS Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
- Zoonotic Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran.
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Waury K, Willemse EAJ, Vanmechelen E, Zetterberg H, Teunissen CE, Abeln S. Bioinformatics tools and data resources for assay development of fluid protein biomarkers. Biomark Res 2022; 10:83. [DOI: 10.1186/s40364-022-00425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractFluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.
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3
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Caoili SEC. Prediction of Variable-Length B-Cell Epitopes for Antipeptide Paratopes Using the Program HAPTIC. Protein Pept Lett 2022; 29:328-339. [PMID: 35125075 DOI: 10.2174/0929866529666220203101808] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/02/2021] [Accepted: 12/13/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND B-cell epitope prediction for antipeptide antibody responses enables peptide-based vaccine design and related translational applications. This entails estimating epitopeparatope binding free-energy changes from antigen sequence; but attempts to do so assuming uniform epitope length (e.g., of hexapeptide sequences, each spanning a typical paratope diameter when fully extended) have neglected empirically established variation in epitope length. OBJECTIVE This work aimed to develop a sequence-based physicochemical approach to variablelength B-cell epitope prediction for antipeptide paratopes recognizing flexibly disordered targets. METHODS Said approach was developed by analogy between epitope-paratope binding and protein folding modeled as polymer collapse, treating paratope structure implicitly. Epitope-paratope binding was thus conceptually resolved into processes of epitope compaction, collapse and contact, with epitope collapse presenting the main entropic barrier limiting epitope length among nonpolyproline sequences. The resulting algorithm was implemented as a computer program, namely the Heuristic Affinity Prediction Tool for Immune Complexes (HAPTIC), which is freely accessible via an online interface (http://badong.freeshell.org/haptic.htm). This was used in conjunction with published data on representative known peptide immunogens. RESULTS HAPTIC predicted immunodominant epitope sequences with lengths limited by penalties for both compaction and collapse, consistent with known paratope-bound structures of flexibly disordered epitopes. In most cases, the predicted association constant was greater than its experimentally determined counterpart but below the predicted upper bound for affinity maturation in vivo. CONCLUSION HAPTIC provides a physicochemically plausible means for estimating the affinity of antipeptide paratopes for sterically accessible and flexibly disordered peptidic antigen sequences by explicitly considering candidate B-cell epitopes of variable length.
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Affiliation(s)
- Salvador E C Caoili
- Biomedical Innovations Research for Translational Health Science (BIRTHS) Laboratory, Department of Biochemistry and Molecular Biology, College of Medicine, University of the Philippines Manila, Manila, Philippines
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Hou Q, Stringer B, Waury K, Capel H, Haydarlou R, Xue F, Abeln S, Heringa J, Feenstra KA. SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes. Bioinformatics 2021; 37:3421-3427. [PMID: 33974039 PMCID: PMC8136078 DOI: 10.1093/bioinformatics/btab321] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/26/2021] [Accepted: 04/26/2021] [Indexed: 11/21/2022] Open
Abstract
Motivation Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein–protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody. Results We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody–antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research. Availability and implementation Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250002, P. R. China.,National institute of health data science of China, Shandong University, Shandong 250002, P. R. China
| | - Bas Stringer
- IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
| | - Katharina Waury
- IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
| | - Henriette Capel
- IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
| | - Reza Haydarlou
- IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250002, P. R. China.,National institute of health data science of China, Shandong University, Shandong 250002, P. R. China
| | - Sanne Abeln
- IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands
| | - Jaap Heringa
- IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands.,AIMMS - Amsterdam Institute for Molecules Medicines and Systems, Vrije Universiteit Amsterdam
| | - K Anton Feenstra
- IBIVU - Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081HV, The Netherlands.,AIMMS - Amsterdam Institute for Molecules Medicines and Systems, Vrije Universiteit Amsterdam
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5
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Norman RA, Ambrosetti F, Bonvin AMJJ, Colwell LJ, Kelm S, Kumar S, Krawczyk K. Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief Bioinform 2020; 21:1549-1567. [PMID: 31626279 PMCID: PMC7947987 DOI: 10.1093/bib/bbz095] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/07/2019] [Accepted: 07/05/2019] [Indexed: 12/31/2022] Open
Abstract
Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.
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Choi Y, Jeong S, Choi JM, Ndong C, Griswold KE, Bailey-Kellogg C, Kim HS. Computer-guided binding mode identification and affinity improvement of an LRR protein binder without structure determination. PLoS Comput Biol 2020; 16:e1008150. [PMID: 32866140 PMCID: PMC7485979 DOI: 10.1371/journal.pcbi.1008150] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 09/11/2020] [Accepted: 07/14/2020] [Indexed: 12/24/2022] Open
Abstract
Precise binding mode identification and subsequent affinity improvement without structure determination remain a challenge in the development of therapeutic proteins. However, relevant experimental techniques are generally quite costly, and purely computational methods have been unreliable. Here, we show that integrated computational and experimental epitope localization followed by full-atom energy minimization can yield an accurate complex model structure which ultimately enables effective affinity improvement and redesign of binding specificity. As proof-of-concept, we used a leucine-rich repeat (LRR) protein binder, called a repebody (Rb), that specifically recognizes human IgG1 (hIgG1). We performed computationally-guided identification of the Rb:hIgG1 binding mode and leveraged the resulting model to reengineer the Rb so as to significantly increase its binding affinity for hIgG1 as well as redesign its specificity toward multiple IgGs from other species. Experimental structure determination verified that our Rb:hIgG1 model closely matched the co-crystal structure. Using a benchmark of other LRR protein complexes, we further demonstrated that the present approach may be broadly applicable to proteins undergoing relatively small conformational changes upon target binding.
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Affiliation(s)
- Yoonjoo Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Sukyo Jeong
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Jung-Min Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Christian Ndong
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Karl E. Griswold
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States of America
- Norris Cotton Cancer Center at Dartmouth, Lebanon, New Hampshire, United States of America
- Department of Biological Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Chris Bailey-Kellogg
- Department of Computer Science, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Hak-Sung Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
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7
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Ndawula C, Amaral Xavier M, Villavicencio B, Cortez Lopes F, Juliano MA, Parizi LF, Verli H, da Silva Vaz I, Ligabue-Braun R. Prediction, mapping and validation of tick glutathione S-transferase B-cell epitopes. Ticks Tick Borne Dis 2020; 11:101445. [PMID: 32354639 DOI: 10.1016/j.ttbdis.2020.101445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 04/02/2020] [Accepted: 04/10/2020] [Indexed: 10/24/2022]
Abstract
In search of ways to address the increasing incidence of global acaricide resistance, tick control through vaccination is regarded as a sustainable alternative approach. Recently, a novel cocktail antigen tick-vaccine was developed based on the recombinant glutathione S-transferase (rGST) anti-sera cross-reaction to glutathione S-transferases of Rhipicephalus appendiculatus (GST-Ra), Amblyomma variegatum (GST-Av), Haemaphysalis longicornis (GST-Hl), Rhipicephalus decoloratus (GST-Rd) and Rhipicephalus microplus (GST-Rm). Therefore, the current study aimed to predict the shared B-cell epitopes within the GST sequences of these tick species. Prediction of B-cell epitopes and proteasomal cleavage sites were performed using immunoinformatics algorithms. The conserved epitopes predicted within the sequences were mapped on the homodimers of the respective tick GSTs, and the corresponding peptides were independently used for rabbit immunization experiments. Based on the dot blot assay, the immunogenicity of the peptides and their potential to be recognized by corresponding rGST anti-sera raised by rabbit immunization in a previous work were investigated. This study revealed that the predicted conserved B-cell epitopes within the five tick GST sequences were localized on the surface of the respective GST homodimers. The epitopes of GST-Ra, GST-Rd, GST-Av, and GST-Hl were also shown to contain a seven residue-long peptide sequence with no proteasomal cleavage sites, whereas proteasomal digestion of GST-Rm was predicted to yield a 4-residue fragment. Given that a few proteasomal cleavage sites were found within the conserved epitope sequences of the four GSTs, the sequences could also contain a T-cell epitope. Finally, the peptide and rGST anti-sera reacted against the corresponding peptide, confirming their immunogenicity. These data support the claim that the rGSTs, used in the previous study, contain conserved B-cell epitopes, which elucidates why the rGST anti-sera cross-reacted to non-homologous tick GSTs. Taken together, the data suggest that the B-cell epitopes predicted in this study could be useful for constituting epitope-based GST tick vaccines.
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Affiliation(s)
- Charles Ndawula
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Marina Amaral Xavier
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Bianca Villavicencio
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Fernanda Cortez Lopes
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Maria Aparecida Juliano
- Departamento de Biofísica, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Luís Fernando Parizi
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Hugo Verli
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Itabajara da Silva Vaz
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
| | - Rodrigo Ligabue-Braun
- Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Departamento de Farmacociências, Universidade Federal das Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brazil
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8
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Hua CK, Gacerez AT, Sentman CL, Ackerman ME, Choi Y, Bailey-Kellogg C. Computationally-driven identification of antibody epitopes. eLife 2017; 6:29023. [PMID: 29199956 PMCID: PMC5739537 DOI: 10.7554/elife.29023] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 12/02/2017] [Indexed: 12/21/2022] Open
Abstract
Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.
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Affiliation(s)
- Casey K Hua
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Albert T Gacerez
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Charles L Sentman
- Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, United States.,Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Lebanon, United States
| | - Yoonjoo Choi
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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Subramaniyan V, Venkatachalam R, Srinivasan P, Palani M. In silico prediction of monovalent and chimeric tetravalent vaccines for prevention and treatment of dengue fever. J Biomed Res 2017; 32:222. [PMID: 29497025 PMCID: PMC6265401 DOI: 10.7555/jbr.31.20160109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 10/27/2017] [Indexed: 11/22/2022] Open
Abstract
Reverse vaccinology method was used to predict the monovalent peptide vaccine candidate to produce antibodies for therapeutic purpose and to predict tetravalent vaccine candidate to act as a common vaccine to cover all the fever dengue virus serotypes. Envelope (E)-proteins of DENV-1-4 serotypes were used for vaccine prediction using NCBI, Uniprot/Swissprot, Swiss-prot viewer, VaxiJen V2.0, TMHMM, BCPREDS, Propred-1, Propred and MHC Pred,. E-proteins of DENV-1-4 serotypes were identified as antigen from which T cell epitopes, through B cell epitopes, were predicted to act as peptide vaccine candidates. Each selected T cell epitope of E-protein was confirmed to act as vaccine and to induce complementary antibody against particular serotype of dengue virus. Chimeric tetravalent vaccine was formed by the conjugation of four vaccines, each from four dengue serotypes to act as a common vaccine candidate for all the four dengue serotypes. It can be justifiably concluded that the monovalent 9-mer T cell epitope for each DENV serotypes can be used to produce specific antibody agaomst dengue virus and a chimeric common tetravalent vaccine candidate to yield a comparative vaccine to cover any of the four dengue virus serotype. This vaccine is expected to act as highly immunogenic against preventing dengue fever.
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Affiliation(s)
- Vijayakumar Subramaniyan
- Computational Phytochemistry Laboratory P.G. and Research Department of Botany and Microbiology, A.V.V.M. Sri Pushpam College (Autonomous), Poondi, Thanjavur district, Tamil Nadu 613503, India
| | - Ramesh Venkatachalam
- Computational Phytochemistry Laboratory P.G. and Research Department of Botany and Microbiology, A.V.V.M. Sri Pushpam College (Autonomous), Poondi, Thanjavur district, Tamil Nadu 613503, India
| | - Prabhu Srinivasan
- Computational Phytochemistry Laboratory P.G. and Research Department of Botany and Microbiology, A.V.V.M. Sri Pushpam College (Autonomous), Poondi, Thanjavur district, Tamil Nadu 613503, India
| | - Manogar Palani
- Computational Phytochemistry Laboratory P.G. and Research Department of Botany and Microbiology, A.V.V.M. Sri Pushpam College (Autonomous), Poondi, Thanjavur district, Tamil Nadu 613503, India
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Computer-Aided Design of an Epitope-Based Vaccine against Epstein-Barr Virus. J Immunol Res 2017; 2017:9363750. [PMID: 29119120 PMCID: PMC5651165 DOI: 10.1155/2017/9363750] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 08/07/2017] [Accepted: 08/20/2017] [Indexed: 02/06/2023] Open
Abstract
Epstein-Barr virus is a very common human virus that infects 90% of human adults. EBV replicates in epithelial and B cells and causes infectious mononucleosis. EBV infection is also linked to various cancers, including Burkitt's lymphoma and nasopharyngeal carcinomas, and autoimmune diseases such as multiple sclerosis. Currently, there are no effective drugs or vaccines to treat or prevent EBV infection. Herein, we applied a computer-aided strategy to design a prophylactic epitope vaccine ensemble from experimentally defined T and B cell epitopes. Such strategy relies on identifying conserved epitopes in conjunction with predictions of HLA presentation for T cell epitope selection and calculations of accessibility and flexibility for B cell epitope selection. The T cell component includes 14 CD8 T cell epitopes from early antigens and 4 CD4 T cell epitopes, targeted during the course of a natural infection and providing a population protection coverage of over 95% and 81.8%, respectively. The B cell component consists of 3 experimentally defined B cell epitopes from gp350 plus 4 predicted B cell epitopes from other EBV envelope glycoproteins, all mapping in flexible and solvent accessible regions. We discuss the rationale for the formulation and possible deployment of this epitope vaccine ensemble.
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11
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In silico designing, cloning, and heterologous expression of novel chimeric human B lymphocyte CD20 extra loop. Tumour Biol 2016; 37:12547-12553. [DOI: 10.1007/s13277-016-5105-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Accepted: 06/09/2016] [Indexed: 01/10/2023] Open
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12
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A Computationally Designed Serological Assay for Porcine Epidemic Diarrhea Virus. J Clin Microbiol 2016; 54:2039-46. [PMID: 27225413 DOI: 10.1128/jcm.00460-16] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 05/17/2016] [Indexed: 12/13/2022] Open
Abstract
The periodic emergence of new infectious agents and the genetic and antigenic evolution of existing agents necessitate the improvement of technology for the rapid development of diagnostic assays. The porcine epidemic diarrhea virus (PEDV) emerged in the United States in 2013, causing severe economic damage to the pork industry. The primary goal of this study was to develop methods to reduce the lead time for serological assay development. An approach involving the computational prediction of diagnostic targets, followed by a rapid synthesis of antigens, was adopted to achieve this objective. To avoid cross-reactivity with other closely related swine coronaviruses, the N protein sequences of PEDV were analyzed to identify sequences unique to PEDV. The potential antigenicity of the identified sequence was predicted computationally using the Jameson-Wolf method. A sequence with a high antigenic index was rapidly synthesized using an in vitro transcription and translation system to yield the diagnostic antigen. The computationally designed enzyme-linked immunosorbent assay (ELISA) was validated using 169 field sera, whose statuses were determined by a PEDV-specific immunofluorescence assay. Comparison of the computationally designed ELISA to a conventionally developed ELISA, using bacterially expressed N protein, and to the immunofluorescence assay showed a high degree of agreement among the three tests (mean kappa statistic, 0.842). The sensitivity and specificity, compared to the conventionally developed assay, were 90.62 and 95.18, respectively. Therefore, the described approach is useful in reducing the development time for serological assays in the face of an infectious disease outbreak.
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Vázquez-Prieto S, Paniagua E, Ubeira FM, González-Díaz H. QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for Predicting “In Silico” New Optimal Peptide Sequences and/or Boundary Conditions for Vaccine Development. Int J Pept Res Ther 2016. [DOI: 10.1007/s10989-016-9524-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Kaumaya PTP. A paradigm shift: Cancer therapy with peptide-based B-cell epitopes and peptide immunotherapeutics targeting multiple solid tumor types: Emerging concepts and validation of combination immunotherapy. Hum Vaccin Immunother 2016; 11:1368-86. [PMID: 25874884 DOI: 10.1080/21645515.2015.1026495] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
There is a recognizable and urgent need to speed the development and application of novel, more efficacious anti-cancer vaccine therapies that inhibit tumor progression and prevent acquisition of tumor resistance. We have created and established a portfolio of validated peptide epitopes against multiple receptor tyrosine kinases and we have identified the most biologically effective combinations of EGFR (HER-1), HER-2, HER-3, VEGF and IGF-1R peptide vaccines/mimics to selectively inhibit multiple receptors and signaling pathways. The strategy is based on the use of chimeric conformational B-cell epitope peptides incorporating "promiscuous" T-cell epitopes that afford the possibility of generating an enduring immune response, eliciting protein-reactive high-affinity anti-peptide antibodies as potential vaccines and peptide mimics that act as antagonists to receptor signaling that drive cancer metastasis. In this review we will summarize our ongoing studies based on the development of combinatorial immunotherapeutic strategies that act synergistically to enhance immune-mediated tumor killing aimed at addressing mechanisms of tumor resistance for several tumor types.
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Affiliation(s)
- Pravin T P Kaumaya
- a Department of Obstetrics and Gynecology; The Ohio State University Wexner Medical Center ; Columbus , OH , USA
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15
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Abstract
Peptides (usually 10-20 amino acid residues in length) can be used as effectively as proteins in raising antibodies producing both polyclonal and monoclonal antibodies routinely with titers higher than 20,000. Peptide antigens do not function as immunogens unless they are conjugated to proteins. Production of high quality antipeptide antibodies is dependent upon peptide sequence selection, the success of peptide synthesis, peptide-carrier protein conjugation, the humoral immune response in the host animal, the adjuvant used, the peptide dose administered, the injection method, and the purification of the antibody. Peptide sequence selection is probably the most critical step in the production of antipeptide antibodies. Although the process for designing peptide antigens is not exact, several guidelines and computational B-cell epitope prediction methods can help maximize the likelihood of producing antipeptide antibodies that recognize the protein. Antibodies raised by peptides have become essential tools in life science research. Virtually all phospho-specific antibodies are now produced using phosphopeptides as antigens. Typically, 5-20 mg of peptide is enough for antipeptide antibody production. It takes 3 months to produce a polyclonal antipeptide antibody in rabbits that yields ~100 mL of serum which corresponds to ~8-10 mg of the specific antibody after affinity purification using a peptide column.
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Affiliation(s)
- Bao-Shiang Lee
- Protein Research Laboratory, Research Resources Center, University of Illinois at Chicago, 835 S. Wolcott Avenue, Chicago, IL, 60612, USA.
| | - Jin-Sheng Huang
- Protein Research Laboratory, Research Resources Center, University of Illinois at Chicago, 835 S. Wolcott Avenue, Chicago, IL, 60612, USA
| | - Lasanthi P Jayathilaka
- Protein Research Laboratory, Research Resources Center, University of Illinois at Chicago, 835 S. Wolcott Avenue, Chicago, IL, 60612, USA
| | - Jenny Lee
- Department of Chemical Engineering, Columbia University, 500 W. 120th Street, New York, NY, 10027, USA
| | - Shalini Gupta
- Protein Research Laboratory, Research Resources Center, University of Illinois at Chicago, 835 S. Wolcott Avenue, Chicago, IL, 60612, USA
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16
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Zheng W, Ruan J, Hu G, Wang K, Hanlon M, Gao J. Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull. PLoS One 2015; 10:e0134835. [PMID: 26244562 PMCID: PMC4526569 DOI: 10.1371/journal.pone.0134835] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 07/14/2015] [Indexed: 01/05/2023] Open
Abstract
The prediction of conformational b-cell epitopes plays an important role in immunoinformatics. Several computational methods are proposed on the basis of discrimination determined by the solvent-accessible surface between epitopes and non-epitopes, but the performance of existing methods is far from satisfying. In this paper, depth functions and the k-th surface convex hull are used to analyze epitopes and exposed non-epitopes. On each layer of the protein, we compute relative solvent accessibility and four different types of depth functions, i.e., Chakravarty depth, DPX, half-sphere exposure and half space depth, to analyze the location of epitopes on different layers of the proteins. We found that conformational b-cell epitopes are rich in charged residues Asp, Glu, Lys, Arg, His; aliphatic residues Gly, Pro; non-charged residues Asn, Gln; and aromatic residue Tyr. Conformational b-cell epitopes are rich in coils. Conservation of epitopes is not significantly lower than that of exposed non-epitopes. The average depths (obtained by four methods) for epitopes are significantly lower than that of non-epitopes on the surface using the Wilcoxon rank sum test. Epitopes are more likely to be located in the outer layer of the convex hull of a protein. On the benchmark dataset, the cumulate 10th convex hull covers 84.6% of exposed residues on the protein surface area, and nearly 95% of epitope sites. These findings may be helpful in building a predictor for epitopes.
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Affiliation(s)
- Wei Zheng
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Jishou Ruan
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, People’s Republic of China
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Michelle Hanlon
- Department of Physical Sciences, Grant MacEwan University, Alberta, Canada
| | - Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
- * E-mail:
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17
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Sela-Culang I, Ofran Y, Peters B. Antibody specific epitope prediction-emergence of a new paradigm. Curr Opin Virol 2015; 11:98-102. [PMID: 25837466 DOI: 10.1016/j.coviro.2015.03.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Revised: 03/11/2015] [Accepted: 03/16/2015] [Indexed: 11/19/2022]
Abstract
The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data.
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Affiliation(s)
- Inbal Sela-Culang
- The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Yanay Ofran
- The Goodman Faculty of Life Sciences, Nanotechnology Building, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 9420 Athena Circle, La Jolla, CA 92037, USA.
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