Estimating design space available for polyepitopes through consideration of major histocompatibility complex binding motifs

Lee, Yvonne; Ferrari, Giacomo; Lee, Stephen Craig

doi:10.1007/s10544-009-9376-7

Cited by 9 publications

(5 citation statements)

References 39 publications

(77 reference statements)

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“…Additional signal sequences (for example, N-terminal ubiquitin, N-terminal leader peptide, and C-terminal fragment of human LAMP-1 protein) could be introduced into target polyepitopes to increase their efficiency of stimulating either CD8+ and/or CD4+ T-cell response [10-14]. However, despite numerous efforts towards constructing artificial polyepitope immunogens and evaluating their immunogenicity and protectivity [2,4-9,15,16] and despite numerous computational methods developed to date for predicting T-cell epitopes [17-20], proteasomal cleavage sites [21-25], and peptide binding to TAP (transporters associated with antigen processing) [24,26-28], only a few computational tools intended for rational design of polyepitope T-cell immunogens were developed to date [29,30]. …”

Section: Resultsmentioning

confidence: 99%

PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens

Антонец

Bazhan

2013

BMC Res Notes

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BackgroundConstruction of artificial polyepitope antigens is one of the most promising strategies for developing more efficient and safer vaccines evoking T-cell immune responses. Epitope rearrangements and utilization of certain spacer sequences have been proven to greatly influence the immunogenicity of polyepitope constructs. However, despite numerous efforts towards constructing and evaluating artificial polyepitope immunogens as well as despite numerous computational methods elaborated to date for predicting T-cell epitopes, peptides binding to TAP and for antigen processing prediction, only a few computational tools were currently developed for rational design of polyepitope antigens.FindingsHere we present a PolyCTLDesigner program that is intended for constructing polyepitope immunogens. Given a set of either known or predicted T-cell epitopes the program selects N-terminal flanking sequences for each epitope to optimize its binding to TAP (if necessary) and joins resulting oligopeptides into a polyepitope in a way providing efficient liberation of potential epitopes by proteasomal and/or immunoproteasomal processing. And it also tries to minimize the number of non-target junctional epitopes resulting from artificial juxtaposition of target epitopes within the polyepitope. For constructing polyepitopes, PolyCTLDesigner utilizes known amino acid patterns of TAP-binding and proteasomal/immunoproteasomal cleavage specificity together with genetic algorithm and graph theory approaches. The program was implemented using Python programming language and it can be used either interactively or through scripting, which allows users familiar with Python to create custom pipelines.ConclusionsThe developed software realizes a rational approach to designing poly-CTL-epitope antigens and can be used to develop new candidate polyepitope vaccines. The current version of PolyCTLDesigner is integrated with our TEpredict program for predicting T-cell epitopes, and thus it can be used not only for constructing the polyepitope antigens based on preselected sets of T-cell epitopes, but also for predicting cytotoxic and helper T-cell epitopes within selected protein antigens. PolyCTLDesigner is freely available from the project’s web site: http://tepredict.sourceforge.net/PolyCTLDesigner.html.

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Section: Resultsmentioning

confidence: 99%

PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens

Антонец

Bazhan

2013

BMC Res Notes

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“…The functional assay showed that Y 367 mutated peptides (Y 367 A and Y 367 K) could induce IFN-γ responses, but that IFN-γ production was lower than that induced by S366-374. That might indicate an important role for the Y side chain in determining the binding affinity to H-2 K d [10].…”

Section: Discussionmentioning

confidence: 99%

“…The surface features of the binding cleft of the class I MHC molecule are complementary to side chains of the anchor residues in the displayed peptide. The amino acid residues lining the binding sites may vary among different class I allelic variants [9,10]. Here, an SARS CoV S protein CTL epitope, S366-374, was identified and the functions of individual residues were evaluated by bioinformatics tool prediction and by IFN-γ responses induced by a series of modified S366-374 peptides.…”

Section: Introductionmentioning

confidence: 99%

Residue analysis of a CTL epitope of SARS-CoV spike protein by IFN-gamma production and bioinformatics prediction

Huang

Cao

et al. 2012

BMC Immunol

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BackgroundSevere acute respiratory syndrome (SARS) is an emerging infectious disease caused by the novel coronavirus SARS-CoV. The T cell epitopes of the SARS CoV spike protein are well known, but no systematic evaluation of the functional and structural roles of each residue has been reported for these antigenic epitopes. Analysis of the functional importance of side-chains by mutational study may exaggerate the effect by imposing a structural disturbance or an unusual steric, electrostatic or hydrophobic interaction.ResultsWe demonstrated that N50 could induce significant IFN-gamma response from SARS-CoV S DNA immunized mice splenocytes by the means of ELISA, ELISPOT and FACS. Moreover, S366-374 was predicted to be an optimal epitope by bioinformatics tools: ANN, SMM, ARB and BIMAS, and confirmed by IFN-gamma response induced by a series of S358-374-derived peptides. Furthermore, each of S366-374 was replaced by alanine (A), lysine (K) or aspartic acid (D), respectively. ANN was used to estimate the binding affinity of single S366-374 mutants to H-2 Kd. Y367 and L374 were predicated to possess the most important role in peptide binding. Additionally, these one residue mutated peptides were synthesized, and IFN-gamma production induced by G368, V369, A371, T372 and K373 mutated S366-374 were decreased obviously.ConclusionsWe demonstrated that S366-374 is an optimal H-2 Kd CTL epitope in the SARS CoV S protein. Moreover, Y367, S370, and L374 are anchors in the epitope, while C366, G368, V369, A371, T372, and K373 may directly interact with TCR on the surface of CD8-T cells.

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“…A commonly reported peptidic spacer for genetic constructs is the GPGPG sequence (Livingston et al ., ). The computational algorithm CANVAC II has been developed to aid the design of junctional epitope‐free polyepitopes (Lee et al ., ). Given two authentic epitopes, a target set of MHC‐binding motifs and a range of desired linker lengths, CANVAC predicts spacer sequences with optimal length and composition.…”

Section: Epitope‐based Vaccine Designmentioning

confidence: 99%

Computer‐aided design of T‐cell epitope‐based vaccines: addressing population coverage

Oyarzún

Kobe

2015

Int J Immunogenetics

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Epitope-based vaccines (EVs) make use of short antigen-derived peptides corresponding to immune epitopes, which are administered to trigger a protective humoral and/or cellular immune response. EVs potentially allow for precise control over the immune response activation by focusing on the most relevant - immunogenic and conserved - antigen regions. Experimental screening of large sets of peptides is time-consuming and costly; therefore, in silico methods that facilitate T-cell epitope mapping of protein antigens are paramount for EV development. The prediction of T-cell epitopes focuses on the peptide presentation process by proteins encoded by the major histocompatibility complex (MHC). Because different MHCs have different specificities and T-cell epitope repertoires, individuals are likely to respond to a different set of peptides from a given pathogen in genetically heterogeneous human populations. In addition, protective immune responses are only expected if T-cell epitopes are restricted by MHC proteins expressed at high frequencies in the target population. Therefore, without careful consideration of the specificity and prevalence of the MHC proteins, EVs could fail to adequately cover the target population. This article reviews state-of-the-art algorithms and computational tools to guide EV design through all the stages of the process: epitope prediction, epitope selection and vaccine assembly, while optimizing vaccine immunogenicity and coping with genetic variation in humans and pathogens.

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Estimating design space available for polyepitopes through consideration of major histocompatibility complex binding motifs

Cited by 9 publications

References 39 publications

PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens

PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens

Residue analysis of a CTL epitope of SARS-CoV spike protein by IFN-gamma production and bioinformatics prediction

Computer‐aided design of T‐cell epitope‐based vaccines: addressing population coverage

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