- Technical Note
- Open Access
PolyCTLDesigner: a computational tool for constructing polyepitope T-cell antigens
© Antonets and Bazhan; licensee BioMed Central Ltd. 2013
- Received: 14 February 2013
- Accepted: 24 September 2013
- Published: 10 October 2013
Construction 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.
Here 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.
The 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.
- T-cell epitope
- Cytotoxic T cell
- Transporters associated with antigen processing
- Directed weighted graph
- Travelling salesman problem
One of the most promising approaches to designing more efficient and safer vaccines is construction of artificial polyepitope antigens [1–6]. Their advantages over conventional vaccines include reduced risk of developing autoimmunity and other pathological conditions since such constructions doesn’t contain whole microbial molecular structures and epitopes, sharing profound similarity to human proteins. Besides, polyepitopes may contain both cytotoxic (CTL) and T-helper epitopes belonging to different antigens, including those of distinct pathogenic microorganisms, thus making possible to induce immune responses with a wider specificity. In addition, polyepitopes may be designed taking into account the prevalences of certain HLA class I molecules allomorphs within the target human population or even within an individual patient. Polyepitopes may also be constructed in a way maximizing efficiency of processing and presentation of the majority of included epitopes [4, 7–9]. 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].
Both interaction of peptides with MHC molecules and peptides binding to TAP complex are sufficiently specific for certain amino acid patterns. Proteasomal and immunoproteasomal cleavage sites are also to a considerable degree determined by the amino acid sequences of antigens and degenerate amino acid motifs that determine the efficiency of proteasomal cleavage sites are currently known [22, 31] as well as the motifs determining the affinities of oligopeptides binding to TAP [26–28]. It has been shown that a concurrent prediction of peptide binding affinities for MHC molecules and TAP decreases false positive rate when predicting T-cell epitopes [26, 27]. As it has been experimentally demonstrated, that introduction of spacer sequences to between individual epitopes could considerably increase the ability of such polyepitope constructs to induce cytotoxic T-cell immune responses [4, 7, 9]. Epitope rearrangements within the polyepitopic constructs were also found to significantly influence their immunogenicity and it was also hypothesized that while constructing the polyepitope antigen one should minimize the number of nontarget junctional epitopes .
It has been shown that longer peptides transported into the endoplasmic reticulum (ER) undergo N-terminal trimming by ER aminopeptidases (ERAPs) to allow them to bind MHC class I molecules , and that C-termini of the epitopes are preferentially generated by proteasomal cleavage [33–35]. Thus individual epitopes within a polyepitope should be arranged in a way providing sufficiently efficient proteasomal cleavage sites at their C-termini while, if necessary, their N-termini might be flanked with certain amino acid residues to optimize their binding to TAP.
The goal of this work was to develop a program intended for rational design of polyepitope T-cell antigens with a special focus on optimizing their immunogenicity via selecting amino acid spacer sequences for each pair of epitopes and choosing the optimal ordering of the epitopes within the polyepitope.
A program named PolyCTLDesigner was developed. Given a set of either known or predicted CTL epitopes, PolyCTLDesigner predicts affinity of their binding to TAP and then N-termini of inefficient binders are extended with certain flanking residues using the model created by Peters et al. , implying that the first three N-terminal amino acid residues of the peptide and the last C-terminal one are the major contributors to its binding to TAP.
where W is the weight (rank) of spacer sequence ss between the epitopes pep1 and pep2; rank HLA is the rank of non-target junctional epitope predicted to be the most efficient binder for HLA class I allele HLA; freq HLA is the genotypic frequency of that allele within the population of interest (HLA alleles genotypic frequencies were taken from dbMHC ); len(ss) is the length of spacer ss; rank pr corresponds to the rank of proteasomal cleavage site predicted at the pep1 C-terminus (this value ranges from 1 to 11 with 1 and 11 corresponding to the most and the least efficient proteasomal cleavage, respectively); rank impr is the rank of immunoproteasomal cleavage site; designates the mean rank HLA value; N eps is the number of predicted junctional epitopes and N HLA is the number of HLA alleles predicted to bind non-target epitopes with sufficient affinity (currently PolyCTLDesigner predicts T-cell epitopes with our program TEpredict , that was recently updated); rank HLA value of 1 corresponds to moderate binding affinity (6.3 ≤ pIC50 < 7.3), the value of 2 corresponds to high affinity (7.3 ≤ pIC50 < 8.3) and 3 corresponds to the highest affinity (with predicted pIC50 value ≥ 8.3). Thus the optimal spacer sequence should have the least weight.
Building the graph is the most time consuming step and for a set of 40–50 peptides it can take about 6–8 hours to be completed on typical desktop personal computer. However in future we plan to implement parallelized algorithm to reduce time consumption.
To illustrate the importance of poly-CTL-epitope optimization we performed a small theoretical analysis: six well-defined HLA-A*02:01-restricted HIV-1 CTL epitopes were used to produce artificial polyepitopes using our PolyCTLDesigner program. It was found that probability of selecting an optimal epitopes permutation at random was less than 0.00139 and only 17% of all possible polyepitope constructs did not contain inefficient proteasomal cleavage sites between target CTL epitopes. Besides, the choice of spacer sequences can have a great impact on proteasomal cleavage efficiency, especially for certain peptides. The detailed description of the study and its results can be found in Additional file 1.
In addition, PolyCTLDesigner is also able to assist in constructing polyepitope fragments containing T-helper epitopes. Currently T-helper epitopes can be predicted with ProPred models  (based on TEpitope models ). From a set of proposed antigens the program selects peptide fragments having 20–40 amino acid residues in length which contain the maximal number of overlapping T-helper epitopes restricted by maximal repertoire of HLA class II allomorphs. Then, each fragment is extended by five amino acid residues at both C- and N-terminals, since residues flanking the core epitope can play an important role in binding to T-cell receptors of CD4+ T lymphocytes . The fragments containing T-helper epitopes can be joined through [KR][KR] motif, which form cleavage sites for several lysosomal cathepsins involved in antigen processing. It has been shown that such motifs can increase the immunogenicity of individual T-cell epitopes [42, 43].
The developed software realizes a rational approach to designing highly immunogenic 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. In addition, PolyCTLDesigner allows the user to select a minimal set of epitopes covering a specified repertoire of allelic variants of HLA molecules with a desired level of redundancy. The program was implemented using Python programming language and can be used either interactively or through scripting, which allows the users familiar with Python to create custom pipelines. PolyCTLDesigner source code can be found in Additional file 2 and it is also freely available at the project’s web site http://tepredict.sourceforge.net/PolyCTLDesigner.html.
PolyCTLDesigner was implemented using the Python programming language. The affinity of peptides binding to TAP was predicted and the flanking sequences were selected using the model developed by Peters et al. . The models by Toes et al.  were used to predict proteasomal and/or immunoproteasomal processing and to select spacer sequences to optimize polyepitope processing. The procedures involving graphs were realized using Python graph library . The poly-CTL-epitope fragment was constructed with the help of genetic algorithm using the PyEvolve library . Biopython library was used to read amino acid sequences written in Fasta or GenBank format . T-helper epitope predictions were based on ProPred models . CTL epitopes predictions were made using our program TEpredict .
Project name: PolyCTLDesigner
Project home page: http://polyctldesigner.sourceforge.net
Operating system(s): platform-independent
Programming language: Python
Other requirements: Python 2.7, TEpredict (provided with the PolyCTLDesigner), Biopython, NumPy, PyEvolve, and Python graph
License: Creative Commons Attribution Non-Commercial License V2.0 (CC BY-NC 2.0)
Any restrictions to use by non-academics: see CC BY-NC 2.0 license
This work was supported by the Ministry of Education and Science of the Russian Federation (contracts #16.512.11.2186 and #8289) and by Russian Foundation for Basic Research (grant #12-04-31746 mol_a).
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