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

Abstract

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.

Artificial Epitope-Based Immunogens in HIV-Vaccine Design

“…The obtained results became the basis for the development of original software TEpredict and PolyCTLDesigner that we consider as a universal platform for rational design of polyepitope immunogens -candidate DNA vaccines for induction of T-cell immunity both against infectious and oncological diseases [71,72] (Figure 5).…”

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“…Успешная полиэпитопная вакцина должна индуцировать Т-клеточный иммунный ответ на множество опухоль-специфических антигенов. Создание такой вакцины возможно на платформе искусственных полиэпитопных иммуногенов с использованием методов биоинформатики и биотехнологии [30,31].…”

Immunomics: a 21st century approach to vaccine development for complex pathogens

“…Widely used algorithms include those in the Immune Epitope DataBase (Kim et al 2012;Vita et al 2015) such as average relative binding (ARB) , or alternate algorithms such as support vector machine for prediction of MHCbinding peptides (SVMHC) (Donnes and Kohlbacher, 2006) or NetMHCII-2·2 (Nielsen and Lund, 2009). More recent prediction algorithms consider additional features, such as proteosomal cleavage sites and transporter associated with antigen processing (TAP)-binding patterns, further enhancing accuracy (Tenzer et al 2005;Antonets and Bazhan, 2013). Current computational models consider quantitative matrices, artificial neural networks, hidden Markov models, support vector machines (SVMs), quantitative structure activity relationship and molecular docking simulations (Brusic et al 2004;Desai and Kulkarni-Kale, 2014).…”