Abstract
BackgroundThe last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders.ResultsWe introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage.ConclusionBased on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.
Highlights
The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases and chronic diseases such as cancer and autoimmune disorders
While there has been some argument concerning the principal feasibility of rational vaccine design [5] we demonstrate procedures on how computational methods can be harnessed to streamline the process of vaccine R&D, to reduce development cost and time, and to increase probability of success in formulating novel as well as improve existing vaccines
Workflow overview We in the following present an example workflow for a vaccine design R&D project centrally resting on computational vaccinology
Summary
The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases and chronic diseases such as cancer and autoimmune disorders. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design. We present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. While there has been some argument concerning the principal feasibility of rational vaccine design [5] we demonstrate procedures on how computational methods can be harnessed to streamline the process of vaccine R&D, to reduce development cost and time, and to increase probability of success in formulating novel as well as improve existing vaccines
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