Limitations of other pipelines

Several computational studies have analysed singular genomes or proteomes of individual pathogenic strain or species (1-5). In contrast, many other researchers have attempted to employ pan-genome strategies to identify potential cross-protective antigens using multiple genomes of the same species, such as group B Streptococcus sp. (6-7). In another study, researchers have used the protein-protein interaction dataset and network biology approach to prioritize vaccine targets in B. burgdorferi (8). Currently, there are a number of tools, resources and databases available in the immunoinformatics domain that have contributed to the development of vaccines in recent past (9-14).

Despite the significant advancements in computational proteomics, machine learning and reverse vaccinology domains, an emphasis must be laid upon the fact that generation of lists of vaccine candidates is not a task of major significance. Furthermore, there has been a pressing need for building pipelines or computational frameworks, to integrate diverse algorithms and databases using single input and provide meaningful results for researchers working on vaccine development. There have been numerous efforts made to build such pipelines that combine immunoinformatics programs sequentially or in parallel mode (15-17). In 2019, Dalsass et al. compared 6 open-source standalone RV programs (18) designed for bacterial pathogens: NERVE (19), VaxiJen (20), Vaxign (21), Bowman-Heinson (22-23), Jenner-predict (24), and VacSol (25) and tested them on 11 different bacterial proteomes. These research studies report several limitations in the existing pipelines. For instance, most of the programs and algorithms have been built around bacterial/prokaryotic systems with only a little work being done on eukaryotic pathogens, including T. cruzi. Also, the bioinformatics programs used to determine the characteristics of proteins (in the existing pipelines) are imprecise, leading to propagation of error in the final output.

References:

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6) D. Maione et al., Identification of a universal Group B streptococcus vaccine by multiple genome screen. Science 309, 148-150 (2005).

7) L. Zeng et al., A novel pan-genome reverse vaccinology approach employing a negative-selection strategy for screening surface-exposed antigens against leptospirosis. Frontiers in microbiology 8, 396 (2017).

8) E. Bencurova, S. K. Gupta, E. Oskoueian, M. Bhide, T. Dandekar, Omics and bioinformatics applied to vaccine development against Borrelia. Molecular omics 14, 330-340 (2018).

9) S. K. Dhanda et al., Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Briefings in bioinformatics 18, 467-478 (2017).

10) G. P. Monterrubio-López, R. M. Ribas-Aparicio, Identification of novel potential vaccine candidates against tuberculosis based on reverse vaccinology. BioMed research international 2015 (2015).

11) J. Zhang et al., Association between vaccination for herpes zoster and risk of herpes zoster infection among older patients with selected immune-mediated diseases. Jama 308, 43-49 (2012).

12) N. I. Cardona, D. M. Moncada, J. E. Gómez-Marin, A rational approach to select immunogenic peptides that induce IFN-γ response against Toxoplasma gondii in human leukocytes. Immunobiology 220, 1337-1342 (2015).

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14) S. D. Allard et al., A phase I/IIa immunotherapy trial of HIV-1-infected patients with Tat, Rev and Nef expressing dendritic

15) S. J. Goodswen, P. J. Kennedy, J. T. Ellis, Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology. Bioinformatics 30, 2381-2383 (2014).

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17) K. Marciniuk, B. Trost, S. Napper, EpIC: a rational pipeline for epitope immunogenicity characterization. Bioinformatics 31, 2388-2390 (2015).

18) M. Dalsass, A. Brozzi, D. Medini, R. Rappuoli, Comparison of open-source reverse vaccinology programs for bacterial vaccine antigen discovery. Frontiers in immunology 10, 113 (2019).

19) S. Vivona, F. Bernante, F. Filippini, NERVE: new enhanced reverse vaccinology environment. BMC biotechnology 6, 35 (2006).

20) I. A. Doytchinova, D. R. Flower, VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics 8, 4 (2007).

21) Y. He, Z. Xiang, H. L. Mobley, Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. BioMed Research International 2010 (2010).

22) B. N. Bowman et al., Improving reverse vaccinology with a machine learning approach. Vaccine 29, 8156-8164 (2011).

23) A. I. Heinson et al., Enhancing the biological relevance of machine learning classifiers for reverse vaccinology. International journal of molecular sciences 18, 312 (2017).

24) V. Jaiswal, S. K. Chanumolu, A. Gupta, R. S. Chauhan, C. Rout, Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions. BMC bioinformatics 14, 211 (2013).

25) M. Rizwan et al., VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology. BMC bioinformatics 18, 106 (2017).