UMD Data Collection

Permanent URI for this collectionhttp://hdl.handle.net/1903/27670

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Author: search.filters.author.Cummings, Michael P.Subject: search.filters.subject.reverse vaccinology

Search Results

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    Supplementary materials for Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data
    (2023) Chou, Renee Ti; Ouattara, Amed; Takala-Harrison, Shannon; Cummings, Michael P.
    Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of unlabeled proteins the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.
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    Supplementary materials for positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum
    (2023) Chou, Renee Ti; Ouattara, Amed; Adams, Matthew; Berry, Andrea A.; Takala-Harrison, Shannon; Cummings, Michael P.
    Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens identified prior to availability of the P. falciparum genome. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute the most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.