Abstract
Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.
Highlights
The underlying procedure to identify protein candidates in an in silico vaccine discovery pipeline is to find and exploit differences between proteins
The results suggest that patterns presented within the secondary structure (SS) properties that define expected exportome and non-exportome proteins in B. bovis are universal to the test species
The foremost objective of the current study was to identify using an in silico approach the most worthy therapeutic candidates from potentially thousands of B. bovis proteins
Summary
The underlying procedure to identify protein candidates in an in silico vaccine discovery pipeline is to find and exploit differences between proteins. A procedure based on a plausible assumption that proteins inducing an immune response in a host must be different to those that induce no response. Immunogenic proteins are expected to contain regions that can trigger a cellular immune response mediated by T or B cells, namely epitopes. An epitope is the minimal structure necessary to invoke an immune response and must come from proteins accessible to the immune. Previous studies (Goodswen et al, 2013a,b) have collated these various predicted characteristics and trained machine learning (ML) models to computationally detect differences, which effectively epitomises the current state-of-the-art approach to in silico vaccine discovery against eukaryotic pathogens
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