Development and validation of a machine learning algorithm prediction for dense granule proteins in Apicomplexa.

Background: Apicomplexa consist of numerous pathogenic parasitic protistan genera that invade host cells and reside and replicate within the parasitophorous vacuole (PV). Through this interface, the parasite exchanges nutrients and affects transport and immune modulation. During the intracellular life-cycle, the specialized secretory organelles of the parasite secrete an array of proteins, among which dense granule proteins (GRAs) play a major role in the modification of the PV. Despite this important role of GRAs, a large number of potential GRAs remain unidentified in Apicomplexa.

Methods: A multi-view attention graph convolutional network (MVA-GCN) prediction model with multiple features was constructed using a combination of machine learning and genomic datasets, and the prediction was performed on selected Neospora caninum protein data. The candidate GRAs were verified by a CRISPR/Cas9 gene editing system, and the complete NcGRA64(a,b) gene knockout strain was constructed and the phenotypes of the mutant were analyzed.

Results: The MVA-GCN prediction model was used to screen N. caninum candidate GRAs, and two novel GRAs (NcGRA64a and NcGRA64b) were verified by gene endogenous tagging. Knockout of complete genes of NcGRA64(a,b) in N. caninum did not affect the parasite's growth and replication in vitro and virulence in vivo.

Conclusions: Our study showcases the utility of the MVA-GCN deep learning model for mining Apicomplexa GRAs in genomic datasets, and the prediction model also has certain potential in mining other functional proteins of apicomplexan parasites.