Identification of novel therapeutic candidates in Cryptosporidium parvum: an in silico approach

Unavailability of vaccines and effective drugs are primarily responsible for the growing menace of cryptosporidiosis. This study has incorporated a bioinformatics-based screening approach to explore potential vaccine candidates and novel drug targets in Cryptosporidium parvum proteome. A systematic strategy was defined for comparative genomics, orthology with related Cryptosporidium species, prioritization parameters and MHC class I and II binding promiscuity. The approach reported cytoplasmic protein cgd7_1830, a signal peptide protein, as a novel drug target. SWISS-MODEL online server was used to generate the 3D model of the protein and was validated by PROCHECK. The model has been subjected to in silico docking study with screened potent lead compounds from the ZINC database, PubChem and ChEMBL database using Flare software package of Cresset (R). Furthermore, the approach reported protein cgd3_1400, as a vaccine candidate. The predicted B- and T-cell epitopes on the proposed vaccine candidate with highest scores were also subjected to docking study with MHC class I and II alleles using ClusPro web server. Results from this study could facilitate selection of proteins which could serve as drug targets and vaccine candidates to efficiently tackle the growing threat of cryptosporidiosis.