Computational Modeling and Validation of Predicted Diagnostic Peptides for Crimean–Congo's Disease

Abstract

The absence of prompt and reliable diagnosis limits the eradication of Crimean-Congo's hemorrhagic fever (CCHF) caused by Hyalomma. Although there have been several recent developments in different points of care and diagnosis, there has been a major drawback caused by the commercial unavailability and limited accessibility of economical serosurveillance assays that pose significant challenges. The primary objective of this study is to predict and design a novel synthetic biomarker composed of immunodominant B- and T-cell lymphocytes (HTL) interleukin 10 (IL-10) epitopes using the antigenic, non-toxigenic, and nonallergenic glycoprotein, nucleocapsid protein, and RNA-dependent RNA polymerase (RdRp). Top highly ranked B-cell epitopes as well as IL-10 inducing epitopes were predicted, screened, selected, and linked with linkers. Then, utilizing a variety of web servers, the biomarker predictions of antigenicity (0.5594), allergenicity (nonallergic), solubility (protein-sol score: 0.623), and physiochemical properties (molecular weight of 56,004.26 Da, instability index of 28.33), secondary (SOPMA) and tertiary structures (AlphaFold2), 3D model refinement (GDT_HA score of 1.0000), and validations were done. Additionally, codon adaptation (G-C of 48.93%, CAI 1.0) and in silico cloning in the pcDNA3 vector were done. The molecular docking (HDock) of the final biomarker with the selected genes was done to see the binding affinity. The model had a docking score of -291.82, a confidence score of 0.9446, and a ligand RSMD of 66.24. For this multi-epitope design to be employed in commercial serodiagnostic kits, further experimental evaluation is required for its development.