Affiliations: Molecular Epidemiology & Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention, Atlanta, GA, USA
Note:  Corresponding author: James Lara, Molecular Epidemiology & Bioinformatics Laboratory, Division of Viral Hepatitis, Centers for Disease Control and Prevention,Atlanta, GA, USA. E-mail: [email protected]
Abstract: Sequence heterogeneity substantially affects antigenic properties of the major epitope in the hepatitis C virus (HCV) NS3 protein. To facilitate protein engineering of NS3 antigens immunologically reactive with antibody against the broad diversity of HCV variants we constructed a set of Bayesian Networks (BN) for predicting antigenicity based on structural parameters. Using homology modeling, tertiary (3D) structures of NS3 variants with known antigenic properties were predicted. Energy force field estimated using the 3D-models was found to be most strongly associated with the antigenic properties. The best BN-models showed 100% accuracy of prediction of immunological reactivity with tested serum specimens in 10-fold cross validation. Bootstrap analyses of BN’s constructed using selected features showed that secondary structure and electrostatic potential assessed from 3D-models are the most robust attributes associated with immunological reactivity of NS3 antigens. The data suggest that the BN models may guide the development of NS3 antigens with improved diagnostically relevant properties.