Artificial neural network for prediction of antigenic activity for a major conformational epitope in the hepatitis C virus NS3 protein

Insufficient knowledge of general principles for accurate quantitative inference of biological properties from sequences is a major obstacle in the rationale design of proteins with predetermined activities. Due to this deficiency, protein engineering frequently relies on the use of computational approaches focused on the identification of quantitative structure-activity relationship (SAR) for each specific task. In the current article, a computational model was developed to define SAR for a major conformational antigenic epitope of the hepatitis C virus (HCV) non-structural protein 3 (NS3) in order to facilitate a rationale design of HCV antigens with improved diagnostically relevant properties.