Prediction of substrate specificity in NS3/4A serine protease by biased sequence search threading

Abstract

Proteases recognize specific substrate sequences and catalyze the hydrolysis of targeted peptide bonds to activate or degrade them. It is particularly important to identify the recognition and binding mechanisms of protease–substrate complex structures in studies of drug development. Cleavage specificity in protease systems is generally determined by the amino acid profile, structural features, and distinct molecular interactions. In this work, substrate variability and substrate specificity of the NS3/4A serine protease encoded by the hepatitis C virus (HCV) was investigated by the biased sequence search threading (BSST) methodology. The available crystal structures of peptide-bound protease were used as templates as well as new complex structures that were generated via docking calculations. Threading various binding and nonbinding sequences as starting sequences over multiple templates, the potential sequence space was efficiently explored by a low-resolution knowledge-based scoring potential. The low-energy substrate sequences generated by the biased search are correlated with the natural substrates with conserved amino acid preferences, although some positions exhibit variability. Specifically, the amino acids which play essential roles in cleavage are mostly preferred. Potential substrate sequences were predicted by statistical probability approaches that consider the pairwise and triplewise interdependencies among residue positions in the low-energy sequences. The predicted substrate sequences also reproduce most of the natural substrate sequences, implying the complex interdependence between the different substrate residues. Consequently, the BSST seems to provide a powerful methodology for predicting the substrate specificity for the NS3/4A protease, which is a target in drug discovery studies for HCV.

Keywords:

• hepatitis C virus (HCV) serine protease
• substrate specificity
• threading
• low-resolution model
• knowledge-based potentials