z-logo
open-access-imgOpen Access
The NS4A Cofactor Dependent Enhancement of HCV NS3 Protease Activity Correlates with a 4D Geometrical Measure of the Catalytic Triad Region
Author(s) -
Hamzah A. Hamad,
Jeremy Thurston,
T. Kent Teague,
Edward Ackad,
Mohammad S. Yousef
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0168002
Subject(s) - catalytic triad , proteases , in silico , protease , cofactor , ns3 , computational biology , biology , chemistry , protein structure , protein subunit , biochemistry , enzyme , active site , gene
We are developing a 4D computational methodology, based on 3D structure modeling and molecular dynamics simulation, to analyze the active site of HCV NS3 proteases, in relation to their catalytic activity. In our previous work, the 4D analyses of the interactions between the catalytic triad residues (His57, Asp81, and Ser139) yielded divergent, gradual and genotype-dependent, 4D conformational instability measures, which strongly correlate with the known disparate catalytic activities among genotypes. Here, the correlation of our 4D geometrical measure is extended to intra-genotypic alterations in NS3 protease activity, due to sequence variations in the NS4A activating cofactor. The correlation between the 4D measure and the enzymatic activity is qualitatively evident, which further validates our methodology, leading to the development of an accurate quantitative metric to predict protease activity in silico . The results suggest plausible “communication” pathways for conformational propagation from the activation subunit (the NS4A cofactor binding site) to the catalytic subunit (the catalytic triad). The results also strongly suggest that the well-sampled (via convergence quantification) structural dynamics are more connected to the divergent catalytic activity observed in HCV NS3 proteases than to rigid structures. The method could also be applicable to predict patients’ responses to interferon therapy and better understand the innate interferon activation pathway.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here