Investigations of the dynamic amino acid networks in the alpha-subunit of tryptophan synthase

Open Access
Author:
Axe, Jennifer Michelle
Graduate Program:
Chemistry
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
December 15, 2014
Committee Members:
  • David D Boehr, Dissertation Advisor
  • Squire J Booker, Committee Member
  • Scott A Showalter, Committee Member
  • William O Hancock, Committee Member
Keywords:
  • tryptophan synthase
  • NMR
  • CHESCA
  • amino acid networks
Abstract:
Amino acid networks are a series of noncovalent interactions between residues that can span from surface-exposed residues to those deep in the active site of the protein. These amino acid networks, or ‘sectors’, have been proposed to be intrinsic to all proteins, and to play a key role in protein allostery. Current methods of identification of networks include computationally-based and experimentally-based methods, though only the latter have the ability to probe amino acid networks under a variety of ligand-bound conditions. A recently developed NMR-based technique is known as the CHEmical Shift Covariance Analysis (CHESCA) method was used to identify and characterize the amino acid networks in isolated α-subunits of tryptophan synthase (αTS). Tryptophan synthase (TS) has long been studied for the intersubunit communication observed between the α- and β-subunits (βTS) as well as the intramolecular channel connecting the two active sites. The conformational states of the two subunits are highly synchronized and it is possible that this extensive intersubunit communication is the result of the amino acid networks connecting αTS and βTS. The use of αTS as the system allowed for the study of the enzyme under working conditions (i.e. while undergoing active catalytic turnover) and under resting conditions (i.e. while in the apo state), as well as two other, single ligand-bound states, revealing the ligand-dependency of the αTS amino acid networks. Most importantly, two clusters of residues were identified in each state and the catalytic residue, Glu49, switches clusters only in the working state. Additionally, the T183V substitution severs crucial hydrogen bonds between the dynamic β2α2 and β6α6 loops. This disruption appears to prevent the cluster switch of Glu49 and results in a 14-fold decrease in catalytic efficiency. These results suggest that amino acid networks play a crucial role in catalysis and association with the correct cluster is key to enzyme function.