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Extension and Limits of the Network of Coupled Motions Correlated to Hydride Transfer in Dihydrofolate Reductase
Author(s) -
Priyanka Singh,
Arundhuti Sen,
Kevin Francis,
Am Kohen
Publication year - 2014
Publication title -
journal of the american chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.115
H-Index - 612
eISSN - 1520-5126
pISSN - 0002-7863
DOI - 10.1021/ja411998h
Subject(s) - dihydrofolate reductase , chemistry , mutant , enzyme , enzyme catalysis , hydride , computational biology , residue (chemistry) , metabolic network , biological system , stereochemistry , biochemistry , gene , organic chemistry , biology , hydrogen
Enzyme catalysis has been studied extensively, but the role of enzyme dynamics in the catalyzed chemical conversion is still an enigma. The enzyme dihydrofolate reductase (DHFR) is often used as a model system to assess a network of coupled motions across the protein that may affect the catalyzed chemical transformation. Molecular dynamics simulations, quantum mechanical/molecular mechanical studies, and bioinformatics studies have suggested the presence of a "global dynamic network" of residues in DHFR. Earlier studies of two DHFR distal mutants, G121V and M42W, indicated that these residues affect the chemical step synergistically. While this finding was in accordance with the concept of a network of functional motions across the protein, two residues do not constitute a network. To better define the extent and limits of the proposed network, the current work studied two remote residues predicted to be part of the same network: W133 and F125. The effect of mutations in these residues on the nature of the chemical step was examined via measurements of the temperature-dependence of the intrinsic kinetic isotope effects (KIEs) and other kinetic parameters, and double mutants were used to tie the findings to G121 and M42. The findings indicate that residue F125, which was implicated by both calculations and bioinformatic methods, is a part of the same global dynamic network as G121 and M42, while W133, implicated only by bioinformatics, is not. These findings extend our understanding of the proposed network and the relations between functional and genomic couplings. Delineating that network illuminates the need to consider remote residues and protein structural dynamics in the rational design of drugs and of biomimetic catalysts.

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