Direct observation of negative cooperativity in a detoxification enzyme at the atomic level by EPR and simulation.
Xiaowei BogettiAnthony BogettiJoshua CastoGordon RuleLillian ChongSunil SaxenaPublished in: Protein science : a publication of the Protein Society (2023)
The catalytic activity of human glutathione S-transferase A1-1 (hGSTA1-1), a homodimeric detoxification enzyme, is dependent on the conformational dynamics of a key C-terminal helix α9 in each monomer. However, the structural details of how the two monomers interact upon binding of substrates is not well understood and the structure of the ligand-free state of the hGSTA1-1 homodimer has not been resolved. Here, we used a combination of EPR distance measurements and weighted ensemble simulations to characterize the conformational ensemble of the ligand-free state at the atomic level. EPR measurements reveal a broad distance distribution between a pair of Cu(II) labels in the ligand-free state that gradually shifts and narrows as a function of increasing ligand concentration. These shifts suggest changes in the relative positioning of the two α9 helices upon ligand binding. Weighted ensemble simulations generated unbiased pathways for the seconds-timescale transition between alternate states of the enzyme, leading to the generation of atomically detailed structures of the ligand-free state. Notably, the simulations provide direct observations of negative cooperativity between the monomers of hGSTA1-1, which involve the mutually exclusive docking of α9 in each monomer as a lid over the active site. We identify key interactions between residues that lead to this negative cooperativity. Negative cooperativity may be essential for interaction of hGSTA1-1 with a wide variety of toxic substrates and their subsequent neutralization. More broadly, this work demonstrates the power of integrating EPR distances with weighted ensemble rare-events sampling strategy to gain mechanistic information on protein function at the atomic level. This article is protected by copyright. All rights reserved.
Keyphrases
- molecular dynamics
- molecular dynamics simulations
- magnetic resonance
- convolutional neural network
- network analysis
- endothelial cells
- neural network
- single molecule
- protein protein
- gene expression
- machine learning
- dna binding
- healthcare
- magnetic resonance imaging
- single cell
- mass spectrometry
- social media
- electron microscopy
- health information
- aqueous solution