Substrate Dynamics Contribute to Enzymatic Specificity in Human and Bacterial Methionine Adenosyltransferases

Abstract

Protein conformational changes can facilitate the binding of noncognate substrates and underlying promiscuous activities. However, the contribution of substrate conformational dynamics to this process is comparatively poorly understood. Here, we analyze human (hMAT2A) and Escherichia coli (eMAT) methionine adenosyltransferases that have identical active sites but different substrate specificity. In the promiscuous hMAT2A, noncognate substrates bind in a stable conformation to allow catalysis. In contrast, noncognate substrates sample stable productive binding modes less frequently in eMAT owing to altered mobility in the enzyme active site. Different cellular concentrations of substrates likely drove the evolutionary divergence of substrate specificity in these orthologues. The observation of catalytic promiscuity in hMAT2A led to the detection of a new human metabolite, methyl thioguanosine, that is produced at elevated levels in a cancer cell line. This work establishes that identical active sites can result in different substrate specificity owing to the effects of substrate and enzyme dynamics.

Nucleoside-driven specificity of DNA Methyltransferase

“…To investigate the specificity of M.TaqI and M.HhaI for different SNM (SAM, SGM, SCM and SUM) analogs, we utilized the enzymatically synthesized SNM cofactors by hMAT2A [29] . To test the methylation activity of M.TaqI and M.HhaI for the different SNM, we used DNA protection against the restriction enzyme assay [34] (Figure 2a).…”

“…Herein, we have investigated the adenosine binding site for two DNA methyltransferases by utilizing novel S-nucleoside methionine (SNM) (S-guanosyl-L-methionine (SGM), S-cytidyl-L-methionine (SCM), S-uridyl-L-methionine (SUM)) cofactors [29] . These SNM cofactors carry different nucleotide bases, making them ideal candidates to study the adenosine binding pocket of DNA methyltransferase.…”

General theory of specific binding: insights from a genetic-mechano-chemical protein model

“…where ∆G C is the optimum binding a nity of the cognate ligand, and ∆∆G i is the required minimal binding energy gap between the cognate ligand Λ C and non-cognate ligand i, Λ NC i ; the degree of speci city required for each noncognate ligand depends on the in vivo concentration, 70 and the cost of incorrect binding.…”

“…where Δ C is the optimum binding a nity of the cognate ligand, and ΔΔ is the required minimal binding energy gap between the cognate ligand Λ C and non-cognate ligand , Λ NC ; the degree of speci city required for each noncognate ligand depends on the in vivo concentration, 70 and the cost of incorrect binding. The di culty is a non-linear function, which we expect to generally increase with: the required speci city (ΔΔ ), the number of similar, non-cognate ligands Λ NC , and how easy it is to distinguish them from the cognate ligand Λ C .…”

“…40 Proteins need to be just exible enough for the cognate ligand to bind, but not the non-cognate ligand. Too much exibility (reduces deformation energy) and both will bind; 70 too rigid and both will partially bind with negligible deformation. The optimal deformation for a given exibility is found to scale linearly with the chemical binding energy ε (Fig.…”