Abstract
Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which
limits comparison to in vivo structure and mechanism. The active site of chorismate mutase
from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used
as a case study to guide construction of QM-cluster models built first from the X-ray crystal
structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network-based ResidUe Selector (RINRUS) software toolkit, developed by our group to
simplify and automate the construction of QM-cluster models, is expanded to handle MD to
QM-cluster model workflows. Several options, some employing novel topological clustering
from Residue Interaction Network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic
framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with Density Functional Theory (DFT). The 250 QM-cluster models sampled
provide a mean ∆G‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4
kcal mol-1 at 25 0C. While the difference between theory and experiment is consequential,
the level of theory used is modest and therefore “chemical” accuracy is unexpected. More
important are the comparisons made between QM-cluster models designed from the X-ray
crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models,
rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the
field of computational enzymology using the model construction framework afforded with
the RINRUS software toolkit.
Supplementary materials
Title
Supplementary material
Description
Supplementary material contains are all starting and final pdbs for all QM-cluster models.
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