All-atom molecular dynamics simulations of butyrylcholinesterase (BChE) sans inhibitor and in complex with each of fifteen dialkyl phenyl phosphate derivatives were conducted to characterize inhibitor binding modes and strengths. Each system was sampled on the 250 ns timescale in explicit ionic solvent, for a total of over 4 μs of simulation time. A K-means algorithm was used to cluster the resulting structures into distinct binding modes, which were further characterized based on atomic-level contacts between inhibitor chemical groups and active site residues. Comparison of experimentally observed inhibition constants (K I) with the resulting contact tables provides structural explanations for relative binding coefficients and highlights several notable interaction motifs. These include ubiquitous contact between glycines in the oxyanion hole and the inhibitor phosphate group; a sterically-driven binding preference for positional isomers that extend aromaticity; a stereochemical binding preference for choline-containing inhibitors, which mimic natural BChE substrates; and the mechanically-induced opening of the omega loop region to fully expose the active site gorge in the presence of choline-containing inhibitors. Taken together, these observations can greatly inform future design of BChE inhibitors, and the approach reported herein is generalizable to other enzyme-inhibitor systems and similar complexes that depend on non-covalent molecular recognition.
The accurate and
reproducible detection and description of thermodynamic
states in computational data is a nontrivial problem, particularly
when the number of states is unknown a priori and
for large, flexible chemical systems and complexes. To this end, we
report a novel clustering protocol that combines high-resolution structural
representation, brute-force repeat clustering, and optimization of
clustering statistics to reproducibly identify the number of clusters
present in a data set (k) for simulated ensembles
of butyrylcholinesterase in complex with two previously studied organophosphate
inhibitors. Each structure within our simulated ensembles was depicted
as a high-dimensionality vector with components defined by specific
protein–inhibitor contacts at the chemical group level and
the magnitudes of these components defined by their respective extents
of pair-wise atomic contact, thus allowing for algorithmic differentiation
between varying degrees of interaction. These surface-weighted
interaction fingerprints were tabulated for each of over
1 million structures from more than 100 μs of all-atom molecular
dynamics simulation per complex and used as the input for repetitive k-means clustering. Minimization of cluster population variance
and range afforded accurate and reproducible identification of k, thereby allowing for the characterization of discrete
binding modes from molecular simulation data in the form of contact
tables that concisely encapsulate the observed intermolecular contact
motifs. While the protocol presented herein to determine k and achieve non-heuristic clustering is demonstrated on data from
massive atomistic simulation, our approach is generalizable to other
data types and clustering algorithms, and is tractable with limited
computational resources.
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