Multiscale Workflow for Modeling Ligand Complexes of Zinc Metalloproteins

Abstract

Zinc metalloproteins are ubiquitous, with protein zinc centers of structural and functional importance, involved in interactions with ligands and substrates and often of pharmacological interest. Biomolecular simulations are increasingly prominent in investigations of protein structure, dynamics, ligand interactions, and catalysis, but zinc poses a particular challenge, in part because of its versatile, flexible coordination. A computational workflow generating reliable models of ligand complexes of biological zinc centers would find broad application. Here, we evaluate the ability of alternative treatments, using (nonbonded) molecular mechanics (MM) and quantum mechanics/molecular mechanics (QM/MM) at semiempirical (DFTB3) and density functional theory (DFT) levels of theory, to describe the zinc centers of ligand complexes of six metalloenzyme systems differing in coordination geometries, zinc stoichiometries (mono- and dinuclear), and the nature of interacting groups (specifically the presence of zinc–sulfur interactions). MM molecular dynamics (MD) simulations can overfavor octahedral geometries, introducing additional water molecules to the zinc coordination shell, but this can be rectified by subsequent semiempirical (DFTB3) QM/MM MD simulations. B3LYP/MM geometry optimization further improved the accuracy of the description of coordination distances, with the overall effectiveness of the approach depending upon factors, including the presence of zinc–sulfur interactions that are less well described by semiempirical methods. We describe a workflow comprising QM/MM MD using DFTB3 followed by QM/MM geometry optimization using DFT (e.g., B3LYP) that well describes our set of zinc metalloenzyme complexes and is likely to be suitable for creating accurate models of zinc protein complexes when structural information is more limited.

Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways

“…The B3LYP hybrid functional , with the 6-31+G** basis set plus the D3 dispersion correction (B3LYP-D3/6-31+G**/C36) were employed as the high-level QM counterpart to refine the single-point energies along the optimized MEPs. We note that the combination of the levels of theory (DFTB3 and B3LYP-D3) has been proposed previously for enzyme catalysis simulations. , It has also been demonstrated that both the DFTB3 and B3LYP methods are applicable for studying concerted reaction steps in QM/MM simulations. , …”

“…We note that the combination of the levels of theory (DFTB3 and B3LYP-D3) has been proposed previously for enzyme catalysis simulations. 66 , 67 It has also been demonstrated that both the DFTB3 and B3LYP methods are applicable for studying concerted reaction steps in QM/MM simulations. 68 , 69 …”

“…We note that the combination of the levels of theory (DFTB3 and B3LYP-D3) has been proposed previously for enzyme catalysis simulations. 66,67 It has also been demonstrated that both the DFTB3 and B3LYP methods are applicable for studying concerted reaction steps in QM/MM simulations. 68,69 The initial guesses of the MEPs were created by linearly intercepting the Cartesian space between the reactants and the acylenzyme products using 50 replicated structures.…”

Highly specific fluorescent probes toward acetic acid via structural transformation of zinc complexes

“…Usually, transition organometallic probes possess the following characteristics: (1) highly luminous efficiency, long lifetime and high stability; (2) large Stokes shift of excitation and emission, and easy regulation of excitation and emission wavelengths; and (3) low cell imaging incubation concentration and less interference of normal cell activity. 26,27 Meanwhile, because of their low price and nontoxicity, easy functional modification and synthesis, photochemical stability, and desirable photophysical properties, [28][29][30][31][32][33][34][35][36][37] zinc complexes are widely used as excellent luminescent materials.…”

Accurate prediction of transition metal ion location via deep learning

“…2,15 In fact, the latest parametrization of the Rosetta energy function (ref2015) 16 did not refit the parameters for the metal ions which originally are from CHARMM27 with empirically derived Lazaridis-Karplus solvation terms. To adequately treat metal sites in proteins quantum mechanical treatments such as in hybrid quantum mechanics/molecular mechanics (QM/MM) simulations 17,18 is needed whose computational cost is prohibitive for regular protein design tasks. QM/MM simulations can however be used to verify coordination chemistry for select candidate proteins.…”