Molecular Dynamics Simulations: Difficulties, Solutions and Strategies for Treating Metalloenzymes

Catalytic role of histidine-114 in the hydrolytic dehalogenation of chlorothalonil by Pseudomonas sp. CTN-3

Chlorothalonil (2,4,5,6-tetrachloroisophthalonitrile; TPN) is an environmentally persistent fungicide that sees heavy use in the USA and is highly toxic to aquatic species and birds, as well as a probable human carcinogen. The chlorothalonil dehalogenase from Pseudomonas sp. CTN-3 (Chd, UniProtKB C9EBR5) degrades TPN to its less toxic 4-OH-TPN analog making it an exciting candidate for the development of a bioremediation process for TPN; however, little is currently known about its catalytic mechanism. Therefore, an active site residue histidine-114 (His114) which forms a hydrogen bond with the Zn(II)-bound water/hydroxide and has been suggested to be the active site acid/base, was substituted by an Ala residue. Surprisingly, ChdH114A exhibited catalytic activity with a kcat value of 1.07 s-1, ~ 5% of wild-type (WT) Chd, and a KM of 32 µM. Thus, His114 is catalytically important but not essential. The electronic and structural aspects of the WT Chd and ChdH114A active sites were examined using UV-Vis and EPR spectroscopy on the catalytically competent Co(II)-substituted enzyme as well as all-atomistic molecular dynamics (MD) simulations. Combination of these data suggest His114 can quickly and reversibly move nearly 2 Å between one conformation that facilitates catalysis and another that enables product egress and active site recharge. In light of experimental and computational data on ChdH114A, Asn216 appears to play a role in substrate binding and preorganization of the transition-state while Asp116 likely facilitates the deprotonation of the Zn(II)-bound water in the absence of His114. Based on these data, an updated proposed catalytic mechanism for Chd is presented.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Extending the Nonbonded Cationic Dummy Model to Account for Ion-Induced Dipole Interactions

Modeling metalloproteins often requires classical molecular dynamics (MD) simulations in order to capture their relevant motions, which in turn necessitates reliable descriptions of the metal centers involved. One of the most successful approaches to date is provided by the "cationic dummy model", where the positive charge of the metal ion is transferred toward dummy particles that are bonded to the central metal ion in a predefined coordination geometry. While this approach allows for ligand exchange, and captures the correct electrostatics as demonstrated for different divalent metal ions, current dummy models neglect ion-induced dipole interactions. In the present work, we resolve this weakness by taking advantage of the recently introduced 12-6-4 type Lennard-Jones potential to include ion-induced dipole interactions. We revise our previous dummy model for Mg²⁺ and demonstrate that the resulting model can simultaneously reproduce the experimental solvation free energy and metal-ligand distances without the need for artificial restraints or bonds. As ion-induced dipole interactions become particularly important for highly charged metal ions, we develop dummy models for the biologically relevant ions Al³⁺, Fe³⁺, and Cr³⁺. Finally, the effectiveness of our new models is demonstrated in MD simulations of several diverse (and highly challenging to simulate) metalloproteins.

Metal Ion Modeling Using Classical Mechanics

Metal ions play significant roles in numerous fields including chemistry, geochemistry, biochemistry, and materials science. With computational tools increasingly becoming important in chemical research, methods have emerged to effectively face the challenge of modeling metal ions in the gas, aqueous, and solid phases. Herein, we review both quantum and classical modeling strategies for metal ion-containing systems that have been developed over the past few decades. This Review focuses on classical metal ion modeling based on unpolarized models (including the nonbonded, bonded, cationic dummy atom, and combined models), polarizable models (e.g., the fluctuating charge, Drude oscillator, and the induced dipole models), the angular overlap model, and valence bond-based models. Quantum mechanical studies of metal ion-containing systems at the semiempirical, ab initio, and density functional levels of theory are reviewed as well with a particular focus on how these methods inform classical modeling efforts. Finally, conclusions and future prospects and directions are offered that will further enhance the classical modeling of metal ion-containing systems.

An electrostatic mechanism for Ca(2+)-mediated regulation of gap junction channels

Gap junction channels mediate intercellular signalling that is crucial in tissue development, homeostasis and pathologic states such as cardiac arrhythmias, cancer and trauma. To explore the mechanism by which Ca(2+) blocks intercellular communication during tissue injury, we determined the X-ray crystal structures of the human Cx26 gap junction channel with and without bound Ca(2+). The two structures were nearly identical, ruling out both a large-scale structural change and a local steric constriction of the pore. Ca(2+) coordination sites reside at the interfaces between adjacent subunits, near the entrance to the extracellular gap, where local, side chain conformational rearrangements enable Ca(2+)chelation. Computational analysis revealed that Ca(2+)-binding generates a positive electrostatic barrier that substantially inhibits permeation of cations such as K(+) into the pore. Our results provide structural evidence for a unique mechanism of channel regulation: ionic conduction block via an electrostatic barrier rather than steric occlusion of the channel pore.

Force field independent metal parameters using a nonbonded dummy model

The cationic dummy atom approach provides a powerful nonbonded description for a range of alkaline-earth and transition-metal centers, capturing both structural and electrostatic effects. In this work we refine existing literature parameters for octahedrally coordinated Mn²⁺, Zn²⁺, Mg²⁺, and Ca²⁺, as well as providing new parameters for Ni²⁺, Co²⁺, and Fe²⁺. In all the cases, we are able to reproduce both M²⁺–O distances and experimental solvation free energies, which has not been achieved to date for transition metals using any other model. The parameters have also been tested using two different water models and show consistent performance. Therefore, our parameters are easily transferable to any force field that describes nonbonded interactions using Coulomb and Lennard-Jones potentials. Finally, we demonstrate the stability of our parameters in both the human and Escherichia coli variants of the enzyme glyoxalase I as showcase systems, as both enzymes are active with a range of transition metals. The parameters presented in this work provide a valuable resource for the molecular simulation community, as they extend the range of metal ions that can be studied using classical approaches, while also providing a starting point for subsequent parametrization of new metal centers.

Coordination-directed self-assembly of M12L24 nanocage: effects of kinetic trapping on the assembly process

We demonstrate the spontaneous formation of spherical complex M12L24, which is composed of 12 palladium ions and 24 bidentate ligands, by molecular dynamics simulations. In contrast to our previous study on the smaller M6L8 cage, we found that the larger M12L24 self-assembly process involves noticeable kinetic trapping at lower nuclearity complexes, e.g., M6L12, M8L16, and M9L18. We also found that the kinetic trapping behaviors sensitively depend on the bend angle of ligands and the metal-ligand binding strength. Our results show that these kinetic effects, that have generally been neglected, are important factor in self-assembly structure determination of larger complexes as M12L24 in this study.

Structures and free energy landscapes of aqueous zinc(II)-bound amyloid-β(1-40) and zinc(II)-bound amyloid-β(1-42) with dynamics

Binding of divalent metal ions with intrinsically disordered fibrillogenic proteins, such as amyloid-β (Aβ), influences the aggregation process and the severity of neurodegenerative diseases. The Aβ monomers and oligomers are the building blocks of the aggregates. In this work, we report the structures and free energy landscapes of the monomeric zinc(II)-bound Aβ40 (Zn:Aβ40) and zinc(II)-bound Aβ42 (Zn:Aβ42) intrinsically disordered fibrillogenic metallopeptides in an aqueous solution by utilizing an approach that employs first principles calculations and parallel tempering molecular dynamics simulations. The structural and thermodynamic properties, including the secondary and tertiary structures and conformational Gibbs free energies of these intrinsically disordered metallopeptide alloforms, are presented. The results show distinct differing characteristics for these metallopeptides. For example, prominent β-sheet formation in the N-terminal region (Asp1, Arg5, and Tyr10) of Zn:Aβ40 is significantly decreased or lacking in Zn:Aβ42. Our findings indicate that blocking multiple reactive residues forming abundant β-sheet structure located in the central hydrophobic core and C-terminal regions of Zn:Aβ42 via antibodies or small organic molecules might help to reduce the aggregation of Zn(II)-bound Aβ42. Furthermore, we find that helix formation increases but β-sheet formation decreases in the C-terminal region upon Zn(II) binding to Aβ. This depressed β-sheet formation in the C-terminal region (Gly33-Gly38) in monomeric Zn:Aβ42 might be linked to the formation of amorphous instead of fibrillar aggregates of Zn:Aβ42.