Force field independent metal parameters using a nonbonded dummy model

Probing the mechanisms for the selectivity and promiscuity of methyl parathion hydrolase

Diverse organophosphate hydrolases have convergently evolved the ability to hydrolyse man-made organophosphates. Thus, these enzymes are attractive model systems for studying the factors shaping enzyme functional evolution. Methyl parathion hydrolase (MPH) is an enzyme from the metallo-β-lactamase superfamily, which hydrolyses a wide range of organophosphate, aryl ester and lactone substrates. In addition, MPH demonstrates metal-ion-dependent selectivity patterns. The origins of this remain unclear, but are linked to open questions about the more general role of metal ions in functional evolution and divergence within enzyme superfamilies. Here, we present detailed mechanistic studies of the paraoxonase and arylesterase activities of MPH complexed with five different transition metal ions, and demonstrate that the hydrolysis reactions proceed via similar pathways and transition states. However, while it is possible to discern a clear structural origin for the selectivity between different substrates, the selectivity between different metal ions appears to lie instead in the distinct electrostatic properties of the metal ions themselves, which causes subtle changes in transition state geometries and metal-metal distances at the transition state rather than significant structural changes in the active site. While subtle, these differences can be significant for shaping the metal-ion-dependent activity patterns observed for this enzyme.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

Conserved methionine dictates substrate preference in Nramp-family divalent metal transporters

Natural resistance-associated macrophage protein (Nramp) family transporters catalyze uptake of essential divalent transition metals like iron and manganese. To discriminate against abundant competitors, the Nramp metal-binding site should favor softer transition metals, which interact either covalently or ionically with coordinating molecules, over hard calcium and magnesium, which interact mainly ionically. The metal-binding site contains an unusual, but conserved, methionine, and its sulfur coordinates transition metal substrates, suggesting a vital role in their transport. Using a bacterial Nramp model system, we show that, surprisingly, this conserved methionine is dispensable for transport of the physiological manganese substrate and similar divalents iron and cobalt, with several small amino acid replacements still enabling robust uptake. Moreover, the methionine sulfur's presence makes the toxic metal cadmium a preferred substrate. However, a methionine-to-alanine substitution enables transport of calcium and magnesium. Thus, the putative evolutionary pressure to maintain the Nramp metal-binding methionine likely exists because it-more effectively than any other amino acid-increases selectivity for low-abundance transition metal transport in the presence of high-abundance divalents like calcium and magnesium.

The good, the bad and the user in soft matter simulations

Molecular dynamics (MD) simulations have become popular in materials science, biochemistry, biophysics and several other fields. Improvements in computational resources, in quality of force field parameters and algorithms have yielded significant improvements in performance and reliability. On the other hand, no method of research is error free. In this review, we discuss a few examples of errors and artifacts due to various sources and discuss how to avoid them. Besides bringing attention to artifacts and proper practices in simulations, we also aim to provide the reader with a starting point to explore these issues further. In particular, we hope that the discussion encourages researchers to check software, parameters, protocols and, most importantly, their own practices in order to minimize the possibility of errors. The focus here is on practical issues. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Direct simulation of electron transfer in the cobalt hexammine(ii/iii) self-exchange reaction

We present an atomistic simulation of electron transfer in a transition metal complex system using path integral methods.

Cooperative Electrostatic Interactions Drive Functional Evolution in the Alkaline Phosphatase Superfamily

It is becoming widely accepted that catalytic promiscuity, i.e., the ability of a single enzyme to catalyze the turnover of multiple, chemically distinct substrates, plays a key role in the evolution of new enzyme functions. In this context, the members of the alkaline phosphatase superfamily have been extensively studied as model systems in order to understand the phenomenon of enzyme multifunctionality. In the present work, we model the selectivity of two multiply promiscuous members of this superfamily, namely the phosphonate monoester hydrolases from Burkholderia caryophylli and Rhizobium leguminosarum. We have performed extensive simulations of the enzymatic reaction of both wild-type enzymes and several experimentally characterized mutants. Our computational models are in agreement with key experimental observables, such as the observed activities of the wild-type enzymes, qualitative interpretations of experimental pH-rate profiles, and activity trends among several active site mutants. In all cases the substrates of interest bind to the enzyme in similar conformations, with largely unperturbed transition states from their corresponding analogues in aqueous solution. Examination of transition-state geometries and the contribution of individual residues to the calculated activation barriers suggest that the broad promiscuity of these enzymes arises from cooperative electrostatic interactions in the active site, allowing each enzyme to adapt to the electrostatic needs of different substrates. By comparing the structural and electrostatic features of several alkaline phosphatases, we suggest that this phenomenon is a generalized feature driving selectivity and promiscuity within this superfamily and can be in turn used for artificial enzyme design.

Development and Application of a Nonbonded Cu(2+) Model That Includes the Jahn-Teller Effect

Metal ions are both ubiquitous to and crucial in biology. In classical simulations, they are typically described as simple van der Waals spheres, making it difficult to provide reliable force field descriptions for them. An alternative is given by nonbonded dummy models, in which the central metal atom is surrounded by dummy particles that each carry a partial charge. While such dummy models already exist for other metal ions, none is available yet for Cu(2+) because of the challenge to reproduce the Jahn-Teller distortion. This challenge is addressed in the current study, where, for the first time, a dummy model including a Jahn-Teller effect is developed for Cu(2+). We successfully validate its usefulness by studying metal binding in two biological systems: the amyloid-β peptide and the mixed-metal enzyme superoxide dismutase. We believe that our parameters will be of significant value for the computational study of Cu(2+)-dependent biological systems using classical models.

Systematic Parameterization of Monovalent Ions Employing the Nonbonded Model

Monovalent ions play fundamental roles in many biological processes in organisms. Modeling these ions in molecular simulations continues to be a challenging problem. The 12-6 Lennard-Jones (LJ) nonbonded model is widely used to model monovalent ions in classical molecular dynamics simulations. A lot of parameterization efforts have been reported for these ions with a number of experimental end points. However, some reported parameter sets do not have a good balance between the two Lennard-Jones parameters (the van der Waals (VDW) radius and potential well depth), which affects their transferability. In the present work, via the use of a noble gas curve we fitted in former work (J. Chem. Theory Comput. 2013, 9, 2733), we reoptimized the 12-6 LJ parameters for 15 monovalent ions (11 positive and 4 negative ions) for three extensively used water models (TIP3P, SPC/E, and TIP4P(EW)). Since the 12-6 LJ nonbonded model performs poorly in some instances for these ions, we have also parameterized the 12-6-4 LJ-type nonbonded model (J. Chem. Theory Comput. 2014, 10, 289) using the same three water models. The three derived parameter sets focused on reproducing the hydration free energies (the HFE set) and the ion-oxygen distance (the IOD set) using the 12-6 LJ nonbonded model and the 12-6-4 LJ-type nonbonded model (the 12-6-4 set) overall give improved results. In particular, the final parameter sets showed better agreement with quantum mechanically calculated VDW radii and improved transferability to ion-pair solutions when compared to previous parameter sets.

Catalytic stimulation by restrained active-site floppiness--the case of high density lipoprotein-bound serum paraoxonase-1

Despite the abundance of membrane-associated enzymes, the mechanism by which membrane binding stabilizes these enzymes and stimulates their catalysis remains largely unknown. Serum paraoxonase-1 (PON1) is a lipophilic lactonase whose stability and enzymatic activity are dramatically stimulated when associated with high-density lipoprotein (HDL) particles. Our mutational and structural analyses, combined with empirical valence bond simulations, reveal a network of hydrogen bonds that connect HDL binding residues with Asn168--a key catalytic residue residing >15Å from the HDL contacting interface. This network ensures precise alignment of N168, which, in turn, ligates PON1's catalytic calcium and aligns the lactone substrate for catalysis. HDL binding restrains the overall motion of the active site and particularly of N168, thus reducing the catalytic activation energy barrier. We demonstrate herein that disturbance of this network, even at its most far-reaching periphery, undermines PON1's activity. Membrane binding thus immobilizes long-range interactions via second- and third-shell residues that reduce the active site's floppiness and pre-organize the catalytic residues. Although this network is critical for efficient catalysis, as demonstrated here, unraveling these long-rage interaction networks is challenging, let alone their implementation in artificial enzyme design.

A helix–coil transition induced by the metal ion interaction with a grafted iron-binding site of the CyaY protein family

An iron-binding motif EExxED from the first α-helical stretch of frataxin was grafted on a foreign peptide scaffold:K_D= 1.9 ± 0.2 μM and 1 : 1 stoichiometry.

Parametrization of DFTB3/3OB for magnesium and zinc for chemical and biological applications

We report the parametrization of the approximate density functional theory, DFTB3, for magnesium and zinc for chemical and biological applications. The parametrization strategy follows that established in previous work that parametrized several key main group elements (O, N, C, H, P, and S). This 3OB set of parameters can thus be used to study many chemical and biochemical systems. The parameters are benchmarked using both gas-phase and condensed-phase systems. The gas-phase results are compared to DFT (mostly B3LYP), ab initio (MP2 and G3B3), and PM6, as well as to a previous DFTB parametrization (MIO). The results indicate that DFTB3/3OB is particularly successful at predicting structures, including rather complex dinuclear metalloenzyme active sites, while being semiquantitative (with a typical mean absolute deviation (MAD) of ∼3–5 kcal/mol) for energetics. Single-point calculations with high-level quantum mechanics (QM) methods generally lead to very satisfying (a typical MAD of ∼1 kcal/mol) energetic properties. DFTB3/MM simulations for solution and two enzyme systems also lead to encouraging structural and energetic properties in comparison to available experimental data. The remaining limitations of DFTB3, such as the treatment of interaction between metal ions and highly charged/polarizable ligands, are also discussed.

Parameterization of highly charged metal ions using the 12-6-4 LJ-type nonbonded model in explicit water

Highly charged metal ions act as catalytic centers and structural elements in a broad range of chemical complexes. The nonbonded model for metal ions is extensively used in molecular simulations due to its simple form, computational speed, and transferability. We have proposed and parametrized a 12-6-4 LJ (Lennard-Jones)-type nonbonded model for divalent metal ions in previous work, which showed a marked improvement over the 12-6 LJ nonbonded model. In the present study, by treating the experimental hydration free energies and ion–oxygen distances of the first solvation shell as targets for our parametrization, we evaluated 12-6 LJ parameters for 18 M(III) and 6 M(IV) metal ions for three widely used water models (TIP3P, SPC/E, and TIP4P_EW). As expected, the interaction energy underestimation of the 12-6 LJ nonbonded model increases dramatically for the highly charged metal ions. We then parametrized the 12-6-4 LJ-type nonbonded model for these metal ions with the three water models. The final parameters reproduced the target values with good accuracy, which is consistent with our previous experience using this potential. Finally, tests were performed on a protein system, and the obtained results validate the transferability of these nonbonded model parameters.