Computing Kinetic Isotope Effects for Chorismate Mutase with High Accuracy. A New DFT/MM Strategy

Acceleration of Ab Initio QM/MM Calculations under Periodic Boundary Conditions by Multiscale and Multiple Time Step Approaches

Development of multiscale ab initio quantum mechanical and molecular mechanical (AI-QM/MM) method for periodic boundary molecular dynamics (MD) simulations and their acceleration by multiple time step approach are described. The developed method achieves accuracy and efficiency by integrating the AI-QM/MM level of theory and the previously developed semiempirical (SE) QM/MM-Ewald sum method [J. Chem. Theory Comput. 2005, 1, 2] extended to the smooth particle-mesh Ewald (PME) summation method. In the developed methods, the total energy of the simulated system is evaluated at the SE-QM/MM-PME level of theory to include long-range QM/MM electrostatic interactions, which is then corrected on the fly using the AI-QM/MM level of theory within the real space cutoff. The resulting energy expression enables decomposition of total forces applied to each atom into forces determined at the low-level SE-QM/MM method and correction forces at the AI-QM/MM level, to integrate the system using the reversible reference system propagator algorithm. The resulting method achieves a substantial speed-up of the entire calculation by minimizing the number of time-consuming energy and gradient evaluations at the AI-QM/MM level. Test calculations show that the developed multiple time step AI-QM/MM method yields MD trajectories and potential of mean force profiles comparable to single time step QM/MM results. The developed method, together with message passing interface (MPI) parallelization, accelerates the present AI-QM/MM MD simulations about 30-fold relative to the speed of single-core AI-QM/MM simulations for the molecular systems tested in the present work, making the method less than one order slower than the SE-QM/MM methods under periodic boundary conditions.

Multiscale Free Energy Simulations: An Efficient Method for Connecting Classical MD Simulations to QM or QM/MM Free Energies Using Non-Boltzmann Bennett Reweighting Schemes

The reliability of free energy simulations (FES) is limited by two factors: (a) the need for correct sampling and (b) the accuracy of the computational method employed. Classical methods (e.g., force fields) are typically used for FES and present a myriad of challenges, with parametrization being a principle one. On the other hand, parameter-free quantum mechanical (QM) methods tend to be too computationally expensive for adequate sampling. One widely used approach is a combination of methods, where the free energy difference between the two end states is computed by, e.g., molecular mechanics (MM), and the end states are corrected by more accurate methods, such as QM or hybrid QM/MM techniques. Here we report two new approaches that significantly improve the aforementioned scheme; with a focus on how to compute corrections between, e.g., the MM and the more accurate QM calculations. First, a molecular dynamics trajectory that properly samples relevant conformational degrees of freedom is generated. Next, potential energies of each trajectory frame are generated with a QM or QM/MM Hamiltonian. Free energy differences are then calculated based on the QM or QM/MM energies using either a non-Boltzmann Bennett approach (QM-NBB) or non-Boltzmann free energy perturbation (NB-FEP). Both approaches are applied to calculate relative and absolute solvation free energies in explicit and implicit solvent environments. Solvation free energy differences (relative and absolute) between ethane and methanol in explicit solvent are used as the initial test case for QM-NBB. Next, implicit solvent methods are employed in conjunction with both QM-NBB and NB-FEP to compute absolute solvation free energies for 21 compounds. These compounds range from small molecules such as ethane and methanol to fairly large, flexible solutes, such as triacetyl glycerol. Several technical aspects were investigated. Ultimately some best practices are suggested for improving methods that seek to connect MM to QM (or QM/MM) levels of theory in FES.

Curvature correction for microiterative optimizations with QM/MM electronic embedding

One of the most common methods to treat the electrostatic effect of the environment in QM/MM calculations is to include the MM atoms as point charges in the QM Hamiltonian. In this case, a microiterative geometry optimization ignoring the QM contributions to the forces in the relaxation of the environment cannot yield exact stationary points. One solution that has been suggested in the literature is based on using a constant additive correction to the MM gradient during the microiterations, determined in the preceding macroiteration. Here, we analyze the convergence properties of the gradient correction method and point out that a smooth relaxation is not ensured if the curvature of the approximate, MM-based description of the potential energy surface of the environment is too small in comparison with the exact one. We suggest a computationally cheap second-order correction that uses an estimated Hessian from the Davidon-Fletcher-Powell method to tackle the problems caused by the too small curvature. Test calculations on four metalloenzymatic systems (∼100 QM atoms, ∼2000 relaxed MM atoms, ∼20,000 atoms in total) show that our approach efficiently restores the convergence where gradient correction alone would lead to oscillations.

Hybrid schemes based on quantum mechanics/molecular mechanics simulations goals to success, problems, and perspectives

The development of characterization techniques, advanced synthesis methods, as well as molecular modeling has transformed the study of systems in a well-established research field. The current research challenges in biocatalysis and biotransformation evolve around enzyme discovery, design, and optimization. How can we find or create enzymes that catalyze important synthetic reactions, even reactions that may not exist in nature? What is the source of enzyme catalytic power? To answer these and other related questions, the standard strategies have evolved from trial-and-error methodologies based on chemical knowledge, accumulated experience, and common sense into a clearly multidisciplinary science that allows one to reach the molecular design of tailor-made enzyme catalysts. This is even more so when one refers to enzyme catalysts, for which the detailed structure and composition are known and can be manipulated to introduce well-defined residues which can be implicated in the chemical rearrangements taking place in the active site. The methods and techniques of theoretical and computational chemistry are becoming more and more important in both understanding the fundamental biological roles of enzymes and facilitating their utilization in biotechnology. Improvement of the catalytic function of enzymes is important from scientific and industrial viewpoints, and to put this fact in the actual perspective as well as the potentialities, we recommend the very recent report of Sanderson [Sanderson, K. (2011). Chemistry: enzyme expertise. Nature 471, 397.]. Great fundamental advances have been made toward the ab initio design of enzyme catalysts based on molecular modeling. This has been based on the molecular mechanistic knowledge of the reactions to be catalyzed, together with the development of advanced synthesis and characterization techniques. The corresponding molecular mechanism can be studied by means of powerful quantum chemical calculations. The catalytic active site can be optimized to improve the transition state analogues (TSA) and to enhance the catalytic activity, even improve the active site to favor a desired direction of some promiscuous enzymes. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are described. The interplay between enzyme design, molecular simulations, and experiments will be presented to emphasize the interdisciplinary nature of this research field. This text highlights the recent advances and examples selected from our laboratory are shown, of how the applications of these tools are a first attempt to de novo design of protein active sites. Identification of neutral/advantageous/deleterious mutation platforms can be exploited to penetrate some of Nature's closely guarded secrets of chemical reactivity. In this chapter, we give a brief introduction, the state of the art, and future prospects and implications of enzyme design. The first part describes briefly how the molecular modeling is carried out. Then, we discuss the requirements of hybrid quantum mechanical/molecular mechanics molecular dynamics (QM/MM MD) simulations, analyzing what are the basis of these theoretical methodologies, how we can use them with a view to its application in the study of enzyme catalysis, and what are the best methodologies for assessing its catalytic potential. In the second part, we focus on some selected examples, taking as a common guide the chorismate to prephenate rearrangement, studying the corresponding molecular mechanism in vacuo, in solution and in an enzyme environment. In addition, examples involving catalytic antibodies (CAs) and promiscuous enzymes will be presented. Finally, a special emphasis is made to provide some hints about the logical evolution that can be anticipated in this research field. Moreover, it helps in understanding the open directions in this area of knowledge and highlights the importance of computational approaches in discovering specific drugs and the impact on the rational design of tailor-made enzymes.

QM/MM methods for biomolecular systems

Combined quantum-mechanics/molecular-mechanics (QM/MM) approaches have become the method of choice for modeling reactions in biomolecular systems. Quantum-mechanical (QM) methods are required for describing chemical reactions and other electronic processes, such as charge transfer or electronic excitation. However, QM methods are restricted to systems of up to a few hundred atoms. However, the size and conformational complexity of biopolymers calls for methods capable of treating up to several 100,000 atoms and allowing for simulations over time scales of tens of nanoseconds. This is achieved by highly efficient, force-field-based molecular mechanics (MM) methods. Thus to model large biomolecules the logical approach is to combine the two techniques and to use a QM method for the chemically active region (e.g., substrates and co-factors in an enzymatic reaction) and an MM treatment for the surroundings (e.g., protein and solvent). The resulting schemes are commonly referred to as combined or hybrid QM/MM methods. They enable the modeling of reactive biomolecular systems at a reasonable computational effort while providing the necessary accuracy.

The reaction mechanism of paraoxon hydrolysis by phosphotriesterase from combined QM/MM simulations

Molecular dynamics simulations employing combined quantum mechanical and molecular mechanical (QM/MM) potentials have been carried out to investigate the reaction mechanism of the hydrolysis of paraoxon by phosphotriesterase (PTE). We used a dual-level QM/MM approach that synthesizes accurate results from high-level electronic structure calculations with computational efficiency of semiempirical QM/MM potentials for free energy simulations. In particular, the intrinsic (gas-phase) energies of the active site in the QM region are determined by using density functional theory (B3LYP) and second-order Møller-Plesset perturbation theory (MP2) and the molecular dynamics free energy simulations are performed by using the mixed AM1:CHARMM potential. The simulation results suggest a revised mechanism for the phosphotriester hydrolysis mechanism by PTE. The reaction free energy profile is mirrored by structural motions of the binuclear metal center in the active site. The two zinc ions occupy a compact conformation with an average zinc-zinc distance of 3.5 +/- 0.1 A in the Michaelis complex, whereas it is elongated to 5.3 +/- 0.3 A at the transition state and product state. The substrate is loosely bound to the more exposed zinc ion (Znbeta2+) at an average distance of 3.8 A +/- 0.3 A. The P=O bond of the substrate paraoxon is activated by adopting a tight coordination to the Znbeta2+, releasing the coordinate to the bridging hydroxide ion and increasing its nucleophilicity. It was also found that a water molecule enters into the binding pocket of the loosely bound binuclear center, originally occupied by the nucleophilic hydroxide ion. We suggest that the proton of this water molecule is taken up by His254 at low pH or released to the solvent at high pH, resulting in a hydroxide ion that pulls the Znbeta2+ ion closer to form the compact configuration and restores the resting state of the enzyme.

Modelling enzyme reaction mechanisms, specificity and catalysis

Modern modelling methods can now give uniquely detailed understanding of enzyme-catalyzed reactions, including the analysis of mechanisms and the identification of determinants of specificity and catalytic efficiency. A new field of computational enzymology has emerged that has the potential to contribute significantly to structure-based design and to develop predictive models of drug metabolism and, for example, of the effects of genetic polymorphisms. This review outlines important techniques in this area, including quantum-chemical model studies and combined quantum-mechanics and molecular-mechanics (QM/MM) methods. Some recent applications to enzymes of pharmacological interest are also covered, showing the types of problems that can be tackled and the insight they can give.