Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives

More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.

A Vision for the Future of Multiscale Modeling

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

Multi-scale simulation of proton diffusion in dislocation cores in BaZrO3

Dislocations are important for their effects on the chemical, electrical, magnetic, and transport properties of oxide materials, especially for electrochemical devices such as solid fuel cells and resistive memories, but these effects are still under-studied at the atomic level. We have developed a quantum mechanical/molecular mechanical (QM/MM)-based multiscale simulation program to reveal the diffusion properties of protons on 〈100〉 edge dislocations in BaZrO₃ perovskite oxide. We find that the large free space and the presence of hydrogen bonds in the dislocation core structure lead to significant trapping of protons. The diffusion properties of protons in dislocation cores were investigated, and no evidence of pipeline diffusion was found from the calculated migration energy barriers, which not only did not accelerate ion diffusion but rather decreases the conductivity of ions. The proton diffusion properties of Y-doped BaZrO₃ (BZY), with a dislocation core structure (BZY-D) and with a grain boundary structure (BZY-GB) were also compared. In all three structures, local lattice deformation occupies an essential part in the proton transfer and rotation processes. The change in bond order is calculated and it is found that the interaction with oxygen and Zr ions during proton transfer and rotation controls the energy barrier for local lattice deformation of the O-B-O motion, which affects the proton diffusion in the structure. Our study provides insight into proton diffusion in dislocations in terms of mechanical behavior, elucidates the origin of the energy barrier associated with proton diffusion in dislocations, and provides guidance for the preparation and application of proton conductors.

Mutation-Specific Differences in Kv7.1 (KCNQ1) and Kv11.1 (KCNH2) Channel Dysfunction and Long QT Syndrome Phenotypes

The electrocardiogram (ECG) empowered clinician scientists to measure the electrical activity of the heart noninvasively to identify arrhythmias and heart disease. Shortly after the standardization of the 12-lead ECG for the diagnosis of heart disease, several families with autosomal recessive (Jervell and Lange-Nielsen Syndrome) and dominant (Romano-Ward Syndrome) forms of long QT syndrome (LQTS) were identified. An abnormally long heart rate-corrected QT-interval was established as a biomarker for the risk of sudden cardiac death. Since then, the International LQTS Registry was established; a phenotypic scoring system to identify LQTS patients was developed; the major genes that associate with typical forms of LQTS were identified; and guidelines for the successful management of patients advanced. In this review, we discuss the molecular and cellular mechanisms for LQTS associated with missense variants in KCNQ1 (LQT1) and KCNH2 (LQT2). We move beyond the "benign" to a "pathogenic" binary classification scheme for different KCNQ1 and KCNH2 missense variants and discuss gene- and mutation-specific differences in K+ channel dysfunction, which can predispose people to distinct clinical phenotypes (e.g., concealed, pleiotropic, severe, etc.). We conclude by discussing the emerging computational structural modeling strategies that will distinguish between dysfunctional subtypes of KCNQ1 and KCNH2 variants, with the goal of realizing a layered precision medicine approach focused on individuals.

Theoretical Study of Macrocyclic Host Molecules: From Supramolecular Recognition to Self-Assembly

Supramolecular chemistry focuses on molecular recognition and self-assembly of various building blocks through weak non-covalent interactions, including anion-π, hydrogen bond (HB), hydrophobic interactions, van der Waals (vdW) interactions, etc, which...

Solvent-scaling as an alternative to coarse-graining in adaptive-resolution simulations: The adaptive solvent-scaling (AdSoS) scheme

A new approach termed Adaptive Solvent-Scaling (AdSoS) is introduced for performing simulations of a solute embedded in a fine-grained (FG) solvent region itself surrounded by a coarse-grained (CG) solvent region, with a continuous FG ↔ CG switching of the solvent resolution across a buffer layer. Instead of relying on a distinct CG solvent model, the AdSoS scheme is based on CG models defined by a dimensional scaling of the FG solvent by a factor s, accompanied by an s-dependent modulation of the atomic masses and interaction parameters. The latter changes are designed to achieve an isomorphism between the dynamics of the FG and CG models, and to preserve the dispersive and dielectric solvation properties of the solvent with respect to a solute at FG resolution. This scaling approach offers a number of advantages compared to traditional coarse-graining: (i) the CG parameters are immediately related to those of the FG model (no need to parameterize a distinct CG model); (ii) nearly ideal mixing is expected for CG variants with similar s-values (ideal mixing holding in the limit of identical s-values); (iii) the solvent relaxation timescales should be preserved (no dynamical acceleration typical for coarse-graining); (iv) the graining level N_G (number of FG molecules represented by one CG molecule) can be chosen arbitrarily (in particular, N_G = s³ is not necessarily an integer); and (v) in an adaptive-resolution scheme, this level can be varied continuously as a function of the position (without requiring a bundling mechanism), and this variation occurs at a constant number of particles per molecule (no occurrence of fractional degrees of freedom in the buffer layer). By construction, the AdSoS scheme minimizes the thermodynamic mismatch between the different regions of the adaptive-resolution system, leading to a nearly homogeneous scaled solvent density s³ρ. Residual density artifacts in and at the surface of the boundary layer can easily be corrected by means of a grid-based biasing potential constructed in a preliminary pure-solvent simulation. This article introduces the AdSoS scheme and provides an initial application to pure atomic liquids (no solute) with Lennard-Jones plus Coulomb interactions in a slab geometry.

Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems

Lipid nanoparticles (LNPs) have been widely applied in drug and gene delivery. More than twenty years ago, DoxilTM was the first LNPs-based drug approved by the US Food and Drug Administration (FDA). Since then, with decades of research and development, more and more LNP-based therapeutics have been used to treat diverse diseases, which often offer the benefits of reduced toxicity and/or enhanced efficacy compared to the active ingredients alone. Here, we provide a review of recent advances in the development of efficient and robust LNPs for drug/gene delivery. We emphasize the importance of rationally combining experimental and computational approaches, especially those providing multiscale structural and functional information of LNPs, to the design of novel and powerful LNP-based delivery systems.

On the Effect of the Various Assumptions and Approximations used in Molecular Simulations on the Properties of Bio-Molecular Systems: Overview and Perspective on Issues

Computer simulations of molecular systems enable structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic or supra-molecular level and plays an increasingly important role in chemistry, biology and physics. To interpret the results of such simulations appropriately, the degree of uncertainty and potential errors affecting the calculated properties must be considered. Uncertainty and errors arise from (1) assumptions underlying the molecular model, force field and simulation algorithms, (2) approximations implicit in the interatomic interaction function (force field), or when integrating the equations of motion, (3) the chosen values of the parameters that determine the accuracy of the approximations used, and (4) the nature of the system and the property of interest. In this overview, advantages and shortcomings of assumptions and approximations commonly used when simulating bio-molecular systems are considered. What the developers of bio-molecular force fields and simulation software can do to facilitate and broaden research involving bio-molecular simulations is also discussed.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.

Energetic Self-Folding Mechanism in α-Helices

A novel energetic route driving the folding of a polyalanine peptide from an extended conformation to its α-helix native conformation is described, supported by a new method to compute mean potential energy surfaces accurately in terms of the dihedral angles of the peptide chain from extensive molecular dynamics simulations. The energetic self-folding (ESF) route arises specifically from the balance between the intrinsic propensity of alanine residues toward the α_R conformation and two, opposite, effects: the destabilizing interaction with neighbor residues and the stabilizing formation of native hydrogen bonds, with the latter being dominant for large peptide lengths. The ESF mechanism provides simple but robust support to the nucleation-elongation or zipper models and offers a quantitative energetic funnel picture of the folding process. The mechanism is validated by the reasonable agreement between the computed folding energies and the experimental values.

From quantum to subcellular scales: multi-scale simulation approaches and the SIRAH force field

Modern molecular and cellular biology profits from astonishing resolution structural methods, currently even reaching the whole cell level. This is encompassed by the development of computational methods providing a deep view into the structure and dynamics of molecular processes happening at very different scales in time and space. Linking such scales is of paramount importance when aiming at far-reaching biological questions. Computational methods at the interface between classical and coarse-grained resolutions are gaining momentum with several research groups dedicating important efforts to their development and tuning. An overview of such methods is addressed herein, with special emphasis on the SIRAH force field for coarse-grained and multi-scale simulations. Moreover, we provide proof of concept calculations on the implementation of a multi-scale simulation scheme including quantum calculations on a classical fine-grained/coarse-grained representation of double-stranded DNA. This opens the possibility to include the effect of large conformational fluctuations in chromatin segments on, for instance, the reactivity of particular base pairs within the same simulation framework.

Linking inhibitor motions to proteolytic stability of sunflower trypsin inhibitor-1

The remarkable capability of an enzyme isn't only determined by its active site but also controlled by the environment. To unravel the environment role in catalysis, the dynamic motions as well as the static mechanism need to be studied. In this work, QM/MM MD simulations were employed to study the proteolysis process of SFTI-1 and BiKF, which revealed that a combination of static non-bonded interactions and dynamic motions along the reaction coordinate can account for the different hydrolysis rates between them. A comparison among SFTI-1 and three analogs with similar non-bonded interactions further revealed a positive correlation between the mobility of inhibitors and the hydrolysis rates. Apart from the cyclic backbone and disulfide bond, intramolecular hydrogen bonds also increase the rigidity of the backbone of inhibitors, and therefore hinder inhibitor motions to resist proteolysis. These new detailed mechanistic insights suggest the need to consider inhibitor motions in the rational design of peptide inhibitors.

Besides the non-bonded interactions, inhibitor motions especially rotation of the scissile bond also influence proteolytic stability.

Molecular insight into chymotrypsin inhibitor 2 resisting proteolytic degradation

Chymotrypsin inhibitor 2 (CI2) is a special serine protease inhibitor which can resist hydrolysis for several days with a rapid equilibrium between the Michaelis complex and acyl-enzyme intermediate. The energies and conformational changes for subtilisin-catalyzed proteolysis of CI2 were examined in this paper for the first time by employing pseudo bond ab initio QM/MM MD simulations. In the acylation reaction, a low-barrier hydrogen bond between His64 and Asp32 in the transition state together with the lack of covalent backbone constraints makes the peptide bonds of CI2 break more easily than in other serine protease inhibitors. After acyl-enzyme formation, molecular dynamics simulations showed that the access of hydrolytic water to the active site requires partial dissociation of the leaving group. However, retention of the leaving group mainly by the intra- and inter-molecular H-bonding networks hinders the access of water and retards the deacylation reaction. Instead of the dissociation constant of inhibitors, we suggest employing the free energy at the acyl-enzyme state to predict the relative hydrolysis rates of CI2 mutants, which are testified by the experimental relative hydrolysis rates.

Adaptive QM/MM for Molecular Dynamics Simulations: 5. On the Energy-Conserved Permuted Adaptive-Partitioning Schemes

In combined quantum-mechanical/molecular-mechanical (QM/MM) dynamics simulations, the adaptive-partitioning (AP) schemes reclassify atoms on-the-fly as QM or MM in a smooth manner. This yields a mobile QM subsystem with contents that are continuously updated as needed. Here, we tailor the Hamiltonian adaptive many-body correction (HAMBC) proposed by Boreboom et al. [J. Chem. Theory Comput.2016, 12, 3441] to the permuted AP (PAP) scheme. The treatments lead to the HAMBC-PAP method (HPAP), which both conserves energy and produces accurate solvation structures in the test of "water-in-water" model system.

Combined Quantum Mechanics and Molecular Mechanics Studies of Enzymatic Reaction Mechanisms

The combined quantum mechanics/molecular mechanics (QM/MM) methods have become a valuable tool in computational biochemistry and received versatile applications for studying the reaction mechanisms of enzymes. The approach combines the calculations of the electronic structure of the active site by QM, with modeling of the protein environment using MM force field, which allows the long-range electrostatics and steric effects on the enzyme reactivity to be accounted for. In this review, we review some key theoretical and computational aspects of the method and we also present some applications to particular enzymatic reactions such as tryptophan-7-halogenase, cyclooxygenase-1, and the epidermal growth factor receptor.

Computational approaches to cell-nanomaterial interactions: keeping balance between therapeutic efficiency and cytotoxicity

Owing to their unique properties, nanomaterials have been widely used in biomedicine since they have obvious inherent advantages over traditional ones. However, nanomaterials may also cause dysfunction in proteins, genes and cells, resulting in cytotoxic and genotoxic responses. Recently, more and more attention has been paid to these potential toxicities of nanomaterials, especially to the risks of nanomaterials to human health and safety. Therefore, when using nanomaterials for biomedical applications, it is of great importance to keep the balance between therapeutic efficiency and cytotoxicity (i.e., increase the therapeutic efficiency as well as decrease the potential toxicity). This requires a deeper understanding of the interactions between various types of nanomaterials and biological systems at the nano/bio interface. In this review, from the point of view of theoretical researchers, we will present the current status regarding the physical mechanism of cytotoxicity caused by nanomaterials, mainly based on recent simulation results. In addition, the strategies for minimizing the nanotoxicity naturally and artificially will also be discussed in detail. Furthermore, we should notice that toxicity is not always bad for clinical use since causing the death of specific cells is the main way of treating disease. Enhancing the targeting ability of nanomaterials to diseased cells and minimizing their side effects on normal cells will always be hugely challenging issues in nanomedicine. By combining the latest computational studies with some experimental verifications, we will provide special insights into recent advances regarding these problems, especially for the design of novel environment-responsive nanomaterials.

Validation of Molecular Simulation: An Overview of Issues

Computer simulation of molecular systems enables structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic, or supra-molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Q^exp of experimentally observable quantities Q. One must consider 1) the accuracy of Q^exp , 2) the accuracy of the function Q(r^N ) used to calculate a Q-value based on a molecular configuration r^N of N particles, 3) the sensitivity of the function Q(r^N ) to the configuration r^N , 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed.

SERS Quantification and Characterization of Proteins and Other Biomolecules

Changes in protein expression levels and protein structure may indicate genomic mutations and may be related to some diseases. Therefore, the precise quantification and characterization of proteins can be used for disease diagnosis. Compared with several other alternative methods, surface-enhanced Raman scattering (SERS) spectroscopy is regarded as an excellent choice for the quantification and structural characterization of proteins. Herein, we review the main advance of using plasmonic nanostructures as SERS sensing platform for this purpose. Three design approaches, including direct SERS, indirect SERS, and SERS-encoded nanoparticles, are discussed in the direction of developing new precise approaches of quantification and characterization of proteins. While this Review is focused on proteins, in order to highlight concepts of SERS-based sensors also detection of other biomolecules will be discussed.

Computational Biochemistry-Enzyme Mechanisms Explored

Understanding enzyme mechanisms is a major task to achieve in order to comprehend how living cells work. Recent advances in biomolecular research provide huge amount of data on enzyme kinetics and structure. The analysis of diverse experimental results and their combination into an overall picture is, however, often challenging. Microscopic details of the enzymatic processes are often anticipated based on several hints from macroscopic experimental data. Computational biochemistry aims at creation of a computational model of an enzyme in order to explain microscopic details of the catalytic process and reproduce or predict macroscopic experimental findings. Results of such computations are in part complementary to experimental data and provide an explanation of a biochemical process at the microscopic level. In order to evaluate the mechanism of an enzyme, a structural model is constructed which can be analyzed by several theoretical approaches. Several simulation methods can and should be combined to get a reliable picture of the process of interest. Furthermore, abstract models of biological systems can be constructed combining computational and experimental data. In this review, we discuss structural computational models of enzymatic systems. We first discuss various models to simulate enzyme catalysis. Furthermore, we review various approaches how to characterize the enzyme mechanism both qualitatively and quantitatively using different modeling approaches.

SimGen: A General Simulation Method for Large Systems

SimGen is a stand-alone computer program that reads a script of commands to represent complex macromolecules, including proteins and nucleic acids, in a structural hierarchy that can then be viewed using an integral graphical viewer or animated through a high-level application programming interface in C++. Structural levels in the hierarchy range from α-carbon or phosphate backbones through secondary structure to domains, molecules, and multimers with each level represented in an identical data structure that can be manipulated using the application programming interface. Unlike most coarse-grained simulation approaches, the higher-level objects represented in SimGen can be soft, allowing the lower-level objects that they contain to interact directly. The default motion simulated by SimGen is a Brownian-like diffusion that can be set to occur across all levels of representation in the hierarchy. Links can also be defined between objects, which, when combined with large high-level random movements, result in an effective search strategy for constraint satisfaction, including structure prediction from predicted pairwise distances. The implementation of SimGen makes use of the hierarchic data structure to avoid unnecessary calculation, especially for collision detection, allowing it to be simultaneously run and viewed on a laptop computer while simulating large systems of over 20,000 objects. It has been used previously to model complex molecular interactions including the motion of a myosin-V dimer "walking" on an actin fibre, RNA stem-loop packing, and the simulation of cell motion and aggregation. Several extensions to this original functionality are described.

Mechanisms for the deamination reaction of 8-oxoguanine catalyzed by 8-oxoguanine deaminase: A combined QM/MM molecular dynamics study

The deamination reaction of 8-oxoguanine (8-oxoG) catalyzed by 8-oxoguanine deaminase (8-oxoGD) plays a critically important role in the DNA repair activity for oxidative damage. In order to elucidate the complete enzymatic catalysis mechanism at the stages of 8-oxoguanine binding, departure of 2-hydroxy-1H-purine-6,8(7H,9H)-dione from the active site, and formation of 8-oxoxanthine, extensive combined QM(PM3)/MM molecular dynamics simulations have been performed. Computations show that the rate-limiting step corresponds to the nucleophilic attack from zinc-coordinate hydroxide group to free 8-oxoguanine. Through conformational analyses, we demonstrate that Trp115, Trp123 and Leu119 connect to O8@8-oxoguanine with hydrogen bonds, and we suggest that mutations of tryptophan (115 and 123) to histidine or phenylalanine and mutation of leucine (119) to alanine could potentially lead to a mutant with enhanced activity. On this ground, a proton transfer mechanism for the formation of 8-oxoxanthine was further discussed. Both Glu218 and water molecule could be used as proton shuttles, and water molecule plays a major role in proton transfer in substrate. On the other hand, comparative simulations on the deamination of guanine and isocytosine reveal that, for the helping of hydrogen bonds between O8@8-oxoguanine and enzyme, O8@8-oxoguanine is the fastest to be deaminated among the three substrates which are also supported by the experimental kinetic constants.

A multi-resolution model to capture both global fluctuations of an enzyme and molecular recognition in the ligand-binding site

In multi-resolution simulations, different system components are simultaneously modeled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of ligand binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modeled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity by exploring the reproduction of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function. Proteins 2016; 84:1902-1913. © 2016 Wiley Periodicals, Inc.

Amide Rotation Hindrance Predicts Proteolytic Resistance of Cystine-Knot Peptides

Cystine-knot peptides have remarkable stability against protease degradation and are attractive scaffolds for peptide-based therapeutic and diagnostic agents. In this work, by studying the hydrolysis reaction of a cystine-knot inhibitor MCTI-A and its variants with ab initio QM/MM molecular dynamics simulations, we have elucidated an amide rotation hindrance mechanism for proteolysis resistance: The proteolysis of MCTI-A is retarded due to the higher free energy cost during the rotation of NH group around scissile peptide bond at the tetrahedral intermediate of acylation, and covalent constraint provided by disulfide bonds is the key factor to hinder this rotation. A nearly linear correlation has been revealed between free energy barriers of the peptide hydrolysis reaction and the amide rotation free energy changes at the protease-peptide Michaelis complex state. This suggests that amide rotation hindrance could be one useful feature to estimate peptide proteolysis stability.

Quantum Mechanics/Molecular Mechanics Method Combined with Hybrid All-Atom and Coarse-Grained Model: Theory and Application on Redox Potential Calculations

We developed a new multiresolution method that spans three levels of resolution with quantum mechanical, atomistic molecular mechanical, and coarse-grained models. The resolution-adapted all-atom and coarse-grained water model, in which an all-atom structural description of the entire system is maintained during the simulations, is combined with the ab initio quantum mechanics and molecular mechanics method. We apply this model to calculate the redox potentials of the aqueous ruthenium and iron complexes by using the fractional number of electrons approach and thermodynamic integration simulations. The redox potentials are recovered in excellent accordance with the experimental data. The speed-up of the hybrid all-atom and coarse-grained water model renders it computationally more attractive. The accuracy depends on the hybrid all-atom and coarse-grained water model used in the combined quantum mechanical and molecular mechanical method. We have used another multiresolution model, in which an atomic-level layer of water molecules around redox center is solvated in supramolecular coarse-grained waters for the redox potential calculations. Compared with the experimental data, this alternative multilayer model leads to less accurate results when used with the coarse-grained polarizable MARTINI water or big multipole water model for the coarse-grained layer.

Reduction of α,β-Unsaturated Ketones by Old Yellow Enzymes: Mechanistic Insights from Quantum Mechanics/Molecular Mechanics Calculations

Enoate reductases catalyze the reduction of activated C═C bonds with high enantioselectivity. The oxidative half-reaction, which involves the addition of a hydride and a proton to opposite faces of the C═C bond, has been studied for the first time by hybrid quantum mechanics/molecular mechanics (QM/MM). The reduction of 2-cyclohexen-1-one by YqjM from Bacillus subtilis was selected as the model system. Two-dimensional QM/MM (B3LYP-D/OPLS2005) reaction pathways suggest that the hydride and proton are added as distinct steps, with the former step preceding the latter. Furthermore, we present interesting insights into the reactivity of this enzyme, including the weak binding of the substrate in the active site, the role of the two active site histidine residues for polarization of the substrate C═O bond, structural details of the transition states to hydride and proton transfer, and the role of Tyr196 as proton donor. The information presented here will be useful for the future design of enantioselective YqjM mutants for other substrates.

Flexible Boundaries for Multiresolution Solvation: An Algorithm for Spatial Multiscaling in Molecular Dynamics Simulations

An algorithm is proposed for performing molecular dynamics (MD) simulations of a biomolecular solute represented at atomistic resolution surrounded by a surface layer of atomistic fine-grained (FG) solvent molecules within a bulk represented by coarse-grained (CG) solvent beads. The method, called flexible boundaries for multiresolution solvation (FBMS), is based on: (i) a three-region layering of the solvent around the solute, involving an FG layer surrounded by a mixed FG-CG buffer layer, itself surrounded by a bulk CG region; (ii) a definition of the layer boundary that relies on an effective distance to the solute surface and is thus adapted to the shape of the solute as well as adjusts to its conformational changes. The effective surface distance is defined by inverse-nth power averaging over the distances to all non-hydrogen solute atoms, and the layering is enforced by means of half-harmonic distance restraints, attractive for the FG molecules and repulsive for the CG beads. A restraint-free region at intermediate distances enables the formation of the buffer layer, where the FG and CG solvents can mix freely. The algorithm is tested and validated using the GROMOS force field and the associated FG (SPC) and CG (polarizable CGW) water models. The test systems include pure-water systems, where one FG molecule plays the role of a solute, and a deca-alanine peptide with two widely different solute shapes considered, α-helical and fully extended. In particular, as the peptide unfolds, the number of FG molecules required to fill its close-range solvation layer increases, with the additional molecules being provided by the buffer layer. Further validation involves simulations of four proteins in multiresolution FG/CG mixtures. The resulting structural, energetic, and solvation properties are found to be similar to those observed in corresponding pure FG simulations.

Understanding DNA under oxidative stress and sensitization: the role of molecular modeling

DNA is constantly exposed to damaging threats coming from oxidative stress, i.e., from the presence of free radicals and reactive oxygen species. Sensitization from exogenous and endogenous compounds that strongly enhance the frequency of light-induced lesions also plays an important role. The experimental determination of DNA lesions, though a difficult subject, is somehow well established and allows to elucidate even extremely rare DNA lesions. In parallel, molecular modeling has become fundamental to clearly understand the fine mechanisms related to DNA defects induction. Indeed, it offers an unprecedented possibility to get access to an atomistic or even electronic resolution. Ab initio molecular dynamics may also describe the time-evolution of the molecular system and its reactivity. Yet the modeling of DNA (photo-)reactions does necessitate elaborate multi-scale methodologies to tackle a damage induction reactivity that takes place in a complex environment. The double-stranded DNA environment is first characterized by a very high flexibility, but also a strongly inhomogeneous electrostatic embedding. Additionally, one aims at capturing more subtle effects, such as the sequence selectivity which is of critical important for DNA damage. The structure and dynamics of the DNA/sensitizers complexes, as well as the photo-induced electron- and energy-transfer phenomena taking place upon sensitization, should be carefully modeled. Finally the factors inducing different repair ratios for different lesions should also be rationalized. In this review we will critically analyze the different computational strategies used to model DNA lesions. A clear picture of the complex interplay between reactivity and structural factors will be sketched. The use of proper multi-scale modeling leads to the in-depth comprehension of DNA lesions mechanisms and also to the rational design of new chemo-therapeutic agents.

Lipid insertion domain unfolding regulates protein orientational transition behavior in a lipid bilayer

We have used coarse-grained (CG) and united atom (UA) molecular dynamics simulations to explore the mechanisms of protein orientational transition of a model peptide (Aβ42) in a phosphatidylcholine/cholesterol (PC/CHO) lipid bilayer. We started with an inserted state of Aβ42 containing a folded (I) or unfolded (II) K28-A42 lipid insertion domain (LID), which was stabilized by the K28-snorkeling and A42-anchoring to the PC polar groups in the lipid bilayer. After a UA-to-CG transformation and a 1000ns-CG simulation for enhancing the sampling of protein orientations, we discovered two transitions: I-to-"deep inserted" state with disrupted K28-snorkeling and II-to-"deep surface" state with disrupted A42-anchoring. The new states remained stable after a CG-to-UA transformation and a 200ns-UA simulation relaxation. Significant changes in the cholesterol-binding domain of Aβ42 and protein-induced membrane disruptions were evident after the transitions. We propose that the conformation of the LID regulates protein orientational transitions in the lipid membrane.

Theoretical and computational investigations of nanoparticle-biomembrane interactions in cellular delivery

With the rapid development of nanotechnology, nanoparticles have been widely used in many applications such as phototherapy, cell imaging, and drug/gene delivery. A better understanding of how nanoparticles interact with bio-system (especially cells) is of great importance for their potential biomedical applications. In this review, the current status and perspective of theoretical and computational investigations is presented on the nanoparticle-biomembrane interactions in cellular delivery. In particular, the determining parameters (including the properties of nanoparticles, cell membranes and environments) that govern the cellular uptake of nanoparticles (direct penetration and endocytosis) are discussed. Further, some special attention is paid to their interactions beyond the translocation of nanoparticles across membranes (e.g., nanoparticles escaping from endosome and entering into nucleus). Finally, a summary is given, and the challenging problems of this field in the future are identified.

Mechanistic insights into a classic wonder drug--aspirin

Aspirin, one of the oldest and most common anti-inflammatory agents, has recently been shown to reduce cancer risks. The principal pharmacological effects of aspirin are known to arise from its covalent modification of cyclooxygenase-2 (COX-2) through acetylation of Ser530, but the detailed mechanism of its biochemical action and specificity remains to be elucidated. In this work, we have filled this gap by employing a state-of-the-art computational approach, Born-Oppenheimer molecular dynamics simulations with ab initio quantum mechanical/molecular mechanical potential and umbrella sampling. Our studies have characterized a substrate-assisted inhibition mechanism for aspirin acetylating COX: it proceeds in two successive stages with a metastable tetrahedral intermediate, in which the carboxyl group of aspirin serves as the general base. The computational results confirmed that aspirin would be 10-100 times more potent against COX-1 than against COX-2, and revealed that this inhibition specificity between the two COX isoforms can be attributed mainly to the difference in kinetics rate of the covalent inhibition reaction, not the aspirin-binding step. The structural origin of this differential inhibition of the COX enzymes by aspirin has also been elucidated.