Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential

To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

Disruption of an Active Site Network Leads to Activation of C2α-Lactylthiamin Diphosphate on the Antibacterial Target 1-Deoxy-d-xylulose-5-phosphate Synthase

The bacterial metabolic enzyme 1-deoxy-d-xylulose-5-phosphate synthase (DXPS) catalyzes the thiamin diphosphate (ThDP)-dependent formation of DXP from pyruvate and d-glyceraldehyde-3-phosphate (d-GAP). DXP is an essential bacteria-specific metabolite that feeds into the biosynthesis of isoprenoids, pyridoxal phosphate (PLP), and ThDP. DXPS catalyzes the activation of pyruvate to give the C2α-lactylThDP (LThDP) adduct that is long-lived on DXPS in a closed state in the absence of the cosubstrate. Binding of d-GAP shifts the DXPS-LThDP complex to an open state which coincides with LThDP decarboxylation. This gated mechanism distinguishes DXPS in ThDP enzymology. How LThDP persists on DXPS in the absence of cosubstrate, while other pyruvate decarboxylases readily activate LThDP for decarboxylation, is a long-standing question in the field. We propose that an active site network functions to prevent LThDP activation on DXPS until the cosubstrate binds. Binding of d-GAP coincides with a conformational shift and disrupts the network causing changes in the active site that promote LThDP activation. Here, we show that the substitution of putative network residues, as well as nearby residues believed to contribute to network charge distribution, predictably affects LThDP reactivity. Substitutions predicted to disrupt the network have the effect to activate LThDP for decarboxylation, resulting in CO2 and acetate production. In contrast, a substitution predicted to strengthen the network fails to activate LThDP and has the effect to shift DXPS toward the closed state. Network-disrupting substitutions near the carboxylate of LThDP also have a pronounced effect to shift DXPS to an open state. These results offer initial insights to explain the long-lived LThDP intermediate and its activation through disruption of an active site network, which is unique to DXPS. These findings have important implications for DXPS function in bacteria and its development as an antibacterial target.

Exploring Routes to Enhance the Calculation of Free Energy Differences via Non-Equilibrium Work SQM/MM Switching Simulations Using Hybrid Charge Intermediates between MM and SQM Levels of Theory or Non-Linear Switching Schemes

Non-equilibrium work switching simulations and Jarzynski's equation are a reliable method for computing free energy differences, ΔAlow→high, between two levels of theory, such as a pure molecular mechanical (MM) and a quantum mechanical/molecular mechanical (QM/MM) description of a system of interest. Despite the inherent parallelism, the computational cost of this approach can quickly become very high. This is particularly true for systems where the core region, the part of the system to be described at different levels of theory, is embedded in an environment such as explicit solvent water. We find that even for relatively simple solute-water systems, switching lengths of at least 5 ps are necessary to compute ΔAlow→high reliably. In this study, we investigate two approaches towards an affordable protocol, with an emphasis on keeping the switching length well below 5 ps. Inserting a hybrid charge intermediate state with modified partial charges, which resembles the charge distribution of the desired high level, makes it possible to obtain reliable calculations with 2 ps switches. Attempts using step-wise linear switching paths, on the other hand, did not lead to improvement, i.e., a faster convergence for all systems. To understand these findings, we analyzed the solutes' properties as a function of the partial charges used and the number of water molecules in direct contact with the solute, and studied the time needed for water molecules to reorient themselves upon a change in the solute's charge distribution.

Tuaimenal A, a Meroterpene from the Irish Deep-Sea Soft Coral Duva florida, Displays Inhibition of the SARS-CoV-2 3CLpro Enzyme

Cold water benthic environments are a prolific source of structurally diverse molecules with a range of bioactivities against human disease. Specimens of a previously chemically unexplored soft coral, Duva florida, were collected during a deep-sea cruise that sampled marine invertebrates along the Irish continental margin in 2018. Tuaimenal A (1), a cyclized merosesquiterpenoid representing a new carbon scaffold with a highly substituted chromene core, was discovered through exploration of the soft coral secondary metabolome via NMR-guided fractionation. The absolute configuration was determined through vibrational circular dichroism. Functional biochemical assays and in silico docking experiments found tuaimenal A selectively inhibits the viral main protease (3CLpro) of SARS-CoV-2.

Obtaining QM/MM binding free energies in the SAMPL8 drugs of abuse challenge: indirect approaches

Accurately predicting free energy differences is essential in realizing the full potential of rational drug design. Unfortunately, high levels of accuracy often require computationally expensive QM/MM Hamiltonians. Fortuitously, the cost of employing QM/MM approaches in rigorous free energy simulation can be reduced through the use of the so-called “indirect” approach to QM/MM free energies, in which the need for QM/MM simulations is avoided via a QM/MM “correction” at the classical endpoints of interest. Herein, we focus on the computation of QM/MM binding free energies in the context of the SAMPL8 Drugs of Abuse host–guest challenge. Of the 5 QM/MM correction coupled with force-matching submissions, PM6-D3H4/MM ranked submission proved the best overall QM/MM entry, with an RMSE from experimental results of 2.43 kcal/mol (best in ranked submissions), a Pearson’s correlation of 0.78 (second-best in ranked submissions), and a Kendall \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau$$\end{document}τ correlation of 0.52 (best in ranked submissions).

Optimizing the Calculation of Free Energy Differences in Nonequilibrium Work SQM/MM Switching Simulations

A key step during indirect alchemical free energy simulations using quantum mechanical/molecular mechanical (QM/MM) hybrid potential energy functions is the calculation of the free energy difference ΔAlow→high between the low level (e.g., pure MM) and the high level of theory (QM/MM). A reliable approach uses nonequilibrium work (NEW) switching simulations in combination with Jarzynski's equation; however, it is computationally expensive. In this study, we investigate whether it is more efficient to use more shorter switches or fewer but longer switches. We compare results obtained with various protocols to reference free energy differences calculated with Crooks' equation. The central finding is that fewer longer switches give better converged results. As few as 200 sufficiently long switches lead to ΔAlow→high values in good agreement with the reference results. This optimized protocol reduces the computational cost by a factor of 40 compared to earlier work. We also describe two tools/ways of analyzing the raw data to detect sources of poor convergence. Specifically, we find it helpful to analyze the raw data (work values from the NEW switching simulations) in a quasi-time series-like manner. Principal component analysis helps to detect cases where one or more conformational degrees of freedom are different at the low and high level of theory.

CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.

Not Drug-like, but Like Drugs: Cnidaria Natural Products

Phylum Cnidaria has been an excellent source of natural products, with thousands of metabolites identified. Many of these have not been screened in bioassays. The aim of this study was to explore the potential of 5600 Cnidaria natural products (after excluding those known to derive from microbial symbionts), using a systematic approach based on chemical space, drug-likeness, predicted toxicity, and virtual screens. Previous drug-likeness measures: the rule-of-five, quantitative estimate of drug-likeness (QED), and relative drug likelihoods (RDL) are based on a relatively small number of molecular properties. We augmented this approach using reference drug and toxin data sets defined for 51 predicted molecular properties. Cnidaria natural products overlap with drugs and toxins in this chemical space, although a multivariate test suggests that there are some differences between the groups. In terms of the established drug-likeness measures, Cnidaria natural products have generally lower QED and RDL scores than drugs, with a higher prevalence of metabolites that exceed at least one rule-of-five threshold. An index of drug-likeness that includes predicted toxicity (ADMET-score), however, found that Cnidaria natural products were more favourable than drugs. A measure of the distance of individual Cnidaria natural products to the centre of the drug distribution in multivariate chemical space was related to RDL, ADMET-score, and the number of rule-of-five exceptions. This multivariate similarity measure was negatively correlated with the QED score for the same metabolite, suggesting that the different approaches capture different aspects of the drug-likeness of individual metabolites. The contrasting of different drug similarity measures can help summarise the range of drug potential in the Cnidaria natural product data set. The most favourable metabolites were around 210-265 Da, quite often sesquiterpenes, with a moderate degree of complexity. Virtual screening against cancer-relevant targets found wide evidence of affinities, with Glide scores <-7 in 19% of the Cnidaria natural products.

Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

Enantiomerization of Axially Chiral Biphenyls: Polarizable MD Simulations in Water and Butylmethylether

In this study, we investigate the influence of chiral and achiral cations on the enantiomerization of biphenylic anions in n-butylmethylether and water. In addition to the impact of the cations and solvent molecules on the free energy profile of rotation, we also explore if chirality transfer between a chiral cation and the biphenylic anion is possible, i.e., if pairing with a chiral cation can energetically favour one conformer of the anion via diastereomeric complex formation. The quantum-mechanical calculations are accompanied by polarizable MD simulations using umbrella sampling to study the impact of solvents of different polarity in more detail. We also discuss how accurate polarizable force fields for biphenylic anions can be constructed from quantum-mechanical reference data.

Hierarchical Markov State Model Building to Describe Molecular Processes

Markov state models can describe ensembles of pathways via kinetic networks but are difficult to create when large free-energy barriers limit unbiased sampling. Chain-of-states simulations allow sampling over large free-energy barriers but are often constructed using a single pathway that is unlikely to thermodynamically average over orthogonal degrees of freedom in complex systems. Here, we combine the advantages of these two approaches in the form of a Markov state model of Markov state models, which we call a Hierarchical Markov state model. In this approach, independent Markov models are constructed in regions of configuration space that are locally well sampled but are separated by large free-energy barriers from other regions. A string method is used to construct an ensemble of pathways connecting the states of these different local Markov models, and the rate through each pathway is then estimated. These rates are then combined with the rate information from the local Markov models in a master equation to predict global rates, fluxes, and populations. By applying this hierarchical approach to tractable systems, a toy potential and dipeptides, we demonstrate that it is more accurate than the conventional single-pathway description. The advantages of this approach are that it (i) is more realistic than the conventional chain-of-states approach, as an ensemble of pathways rather than a single pathway is used to describe processes in high-dimensional systems, and (ii) it resolves the issue of poor sampling in Markov State model building when large free-energy barriers are present. The divide-and-conquer strategy inherent to this approach should make this procedure straightforward to apply to more complex systems.

Use of Interaction Energies in QM/MM Free Energy Simulations

The use of the most accurate (i.e., QM or QM/MM) levels of theory for free energy simulations (FES) is typically not possible. Primarily, this is because the computational cost associated with the extensive configurational sampling needed for converging FES is prohibitive. To ensure the feasibility of QM-based FES, the "indirect" approach is generally taken, necessitating a free energy calculation between the MM and QM/MM potential energy surfaces. Ideally, this step is performed with standard free energy perturbation (Zwanzig's equation) as it only requires simulations be carried out at the low level of theory; however, work from several groups over the past few years has conclusively shown that Zwanzig's equation is ill-suited to this task. As such, many approximations have arisen to mitigate difficulties with Zwanzig's equation. One particularly popular notion is that the convergence of Zwanzig's equation can be improved by using interaction energy differences instead of total energy differences. Although problematic numerical fluctuations (a major problem when using Zwanzig's equation) are indeed reduced, our results and analysis demonstrate that this "interaction energy approximation" (IEA) is theoretically incorrect, and the implicit approximation invoked is spurious at best. Herein, we demonstrate this via solvation free energy calculations using IEA from two different low levels of theory to the same target high level. Results from this proof-of-concept consistently yield the wrong results, deviating by ∼1.5 kcal/mol from the rigorously obtained value.

Abstract 4826: Enhancement of PTEN activity via peptidomimetics

Compromised PTEN function is associated with multiple cancers. As per the continuum model, variable degree of PTEN inactivation drives distinct cancer phenotypes. Loss of PTEN activity, via genomic/non-genomic mechanisms, leads to enhanced oncogenic PI3K signaling. While kinase inhibitors have proved effective in the clinic, they are increasingly met with off-target effects and therapy-resistance due to compensatory feedback mechanisms. Although PTEN restoration therapy has proved promising in experimental models; implementation of these therapies in the clinic remains challenging. To fill this therapy gap, we have targeted endogenous PTEN for activation via peptidomimetics, mitigating PI3K signaling. Our selected peptidomimetics reduced cell proliferation, migration and cell cycle activity after treatment of non-small cell lung cancer cells expressing endogenous PTEN. Computational studies utilizing PTEN crystal structures revealed a binding site at the interface of the Phosphatase Domain (PD) and C2 Domain (C2D). Induced fit docking indicated that a unique functional group is responsible for peptidomimetic-mediated enhancement of PTEN activity. Molecular dynamics analysis revealed energetically favorable binding of our lead peptidomimetic at the PD/C2D interface, which allosterically modulated the conformation of the active site. Binding of our peptidomimetic altered the active site orientation of PIP3, increasing its binding affinity. In summary, we have developed the first-known direct PTEN activators and analyzed their effects in suppressing oncogenic activities in lung cancer. Refinement of our peptidomimetics, complemented with in vivo studies, will provide a novel clinical rationale to reduce doses of standard therapy and their associated toxicities, decreasing morbidity and mortality observed in the clinic.

Citation Format: Emily Palumbo, Peng Teng, Prerna Malaney, Jacob Wilson, Fiona Kearns, Michael T. Kemp, Zhi Tian, Vladimir Uversky, Diane Allen-Gipson, Yu Chen, H. Lee Woodcock, Jianfeng Cai, Vrushank Dave. Enhancement of PTEN activity via peptidomimetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4826.

Modeling Boronic Acid Based Fluorescent Saccharide Sensors: Computational Investigation of d-Fructose Binding to Dimethylaminomethylphenylboronic Acid

Designing organic saccharide sensors for use in aqueous solution is a nontrivial endeavor. Incorporation of hydrogen bonding groups on a sensor's receptor unit to target saccharides is an obvious strategy but not one that is likely to ensure analyte-receptor interactions over analyte-solvent or receptor-solvent interactions. Phenylboronic acids are known to reversibly and covalently bind saccharides (diols in general) with highly selective affinity in aqueous solution. Therefore, recent work has sought to design such sensors and understand their mechanism for allowing fluorescence with bound saccharides. In past work, binding orientations of several saccharides were determined to dimethylaminomethylphenylboronic acid (DMPBA) receptors with an anthracene fluorophore; however, the binding orientation of d-fructose to such a sensor could not be determined. In this work, we investigate the potential binding modes by generating 20 possible bidentate and six possible tridentate modes between fructose and DMPBA, a simplified receptor model. Gas phase and implicit solvent geometry optimizations, with a myriad functional/basis set pairs, were carried out to identify the lowest energy bidentate and tridentate binding modes of d-fructose to DMPBA. An interesting hydrogen transfer was observed during selected bidentate gas phase optimizations; this transfer suggests a strong sharing of the hydrogen atom between the boronate hydroxyl and amine nitrogen.

The Good, the Bad, and the Ugly: "HiPen", a New Dataset for Validating (S)QM/MM Free Energy Simulations

Indirect (S)QM/MM free energy simulations (FES) are vital to efficiently incorporating sufficient sampling and accurate (QM) energetic evaluations when estimating free energies of practical/experimental interest. Connecting between levels of theory, i.e., calculating Δ A l o w → h i g h , remains to be the most challenging step within an indirect FES protocol. To improve calculations of Δ A l o w → h i g h , we must: (1) compare the performance of all FES methods currently available; and (2) compile and maintain datasets of Δ A l o w → h i g h calculated for a wide-variety of molecules so that future practitioners may replicate or improve upon the current state-of-the-art. Towards these two aims, we introduce a new dataset, "HiPen", which tabulates Δ A g a s M M → 3 o b (the free energy associated with switching from an M M to an S C C - D F T B molecular description using the 3ob parameter set in gas phase), calculated for 22 drug-like small molecules. We compare the calculation of this value using free energy perturbation, Bennett's acceptance ratio, Jarzynski's equation, and Crooks' equation. We also predict the reliability of each calculated Δ A g a s M M → 3 o b by evaluating several convergence criteria including sample size hysteresis, overlap statistics, and bias metric ( Π ). Within the total dataset, three distinct categories of molecules emerge: the "good" molecules, for which we can obtain converged Δ A g a s M M → 3 o b using Jarzynski's equation; "bad" molecules which require Crooks' equation to obtain a converged Δ A g a s M M → 3 o b ; and "ugly" molecules for which we cannot obtain reliably converged Δ A g a s M M → 3 o b with either Jarzynski's or Crooks' equations. We discuss, in depth, results from several example molecules in each of these categories and describe how dihedral discrepancies between levels of theory cause convergence failures even for these gas phase free energy simulations.

Accelerating QM/MM Free Energy Computations via Intramolecular Force Matching

The calculation of free energy differences between levels of theory has numerous potential pitfalls. Chief among them is the lack of overlap, i.e., ensembles generated at one level of theory (e.g., "low") not being good approximations of ensembles at the other (e.g., "high"). Numerous strategies have been devised to mitigate this issue. However, the most straightforward approach is to ensure that the "low" level ensemble more closely resembles that of the "high". Ideally, this is done without increasing computational cost. Herein, we demonstrate that by reparametrizing classical intramolecular potentials to reproduce high level forces (i.e., force matching) configurational overlap between a "low" (i.e., classical) and "high" (i.e., quantum) level can be significantly improved. This procedure is validated on two test cases and results in vastly improved convergence of free energy simulations.

Force matching as a stepping stone to QM/MM CB[8] host/guest binding free energies: a SAMPL6 cautionary tale

Use of quantum mechanical/molecular mechanical (QM/MM) methods in binding free energy calculations, particularly in the SAMPL challenge, often fail to achieve improvement over standard additive (MM) force fields. Frequently, the implementation is through use of reference potentials, or the so-called "indirect approach", and inherently relies on sufficient overlap existing between MM and QM/MM configurational spaces. This overlap is generally poor, particularly for the use of free energy perturbation to perform the MM to QM/MM free energy correction at the end states of interest (e.g., bound and unbound states). However, by utilizing MM parameters that best reproduce forces obtained at the desired QM level of theory, it is possible to lessen the configurational disparity between MM and QM/MM. To this end, we sought to use force matching to generate MM parameters for the SAMPL6 CB[8] host-guest binding challenge, classically compute binding free energies, and apply energetic end state corrections to obtain QM/MM binding free energy differences. For the standard set of 11 molecules and the bonus set (including three additional challenge molecules), error statistics, such as the root mean square deviation (RMSE) were moderately poor (5.5 and 5.4 kcal/mol). Correlation statistics, however, were in the top two for both standard and bonus set submissions ([Formula: see text] of 0.42 and 0.26, [Formula: see text] of 0.64 and 0.47 respectively). High RMSE and moderate correlation strongly indicated the presence of systematic error. Identifiable issues were ameliorated for two of the guest molecules, resulting in a reduction of error and pointing to strong prospects for the future use of this methodology.

Origins of the Mechanochemical Coupling of Peptide Bond Formation to Protein Synthesis

Mechanical forces acting on the ribosome can alter the speed of protein synthesis, indicating that mechanochemistry can contribute to translation control of gene expression. The naturally occurring sources of these mechanical forces, the mechanism by which they are transmitted 10 nm to the ribosome's catalytic core, and how they influence peptide bond formation rates are largely unknown. Here, we identify a new source of mechanical force acting on the ribosome by using in situ experimental measurements of changes in nascent-chain extension in the exit tunnel in conjunction with all-atom and coarse-grained computer simulations. We demonstrate that when the number of residues composing a nascent chain increases, its unstructured segments outside the ribosome exit tunnel generate piconewtons of force that are fully transmitted to the ribosome's P-site. The route of force transmission is shown to be through the nascent polypetide's backbone, not through the wall of the ribosome's exit tunnel. Utilizing quantum mechanical calculations we find that a consequence of such a pulling force is to decrease the transition state free energy barrier to peptide bond formation, indicating that the elongation of a nascent chain can accelerate translation. Since nascent protein segments can start out as largely unfolded structural ensembles, these results suggest a pulling force is present during protein synthesis that can modulate translation speed. The mechanism of force transmission we have identified and its consequences for peptide bond formation should be relevant regardless of the source of the pulling force.

Mechanistic Studies of 1-Deoxy-D-Xylulose-5-Phosphate Synthase from Deinococcus radiodurans

The non-mevalonate dependent (NMVA) pathway for the biosynthesis of isopentenyl pyrophosphate and dimethylallyl pyrophosphate is the sole source of these terpenoids for the production of isoprenoids in the apicomplexan parasites, in many eubacteria, and in plants. The absence of this pathway in higher organisms has opened a new platform for the development of novel antibiotics and anti-malarials. The enzyme catalyzing the first step of the NMVA pathway is 1-deoxy-D-xylulose-5-phosphate synthase (DXPS). DXPS catalyzes the thiamine pyrophosphate- and Mg (II)-dependent conjugation of pyruvate and D-glyceraldehyde-3-phosphate to form 1-deoxy-D-xylulose-5-phosphate and CO₂. The kinetic mechanism of DXPS from Deinococcus radiodurans most consistent with our data is random sequential as shown using a combination of kinetic analysis and product and dead-end inhibition studies. The role of active site amino acids, identified by sequence alignment to other DXPS proteins, was probed by constructing and analyzing the catalytic efficacy of a set of targeted site-directed mutants.

Thiamin Diphosphate Activation in 1-Deoxy-d-xylulose 5-Phosphate Synthase: Insights into the Mechanism and Underlying Intermolecular Interactions

1-Deoxy-d-xylulose 5-phosphate synthase (DXS) is a thiamin diphosphate (TDP) dependent enzyme that marks the beginning of the methylerythritol 4-phosphate isoprenoid biosynthesis pathway. The mechanism of action for DXS is still poorly understood and begins with the formation of a thiazolium ylide. This TDP activation step is thought to proceed through an intramolecular deprotonation by the 4'-aminopyrimidine ring of TDP; however, this step would occur only after an initial deprotonation of its own 4'-amino group. The mechanism of the initial deprotonation has been hypothesized, by analogy to transketolases, to occur via a histidine or an active site water molecule. Results from hybrid quantum mechanical/molecular mechanical (QM/MM) reaction path calculations reveal an ∼10 kcal/mol difference in transition state energies, favoring a water mediated mechanism over direct deprotonation by histidine. This difference was determined to be largely governed by electrostatic changes induced by conformational variations in the active site. Additionally, mutagenesis studies reveal DXS to be an evolutionarily resilient enzyme. Particularly, we hypothesize that residues H82 and H304 may act in a compensatory fashion if the other is lost due to mutation. Further, nucleus-independent chemical shifts (NICSs) and aromatic stabilization energy (ASE) calculations suggest that reduction in TDP aromaticity also serves as a factor for regulating ylide formation and controlling reactivity.

Methods for Efficiently and Accurately Computing Quantum Mechanical Free Energies for Enzyme Catalysis

Enzyme activity is inherently linked to free energies of transition states, ligand binding, protonation/deprotonation, etc.; these free energies, and thus enzyme function, can be affected by residue mutations, allosterically induced conformational changes, and much more. Therefore, being able to predict free energies associated with enzymatic processes is critical to understanding and predicting their function. Free energy simulation (FES) has historically been a computational challenge as it requires both the accurate description of inter- and intramolecular interactions and adequate sampling of all relevant conformational degrees of freedom. The hybrid quantum mechanical molecular mechanical (QM/MM) framework is the current tool of choice when accurate computations of macromolecular systems are essential. Unfortunately, robust and efficient approaches that employ the high levels of computational theory needed to accurately describe many reactive processes (ie, ab initio, DFT), while also including explicit solvation effects and accounting for extensive conformational sampling are essentially nonexistent. In this chapter, we will give a brief overview of two recently developed methods that mitigate several major challenges associated with QM/MM FES: the QM non-Boltzmann Bennett's acceptance ratio method and the QM nonequilibrium work method. We will also describe usage of these methods to calculate free energies associated with (1) relative properties and (2) along reaction paths, using simple test cases with relevance to enzymes examples.

Identification of Ecdysone Hormone Receptor Agonists as a Therapeutic Approach for Treating Filarial Infections

Background

A homologue of the ecdysone receptor has previously been identified in human filarial parasites. As the ecdysone receptor is not found in vertebrates, it and the regulatory pathways it controls represent attractive potential chemotherapeutic targets.

Methodology/ Principal Findings

Administration of 20-hydroxyecdysone to gerbils infected with B. malayi infective larvae disrupted their development to adult stage parasites. A stable mammalian cell line was created incorporating the B. malayi ecdysone receptor ligand-binding domain, its heterodimer partner and a secreted luciferase reporter in HEK293 cells. This was employed to screen a series of ecdysone agonist, identifying seven agonists active at sub-micromolar concentrations. A B. malayi ecdysone receptor ligand-binding domain was developed and used to study the ligand-receptor interactions of these agonists. An excellent correlation between the virtual screening results and the screening assay was observed. Based on both of these approaches, steroidal ecdysone agonists and the diacylhydrazine family of compounds were identified as a fruitful source of potential receptor agonists. In further confirmation of the modeling and screening results, Ponasterone A and Muristerone A, two compounds predicted to be strong ecdysone agonists stimulated expulsion of microfilaria and immature stages from adult parasites.

Conclusions

The studies validate the potential of the B. malayi ecdysone receptor as a drug target and provide a means to rapidly evaluate compounds for development of a new class of drugs against the human filarial parasites.

The human filarial parasites are the causative agents of two neglected tropical diseases targeted for elimination by the international community. The current elimination programs rely upon the mass distribution of a limited number of drugs, leaving the programs open to failure in the event that resistance develops. Thus, there is a critical need for novel chemotherapeutic agents to supplement the current arsenal. The filarial parasites are ecdysozoans, whose developmental processes are controlled by a master regulator, the ecdysone receptor. Here we validate the potential of the filarial ecdysone receptor as a chemotherapeutic target and report the development of high throughput and virtual screening assays that may be used to compounds that target it.

The conserved active site histidine-glutamate pair of ferrochelatase coordinately catalyzes porphyrin metalation

Ferrochelatase catalyzes the insertion of ferrous iron into protoporphyrin IX to generate heme. Despite recent research on the reaction mechanism of ferrochelatase, the precise roles and localization of individual active site residues in catalysis, particularly those involved in the insertion of the ferrous iron into the protoporphyrin IX substrate, remain controversial. One outstanding question is from which side of the macrocycle of the bound porphyin substrate is the ferrous iron substrate inserted. Pre-steady state kinetic experiments done under single-turnover conditions conclusively demonstrate that metal ion insertion is pH-dependent, and that the conserved active site His-Glu pair coordinately catalyzes the metal ion insertion reaction. Further, p[Formula: see text] calculations and molecular dynamic simulations indicate that the active site His is deprotonated and the protonation state of the Glu relates to the conformational state of ferrochelatase. Specifically, the conserved Glu in the open conformation of ferrochelatase is deprotonated, while it remains protonated in the closed conformation. These findings support not only the role of the His-Glu pair in catalyzing metal ion insertion, as these residues need to be deprotonated to bind the incoming metal ion, but also the importance of the relationship between the protonation state of the Glu residue and the conformation of ferrochelatase. Finally, the results of this study are consistent with our previous proposal that the unwinding of the [Formula: see text]-helix, the major structural determinant of the closed to open conformational transition in ferrochelatase, is associated with the Glu residue binding the Fe[Formula: see text] substrate from a mitochondrial Fe[Formula: see text] donor.

Computation of Hydration Free Energies Using the Multiple Environment Single System Quantum Mechanical/Molecular Mechanical Method

A recently developed MESS-E-QM/MM method (multiple-environment single-system quantum mechanical molecular/mechanical calculations with a Roothaan-step extrapolation) is applied to the computation of hydration free energies for the blind SAMPL4 test set and for 12 small molecules. First, free energy simulations are performed with a classical molecular mechanics force field using fixed-geometry solute molecules and explicit TIP3P solvent, and then the non-Boltzmann-Bennett method is employed to compute the QM/MM correction (QM/MM-NBB) to the molecular mechanical hydration free energies. For the SAMPL4 set, MESS-E-QM/MM-NBB corrections to the hydration free energy can be obtained 2 or 3 orders of magnitude faster than fully converged QM/MM-NBB corrections, and, on average, the hydration free energies predicted with MESS-E-QM/MM-NBB fall within 0.10-0.20 kcal/mol of full-converged QM/MM-NBB results. Out of five density functionals (BLYP, B3LYP, PBE0, M06-2X, and ωB97X-D), the BLYP functional is found to be most compatible with the TIP3P solvent model and yields the most accurate hydration free energies against experimental values for solute molecules included in this study.

Use of Nonequilibrium Work Methods to Compute Free Energy Differences Between Molecular Mechanical and Quantum Mechanical Representations of Molecular Systems

Carrying out free energy simulations (FES) using quantum mechanical (QM) Hamiltonians remains an attractive, albeit elusive goal. Renewed efforts in this area have focused on using "indirect" thermodynamic cycles to connect "low level" simulation results to "high level" free energies. The main obstacle to computing converged free energy results between molecular mechanical (MM) and QM (ΔA(MM→QM)), as recently demonstrated by us and others, is differences in the so-called "stiff" degrees of freedom (e.g., bond stretching) between the respective energy surfaces. Herein, we demonstrate that this problem can be efficiently circumvented using nonequilibrium work (NEW) techniques, i.e., Jarzynski's and Crooks' equations. Initial applications of computing ΔA(NEW)(MM→QM), for blocked amino acids alanine and serine as well as to generate butane's potentials of mean force via the indirect QM/MM FES method, showed marked improvement over traditional FES approaches.

Ligand-Induced Proton Transfer and Low-Barrier Hydrogen Bond Revealed by X-ray Crystallography

Ligand binding can change the pKa of protein residues and influence enzyme catalysis. Herein, we report three ultrahigh resolution X-ray crystal structures of CTX-M β-lactamase, directly visualizing protonation state changes along the enzymatic pathway: apo protein at 0.79 Å, precovalent complex with nonelectrophilic ligand at 0.89 Å, and acylation transition state (TS) analogue at 0.84 Å. Binding of the noncovalent ligand induces a proton transfer from the catalytic Ser70 to the negatively charged Glu166, and the formation of a low-barrier hydrogen bond (LBHB) between Ser70 and Lys73, with a length of 2.53 Å and the shared hydrogen equidistant from the heteroatoms. QM/MM reaction path calculations determined the proton transfer barrier to be 1.53 kcal/mol. The LBHB is absent in the other two structures although Glu166 remains neutral in the covalent complex. Our data represents the first X-ray crystallographic example of a hydrogen engaged in an enzymatic LBHB, and demonstrates that desolvation of the active site by ligand binding can provide a protein microenvironment conducive to LBHB formation. It also suggests that LBHBs may contribute to stabilization of the TS in general acid/base catalysis together with other preorganized features of enzyme active sites. These structures reconcile previous experimental results suggesting alternatively Glu166 or Lys73 as the general base for acylation, and underline the importance of considering residue protonation state change when modeling protein-ligand interactions. Additionally, the observation of another LBHB (2.47 Å) between two conserved residues, Asp233 and Asp246, suggests that LBHBs may potentially play a special structural role in proteins.

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms-Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity.

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-dockings" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

Efficiently computing pathway free energies: New approaches based on chain-of-replica and Non-Boltzmann Bennett reweighting schemes

BACKGROUND

Accurately modeling condensed phase processes is one of computation's most difficult challenges. Include the possibility that conformational dynamics may be coupled to chemical reactions, where multiscale (i.e., QM/MM) methods are needed, and this task becomes even more daunting.

METHODS

Free energy simulations (i.e., molecular dynamics), multiscale modeling, and reweighting schemes.

RESULTS

Herein, we present two new approaches for mitigating the aforementioned challenges. The first is a new chain-of-replica method (off-path simulations, OPS) for computing potentials of mean force (PMFs) along an easily defined reaction coordinate. This development is coupled with a new distributed, highly-parallel replica framework (REPDstr) within the CHARMM package. Validation of these new schemes is carried out on two processes that undergo conformational changes. First is the simple torsional rotation of butane, while a much more challenging glycosidic rotation (in vacuo and solvated) is the second. Additionally, a new approach that greatly improves (i.e., possibly an order of magnitude) the efficiency of computing QM/MM PMFs is introduced and compared to standard schemes. Our efforts are grounded in the recently developed method for efficiently computing QM-based free energies (i.e., QM-Non-Boltzmann Bennett, QM-NBB). Again, we validate this new technique by computing the QM/MM PMF of butane's torsional rotation.

CONCLUSIONS

The OPS-REPDstr method is a promising new approach that overcomes many limitations of standard pathway simulations in CHARMM. The combination of QM-NBB with pathway techniques is very promising as it offers significant advantages over current procedures.

GENERAL SIGNIFICANCE

Efficiently computing potentials of mean force is a major, unresolved, area of interest. This article is part of a Special Issue entitled Recent developments of molecular dynamics.

Web-based computational chemistry education with CHARMMing I: Lessons and tutorial

This article describes the development, implementation, and use of web-based “lessons” to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e., interactive step-by-step instructions for performing common molecular simulation tasks, are integrated into the collaboratively developed CHARMM INterface and Graphics (CHARMMing) web user interface (http://www.charmming.org). Several lessons have already been developed with new ones easily added via a provided Python script. In addition to CHARMMing's new lessons functionality, web-based graphical capabilities have been overhauled and are fully compatible with modern mobile web browsers (e.g., phones and tablets), allowing easy integration of these advanced simulation techniques into coursework. Finally, one of the primary objections to web-based systems like CHARMMing has been that “point and click” simulation set-up does little to teach the user about the underlying physics, biology, and computational methods being applied. In response to this criticism, we have developed a freely available tutorial to bridge the gap between graphical simulation setup and the technical knowledge necessary to perform simulations without user interface assistance.

Web-based computational chemistry education with CHARMMing II: Coarse-grained protein folding

A lesson utilizing a coarse-grained (CG) G-like model has been implemented into the CHARMM INterface and Graphics (CHARMMing) web portal (www.charmming.org) to the Chemistry at HARvard Macromolecular Mechanics (CHARMM) molecular simulation package. While widely used to model various biophysical processes, such as protein folding and aggregation, CG models can also serve as an educational tool because they can provide qualitative descriptions of complex biophysical phenomena for a relatively cheap computational cost. As a proof of concept, this lesson demonstrates the construction of a CG model of a small globular protein, its simulation via Langevin dynamics, and the analysis of the resulting data. This lesson makes connections between modern molecular simulation techniques and topics commonly presented in an advanced undergraduate lecture on physical chemistry. It culminates in a straightforward analysis of a short dynamics trajectory of a small fast folding globular protein; we briefly describe the thermodynamic properties that can be calculated from this analysis. The assumptions inherent in the model and the data analysis are laid out in a clear, concise manner, and the techniques used are consistent with those employed by specialists in the field of CG modeling. One of the major tasks in building the G-like model is determining the relative strength of the nonbonded interactions between coarse-grained sites. New functionality has been added to CHARMMing to facilitate this process. The implementation of these features into CHARMMing helps automate many of the tedious aspects of constructing a CG G model. The CG model builder and its accompanying lesson should be a valuable tool to chemistry students, teachers, and modelers in the field.

Web-based computational chemistry education with CHARMMing III: Reduction potentials of electron transfer proteins

A module for fast determination of reduction potentials, E°, of redox-active proteins has been implemented in the CHARMM INterface and Graphics (CHARMMing) web portal (www.charmming.org). The free energy of reduction, which is proportional to E°, is composed of an intrinsic contribution due to the redox site and an environmental contribution due to the protein and solvent. Here, the intrinsic contribution is selected from a library of pre-calculated density functional theory values for each type of redox site and redox couple, while the environmental contribution is calculated from a crystal structure of the protein using Poisson-Boltzmann continuum electrostatics. An accompanying lesson demonstrates a calculation of E°. In this lesson, an ionizable residue in a [4Fe-4S]-protein that causes a pH-dependent E° is identified, and the E° of a mutant that would test the identification is predicted. This demonstration is valuable to both computational chemistry students and researchers interested in predicting sequence determinants of E° for mutagenesis.

Can molecular dynamics and QM/MM solve the penicillin binding protein protonation puzzle?

Benzylpenicillin, a member of the β-lactam antibiotic class, has been widely used to combat bacterial infections since 1947. The general mechanism is well-known: a serine protease enzyme (i.e., DD-peptidase) forms a long lasting intermediate with the lactam ring of the antibiotic known as acylation, effectively preventing biosynthesis of the bacterial cell wall. Despite this overall mechanistic understanding, many details of binding and catalysis are unclear. Specifically, there is ongoing debate about active site protonation states and the role of general acids/bases in the reaction. Herein, a unique combination of MD simulations, QM/MM minimizations, and QM/MM orbital analyses is combined with systematic variation of active site residue protonation states. Critical interactions that maximize the stability of the bound inhibitor are examined and used as metrics. This approach was validated by examining cefoxitin interactions in the CTX-M β-lactamase from E. coli and compared to an ultra high-resolution (0.88 Å) crystal structure. Upon confirming the approach used, an investigation of the preacylated Streptomyces R61 active site with bound benzylpenicillin was performed, varying the protonation states of His298 and Lys65. We concluded that protonated His298 and deprotonated Lys65 are most likely to exist in the R61 active site.

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.

Identification and Characterization of Noncovalent Interactions That Drive Binding and Specificity in DD-Peptidases and β-Lactamases

Bacterial resistance to standard (i.e., β-lactam-based) antibiotics has become a global pandemic. Simultaneously, research into the underlying causes of resistance has slowed substantially, although its importance is universally recognized. Key to unraveling critical details is characterization of the noncovalent interactions that govern binding and specificity (DD-peptidases, antibiotic targets, versus β-lactamases, the evolutionarily derived enzymes that play a major role in resistance) and ultimately resistance as a whole. Herein, we describe a detailed investigation that elicits new chemical insights into these underlying intermolecular interactions. Benzylpenicillin and a novel β-lactam peptidomimetic complexed to the Stremptomyces R61 peptidase are examined using an arsenal of computational techniques: MD simulations, QM/MM calculations, charge perturbation analysis, QM/MM orbital analysis, bioinformatics, flexible receptor/flexible ligand docking, and computational ADME predictions. Several key molecular level interactions are identified that not only shed light onto fundamental resistance mechanisms, but also offer explanations for observed specificity. Specifically, an extended π–π network is elucidated that suggests antibacterial resistance has evolved, in part, due to stabilizing aromatic interactions. Additionally, interactions between the protein and peptidomimetic substrate are identified and characterized. Of particular interest is a water-mediated salt bridge between Asp217 and the positively charged N-terminus of the peptidomimetic, revealing an interaction that may significantly contribute to β-lactam specificity. Finally, interaction information is used to suggest modifications to current β-lactam compounds that should both improve binding and specificity in DD-peptidases and their physiochemical properties.

How does catalase release nitric oxide? A computational structure-activity relationship study

Hydroxyurea (HU) is the only FDA approved medication for treating sickle cell disease in adults. The primary mechanism of action is pharmacological elevation of nitric oxide (NO) levels which induces propagation of fetal hemoglobin. HU is known to undergo redox reactions with heme based enzymes like hemoglobin and catalase to produce NO. However, specific details about the HU based NO release remain unknown. Experimental studies indicate that interaction of HU with human catalase compound I produces NO. Presently, we combine flexible receptor-flexible substrate induced fit docking (IFD) with energy decomposition analyses to examine the atomic level details of a possible key step in the clinical conversion of HU to NO. Substrate binding modes of nine HU analogs with catalase compound I were investigated to determine the essential properties necessary for effective NO release. Three major binding orientations were found that provide insight into the possible reaction mechanisms for producing NO. Further results show that anion/radical intermediates produced as part of these mechanisms would be stabilized by hydrogen bonding interactions from distal residues His75, Asn148, Gln168, and oxoferryl-heme. These details will ideally contribute to both a clearer mechanistic picture and provide insights for future structure based drug design efforts.

Remediation of Cu(II), Ni(II), and Cr(III) ions from simulated wastewater by dendrimer/titania composites

Generation 4 polyamidoamine (PAMAM) dendrimers with ethylenediamine cores (G4-OH) were immobilized on titania (TiO(2)) and examined as novel metal chelation materials. Characterization results indicate both the effective immobilization of dendrimers onto titania and retention of the dendrimer on titania following remediation. The effective remediation of Cu(II), Ni(II), and Cr(III), which are model pollutants commonly found in industrial electroplating wastewater, is demonstrated in this work. Important parameters that influence the efficiency of metal ion removal were investigated; e.g. solution pH, retention time, metal ion concentration, and composite material dosage. Metal ion removal was achieved over a wide metal concentration range within a 1 h equilibration time. Maximum metal ion removal was achieved at pH ≥7 for both Cu(II) and Cr(III), and pH ≥9 for Ni(II). Further, the dendrimer/titania composite materials were even more effective when metal ion mixtures were tested. Specifically, a dramatic increase was observed for Ni(II) chelation when in a mixture was compared to a pure nickel solution. These findings suggest new strategies for improving metal ion removal from industrial wastewater.

Virtual target screening: validation using kinase inhibitors

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.

Unlocking the binding and reaction mechanism of hydroxyurea substrates as biological nitric oxide donors

Hydroxyurea is the only FDA approved treatment of sickle cell disease. It is believed that the primary mechanism of action is associated with the pharmacological elevation of nitric oxide in the blood; however, the exact details of this are still unclear. In the current work, we investigate the atomic level details of this process using a combination of flexible-ligand/flexible-receptor virtual screening coupled with energetic analysis that decomposes interaction energies. Utilizing these methods, we were able to elucidate the previously unknown substrate binding modes of a series of hydroxyurea analogs to hemoglobin and the concomitant structural changes of the enzyme. We identify a backbone carbonyl that forms a hydrogen bond with bound substrates. Our results are consistent with kinetic and electron paramagnetic resonance (EPR) measurements of hydroxyurea-hemoglobin reactions, and a full mechanism is proposed that offers new insights into possibly improving substrate binding and/or reactivity.

Understanding ion sensing in Zn(II) porphyrins: spectroscopic and computational studies of nitrite/nitrate binding

The development of effective sensor elements relies on the ability of a chromophore to bind an analyte selectively and then study the binding through changes in spectroscopic signals. In this report the ability of Zn(II) Tetraphenyl Porphyrin (ZnTPP) to selectively bind nitrite over nitrate ions is examined. The results of Benesi-Hildebrand analysis reveals that ZnTPP binds NO(2)(-) and NO(3)(-) ions with association constants of 739 ± 70 M(-1) and 134 ± 15 M(-1), respectively. Interestingly, addition of a pyridine ligand to the fifth coordination site of the Zn(II) center enhances ion binding with the association constants increasing to 71,300 ± 8,000 M(-1) and 18,900 ± 3,000 M(-1) for nitrite and nitrate, respectively. Density functional theory calculations suggest a binding mechanism through which Zn(II)-porphyrin interactions are disrupted by ligand and base coordination to Zn(II), with Zn(II) having more favorable overlap with nitrite orbitals, which are less delocalized than nitrate orbitals. Overall, these provide new insights into the ability to tune the affinity and selectivity of porphyrin based sensors utilizing electronic factors associated with the central Zn(II) ion.