The global type 2 diabetes epidemic is a major health crisis. Although the microbiome has roles in the onset of insulin resistance (IR), low-grade inflammation and diabetes, the microbial compounds controlling these processes remain to be discovered. Here, we show that the microbial metabolite trimethylamine (TMA) decouples inflammation and IR from diet-induced obesity by inhibiting interleukin-1 receptor-associated kinase 4 (IRAK4), a central kinase in the Toll-like receptor pathway sensing danger signals. TMA blunts TLR4 signalling in primary human hepatocytes and peripheral blood monocytic cells and rescues mouse survival after lipopolysaccharide-induced septic shock. Genetic deletion and chemical inhibition of IRAK4 result in metabolic and immune improvements in high-fat diets. Remarkably, our results suggest that TMA—unlike its liver co-metabolite trimethylamine N-oxide, which is associated with cardiovascular disease—improves immune tone and glycemic control in diet-induced obesity. Altogether, this study supports the emerging role of the kinome in the microbial–mammalian chemical crosstalk. The microbial metabolite trimethylamine (TMA), the precursor of TMAO, which is associated with adverse cardiometabolic outcomes, is shown to have beneficial metabolic and anti-inflammatory effects in the host in the context of obesity.

We present the SSIPTools suite of programs. SSIPTools is a collection of software modules enabling the use of the Surface Site Interaction Point (SSIP) molecular descriptors, used for the modeling of noncovalent interactions in neutral organic molecules. It contains an implementation of the workflow for the generation of the SSIP descriptors, as well as the Functional Group Interaction Profiles (FGIPs) and Solvent Similarity Indexes (SSIs) applications, based on the SSIMPLE (Surface Site Interaction model for the Properties of Liquids at Equilibria) approach.

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.

Dissipative particle dynamics (DPD) is a coarse-grained approach to the simulation of large supramolecular systems, but one limitation has been that the parameters required to describe the noncovalent interactions between beads are not readily accessible. A first-principles computational method has been developed so that bead interaction parameters can be calculated directly from ab initio gas-phase molecular electrostatic potential surfaces of the molecular fragments that represent the beads. A footprinting algorithm converts the molecular electrostatic potential surfaces into a discrete set of surface site interaction points (SSIPs), and these SSIPs are used in the SSIMPLE (surface site interaction model for the properties of liquids at equilibrium) algorithm to calculate the free energies of transfer of one bead into a solution of any other bead. The bead transfer free energies are then converted into the required DPD interaction parameters for all pairwise combinations of different beads. The reliability of the parameters was demonstrated using DPD simulations of a range of alkyl ethoxylate surfactants. The simulations reproduce the experimentally determined values of the critical micelle concentration and mean aggregation number well for all 22 surfactants studied.

Mre11 and Rad50 (M/R) proteins are part of an evolutionarily conserved macromolecular apparatus that maintains genomic integrity through repair pathways. Prior structural studies have revealed that this apparatus is extremely dynamic, displaying flexibility in the long coiled-coil regions of Rad50, a member of the structural maintenance of chromosome (SMC) superfamily of ATPases. However, many details of the mechanics of M/R chromosomal manipulation during DNA-repair events remain unclear. Here, we investigate the properties of the thermostable M/R complex from the archaeon Sulfolobus acidocaldarius using atomic force microscopy (AFM) to understand how this macromolecular machinery orchestrates DNA repair. While previous studies have observed canonical interactions between the globular domains of M/R and DNA, we observe transient interactions between DNA substrates and the Rad50 coiled coils. Fast-scan AFM videos (at 1-2 frames per second) of M/R complexes reveal that these interactions result in manipulation and translocation of the DNA substrates. Our study also shows dramatic and unprecedented ATP-dependent DNA unwinding events by the M/R complex, which extend hundreds of base pairs in length. Supported by molecular dynamic simulations, we propose a model for M/R recognition at DNA breaks in which the Rad50 coiled coils aid movement along DNA substrates until a DNA end is encountered, after which the DNA unwinding activity potentiates the downstream homologous recombination (HR)-mediated DNA repair.

Solvation has profound effects on the behaviour of supramolecular systems, but the effects can be difficult to predict even at a qualitative level. Functional group interaction profiles (FGIPs) provide a simple visual method for understanding how solvent affects the free energy contribution due to a single point interaction, such as a hydrogen bond, between two solute functional groups. A generalised theoretical approach has been developed, which allows calculation of FGIPs for any solvent or solvent mixture, and FGIPs for 300 different solvents have been produced, providing a comprehensive description of solvent effects on non-covalent chemistry. The free energy calculations have been validated using experimental measurements of association constants for hydrogen bonded complexes in multiple solvent mixtures. The calculated FGIPs provide good descriptions of the solvation of polar solutes, solvophobic interactions between non-polar solutes in polar solvents like water, and preferential solvation in solvent mixtures. Applications are explored of the use of FGIPs in drug design, for optimising receptor-ligand interactions, and in enantioselective catalysis for solvent selection to optimise selectivity.

Enteroviruses cause various acute and chronic diseases. The most promising therapeutics for these infections are capsid-binding molecules. These can act against a broad spectrum of enteroviruses, but emerging resistant virus variants threaten their efficacy. All known enterovirus variants with high-level resistance toward capsid-binding molecules have mutations of residues directly involved in the formation of the hydrophobic binding site. This is a first report of substitutions outside the binding pocket causing this type of drug resistance: I1207K and I1207R of the viral capsid protein 1 of coxsackievirus B3. Both substitutions completely abolish the antiviral activity of pleconaril (a capsid-binding molecule) but do not affect viral replication rates in vitro. Molecular dynamics simulations indicate that the resistance mechanism is mediated by a conformational rearrangement of R1095, which is a neighboring residue of 1207 located at the heel of the binding pocket. These insights provide a basis for the design of resistance-breaking inhibitors.

BACKGROUND:The prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository.RESULTS:It is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively.CONCLUSIONS:2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme.

Understanding which physicochemical properties, or property distributions, are favorable for successful design and development of drugs, nutritional supplements, cosmetics, and agrochemicals is of great importance. In this study we have analyzed molecules from three distinct chemical spaces (i) approved drugs, (ii) human metabolites, and (iii) traditional Chinese medicine (TCM) to investigate four aspects determining the disposition of small organic molecules. First, we examined the physicochemical properties of these three classes of molecules and identified characteristic features resulting from their distinctive biological functions. For example, human metabolites and TCM molecules can be larger and more hydrophobic than drugs, which makes them less likely to cross membranes. We then quantified the shifts in physicochemical property space induced by metabolism from a holistic perspective by analyzing a data set of several thousand experimentally observed metabolic trees. Results show how the metabolic system aims to retain nutrients/micronutrients while facilitating a rapid elimination of xenobiotics. In the third part we compared these global shifts with the contributions made by individual metabolic reactions. For better resolution, all reactions were classified into phase I and phase II biotransformations. Interestingly, not all metabolic reactions lead to more hydrophilic molecules. We were able to identify biotransformations leading to an increase of logP by more than one log unit, which could be used for the design of drugs with enhanced efficacy. The study closes with the analysis of the physicochemical properties of metabolites found in the bile, faeces, and urine. Metabolites in the bile can be large and are often negatively charged. Molecules with molecular weight >500 Da are rarely found in the urine, and most of these large molecules are charged phase II conjugates.

Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.

Understanding the metabolic fate of small organic molecules is of fundamental importance to the successful design and development of drugs, nutritional supplements, cosmetics and agrochemicals [1,2]. In the current study we investigated how the products of metabolism differ from their parent molecules by analysing a large dataset of experimentally determined metabolic transformations (Figure ​(Figure1).1). This dataset was split into three specific chemical domains representing approved drug molecules, human metabolites and molecules from traditional Chinese medicines to allow individual analysis. We also quantified the impact of individual Phase I and Phase II metabolic reactions on calculated chemical descriptors using MetaPrint2D [3] and suggest new approaches to utilise metabolism for the design of drugs and cosmetics. The last section of this study investigates the properties of metabolites found in the bile, faeces and urine and analyses their commonalities and differences. Figure 1 Four important questions pertinent to the design and development of new molecules with favourable ADME properties addressed in this work. d, approved drugs; h, human metabolites; t, molecules from traditional Chinese medicines; MW, molecular weight.

FAst MEtabolizer (FAME) is a fast and accurate predictor of sites of metabolism (SoMs). It is based on a collection of random forest models trained on diverse chemical data sets of more than 20 000 molecules annotated with their experimentally determined SoMs. Using a comprehensive set of available data, FAME aims to assess metabolic processes from a holistic point of view. It is not limited to a specific enzyme family or species. Besides a global model, dedicated models are available for human, rat, and dog metabolism; specific prediction of phase I and II metabolism is also supported. FAME is able to identify at least one known SoM among the top-1, top-2, and top-3 highest ranked atom positions in up to 71%, 81%, and 87% of all cases tested, respectively. These prediction rates are comparable to or better than SoM predictors focused on specific enzyme families (such as cytochrome P450s), despite the fact that FAME uses only seven chemical descriptors. FAME covers a very broad chemical space, which together with its inter- and extrapolation power makes it applicable to a wide range of chemicals. Predictions take less than 2.5 s per molecule in batch mode on an Ultrabook. Results are visualized using Jmol, with the most likely SoMs highlighted.

We present an implementation of generalized Born implicit solvent all-atom classical molecular dynamics (MD) within the AMBER program package that runs entirely on CUDA enabled NVIDIA graphics processing units (GPUs). We discuss the algorithms that are used to exploit the processing power of the GPUs and show the performance that can be achieved in comparison to simulations on conventional CPU clusters. The implementation supports three different precision models in which the contributions to the forces are calculated in single precision floating point arithmetic but accumulated in double precision (SPDP), or everything is computed in single precision (SPSP) or double precision (DPDP). In addition to performance, we have focused on understanding the implications of the different precision models on the outcome of implicit solvent MD simulations. We show results for a range of tests including the accuracy of single point force evaluations and energy conservation as well as structural properties pertainining to protein dynamics. The numerical noise due to rounding errors within the SPSP precision model is sufficiently large to lead to an accumulation of errors which can result in unphysical trajectories for long time scale simulations. We recommend the use of the mixed-precision SPDP model since the numerical results obtained are comparable with those of the full double precision DPDP model and the reference double precision CPU implementation but at significantly reduced computational cost. Our implementation provides performance for GB simulations on a single desktop that is on par with, and in some cases exceeds, that of traditional supercomputers.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Understanding the mechanisms and improving the efficiency of the hydrolysis of cellulose for the production of liquid biofuels is critical for establishing a sustainable energy future. The mechanism of action of cellulose-degrading enzymes is being illuminated through a multidisciplinary collaboration that uses molecular dynamics (MD) and hybrid quantum mechanics molecular mechanics (QMMM) simulations. This paper details our efforts to improve the efficiency of MD codes, as well as the addition of PM6 semiempirical hybrid QMMM functionality to such codes. Our efforts are extending the capabilities of MD codes to allow simulations of enzymes and substrates on large-scale HPC resources.

Over the past few years competition within the computer game market coupled with the emergence of application programming interfaces to support general purpose computation on graphics processing units (GPUs) has led to an explosion in the use of GPUs for acceleration of scientific applications. Here we explore the use of GPUs within the context of condensed phase molecular dynamics (MD) simulations. We discuss the algorithmic differences that the GPU architecture imposes on MD codes, an overview of the challenges involved in using GPUs for MD, followed by a critical survey of contemporary MD simulation packages that are attempting to utilize GPUs. Finally we discuss the possible outlook for this field.

The similarity of the AMBER force field's energy functional form with that of the CHARMM force field, gives the potential for direct translation of common bonding and nonbonding terms, along with their parameters, present in CHARMM topology and parameter files, with the intent of evaluation within the AMBER software; specifically the SANDER and PMEMD dynamics engines. To this extent, we have created a tool, CHAMBER, which can take a CHARMM protein structure file (PSF), coordinate file (COR) and associated forcefield files, and convert these to an AMBER topology file (prmtop) and associated coordinate file (inpcrd). CHAMBER opens a conversion route which enables the simulation of CHARMM parameterized models using AMBER's PMEMD engine; thus providing improved serial efficiency as well as parallel efficiency over large numbers of CPUs. Significant effort has been expended in ensuring a true representation of the CHARMM force field in AMBER providing energies and forces that are the same to the limits of machine precision. This software will be released in the upcoming version 1.3 of the free AMBERTools suite. (C) 2009 Wiley Periodicals, Inc. Int J Quantum Chem 109: 3767-3772, 2009