Lars Ridder

Substructure‐based annotation of high‐resolution multistage MSn spectral trees

RATIONALE High-resolution multistage MS(n) data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation. METHODS We present an extended substructure-based approach which allows annotation of hierarchical spectral trees obtained from high-resolution multistage MS(n) experiments. The algorithm yields a hierarchical tree of substructures of a candidate molecule to explain the fragment peaks observed at consecutive levels of the multistage MS(n) spectral tree. A matching score is calculated that indicates how well the candidate structure can explain the observed hierarchical fragmentation pattern. RESULTS The method is applied to MS(n) spectral trees of a set of compounds representing important chemical classes in metabolomics. Based on the calculated score, the correct molecules were successfully prioritized among extensive sets of candidates structures retrieved from the PubChem database. CONCLUSIONS The results indicate that the inclusion of subsequent levels of fragmentation in the automatic annotation of MS(n) data improves the identification of the correct compounds. We show that, especially in the case of lower mass accuracy, this improvement is not only due to the inclusion of additional fragment ions in the analysis, but also to the specific hierarchical information present in the MS(n) spectral trees. This method may significantly reduce the time required by MS experts to analyze complex MS(n) data.

Mechanism and structure-reactivity relationships for aromatic hydroxylation by cytochrome P450

Cytochrome P450 enzymes play a central role in drug metabolism, and models of their mechanism could contribute significantly to pharmaceutical research and development of new drugs. The mechanism of cytochrome P450 mediated hydroxylation of aromatics and the effects of substituents on reactivity have been investigated using B3LYP density functional theory computations in a realistic porphyrin model system. Two different orientations of substrate approach for addition of Compound I to benzene, and also possible subsequent rearrangement pathways have been explored. The rate-limiting Compound I addition to an aromatic carbon atom proceeds on the doublet potential energy surface via a transition state with mixed radical and cationic character. Subsequent formation of epoxide, ketone and phenol products is shown to occur with low barriers, especially starting from a cation-like rather than a radical-like tetrahedral adduct of Compound I with benzene. Effects of ring substituents were explored by calculating the activation barriers for Compound I addition in the meta and para-position for a range of monosubstituted benzenes and for more complex polysubstituted benzenes. Two structure-reactivity relationships including 8 and 10 different substituted benzenes have been determined using (i) experimentally derived Hammett sigma-constants and (ii) a theoretical scale based on bond dissociation energies of hydroxyl adducts of the substrates, respectively. In both cases a dual-parameter approach that employs a combination of radical and cationic electronic descriptors gave good relationships with correlation coefficients R2 of 0.96 and 0.82, respectively. These relationships can be extended to predict the reactivity of other substituted aromatics, and thus can potentially be used in predictive drug metabolism models.

Automatic Chemical Structure Annotation of an LC-MSn Based Metabolic Profile from Green Tea

Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method ( http://www.emetabolomics.org/magma ) to automatically process and annotate the LC-MS(n) data sets on the basis of candidate molecules from chemical databases, such as PubChem or the Human Metabolite Database. Multistage MS(n) spectral data is automatically annotated with hierarchical trees of in silico generated substructures of candidate molecules to explain the observed fragment ions and alternative candidates are ranked on the basis of the calculated matching score. We tested this method on an untargeted LC-MS(n) (n ≤ 3) data set of a green tea extract, generated on an LC-LTQ/Orbitrap hybrid MS system. For the 623 spectral trees obtained in a single LC-MS(n) run, a total of 116,240 candidate molecules with monoisotopic masses matching within 5 ppm mass accuracy were retrieved from the PubChem database, ranging from 4 to 1327 candidates per molecular ion. The matching scores were used to rank the candidate molecules for each LC-MS(n) component. The median and third quartile fractional ranks for 85 previously identified tea compounds were 3.5 and 7.5, respectively. The substructure annotations and rankings provided detailed structural information of the detected components, beyond annotation with elemental formula only. Twenty-four additional components were putatively identified by expert interpretation of the automatically annotated data set, illustrating the potential to support systematic and untargeted metabolite identification.

Insights into enzyme catalysis from QM/MM modelling: transition state stabilization in chorismate mutase

Chorismate mutase provides an important test of theories of enzyme catalysis, and of modelling methods. The Claisen rearrangement of chorismate to prephenate in the enzyme has been modelled here by a combined quantum mechanics/molecular mechanics (QM/MM) method. Several pathways have been calculated. The sensitivity of the results to details of model preparation and pathway calculation is tested, and the results are compared in detail to previous similar studies and experiments. The potential energy barrier for the enzyme reaction is estimated at 24.5—31.6 kcal mol−1 (AMl/CHARMM), and 2.7—11.9 kcal mol−1 with corrections (e.g. B3LYP/6-31 + G(d)). In agreement with previous studies, the present analysis of the calculated paths provides unequivocal evidence of significant transition state stabilization by the enzyme, indicating that this is central to catalysis by the enzyme. The active site is exquisitely complementary to the transition state, stabilizing it more than the substrate, so reducing the barrier to reaction. A number of similar pathways for reaction exist in the protein, as expected. Small structural differences give rise to differences in energetic contributions. Major electrostatic contributions to transition state stabilization come in all cases from Arg90, Arg7, one or two water molecules, and Glu78 (Glu78 destabilizes the transition state less than the substrate), while Arg63 contributes significantly in one model.

Determinants of Reactivity and Selectivity in Soluble Epoxide Hydrolase from Quantum Mechanics/Molecular Mechanics Modeling

Soluble epoxide hydrolase (sEH) is an enzyme involved in drug metabolism that catalyzes the hydrolysis of epoxides to form their corresponding diols. sEH has a broad substrate range and shows high regio- and enantioselectivity for nucleophilic ring opening by Asp333. Epoxide hydrolases therefore have potential synthetic applications. We have used combined quantum mechanics/molecular mechanics (QM/MM) umbrella sampling molecular dynamics (MD) simulations (at the AM1/CHARMM22 level) and high-level ab initio (SCS-MP2) QM/MM calculations to analyze the reactions, and determinants of selectivity, for two substrates: trans-stilbene oxide (t-SO) and trans-diphenylpropene oxide (t-DPPO). The calculated free energy barriers from the QM/MM (AM1/CHARMM22) umbrella sampling MD simulations show a lower barrier for phenyl attack in t-DPPO, compared with that for benzylic attack, in agreement with experiment. Activation barriers in agreement with experimental rate constants are obtained only with the highest level of QM theory (SCS-MP2) used. Our results show that the selectivity of the ring-opening reaction is influenced by several factors, including proximity to the nucleophile, electronic stabilization of the transition state, and hydrogen bonding to two active site tyrosine residues. The protonation state of His523 during nucleophilic attack has also been investigated, and our results show that the protonated form is most consistent with experimental findings. The work presented here illustrates how determinants of selectivity can be identified from QM/MM simulations. These insights may also provide useful information for the design of novel catalysts for use in the synthesis of enantiopure compounds.

Molecular mechanisms of antibiotic resistance: QM/MM modelling of deacylation in a class A β-lactamase

Modelling of the first step of the deacylation reaction of benzylpenicillin in the E. coli TEM1 beta-lactamase (with B3LYP/6-31G + (d)//AM1-CHARMM22 quantum mechanics/molecular mechanics methods) shows that a mechanism in which Glu166 acts as the base to deprotonate a conserved water molecule is both energetically and structurally consistent with experimental data; the results may assist the design of new antibiotics and beta-lactamase inhibitors.

Modeling Biotransformation Reactions by Combined Quantum Mechanical / Molecular Mechanical Approaches: From Structure to Activity

An overview of the combined quantum mechanical/molecular mechanical (QM/MM) approach and its application to studies of biotransformation enzymes and drug metabolism is given. Theoretical methods to simulate enzymatic reactions have rapidly developed during the last decade. In particular, QM/MM methods provide detailed insights into enzyme catalyzed reactions, which can be extremely valuable in complementing experimental research. QM/MM methods allow the reacting groups in the active site of an enzyme to be studied at a quantum mechanical level, while the surrounding protein and solvent is included at a classical (and computationally less expensive) molecular mechanical level. Existing QM/MM implementations vary in the level of interaction between the QM and MM regions and in the way the partitioning into QM and MM regions is setup. Some general considerations concerning reaction modeling are discussed and a number of QM/MM studies related to drug metabolism are described. These studies illustrate that theoretical modeling of important metabolic reactions provides detailed insights into mechanisms of reaction and specific catalytic effects of enzyme residues as well as explaining variation in rates of conversion of different metabolites. Such information is essential in the development of methods to predict metabolism of drugs and to understand metabolic effects of genetic polymorphism in biotransformation enzymes.

Structural elucidation of low abundant metabolites in complex sample matrices

Identification of metabolites is a major challenge in biological studies and relies in principle on mass spectrometry (MS) and nuclear magnetic resonance (NMR) methods. The increased sensitivity and stability of both NMR and MS systems have made dereplication of complex biological samples feasible. Metabolic databases can be of help in the identification process. Nonetheless, there is still a lack of adequate spectral databases that contain high quality spectra, but new developments in this area will assist in the (semi-)automated identification process in the near future. Here, we discuss new developments for the structural elucidation of low abundant metabolites present in complex sample matrices. We describe how a recently developed combination of high resolution MS multistage fragmentation (MSn) and high resolution one dimensional (1D)-proton (1H)-NMR of liquid chromatography coupled to solid phase extraction (LC–SPE) purified metabolites can circumvent the need for isolating extensive amounts of the compounds of interest to elucidate their structures. The LC–MS–SPE–NMR hardware configuration in conjunction with high quality databases facilitates complete structural elucidation of metabolites even at sub-microgram levels of compound in crude extracts. However, progress is still required to optimally exploit the power of an integrated MS and NMR approach. Especially, there is a need to improve and expand both MSn and NMR spectral databases. Adequate and user-friendly software is required to assist in candidate selection based on the comparison of acquired MS and NMR spectral information with reference data. It is foreseen that these focal points will contribute to a better transfer and exploitation of structural information gained from diverse analytical platforms.

In Silico Prediction and Automatic LC–MSn Annotation of Green Tea Metabolites in Urine

The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27,245 potential metabolites. All matching precursor ions in the urine LC-MS(n) data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77% of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC-MS(n) data from nutritional metabolite profiling experiments.

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking. In 6 of the 12 submitted solutions in category 2, the correct chemical structure obtained the best score, whereas 3 molecules were ranked outside the top 5. All top ranked molecular formulas submitted in category 1 were correct. In addition, we present MAGMa results generated retrospectively for the remaining challenges. Successful application of the MAGMa algorithm required inclusion of the relevant candidate molecules, application of the appropriate mass tolerance and a sufficient degree of in silico fragmentation of the candidate molecules. Furthermore, the effect of the exhaustiveness of the candidate lists and limitations of substructure based scoring are discussed.