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Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ($$\hbox {F1-score} > 93.31\%$$ F1-score > 93.31 % ), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ($$\hbox {r}=.60$$ r = . 60 ). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model’s prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-021-00558-4.

Poster presentedat ICTOPEN 2020.

This report describes a density functional theory investigation into the reactivities of a series of aza-1,3-dipoles with ethylene at the BP86/TZ2P level. A benchmark study was carried out using QMflows, a newly developed program for automated workflows of quantum chemical calculations. In total, 24 1,3-dipolar cycloaddition (1,3-DCA) reactions were benchmarked using the highly accurate G3B3 method as a reference. We screened a number of exchange and correlation functionals, including PBE, OLYP, BP86, BLYP, both with and without explicit dispersion corrections, to assess their accuracies and to determine which of these computationally efficient functionals performed the best for calculating the energetics for cycloaddition reactions. The BP86/TZ2P method produced the smallest errors for the activation and reaction enthalpies. Then, to understand the factors controlling the reactivity in these reactions, seven archetypal aza-1,3-dipolar cycloadditions were investigated using the activation strain model and energy decomposition analysis. Our investigations highlight the fact that differences in activation barrier for these 1,3-DCA reactions do not arise from differences in strain energy of the dipole, as previously proposed. Instead, relative reactivities originate from differences in interaction energy. Analysis of the 1,3-dipole–dipolarophile interactions reveals the reactivity trends primarily result from differences in the extent of the primary orbital interactions.

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely\ud used and sensitive technique to obtain structural information on complex mixtures. However, just\ud knowing the molecular masses of the mixture’s constituents is almost always insufficient for\ud confident assignment of the associated chemical structures. Structural information can be\ud augmented through MS fragmentation experiments whereby detected metabolites are\ud fragmented giving rise to MS/MS spectra. However, how can we maximize the structural\ud information we gain from fragmentation spectra?\ud We recently proposed a substructure-based strategy to enhance metabolite annotation for\ud complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that\ud we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows\ud us to discover - unsupervised - groups of mass fragments and/or neutral losses termed\ud Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs\ud can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural\ud annotations for many molecules that are not present in spectral databases.\ud Here, we describe how additional strategies, taking advantage of i) combinatorial in-silico\ud matching of experimental mass features to substructures of candidate molecules, and ii)\ud automated machine learning classification of molecules, can facilitate semi-automated annotation\ud of substructures. We show how our approach accelerates the Mass2Motif annotation process and\ud therefore broadens the chemical space spanned by characterized motifs. Our machine learning\ud model used to classify fragmentation spectra learns the relationships between fragment spectra\ud and chemical features. Classification prediction on these features can be aggregated for all\ud molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations.\ud To make annotated Mass2Motifs available to the community, we also present motifDB: an open\ud database of Mass2Motifs that can be browsed and accessed programmatically through an\ud Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users\ud to efficiently search for characterized motifs in their own experiments. We expect that with an\ud increasing number of Mass2Motif annotations available through a growing database we can more\ud quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards\ud novel or unexpected chemistries and faster recognition of known biochemical building blocks.

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are considered characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics.Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of >100,000 mass spectra of about 15,000 unique known compounds, MS2DeepScore learns to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model’s prediction uncertainty. On 3,600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and predicts Tanimoto scores with a root mean squared error of about 0.15. The prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. We demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity metrics have great potential for a range of metabolomics data processing pipelines.

International audience; Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance.We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression.We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive or outperforms state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

We present the QMflows Python package for quantum chemistry workflow automatization. QMflows allows users to write complex workflows in terms of simple Python scripts. It supports the development of interoperable workflows involving multiple quantum chemistry codes and executes them efficiently on large scale parallel computers. This open source library provides standardized interfaces to a number of quantum chemistry packages and can be easily extended to accommodate additional codes. QMflows features are described and illustrated with a number of representative applications.

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds., Author summary Most metabolomics analyses rely upon matching observed fragmentation mass spectra to library spectra for structural annotation or compare spectra with each other through network analysis. As a key part of such processes, scoring functions are used to assess the similarity between pairs of fragment spectra. No studies have so far proposed scores fundamentally different to the popular cosine-based similarity score, despite the fact that its limitations are well understood. We propose a novel spectral similarity score known as Spec2Vec which adapts algorithms from natural language processing to learn relationships between peaks from co-occurrences across large spectra datasets. We find that similarities computed with Spec2Vec i) correlate better to structural similarity than cosine-based scores, ii) subsequently gives better performance in library matching tasks, and iii) is computationally more scalable than cosine-based scores. Given the central place of similarity scoring in key metabolomics analysis tasks such as library matching and spectral networking, we expect Spec2Vec to make a broad impact in all fields that rely upon untargeted metabolomics.

Strenuous exercise induces different stress-related physiological changes, potentially including changes in intestinal barrier function. In the Protege Study (ISRCTN14236739; www.isrctn.com) we determined the test-retest repeatability in responses to exercise in well-trained individuals. Eleven well-trained males (27 ± 4 years old) completed an exercise protocol that consisted of intensive cycling intervals, followed by an overnight fast and an additional 90 min cycling phase at 50% Wmax the next morning. The day before (rest), and immediately after the exercise protocol (exercise) a lactulose/rhamnose solution was ingested. Markers of energy metabolism, lactulose/rhamnose ratio, several cytokines and potential stress-related markers were measured at rest and during exercise. In addition, untargeted urine metabolite profiles were obtained. The complete procedure (Test) was repeated one week later (Retest) to assess repeatability. Metabolic effect parameters with regard to energy metabolism and urine metabolomics were similar for both the Test and Retest period, underlining comparable exercise load. Following exercise, intestinal permeability (one hour plasma lactulose/rhamnose ratio), serum interleukin-6, interleukin-10, fibroblast growth factor-21, and muscle creatine kinase levels were only significantly increased compared to rest during the first test and not when the test was repeated. Responses to strenuous exercise in well-trained young men, as indicated by intestinal markers and myokines, show adaptation in Test-Retest outcome. This might be due to a carry-over effect of the defense mechanisms triggered during the Test. This finding has implications for the design of studies aimed at evaluating physiological responses to exercise. This article is protected by copyright. All rights reserved

Somatic embryogenesis receptor kinases (SERKs) are ligand-binding coreceptors that are able to combine with different ligand-perceiving receptors such as BRASSINOSTEROID INSENSITIVE1 (BRI1) and FLAGELLIN-SENSITIVE2. Phenotypical analysis of serk single mutants is not straightforward because multiple pathways can be affected, while redundancy is observed for a single phenotype. For example, serk1serk3 double mutant roots are insensitive toward brassinosteroids but have a phenotype different from bri1 mutant roots. To decipher these effects, 4-d-old Arabidopsis (Arabidopsis thaliana) roots were studied using microarray analysis. A total of 698 genes, involved in multiple biological processes, were found to be differentially regulated in serk1-3serk3-2 double mutants. About half of these are related to brassinosteroid signaling. The remainder appear to be unlinked to brassinosteroids and related to primary and secondary metabolism. In addition, methionine-derived glucosinolate biosynthesis genes are up-regulated, which was verified by metabolite profiling. The results also show that the gene expression pattern in serk3-2 mutant roots is similar to that of the serk1-3serk3-2 double mutant roots. This confirms the existence of partial redundancy between SERK3 and SERK1 as well as the promoting or repressive activity of a single coreceptor in multiple simultaneously active pathways.

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

Poster presented at the Analytical Tools for Cutting-edge Metabolomics meeting in London, 30 April 2014

Contains fulltext : 169642.pdf (Publisher’s version ) (Open Access) 3D-e-Chem-VM is an open source, freely available Virtual Machine ( http://3d-e-chem.github.io/3D-e-Chem-VM/ ) that integrates cheminformatics and bioinformatics tools for the analysis of protein-ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein-ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPOdb).

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.

What is the central question of this study? Exercise is known to induce stress-related physiological responses, such as changes in intestinal barrier function. Our aim was to determine the test-retest repeatability of these responses in well-trained individuals. What is the main finding and its importance? Responses to strenuous exercise, as indicated by stress-related markers such as intestinal integrity markers and myokines, showed high test-retest variation. Even in well-trained young men an adapted response is seen after a single repetition after 1 week. This finding has implications for the design of studies aimed at evaluating physiological responses to exercise. Strenuous exercise induces different stress-related physiological changes, potentially including changes in intestinal barrier function. In the Protégé Study (ISRCTN14236739; www.isrctn.com), we determined the test-retest repeatability in responses to exercise in well-trained individuals. Eleven well-trained men (27 ± 4 years old) completed an exercise protocol that consisted of intensive cycling intervals, followed by an overnight fast and an additional 90 min cycling phase at 50% of maximal workload the next morning. The day before (rest), and immediately after the exercise protocol (exercise) a lactulose and rhamnose solution was ingested. Markers of energy metabolism, lactulose-to-rhamnose ratio, several cytokines and potential stress-related markers were measured at rest and during exercise. In addition, untargeted urine metabolite profiles were obtained. The complete procedure (Test) was repeated 1 week later (Retest) to assess repeatability. Metabolic effect parameters with regard to energy metabolism and urine metabolomics were similar for both the Test and Retest period, underlining comparable exercise load. Following exercise, intestinal permeability (1 h plasma lactulose-to-rhamnose ratio) and the serum interleukin-6, interleukin-10, fibroblast growth factor-21 and muscle creatine kinase concentrations were significantly increased compared with rest only during the first test and not when the test was repeated. Responses to strenuous exercise in well-trained young men, as indicated by intestinal markers and myokines, show adaptation in Test-Retest outcome. This might be attributable to a carry-over effect of the defense mechanisms triggered during the Test. This finding has implications for the design of studies aimed at evaluating physiological responses to exercise.

RATIONALE High-resolution multistage MSn 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 MSn 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 MSn 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 MSn 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 MSn 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 MSn spectral trees. This method may significantly reduce the time required by MS experts to analyze complex MSn data. Copyright (c)proves 2012 John Wiley & Sons, Ltd.

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.

Predictions of potential metabolites based on chemical structure are becoming increasingly important in drug discovery to guide medicinal chemistry efforts that address metabolic issues and to support experimental metabolite screening and identification. Herein we present a novel rule-based method, SyGMa (Systematic Generation of potential Metabolites), to predict the potential metabolites of a given parent structure. A set of reaction rules covering a broad range of phase 1 and phase 2 metabolism has been derived from metabolic reactions reported in the Metabolite Database to occur in humans. An empirical probability score is assigned to each rule representing the fraction of correctly predicted metabolites in the training database. This score is used to refine the rules and to rank predicted metabolites. The current rule set of SyGMa covers approximately 70 % of biotransformation reactions observed in humans. Evaluation of the rule-based predictions demonstrated a significant enrichment of true metabolites in the top of the ranking list: while in total, 68 % of all observed metabolites in an independent test set were reproduced by SyGMa, a large part, 30 % of the observed metabolites, were identified among the top three predictions. From a subset of cytochrome P450 specific metabolites, 84 % were reproduced overall, with 66 % in the top three predicted phase 1 metabolites. A similarity analysis of the reactions present in the database was performed to obtain an overview of the metabolic reactions predicted by SyGMa and to support ongoing efforts to extend the rules. Specific examples demonstrate the use of SyGMa in experimental metabolite identification and the application of SyGMa to suggest chemical modifications that improve the metabolic stability of compounds.

Flavonoids are a class of natural compounds essentially produced by plants that are part of animal and human diets and have assumed health-promoting benefits. Upon human consumption, these flavonoids are to a modest extent absorbed in the small intestines. The major part arrives in the colon where the microflora utilises and converts the flavonoids to a wide range of products. Many of these products are absorbed in the major intestines and subsequently metabolised by the host. To understand the impact of the microflora on the metabolism and possible effects on human health, complete (and quantitative) identification of the microbial as well as human metabolic conversion products of flavonoids is required. This is a challenging task, as these bioconversion products are often present in relatively small amounts, making classical identification strategies based on (accurate) mass information or nuclear magnetic resonance, not straightforward. In the absence of reference compounds, annotation of a component may be achieved by detailed expert evaluation, e.g. by searching for similar fragmentation patterns in spectral databases of known compounds. However, such manual analysis is a tedious task, and in advanced metabolite profiling experiments, with large numbers of unknown metabolites, this is a major bottleneck. Therefore, new strategies are needed for quick and reliable identification of the diverse range of molecules in complex matrices (faeces, blood, urine). Intelligent software for annotation and identification of unknowns is crucial to fully exploit complex datasets. We developed a new software tool (MAGMA) for (sub)structure-based annotation of LC-MSn datasets which, combined with a newly established database for phenolic molecules (MetIDB), enables semiautomated identification of flavonoid derivatives.

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...

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.

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.

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.

The simulation of enzymatic reactions, using computer models, is becoming a powerful tool in the most fundamental challenge in biochemistry: to relate the catalytic activity of enzymes to their structure. In the present study, various computed parameters were correlated with the natural logarithm of experimental rate constants for the hydroxylation of various substrate derivatives catalysed by wild-type para-hydroxybenzoate hydroxylase (PHBH) as well as for the hydroxylation of the native substrate (p-hydroxybenzoate) by PHBH reconstituted with a series of 8-substituted flavins. The following relative parameters have been calculated and tested: (a) energy barriers from combined quantum mechanical/molecular mechanical (QM/MM) (AM1/CHARMM) reaction pathway calculations, (b) gas-phase reaction enthalpies (AM1) and (c) differences between the HOMO and LUMO energies of the isolated substrate and cofactor molecules (AM1 and B3LYP/6-31+G(d)). The gas-phase approaches yielded good correlations, as long as similarly charged species are involved. The QM/MM approach resulted in a good correlation, even including differently charged species. This indicates that the QM/MM model accounts quite well for the solvation effects of the active site surroundings, which vary for differently charged species. The correlations obtained demonstrate quantitative structure activity relationships for an enzyme-catalysed reaction including, for the first time, substitutions on both substrate and cofactor.

A human diet containing a significant amount of flavonoids, such as present in tea, red wine, apple, and cocoa has been associated with reduced disease risks. After consumption, a part of these flavonoids can be directly absorbed by the small intestine, but the greatest part passages towards the large intestine where microbes break the flavonoids down into phenolic metabolites. After absorption into the blood, both intact and metabolized flavonoids are subsequently methylated, sulphated, and glucuronidated or a combination thereof. The exact chemical structural elucidation and quantification of these conjugates present in the human body are key to identify potential bioactive components. However, this is still a tedious task due to their relative low abundance in a complex background of other high-abundant metabolites and the many possible isomeric forms. Therefore, we aimed to systematically identify these conjugates by using a combination of pre-concentration and separation by solid phase extraction (SPE) followed by LC-FTMSn and 1D 1H NMR. The combination of LC-FTMSn and HPLC-TOF-MS-SPE-NMR resulted in the efficient identification and quantification of low abundant polyphenol metabolites down to micromolar concentrations and thus opens up new perspectives for in depth studying of the bioavailability and the possible mode of action of flavonoids like flavan-3-ols and their gut-microbial break-down products circulating in the human body.

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.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.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.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.

In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. In order to determine the biological activity of specific components, it is necessary to have the correct structures and the quantification of the polyphenol-derived conjugates present in the human body. We present a procedure for identification and quantification of metabolites and conjugates excreted in human urine after single bolus intake of black or green tea. A combination of a solid phase extraction (SPE) preparation step and two high pressure liquid chromatography (HPLC)-based analytical platforms was used; namely, accurate mass fragmentation (HPLC-FTMSn) and mass-guided SPE-trapping of selected compounds for nuclear magnetic resonance spectroscopy (NMR) measurements (HPLC-TOFMS-SPE-NMR). HPLC-FTMSn analysis led to the annotation of 138 urinary metabolites, including 48 valerolactone and valeric acid conjugates. By combining the results from MSn fragmentation with the one dimensional (1D)-1H-NMR spectra of HPLC-TOFMS-SPE trapped compounds, we elucidated the structures of 36 phenolic conjugates, including the glucuronides of 3’,4’-di, and 3’,4’,5’-trihydroxyphenyl-¿-valerolactone, three urolithin glucuronides, and indole-3-acetic acid glucuronide. We also obtained 26 hours of quantitative excretion profiles for specific valerolactone conjugates. The combination of the HPLC-FTMSn and HPLC-TOFMS-SPE-NMR platforms results in the efficient identification and quantification of low abundant phenolic conjugates down to nanomoles of trapped amounts of metabolite corresponding to micromolar metabolite concentrations in urine

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Modeling methods allow the identification and analysis of determinants of reactivity and specificity in enzymes. The reaction between glutathione and 1-chloro-2,4-dinitrobenzene (CDNB) is widely used as a standard activity assay for glutathione S-transferases (GSTs). It is important to understand the causes of differences between catalytic GST isoenzymes and the effects of mutations and genetic polymorphisms. Quantum mechanical/molecular mechanical (QM/MM) molecular dynamics simulations have been performed here to investigate the addition of the glutathione anion to CDNB in the wild-type M1-1 GST isoenzyme from rat and in three single point mutant (Tyr6Phe, Tyr115Phe, and Met108Ala) M1-1 GST enzymes. We have developed a specifically parameterized QM/MM method (AM1-SRP/CHARMM22) to model this reaction by fitting to experimental heats of formation and ionization potentials. Free energy profiles were obtained from molecular dynamics simulations of the reaction using umbrella sampling and weighted histogram analysis techniques. The reaction in solution has also been simulated and is compared to the enzymatic reaction. The free energies are in excellent agreement with experimental results. Overall the results of the present study show that QM/MM reaction pathway analysis provides detailed insight into the chemistry of GST and can be used to obtain mechanistic insight into the effects of specific mutations on this catalytic process.

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.

Understanding the mechanisms by which beta-lactamases destroy beta-lactam antibiotics is potentially vital in developing effective therapies to overcome bacterial antibiotic resistance. Class A beta-lactamases are the most important and common type of these enzymes. A key process in the reaction mechanism of class A beta-lactamases is the acylation of the active site serine by the antibiotic. We have modeled the complete mechanism of acylation with benzylpenicillin, using a combined quantum mechanical and molecular mechanical (QM/MM) method (B3LYP/6-31G+(d)//AM1-CHARMM22). All active site residues directly involved in the reaction, and the substrate, were treated at the QM level, with reaction energies calculated at the hybrid density functional (B3LYP/6-31+Gd) level. Structures and interactions with the protein were modeled by the AM1-CHARMM22 QM/MM approach. Alternative reaction coordinates and mechanisms have been tested by calculating a number of potential energy surfaces for each step of the acylation mechanism. The results support a mechanism in which Glu166 acts as the general base. Glu166 deprotonates an intervening conserved water molecule, which in turn activates Ser70 for nucleophilic attack on the antibiotic. This formation of the tetrahedral intermediate is calculated to have the highest barrier of the chemical steps in acylation. Subsequently, the acylenzyme is formed with Ser130 as the proton donor to the antibiotic thiazolidine ring, and Lys73 as a proton shuttle residue. The presented mechanism is both structurally and energetically consistent with experimental data. The QM/MM energy barrier (B3LYP/ 6-31G+(d)//AM1-CHARMM22) for the enzymatic reaction of 9 kcal mol(-1) is consistent with the experimental activation energy of about 12 kcal mol(-1). The effects of essential catalytic residues have been investigated by decomposition analysis. The results demonstrate the importance of the "oxyanion hole" in stabilizing the transition state and the tetrahedral intermediate. In addition, Asn132 and a number of charged residues in the active site have been identified as being central to the stabilizing effect of the enzyme. These results will be potentially useful in the development of stable beta-lactam antibiotics and for the design of new inhibitors.

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.

The mechanism and selectivity of aromatic hydroxylation by cytochrome P450 enzymes is explored using new B3LYP density functional theory computations. The calculations, using a realistic porphyrin model system, show that rate-determining addition of compound I to an aromatic carbon atom proceeds via a transition state with partial radical and cationic character. Reactivity is shown to depend strongly on ring substituents, with both electron-withdrawing and -donating groups strongly decreasing the addition barrier in the para position, and it is shown that the calculated barrier heights can be reproduced by a new dual-parameter equation based on radical and cationic Hammett sigma parameters.

Glutathione S-transferases (GSTs) play an important role in the detoxification of xenobiotics in mammals. They catalyze the conjugation of glutathione to a wide range of electrophilic compounds. Phenanthrene 9,10-oxide is a model substrate for GSTs, representing an important group of epoxide substrates. In the present study, combined quantum mechanical/molecular mechanical (QM/MM) simulations of the conjugation of glutathione to phenanthrene 9,10-oxide, catalyzed by the M1-1 isoenzyme from rat, have been carried out to obtain insight into details of the reaction mechanism and the role of solvent present in the highly solvent accessible active site. Reaction-specific AM1 parameters for sulfur have been developed to obtain an accurate modeling of the reaction, and QM/MM solvent interactions in the model have been calibrated. Free energy profiles for the formation of two diastereomeric products were obtained from molecular dynamics simulations of the enzyme, using umbrella sampling and weighted histogram analysis techniques. The barriers (20 kcal/mol) are in good agreement with the overall experimental rate constant and with the formation of equal amounts of the two diastereomeric products, as experimentally observed. Along the reaction pathway, desolvation of the thiolate sulfur of glutathione is observed, in agreement with solvent isotope experiments, as well as increased solvation of the epoxide oxygen of phenanthrene 9,10-oxide, illustrating an important stabilizing role for active site solvent molecules. Important active site interactions have been identified and analyzed. The catalytic effect of Tyr115 through a direct hydrogen bond with the epoxide oxygen of the substrate, which was proposed on the basis of the crystal structure of the (9S,10S) product complex, is supported by the simulations. The indirect interaction through a mediating water molecule, observed in the crystal structure of the (9R,10R) product complex, cannot be confirmed to play a role in the conjugation step. A selection of mutations is modeled. The Asn8Asp mutation, representing one of the differences between the M1-1 and M2-2 isoenzymes, is identified as a possible factor contributing to the difference in the ratio of product formation by these two isoenzymes. The QM/MM reaction pathway simulations provide new and detailed insight into the reaction mechanism of this important class of detoxifying enzymes and illustrate the potential of QM/MM modeling to complement experimental data on enzyme reaction mechanisms.

A combined quantum mechanical and molecular mechanical (QM/MM) method (AM1/CHARMM) was used to investigate the mechanism of the aromatic hydroxylation of phenol by a flavin dependent phenol hydroxylase (PH), an essential reaction in the degradation of a wide range of aromatic compounds. The model for the reactive flavin intermediate (C4a-hydroperoxyflavin) bound to PH was constructed on the basis of the crystal structure of the enzyme-substrate complex. A potential energy surface (PES) was calculated as a function of the reaction coordinates for hydroxylation of phenol (on C6) and for proton transfer from phenol (O1) to an active-site base Asp54 (OD1). The results support a reaction mechanism in which phenol is activated through deprotonation by Asp54, after which the phenolate is hydroxylated through an electrophilic aromatic substitution. Ab initio test calculations were performed to verify these results of the QM/MM model. Furthermore, the variation in the calculated QM/MM activation energies for hydroxylation of a series of substrate derivatives was shown to correlate very well (R = 0.98) with the natural logarithm of the experimental rate constants for their overall conversion by PH (25 C, pH 7.6). This correlation validates the present QM/MM model and supports the proposal of an electrophilic aromatic substitution mechanism in which the electrophilic attack of the C4a-hydroperoxyflavin cofactor on the activated (deprotonated) substrate is the rate-limiting step at 25 C and pH 7.6. The correlation demonstrates the potential of the QM/MM technique for predictions of catalytic activity on the basis of protein structure. Analysis of the residue contributions identifies a catalytic role for the backbone carbonyl of a conserved proline residue, Pro364, in specific stabilization of the transition state for hydroxylation. A crystal water appears to assist in the hydroxylation reaction by stabilizing the deprotonated C4a-hydroxyflavin product. Comparison of the present results with previous QM/MM results for the related p-hydroxybenzoate hydroxylase (Ridder et al. J. Am. Chem. Soc. 1998, 120, 7641-7642) identifies common mechanistic features, providing detailed insight into the relationship between these enzymes.

With the continuously increasing power of computers, quantum chemistry is becoming a valuable theoretical tool in enzyme research. Molecules as large as flavins can now be treated by computational methods of reasonable theoretical level. The present chapter focuses on the possibilities and restrictions of some quantum chemical methods with respect to research on the chemistry of flavin cofactors in enzyme catalysis.