Publications by authors named "Lars Ridder"

28 Publications

  • Page 1 of 1

Towards FAIR protocols and workflows: the OpenPREDICT use case.

PeerJ Comput Sci 2020 21;6:e281. Epub 2020 Sep 21.

Institute of Data Science, Maastricht University, Maastricht, Netherlands.

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.
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http://dx.doi.org/10.7717/peerj-cs.281DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924452PMC
September 2020

Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships.

PLoS Comput Biol 2021 02 16;17(2):e1008724. Epub 2021 Feb 16.

Bioinformatics Group, Wageningen University, Wageningen, the Netherlands.

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.
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http://dx.doi.org/10.1371/journal.pcbi.1008724DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909622PMC
February 2021

Sleep classification from wrist-worn accelerometer data using random forests.

Sci Rep 2021 01 8;11(1):24. Epub 2021 Jan 8.

Netherlands eScience Center, Amsterdam, The Netherlands.

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 ([Formula: see text]), 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 ([Formula: see text]). 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.
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http://dx.doi.org/10.1038/s41598-020-79217-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794504PMC
January 2021

QMflows: A Tool Kit for Interoperable Parallel Workflows in Quantum Chemistry.

J Chem Inf Model 2019 07 1;59(7):3191-3197. Epub 2019 Jul 1.

Division of Theoretical Chemistry, Faculty of Science , Vrije Universiteit Amsterdam , de Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands.

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.
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http://dx.doi.org/10.1021/acs.jcim.9b00384DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6651270PMC
July 2019

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra.

Faraday Discuss 2019 08;218(0):284-302

Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique for obtaining structural information of complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented, giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses, termed Mass2Motifs, that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of (i) combinatorial in silico matching of experimental mass features to substructures of candidate molecules, and (ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present MotifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database, we can more quickly gain insight into the constituents of complex mixtures. This will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.
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http://dx.doi.org/10.1039/c8fd00235eDOI Listing
August 2019

3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine.

J Chem Inf Model 2017 02 14;57(2):115-121. Epub 2017 Feb 14.

Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , 1081 HZ Amsterdam, The Netherlands.

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).
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http://dx.doi.org/10.1021/acs.jcim.6b00686DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5342320PMC
February 2017

Adaptation of exercise-induced stress in well-trained healthy young men.

Exp Physiol 2017 01 11;102(1):86-99. Epub 2016 Dec 11.

Human Nutrition, Wageningen University and Research, Wageningen, The Netherlands.

New Findings: 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.
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http://dx.doi.org/10.1113/EP086025DOI Listing
January 2017

Transcriptional Analysis of serk1 and serk3 Coreceptor Mutants.

Plant Physiol 2016 12 1;172(4):2516-2529. Epub 2016 Nov 1.

Laboratory of Biochemistry, Wageningen University, 6708 WE Wageningen, The Netherlands

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.
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http://dx.doi.org/10.1104/pp.16.01478DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5129729PMC
December 2016

Enhanced acylcarnitine annotation in high-resolution mass spectrometry data: fragmentation analysis for the classification and annotation of acylcarnitines.

Front Bioeng Biotechnol 2015 9;3:26. Epub 2015 Mar 9.

Glasgow Polyomics, University of Glasgow , Glasgow , UK.

Metabolite annotation and identification are primary challenges in untargeted metabolomics experiments. Rigorous workflows for reliable annotation of mass features with chemical structures or compound classes are needed to enhance the power of untargeted mass spectrometry. High-resolution mass spectrometry considerably improves the confidence in assigning elemental formulas to mass features in comparison to nominal mass spectrometry, and embedding of fragmentation methods enables more reliable metabolite annotations and facilitates metabolite classification. However, the analysis of mass fragmentation spectra can be a time-consuming step and requires expert knowledge. This study demonstrates how characteristic fragmentations, specific to compound classes, can be used to systematically analyze their presence in complex biological extracts like urine that have undergone untargeted mass spectrometry combined with data dependent or targeted fragmentation. Human urine extracts were analyzed using normal phase liquid chromatography (hydrophilic interaction chromatography) coupled to an Ion Trap-Orbitrap hybrid instrument. Subsequently, mass chromatograms and collision-induced dissociation and higher-energy collisional dissociation (HCD) fragments were annotated using the freely available MAGMa software. Acylcarnitines play a central role in energy metabolism by transporting fatty acids into the mitochondrial matrix. By filtering on a combination of a mass fragment and neutral loss designed based on the MAGMa fragment annotations, we were able to classify and annotate 50 acylcarnitines in human urine extracts, based on high-resolution mass spectrometry HCD fragmentation spectra at different energies for all of them. Of these annotated acylcarnitines, 31 are not described in HMDB yet and for only 4 annotated acylcarnitines the fragmentation spectra could be matched to reference spectra. Therefore, we conclude that the use of mass fragmentation filters within the context of untargeted metabolomics experiments is a valuable tool to enhance the annotation of small metabolites.
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http://dx.doi.org/10.3389/fbioe.2015.00026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4353373PMC
March 2015

In silico prediction and automatic LC-MS(n) annotation of green tea metabolites in urine.

Anal Chem 2014 May 29;86(10):4767-74. Epub 2014 Apr 29.

Laboratory of Biochemistry, Wageningen University , Dreijenlaan 3, 6703 HA, Wageningen, The Netherlands.

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.
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http://dx.doi.org/10.1021/ac403875bDOI Listing
May 2014

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

Mass Spectrom (Tokyo) 2014 2;3(Spec Iss 2):S0033. Epub 2014 Jul 2.

Netherlands eScience Center.

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.
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http://dx.doi.org/10.5702/massspectrometry.S0033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4321337PMC
January 2016

Identification of drug metabolites in human plasma or serum integrating metabolite prediction, LC-HRMS and untargeted data processing.

Bioanalysis 2013 Sep;5(17):2115-28

TNO Triskelion BV, Utrechtseweg 48, 3704 HE Zeist, The Netherlands.

Background: Comprehensive identification of human drug metabolites in first-in-man studies is crucial to avoid delays in later stages of drug development. We developed an efficient workflow for systematic identification of human metabolites in plasma or serum that combines metabolite prediction, high-resolution accurate mass LC-MS and MS vendor independent data processing. Retrospective evaluation of predictions for 14 (14)C-ADME studies published in the period 2007-January 2012 indicates that on average 90% of the major metabolites in human plasma can be identified by searching for accurate masses of predicted metabolites. Furthermore, the workflow can identify unexpected metabolites in the same processing run, by differential analysis of samples of drug-dosed subjects and (placebo-dosed, pre-dose or otherwise blank) control samples. To demonstrate the utility of the workflow we applied it to identify tamoxifen metabolites in serum of a breast cancer patient treated with tamoxifen.

Results & Conclusion: Previously published metabolites were confirmed in this study and additional metabolites were identified, two of which are discussed to illustrate the advantages of the workflow.
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http://dx.doi.org/10.4155/bio.13.178DOI Listing
September 2013

Automatic chemical structure annotation of an LC-MS(n) based metabolic profile from green tea.

Anal Chem 2013 Jun 31;85(12):6033-40. Epub 2013 May 31.

Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703 HA Wageningen, The Netherlands.

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.
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http://dx.doi.org/10.1021/ac400861aDOI Listing
June 2013

Substructure-based annotation of high-resolution multistage MS(n) spectral trees.

Rapid Commun Mass Spectrom 2012 Oct;26(20):2461-71

Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands.

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.
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http://dx.doi.org/10.1002/rcm.6364DOI Listing
October 2012

Structural elucidation and quantification of phenolic conjugates present in human urine after tea intake.

Anal Chem 2012 Aug 2;84(16):7263-71. Epub 2012 Aug 2.

Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703 HA, Wageningen, The Netherlands.

In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. 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-FTMS(n)) and mass-guided SPE-trapping of selected compounds for nuclear magnetic resonance spectroscopy (NMR) measurements (HPLC-TOFMS-SPE-NMR). HPLC-FTMS(n) analysis led to the annotation of 138 urinary metabolites, including 48 valerolactone and valeric acid conjugates. By combining the results from MS(n) fragmentation with the one-dimensional (1D)-(1)H 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-γ-valerolactone, three urolithin glucuronides, and indole-3-acetic acid glucuronide. We also obtained 26 h-quantitative excretion profiles for specific valerolactone conjugates. The combination of the HPLC-FTMS(n) and HPLC-TOFMS-SPE-NMR platforms results in the efficient identification and quantification of less abundant phenolic conjugates down to nanomoles of trapped amounts of metabolite corresponding to micromolar metabolite concentrations in urine.
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http://dx.doi.org/10.1021/ac3017339DOI Listing
August 2012

Determinants of reactivity and selectivity in soluble epoxide hydrolase from quantum mechanics/molecular mechanics modeling.

Biochemistry 2012 Feb 10;51(8):1774-86. Epub 2012 Feb 10.

Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK.

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.
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http://dx.doi.org/10.1021/bi201722jDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3290109PMC
February 2012

Revisiting the rule of five on the basis of pharmacokinetic data from rat.

ChemMedChem 2011 Nov 20;6(11):1967-70. Epub 2011 Sep 20.

Molecular Design and Informatics, Merck Research Laboratories, Molenstraat 110, 5342 CC Oss, The Netherlands.

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http://dx.doi.org/10.1002/cmdc.201100306DOI Listing
November 2011

SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites.

ChemMedChem 2008 May;3(5):821-32

Molecular Design and Informatics, Organon, part of Schering-Plough Corporation, P.O. Box 20, 5340 BH Oss, The Netherlands.

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.
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http://dx.doi.org/10.1002/cmdc.200700312DOI Listing
May 2008

Molecular determinants of xenobiotic metabolism: QM/MM simulation of the conversion of 1-chloro-2,4-dinitrobenzene catalyzed by M1-1 glutathione S-transferase.

Biochemistry 2007 May 5;46(21):6353-63. Epub 2007 May 5.

Centre for Computational Chemistry, School of Chemistry, University of Bristol, Cantock's Close, UK.

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.
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http://dx.doi.org/10.1021/bi0622827DOI Listing
May 2007

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

Org Biomol Chem 2006 Jan 9;4(2):206-10. Epub 2005 Dec 9.

School of Chemistry, University of Bristol, Bristol, BS8 1TS, U.K.

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.
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http://dx.doi.org/10.1039/b512969aDOI Listing
January 2006

Mechanisms of antibiotic resistance: QM/MM modeling of the acylation reaction of a class A beta-lactamase with benzylpenicillin.

J Am Chem Soc 2005 Mar;127(12):4454-65

Institut für Pharmazeutische Chemie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany.

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.
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http://dx.doi.org/10.1021/ja044210dDOI Listing
March 2005

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

Org Biomol Chem 2004 Oct 28;2(20):2998-3005. Epub 2004 Sep 28.

School of Chemistry and Centre for Computational Chemistry, University of Bristol, UK.

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.
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http://dx.doi.org/10.1039/B410729BDOI Listing
October 2004

Transition state stabilization and substrate strain in enzyme catalysis: ab initio QM/MM modelling of the chorismate mutase reaction.

Org Biomol Chem 2004 Apr 3;2(7):968-80. Epub 2004 Mar 3.

Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK BS8 1TS.

To investigate fundamental features of enzyme catalysis, there is a need for high-level calculations capable of modelling crucial, unstable species such as transition states as they are formed within enzymes. We have modelled an important model enzyme reaction, the Claisen rearrangement of chorismate to prephenate in chorismate mutase, by combined ab initio quantum mechanics/molecular mechanics (QM/MM) methods. The best estimates of the potential energy barrier in the enzyme are 7.4-11.0 kcal mol(-1)(MP2/6-31+G(d)//6-31G(d)/CHARMM22) and 12.7-16.1 kcal mol(-1)(B3LYP/6-311+G(2d,p)//6-31G(d)/CHARMM22), comparable to the experimental estimate of Delta H(++)= 12.7 +/- 0.4 kcal mol(-1). The results provide unequivocal evidence of transition state (TS) stabilization by the enzyme, with contributions from residues Arg90, Arg7, and Arg63. Glu78 stabilizes the prephenate product (relative to substrate), and can also stabilize the TS. Examination of the same pathway in solution (with a variety of continuum models), at the same ab initio levels, allows comparison of the catalyzed and uncatalyzed reactions. Calculated barriers in solution are 28.0 kcal mol(-1)(MP2/6-31+G(d)/PCM) and 24.6 kcal mol(-1)(B3LYP/6-311+G(2d,p)/PCM), comparable to the experimental finding of Delta G(++)= 25.4 kcal mol(-1) and consistent with the experimentally-deduced 10(6)-fold rate acceleration by the enzyme. The substrate is found to be significantly distorted in the enzyme, adopting a structure closer to the transition state, although the degree of compression is less than predicted by lower-level calculations. This apparent substrate strain, or compression, is potentially also catalytically relevant. Solution calculations, however, suggest that the catalytic contribution of this compression may be relatively small. Consideration of the same reaction pathway in solution and in the enzyme, involving reaction from a 'near-attack conformer' of the substrate, indicates that adoption of this conformation is not in itself a major contribution to catalysis. Transition state stabilization (by electrostatic interactions, including hydrogen bonds) is found to be central to catalysis by the enzyme. Several hydrogen bonds are observed to shorten at the TS. The active site is clearly complementary to the transition state for the reaction, stabilizing it more than the substrate, so reducing the barrier to reaction.
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http://dx.doi.org/10.1039/b313759gDOI Listing
April 2004

Aromatic hydroxylation by cytochrome P450: model calculations of mechanism and substituent effects.

J Am Chem Soc 2003 Dec;125(49):15004-5

School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK.

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.
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http://dx.doi.org/10.1021/ja035590qDOI Listing
December 2003

Identification of Glu166 as the general base in the acylation reaction of class A beta-lactamases through QM/MM modeling.

J Am Chem Soc 2003 Aug;125(32):9590-1

Institut für Pharmazeutische Chemie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany.

Bacterial class A beta-lactamases are responsible for the most known resistance against beta-lactam antibiotics. With the continuing rise in antibiotic resistance, improved knowledge of the mechanisms of action of these enzymes is needed in the development of effective therapeutic agents and strategies. The mechanism of the deacylation step in class A beta-lactamases is well accepted. In contrast, the mechanism of the acylation step has been uncertain, with several conflicting proposals put forward. We have modeled the acylation step in a class A beta-lactamase, using a combined quantum mechanics/molecular mechanics approach. The results provide an atomic level description of the reaction and show that Glu166 acts as the general base in the reaction, deprotonating Ser70 via an intervening water molecule. Ser70 acts as the nucleophile for attack on the lactam ring in a concerted reaction. The results do not rule out the importance of Lys73 in catalysis, in agreement with experimental data.
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http://dx.doi.org/10.1021/ja034434gDOI Listing
August 2003

Modeling biotransformation reactions by combined quantum mechanical/molecular mechanical approaches: from structure to activity.

Curr Top Med Chem 2003 ;3(11):1241-56

Molecular Design & Informatics, N.V. Organon, P.O. Box 20, 5430 BH Oss, The Netherlands.

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.
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http://dx.doi.org/10.2174/1568026033452005DOI Listing
September 2003

Quantum mechanical/molecular mechanical free energy simulations of the glutathione S-transferase (M1-1) reaction with phenanthrene 9,10-oxide.

J Am Chem Soc 2002 Aug;124(33):9926-36

School of Chemistry, University of Bristol, Bristol BS8 1TS, United Kingdom.

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.
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http://dx.doi.org/10.1021/ja0256360DOI Listing
August 2002
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