140 research outputs found

    “Ask Ernö”: a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra

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    Background: We present "Ask Erno", a self-learning system for the automatic analysis of NMR spectra, consisting of integrated chemical shift assignment and prediction tools. The output of the automatic assignment component initializes and improves a database of assigned protons that is used by the chemical shift predictor. In turn, the predictions provided by the latter facilitate improvement of the assignment process. Iteration on these steps allows Ask Erno to improve its ability to assign and predict spectra without any prior knowledge or assistance from human experts. Results: This concept was tested by training such a system with a dataset of 2341 molecules and their H-1-NMR spectra, and evaluating the accuracy of chemical shift predictions on a test set of 298 partially assigned molecules (2007 assigned protons). After 10 iterations, Ask Erno was able to decrease its prediction error by 17 %, reaching an average error of 0.265 ppm. Over 60 % of the test chemical shifts were predicted within 0.2 ppm, while only 5 % still presented a prediction error of more than 1 ppm. Conclusions: Ask Erno introduces an innovative approach to automatic NMR analysis that constantly learns and improves when provided with new data. Furthermore, it completely avoids the need for manually assigned spectra. This system has the potential to be turned into a fully autonomous tool able to compete with the best alternatives currently available

    Automatic learning for the classification of chemical reactions and in statistical thermodynamics

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    This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations

    Predicting NMR parameters from the molecular structure

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    Robust automatic assignment of nuclear magnetic resonance spectra for small molecules

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    Abstract. In this document we describe a fully automatic assignment system for Nuclear Magnetic Resonance (NMR) for small molecules. This system has 3 main features: 1. it uses as input raw NMR data. Which means it should be able to extract from them the information that is useful while ignores the noise; 2. it assigns the signals to atoms in the structure, and associates to each assignment a confidence value, which is used to sort all possible solutions; 3. it does not depend on chemical shifts predictions. So it can use the connectivity information observed in 2D NMR spectra and integrals to perform an assignment(coupling constants are also a possibility, but were not explored in this work). However, the system can use chemical shifts if available.; 4. it can learn in an unsupervised fashion, the relation between configurations of atoms and chemical shifts while solving assignment problems, which allows the system to improve while working. Analogous to the way a human works. This system is completely open source, as well as the data used in this work.En este trabajo describimos un sistema completamente automático de asignación de espectros de Resonancia Magnética Nuclear(RMN) para moléculas pequeñas. Este sistema tiene la siguientes características: 1. usa como entrada datos de RMN crudos. Lo que significa que debe ser capaz de extraer de ellos, la información que es útil y dejar de lado el ruido; 2. asigna las señales a átomos en la estructura, y asocia a cada asignación un valor de confianza, que es usado para ordenar todas las posibles soluciones; 3. no depende de predicciones de desplazamientos químicos, de forma que puede usar solo la información de conectividad observada en los espectros de RMN 2D y las integrales( las constantes de acople también son una posibilidad, pero no fueron exploradas en este trabajo). Sin embargo el sistema puede usar los desplazamientos químicos si están disponibles; 4. puede aprender de forma no supervisada, la relación entre configuraciones de átomos y desplazamientos químicos mientras resuelve problemas de asignación, lo que le permite mejorar mientras trabaja, de forma análoga a como lo hace un humano. Este sistema es completamente de código abierto, al igual que los datos que se usaron en este trabajo.Doctorad

    Molecular MRI in the Earth's Magnetic Field Using Continuous Hyperpolarization of a Biomolecule in Water

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    In this work, we illustrate a method to continuously hyperpolarize a biomolecule, nicotinamide, in water using parahydrogen and signal amplification by reversible exchange (SABRE). Building on the preparation procedure described recently by Truong et al. [ J. Phys. Chem. B, 2014, 118, 13882-13889 ], aqueous solutions of nicotinamide and an Ir-IMes catalyst were prepared for low-field NMR and MRI. The 1H-polarization was continuously renewed and monitored by NMR experiments at 5.9 mT for more than 1000 s. The polarization achieved corresponds to that induced by a 46 T magnet (P = 1.6 × 10-4) or an enhancement of 104. The polarization persisted, although reduced, if cell culture medium (DPBS with Ca2+ and Mg2+) or human cells (HL-60) were added, but was no longer observable after the addition of human blood. Using a portable MRI unit, fast 1H-MRI was enabled by cycling the magnetic field between 5 mT and the Earth's field for hyperpolarization and imaging, respectively. A model describing the underlying spin physics was developed that revealed a polarization pattern depending on both contact time and magnetic field. Furthermore, the model predicts an opposite phase of the dihydrogen and substrate signal after one exchange, which is likely to result in the cancelation of some signal at low field

    Phytochemical study and biological activities of diterpenes and derivatives from Plectranthus species

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    Tese de doutoramento, Farmácia (Química Farmacêutica e Terapêutica), Universidade de Lisboa, Faculdade de Farmácia, 2011This study focused on the research of new bioactive constituents from four species of the Plectranthus plants. Previous works on plants of the genus Plectranthus (Lamiaceæ) evidenced that some of their constituents possess interesting biological activities. The antimicrobial activity of the plant extracts and of the isolated metabolites was thoroughly searched. Antioxidant, anticholinesterase and anti-inflammatory properties of some compounds were also screened. The phytochemical study of the acetone extracts of Plectranthus ornatus Codd., P. ecklonii Benth., P. porcatus Winter & Van Jaarsv and P. saccatus Benth. rendered several terpenoid constituents mostly diterpenes. From P. ornatus three new forskolin-like labdane diterpenes (6-O-acetylforskolin, 1,6-di-O-acetylforskolin and 1,6-di-O-acetyl-9-deoxyforskolin), a new diterpene with the rare halimane skeleton (11R*-acetoxyhalima-5,13E-dien-15-oic acid), and two known labdane diterpenes were isolated; the rhinocerotinoic acid which was found in Plectranthus species for the first time, and plectrornatin C. Six known triterpenoids were also identified as mixtures. The study of P. ecklonii led to the isolation of two known abietanes, sugiol and parvifloron D. Sugiol was obtained from Plectranthus species for the first time. Four known triterpenoids were also identified as mixtures. P. porcatus, a plant not hitherto studied, yield a new spiro-abietane diterpene [(13S,15S)-6β,7α,12α,19-tetrahydroxy-13β,16-cyclo-8-abietene-11,14-dione]. A new beyerane diterpene (ent-7α-acetoxy-15-beyeren-18-oic acid) was isolated from P. saccatus. Attempting to find novel bioactive prototypes from the more potent antibacterial diterpenes, isolated in higher yields, some diterpene derivatives were prepared. Nine new derivatives were obtained from (11R*,13E)-11-acetoxyhalima-5,13-dien-15-oic acid (P. ornatus). A new 2β-(4-hydroxy)benzoyloxy derivative of microstegiol was prepared from parvifloron D (P. ecklonii). From the 7α-acetoxy-6β-hydroxyroyleanone (isolated in the past from P. grandidentatus) thirteen ester derivatives were synthesized, whereof ten were new compounds. The unequivocal chemical structures of pure compounds (natural and derivatives) were deduced from their spectroscopic (IR, MS, 1D and 2D NMR experiments) and physico-chemical data, as well as from literature information. The preliminary antimicrobial activity screenings of all the isolated metabolites showed that several diterpenes inhibited the growth of the Gram positive bacteria tested. In addition, the minimum inhibitory concentration against standard and clinical isolates of sensitive and resistant Staphylococcus and Enterococcus strains was determined for the antibacterial metabolites and their synthesized derivatives. The (11R*,13E)-11-acetoxyhalima-5,13-dien-15-oic acid and its (11R*,13E)-halima-5,13-diene-11,15-diol derivative were the more active halimanes. Parvifloron D was less active than its microstegiol 2β-(4-hydroxy)benzoate derivative, but both showed more potent antibacterial activities than the halimane diterpenoids. The three 12-O-benzoyl esters derivatives of the 7α-acetoxy-6β-hydroxyroyleanone prototype revealed to be more potent growth inhibitors against Staphylococcus and Enterococcus strains than the prototype. The 6β-propionyloxy-12-O-propionyl derivative also showed to be more active against Enterococcus than the viii prototype. Generally, the 12-esters and the 6,12-diesters were more active against Enterococcus than Staphylococcus strains. The hydrophobic extra-interactions with the bacterial targets seem to play an important role on the activity of royleanones derivatives prepared. Taking into account the IC50 values which expressed the scavenging DPPH radical ability, the isolated metabolite parvifloron D as well as 7α-acetoxy-6β-hydroxyroyleanone showed in vitro antioxidant activity. The in vitro acetylcholinesterase assay did not detect any activity for all the newly isolated diterpenes and 7α-acetoxy-6β-hydroxyroyleanone. The COX inhibitor screening assay was tested on 6-O-acetylforskolin, rhinocerotinoic acid, plectrornatin C, (11R*,13E)-halima-5,13-diene-11,15- diol, 11R*-acetoxyhalima-5,13E-dien-15-oic acid and on its methyl ester, for their ability to inhibit COX-2. The preliminary results encourage further studies aiming to confirm and to examine its potential anti-inflammatory activity in a more robust approach.estudo teve como objectivo a pesquisa de novos constituintes bioactivos de quatro espécies de plantas do género Plectranthus. A actividade antimicrobiana dos extractos obtidos e dos metabolitos isolados foi realizada e foram testadas as propriedades anti-oxidante, anti-colinesterase e anti-inflamatória de alguns compostos. O estudo fitoquímico dos extractos de acetona de Plectranthus ornatus Codd., P. ecklonii Benth., P. porcatus Winter & Van Jaarsv. e P. saccatus Benth. originou diversos constituintes terpénicos, principalmente diterpenos. Três novos diterpenos do tipo forskolina (6-O-acetilforskolina; 1,6-di-O-acetilforskolina e 1,6-di-O-acetil-9-deoxiforskolina) foram isolados de P. ornatus. Foram também identificados um novo diterpeno com o raro esqueleto de halimano (ácido 11R*-acetoxihalima-5,13E-dien-15-óico), dois diterpenos labdânicos conhecidos; o ácido rinocerotinóico encontrado pela primeira vez em espécies do género Plectranthus, e a plectrornatina C. Seis triterpenos já conhecidos foram igualmente identificados na forma de misturas. O estudo de P. ecklonii originou o isolamento de dois abietanos conhecidos: o sugiol e a parviflorona D. O sugiol foi isolado pela primeira vez de espécies Plectranthus. Outros quatro triterpenos conhecidos foram identificados também como misturas. A planta P. porcatus, até à data não estudada, originou um novo diterpeno spiro-abietânico [(13S,15S)-6β,7α,12α,19-tetrahidroxi-13β,16-ciclo-8-abietene-11,14-diona]. Um novo diterpeno com esqueleto de beierano (ácido ent-7α-acetoxi-15-beieren-18-óico) foi isolado de P. saccatus. Na tentativa de obter novos protótipos bioactivos, vários derivados foram preparados, a partir dos diterpenos antibacterianos mais potentes e isolados em maior quantidade. Nove novos derivados foram obtidos do ácido (11R*,13E)-11-acetoxihalima-5,13-dien-15-óico (P. ornatus). Um novo derivado 2β-(4-hidroxi)benzoilado do microstegiol, foi preparado a partir da parviflorona D (P. ecklonii). Treze ésteres derivados da 7α-acetoxi-6β-hidroxiroyleanona (isolada anteriormente de P. grandidentatus) foram sintetizados, sendo de assinalar que dez dos derivados são compostos novos. A determinação estrutural dos compostos puros (naturais e derivados) foi deduzida por espectroscopia (IV, EM, RMN 1D e 2D), propriedades físico-químicas e com base na informação obtida da literatura. O estudo preliminar da actividade antimicrobiana de todos os metabolitos isolados, mostrou que diversos diterpenos inibem o crescimento de bactérias de Gram positivo. A concentração mínima inibitória (CMI) dos metabolitos e seus derivados foi determinada em estirpes de Staphylococcus e Enterococcus, tanto em bactérias padrão como em isolados clínicos resistentes e sensíveis a antibióticos. O ácido (11R*,13E)-11-acetoxihalima-5,13-dien-15-óico e o seu derivado (11R*,13E)-halima-5,13-diene-11,15-diol foram os halimanos mais activos. A parviflorona D foi menos activa do que o seu correspondente derivado 2β-(4-hidroxi)benzoilado, mas ambos apresentaram uma actividade antibacteriana mais potente do que os diterpenos com esqueleto de halimano. Os três 12-O-benzoil-ésteres derivados do protótipo 7α-acetoxi-6β-hidroxiroyleanona revelaram ser inibidores mais potentes do que a royleanona-protótipo, contra as estirpes testadas de Staphylococcus e Enterococcus. O derivado 6β-propioniloxi-12-O-propionilo mostrou ser o mais activo contra as estirpes testadas de Enterococcus do que o protótipo. De um modo geral, os derivados 12-ésteres e os 6,12-diésteres foram mais activos contra as estirpes de Enterococcus do que as estirpes de Staphylococcus testadas. As interacções hidrofóbicas com os alvos bacterianos parecem ter um papel importante na actividade antibacteriana dos derivados de royleanona preparados. Os metabolitos parviflorona D e a 7α-acetoxi-6β-hidroxiroyleanona demostraram possuir actividade antioxidante in vitro, tendo em conta os valores de IC50 que expressam a actividade anti-oxidante com base na captura do radical DPPH. Todos os novos diterpenos isolados e derivados obtidos neste trabalho foram testados e não revelaram possuir actividade inibitória da acetilcolinesterase in vitro. A actividade anti-inflamatória foi testada nos compostos 6-O-acetilforskolina, ácido rinocerotinóico, plectrornatina C, (11R*,13E)-halima-5,13-diene-11,15-diol, ácido 11R*-acetoxihalima-5,13E-dien-15-óico e no seu éster metílico, através da sua capacidade de inibir a COX-2. Os resultados preliminares obtidos apoiam a necessidade de estudos futuros de forma a confirmar, explorar e discutir uma potencial actividade anti-inflamatória.The research work was performed, mostly, in the Faculdade de Farmácia da Universidade de Lisboa at the Medicinal Chemistry Group (former Centro de Estudos de Ciências Farmacêuticas – CECF) of the Institute for Medicines and Pharmaceutical Sciences (iMed.UL). Funding to these research centres and the attribution of a Doctoral degree grant (SFRH/BD/19250/2004) were provided by the Fundação para a Ciência e a Tecnologia - Ministério da Ciência, Tecnologia e Ensino Superior (FCT-MCTES)

    Reducing CO2 and Corrosion: Insights from Thermodynamic Descriptors Calculated with Density Functional Theory

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    Catalytic reaction mechanisms can be extremely complex, and it is difficult to determine all the factors that control reaction rates. Fortunately, complex chemical phenomena can frequently be described by thermodynamic properties (such as molecular pKas and reaction overpotentials) that correlate with catalytic reaction rates. While these properties can be difficult or time intensive to measure experimentally, they can be easily computed using Kohn-Sham density functional theory (KS-DFT). We have developed a thermodynamic descriptor-based model that uses molecular pKas and redox potentials calculated with KS-DFT to predict the electrochemical conditions at which aromatic N-heterocycle (ANH) molecules could facilitate multi-proton and multi-electron reduction reactions. By automating this procedure using the ADF modeling suite, we can rapidly screen through potential catalysts with minimal user input. To establish a baseline procedure for studying the chemical reduction of CO2 via hydride transfers from ANH molecules, we characterized the chemical reduction of CO2 by hydride transfers from sodium borohydride. We located hydride transfer pathways with nudged elastic band calculations and obtained free energy barriers from potentials of mean force derived from constrained molecular dynamics simulations along the reaction pathways. These simulations provided reaction energetics at realistic operating conditions and highlighted the potential pitfalls of only studying reaction pathways at 0 K. Cathodic reduction reactions can limit galvanic corrosion rates in atmospheric environments. To help guide the design of titanium alloys that resist galvanic corrosion, we used density functional theory to predict dopants that inhibit cathodic reduction reaction kinetics on oxide surfaces. We calculated overpotentials for the oxygen reduction reaction (ORR) occurring on metal dopants in an amorphous TiO2 surface. These overpotential trends successfully predicted six dopants that have been experimentally verified to inhibit ORR activity by up to 77% (Sn, Cr, Co, Al, Mn, and V). Next, we used this approach to study the native oxides of Ti-6Al-4V, a Ti alloy with improved corrosion resistance. We used Behler-Parrinello neural networks to create defective and amorphous surface models for TiAl2O5 (the oxide that forms on Ti-6Al-4V surfaces in addition to TiO2) and predicted how ORR activity was altered by different complex oxide surface morphologies

    Extraction of Cashew Nutshell Liquid from Cashew Nutshells Using the Polyol Induced Extraction (PIE) Method

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    Polyol-induced extraction (PIE) is applied to the extraction of cashew nutshell liquid (CNSL) from cashew nutshells using glycerol as a mass-separating agent. In this process, a 1:1 mixture of acetonitrile (ACN) and water (H2O) exists as a completely miscible homogeneous phase. Cashew nutshells are soaked in this mixture at room temperature. After soaking the nutshells in acetonitrile/water, a polyol, glycerol, is added to the mixture, and a phase separation is observed. Upon cooling to –20 °C, the phase separation increases with an acetonitrile-rich top layer containing analytes that are soluble in acetonitrile. Initially, the PIE process was studied with pure caffeine as a test molecule. Using a 1:1 mixture of acetonitrile and water, 20 % (w/v) of glycerol and cooling to -21 °C, an acetonitrile-rich upper layer formed in 30 % of the total volume. The partition coefficient for caffeine (KPC) was 1.0. Extraction of 2.06 g of cashew nutshells produced 0.33 g of CNSL in 16.0 % yield. By comparison, extraction of 2.05 g of cashew nutshells with cyclohexane produced 0.38 g of CNSL in 18.5 % yield. While extraction of 12.01 g of cashew nutshells by the Soxhlet method using acetonitrile as a solvent produced 2.45 g CNSL in 20.4 % yield. The extracts were analyzed by HPLC, GC-MS, and NMR instruments. The HPLC peak area analysis revealed that the saturated form of anacardic acid was only a minor component (about 2 %). Moreover, 1H NMR analysis indicates that the major component of the extract is a decarboxylated derivate of anacardic acid (Figure 1) which is cardanol (Figure 2). This decarboxylation reaction occurred in the GC-MS due to the loss of the carboxylic acid group from the anacardic acid

    Development and application of Nuclear Magnetic Resonance spectroscopy and chemometric methods for the analysis of the metabolome of Saccharomyces cerevisiae under different growing conditions

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    [eng] Nuclear Magnetic Resonance (NMR) spectroscopy is able to produce by a single direct measurement a very high amount of chemical information. However, this information is not always easy to interpret. In fact, the complexity of the NMR spectral data analysis is proportional to the number of compounds present simultaneously in the analyzed sample, as resonances from different compounds overlap. One of the most extreme situations can be found for NMR spectra of samples from metabolomics studies, from which approximately fifty compounds can be detected in a single measurement. In the study of the chemical processes involving metabolites (metabolomics), the most commonly used NMR spectra are the one-dimensional proton (1D 1H) NMR spectra, since they are relatively fast to acquire and proton sensitivity is the highest. The 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra are also frequently used in metabolomics for an improved structural characterization of the detected metabolites. In this Thesis, we have developed different data analysis strategies of 1H NMR and 1H-13C HSQC NMR metabolomics datasets. The investigated NMR spectra were acquired from extracts of Saccharomyces cerevisiae cells previously exposed to different environmental perturbations. The aim of these studies was to better understand the different metabolic processes that regulate the yeast metabolism acclimation to different growing conditions. From the study of these NMR metabolomics experiments, we designed new strategies and protocols for the analysis of these datasets that include the steps of data import, data pre-treatment, resonance assignment and metabolite quantification. Moreover, different chemometric methods were applied for the identification of the possible biomarkers that define the metabolic states of yeast cells and to extract the main metabolic profiles that describe the observed changes in the metabolome. Furthermore, two chemometric strategies were proposed for the untargeted analysis of 1H NMR and 1H-13C HSQC NMR, respectively. For the study of 1H NMR spectra of metabolomics samples, the application of the Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) chemometric method allowed the satisfactory resolution of the individual 1H NMR spectra and concentrations of the different metabolites. On the other hand, the investigation of metabolomics datasets by 1H-13C HSQC NMR revealed that most of the data values in these NMR spectra are only descriptive of noise, hampering their chemometric data analysis. In this context, a new strategy to filter the variables relative to noise, named ‘Variables of Interest’ (or VOI) is proposed. After the application of this procedure, we observed that the analysis of the noise-filtered 1H-13C HSQC NMR spectra produced similar results to the corresponding analysis of 1H NMR spectra. Due to the existence of the second dimension in the 1H-13C HSQC NMR spectra, resonances are less overlapped and they could be integrated without using deconvolution approaches. For all these reasons, and linked to the fact that more chemical information is contained in the 1H-13C HSQC NMR spectra than in the 1H NMR spectra, the analysis of noise-filtered 1H-13C HSQC NMR spectra allow a more accurate characterization of the metabolomic system, in a reduced amount of time in comparison to the analysis of the corresponding 1H NMR spectra.[cat] L'espectroscòpia de ressonància magnètica nuclear (RMN) és capaç de generar mitjançant una mesura simple i directa una gran quantitat d'informació química. Tanmateix, aquesta informació no sempre és fàcil d'interpretar. De fet, la complexitat de l'anàlisi espectral és proporcional al nombre de compostos presents en la mostra analitzada, ja que les ressonàncies dels diferents compostos es troben superposades. Una de les situacions més extremes la podem trobar en el cas dels espectres de RMN de mostres obtingudes en estudis de metabolòmica, en les que es poden arribar a detectar al voltant d’una cinquantena de compostos en una sola mesura. En l'estudi dels processos químics relacionats amb els metabòlits (metabolòmica), els espectres de RMN més utilitzats són els espectres monodimensionals de protó (1D 1H), ja que són relativament ràpids d'adquirir i la sensibilitat del protó és la més alta. És també corrent utilitzar en estudis de metabolòmica els espectres de RMN bidimensionals 1H-13C heteronuclears de coherència quàntica única (2D 1H-13C HSQC), els quals permeten obtenir una millor caracterització estructural dels metabòlits detectats. En aquesta Tesi, s’han desenvolupat diferents estratègies d'anàlisi d’espectres de RMN de 1H i de 1H-13C HSQC de mostres de metabolòmica. Els espectres de RMN van ser adquirits d’extractes de llevat Saccharomyces cerevisiae que prèviament havia estat exposat a diferents pertorbacions mediambientals. L’objectiu d’aquests estudis ha estat millorar la comprensió dels diferents processos metabòlics que regulen l'aclimatació de les cèl·lules de llevat a diferents condicions de creixement. A partir d’aquests estudis de metabolòmica realitzats, es van dissenyar noves estratègies i protocols d'anàlisi de dades de RMN que inclouen la seva importació, el seu preprocessament, l'assignació de les ressonàncies i la seva integració. A més, es van aplicar diferents mètodes quimiomètrics que van permetre identificar els biomarcadors de l’estat metabòlic de les cèl·lules del llevat i extreure els principals perfils metabòlics que descriuen els canvis en el seu metabolisme. Es van proposar a més, dues estratègies quimiomètriques per a l’anàlisi no dirigida d’espectres de RMN de 1H i de 1H-13C HSQC, respectivament. En el cas dels estudis d’espectres de RMN de 1H, l'aplicació del mètode de resolució multivariant de corbes per mínims quadrats alternats (MCR-ALS) va permetre resoldre satisfactòriament les concentracions i els espectres individuals dels diferents metabòlits. D’altra banda, la investigació de l’estructura de les dades dels espectres de RMN de 1H-13C HSQC va revelar que la majoria dels valors espectrals són descriptius del soroll, cosa que dificulta la seva anàlisi. En aquest context, s’ha desenvolupat una nova estratègia per filtrar les variables descriptives del soroll, anomenada selecció de les variables d'interès (Variables of Interest, VOI). Després d’aplicar aquest procediment, es va observar que l'anàlisi dels espectres 1H-13C HSQC filtrats produeix resultats similars als obtinguts amb els espectres corresponents de 1H. Degut a l’existència de la segona dimensió en els espectres de 1H-13C HSQC, les ressonàncies estan menys solapades i es poden integrar sense fer servir estratègies basades en la seva deconvolució. Degut a tot això i al fet que els espectres de 1H-13C HSQC contenen més informació química que els de 1H, l’anàlisi dels espectres de 1H-13C HSQC filtrats amb aquest procediment permet una caracterització del sistema metabolòmic més acurada i amb temps d’anàlisis més curts, en comparació a l’anàlisi dels espectres de 1H corresponents
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