Biomimetic mineralization: encapsulation in calcium carbonate shells

Calcium carbonate biomineralization is a self-assembly process that has been studied to be applied in the biomedical field to encapsulate biomolecules. Advantages of engineering mineral capsules include improved drug loading efficiencies and protection against external environment. However, common production methods result in heterogeneous capsules and subject biomolecules to heat and vibration which cause irreversible damage. To overcome these issues, a microfluidic device was designed, manufactured and tested in terms of selectivity for water and oil to produce a W/O/W emulsion. During the development of this work there was one critical challenge: the selective functionalization in closed microfluidic channels. Wet chemical oxidation of PDMS with 1M NaOH, confirmed by FTIR, followed by adsorption of polyelectrolytes - PDADMAC/PSS - confirmed by UV-Vis and AFM results, render the surface of PDMS hydrophilic. UV-Vis spectroscopy also confirmed that this modification did not affect PDMS optical properties, making possible to monitor fluids and droplets. More important, with this approach PDMS remains hydrophilic over time. However, due to equipment constrains selectivity in microchannels was not achieved. Therefore, emulsion studies took place with conventional methods. Several systems were tried, with promising results achieved with CaCO3 in-situ precipitation, without the use of polymers or magnesium. This mineral stabilizes oil droplets in water, but not in air due to incomplete capsule formation.

Formation of a stable cyano-bridged dinuclear iron cluster following oxidation of the superoxide reductases from Treponema pallidum and Desulfovibrio vulgaris with K(3)Fe(CN)(6)

Inorg. Chem., 2003, 42 (4), pp 938–940 DOI: 10.1021/ic0262886

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

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.

Chemical reaction network of flavylium ions in heterogeneous media

Dissertação apresentada para a obtenção do Grau de Doutor em Química, especialidade em Química-Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

MBR activated sludge filterability characterization in cross flow filtration

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Sanitária

Spectroscopic Studies and Characterization of a Novel Electron-Transfer Chain

A novel two-component enzyme system from Escherichia coli involving a flavorubredoxin (FlRd) and its reductase was studied in terms of spectroscopic, redox, and biochemical properties of its constituents. FlRd contains one FMN and one rubredoxin (Rd) center per monomer. To assess the role of the Rd domain, FlRd and a truncated form lacking the Rd domain (FlRd¢Rd), were characterized. FlRd contains 2.9 ( 0.5 iron atoms/subunit, whereas FlRd¢Rd contains 2.1 ( 0.6 iron atoms/subunit. While for FlRd one iron atom corresponds to the Rd center, the other two irons, also present in FlRd¢Rd, are most probably due to a di-iron site. Redox titrations of FlRd using EPR and visible spectroscopies allowed us to determine that the Rd site has a reduction potential of -140 ( 15 mV, whereas the FMN undergoes reduction via a red-semiquinone, at -140 ( 15 mV (Flox/Flsq) and -180 ( 15 mV (Flsq/Flred), at pH 7.6. The Rd site has the lowest potential ever reported for a Rd center, which may be correlated with specific amino acid substitutions close to both cysteine clusters. The gene adjacent to that encoding FlRd was found to code for an FAD-containing protein, (flavo)rubredoxin reductase (FlRd-reductase), which is capable of mediating electron transfer from NADH to DesulfoVibrio gigas Rd as well as to E. coli FlRd. Furthermore, electron donation was found to proceed through the Rd domain of FlRd as the Rd-truncated protein does not react with FlRd-reductase. In vitro, this pathway links NADH oxidation with dioxygen reduction. The possible function of this chain is discussed considering the presence of FlRd homologues in all known genomes of anaerobes and facultative aerobes.

Chemical analysis of 17th century Millefiori glasses excavated in the Monastery of Sta. Clara‐a‐Velha: comparison with Venetian and façon‐de‐Venise production

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Conservação e Restauro,Área de especialização Cerâmica e Vidro

Real fábrica de vidros de Coina – Chemical analysis of archaeological glass fragments

Actas do 17º Congresso da Associação Internacional para a História do Vidro

The removal of xenobiotic compounds from wastewater through the use of biological processes and advanced oxidation technologies

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Química e Bioquímica pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Decavanadate interactions with actin: cysteine oxidation and vanadyl formation

Dalton Trans., 2009, 7985–7994

Fsp27/CIDEC is a CREB target gene induced during fasting and regulated by fatty acid oxidation rate

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Ligand K-edge X-ray absorption spectroscopy and DFT calculations on [Fe3S4]0,+ clusters: delocalization, redox, and effect of the protein environment

Ligand K-edge XAS of an [Fe3S4]0 model complex is reported. The pre-edge can be resolved into contributions from the í2Ssulfide, í3Ssulfide, and Sthiolate ligands. The average ligand-metal bond covalencies obtained from these pre-edges are further distributed between Fe3+ and Fe2.5+ components using DFT calculations. The bridging ligand covalency in the [Fe2S2]+ subsite of the [Fe3S4]0 cluster is found to be significantly lower than its value in a reduced [Fe2S2] cluster (38% vs 61%, respectively). This lowered bridging ligand covalency reduces the superexchange coupling parameter J relative to its value in a reduced [Fe2S2]+ site (-146 cm-1 vs -360 cm-1, respectively). This decrease in J, along with estimates of the double exchange parameter B and vibronic coupling parameter ì2/k-, leads to an S ) 2 delocalized ground state in the [Fe3S4]0 cluster. The S K-edge XAS of the protein ferredoxin II (Fd II) from the D. gigas active site shows a decrease in covalency compared to the model complex, in the same oxidation state, which correlates with the number of H-bonding interactions to specific sulfur ligands present in the active site. The changes in ligand-metal bond covalencies upon redox compared with DFT calculations indicate that the redox reaction involves a two-electron change (one-electron ionization plus a spin change of a second electron) with significant electronic relaxation. The presence of the redox inactive Fe3+ center is found to decrease the barrier of the redox process in the [Fe3S4] cluster due to its strong antiferromagnetic coupling with the redox active Fe2S2 subsite.

The mechanism of formate oxidation by metal-dependent formate dehydrogenases

J Biol Inorg Chem (2011) 16:1255–1268 DOI 10.1007/s00775-011-0813-8

Low-Spin Heme b3 in the Catalytic Center of Nitric Oxide Reductase from Pseudomonas nautica

Biochemistry, 2011, 50 (20), pp 4251–4262 DOI: 10.1021/bi101605p

Kinetic, Structural, and EPR Studies Reveal That Aldehyde Oxidoreductase from Desulfovibrio gigas Does Not Need a Sulfido Ligand for Catalysis and Give Evidence for a Direct Mo-C Interaction in a Biological System

J. Am. Chem. Soc., 2009, 131 (23), pp 7990–7998 DOI: 10.1021/ja809448r