Benefits of membrane electrodes in the electrochemistry of metalloproteins: mediated catalysis of Paracoccus pantotrophus cytochrome c peroxidase by horse cytochrome c: a case study

J Biol Inorg Chem. 2008 Jun;13(5):779-87. doi: 10.1007/s00775-008-0365-8

One-pot enzymatic resolution/separation of enantiomers using green solvents

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Biotransformation/separation routes to obtain pure enantiomers

The objective of the work presented in this thesis was the development of an innovative approach for the separation of enantiomers of secondary alcohols, combining the use of an ionic liquid (IL) - both as solvent for conducting enzymatic kinetic resolution and as acylating agent - with the use of carbon dioxide (CO2) as solvent for extraction. Menthol was selected for testing this reaction/separation approach due to the increasing demand for this substance, which is widely used in the pharmaceutical, cosmetics and food industries. With a view to using an ionic ester as acylating agent, whose conversion led to the release of ethanol, and due to the need to remove this alcohol so as to drive reaction equilibrium forward, a phase equilibrium study was conducted for the ehtanol/(±)-menthol/CO2 system, at pressures between 8 and 10 MPa and temperatures between 40 and 50 oC. It was found that CO2 is more selective towards ethanol, especially at the lowest pressure and highest temperature tested, leading to separation factors in the range 1.6-7.6. The pressure-temperature-composition data obtained were correlated with the Peng-Robinson equation of state and the Mathias-Klotz-Prausnitz mixing rule. The model fit the experimental results well, with an average absolute deviation (AAD) of 3.7 %. The resolution of racemic menthol was studied using two lipases, namely lipase from Candida rugosa (CRL) and immobilized lipase B from Candida antarctica (CALB), and two ionic acylating esters. No reaction was detected in either case. (R,S)-1-phenylethanol was used next, and it was found that with CRL low, nonselective, conversion of the alcohol took place, whereas CALB led to an enantiomeric excess (ee) of the substrate of 95%, at 30% conversion. Other acylating agents were tested for the resolution of (±)-menthol, namely vinyl esters and acid anhydrides, using several lipases and varying other parameters that affect conversion and enantioselectivity, such as substrate concentration, solvent and temperature. One such acylating agent was propionic anhydride. It was thus performed a phase equilibrium study on the propionic anhydride/CO2 system, at temperatures between 35 and 50 oC. This study revealed that, at 35 oC and pressures from 7 MPa, the system is monophasic for all compositions. The enzymatic catalysis studies carried out with propionic anhydride revealed that the extent of noncatalyzed reaction was high, with a negative effect on enantioselectivity. These studies showed also that it was possible to reduce considerably the impact of the noncatalyzed reaction relative to the reaction catalyzed by CRL by lowering temperature to 4 oC. Vinyl decanoate was shown to lead to the best results at conditions amenable to a process combining the use of supercritical CO2 as agent for post-reaction separation. The use of vinyl decanoate in a number of IL solvents, namely [bmim][PF6], [bmim][BF4], [hmim][PF6], [omim][PF6], and [bmim][Tf2N], led to an enantiomeric excess of product (eep) values of over 96%, at about 50% conversion, using CRL. In n-hexane and supercritical CO2, reaction progressed more slowly.(...)

Enzymatic activity mastered by altering metal coordination spheres

J Biol Inorg Chem (2008) 13:1185–1195 DOI 10.1007/s00775-008-0414-3

Development of new methodologies in sample treatment for proteomics workflow based on enzymatic probe sonication technology and mass spectrometry

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia, for the degree of Doctor of Philosophy in Biochemistry

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

Mediated catalysis of Paracoccus pantotrophus cytochrome c peroxidase by P. pantotrophus pseudoazurin: kinetics of intermolecular electron transfer

J Biol Inorg Chem (2007) 12:691–698 DOI 10.1007/s00775-007-0219-9

“One-pot” enzymatic conversion of CO2 to methanol

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Valorisation of vegetable oil deodorizer distillate by enzymatic reaction and membrane processing

Dissertação para obtenção do Grau de Doutora em Engenharia Química e Bioquímica

Multi-Enzymatic Systems for Cleaning-up Synthetic Dyes from the Environment

Environmental pollution is one of the major and most important problems of the modern world. In order to fulfill the needs and demands of the overgrowing human population, developments in agriculture, medicine, energy sources, and all chemical industries are necessary (Ali 2010). Over the last century, the increased industrialization and continued population growth led to an augmented production of environmental pollutants that are released into air, water, and soil, with significant impact in the degradation of various ecosystems (Ali 2010, Khan et al. 2013).(...)

Valorization of agroindustrial waste

The main objective of this work is the valorization of residues from agro-industry giving them an added value. The valorization was performed by using a "green" and sustainable solvent - supercritical fluid, in this case carbon dioxide. Two residues and one biomass were used to produce two different final products, thereby emphasizing the versatility of the waste recovery - spent coffee grounds and microalgae Chlorella protothecoides to produce biodiesel, and tomato pomace to extract carotenoids. In the first part of this work it was demonstrated the possibility to obtain a conversion of coffee spent grounds oil into biodiesel, through an enzymatic transesterification reaction, of 98.01% with the following operating conditions: molar ratio oil:methanol 1:24, residence time 0.8 min, pressure 25 MPa, temperature 313,15K. In this first phase, it was also used the microalgae Chlorella protothecoides, a biomass, to produce biodiesel and favorable results were obtained with this green process compared with a traditional process - basic catalysis / acid. In the second part of this work, by an extraction with supercritical CO2 it was obtained 3.38% oil from tomato pomace under the following conditions: pressure 35.1 MPa, temperature 313,15K. It was found that this oil contains various carotenoids: β-carotene, lutein and lycopene. The latter is present in larger amount.

Experimental design methodologies for the identification of Michaelis-Menten type kinetics

The main objective of this work was to investigate the application of experimental design techniques for the identification of Michaelis-Menten kinetic parameters. More specifically, this study attempts to elucidate the relative advantages/disadvantages of employing complex experimental design techniques in relation to equidistant sampling when applied to different reactor operation modes. All studies were supported by simulation data of a generic enzymatic process that obeys to the Michaelis-Menten kinetic equation. Different aspects were investigated, such as the influence of the reactor operation mode (batch, fed-batch with pulse wise feeding and fed-batch with continuous feeding) and the experimental design optimality criteria on the effectiveness of kinetic parameters identification. The following experimental design optimality criteria were investigated: 1) minimization of the sum of the diagonal of the Fisher information matrix (FIM) inverse (A-criterion), 2) maximization of the determinant of the FIM (D-criterion), 3) maximization of the smallest eigenvalue of the FIM (E-criterion) and 4) minimization of the quotient between the largest and the smallest eigenvalue (modified E-criterion). The comparison and assessment of the different methodologies was made on the basis of the Cramér-Rao lower bounds (CRLB) error in respect to the parameters vmax and Km of the Michaelis-Menten kinetic equation. In what concerns the reactor operation mode, it was concluded that fed-batch (pulses) is better than batch operation for parameter identification. When the former operation mode is adopted, the vmax CRLB error is lowered by 18.6 % while the Km CRLB error is lowered by 26.4 % when compared to the batch operation mode. Regarding the optimality criteria, the best method was the A-criterion, with an average vmax CRLB of 6.34 % and 5.27 %, for batch and fed-batch (pulses), respectively, while presenting a Km’s CRLB of 25.1 % and 18.1 %, for batch and fed-batch (pulses), respectively. As a general conclusion of the present study, it can be stated that experimental design is justified if the starting parameters CRLB errors are inferior to 19.5 % (vmax) and 45% (Km), for batch processes, and inferior to 42 % and to 50% for fed-batch (pulses) process. Otherwise equidistant sampling is a more rational decision. This conclusion clearly supports that, for fed-batch operation, the use of experimental design is likely to largely improve the identification of Michaelis-Menten kinetic parameters.

Copper-containing nitrite reductase from Pseudomonas chlororaphis DSM 50135

Eur. J. Biochem. 271, 2361–2369 (2004)

Kinetic behavior of Desulfovibrio gigas aldehyde oxidoreductase encapsulated in reverse micelles

Biochemical and Biophysical Research Communications 308 (2003) 73–78

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.