Employing correlation consistent basis sets coupled with electronic structure methods has enabled accurate predictions of chemical properties for second- and third-row main group and transition metal molecular species. For third-row (Ga-Kr) molecules, the performance of the correlation consistent basis sets (cc-pVnZ, n=D, T, Q, 5) for computing energetic (e.g., atomization energies, ionization energies, electron and proton affinities) and structural properties using the ab initio coupled cluster method including single, double, and quasiperturbative triple excitations [CCSD(T)] and the B3LYP density functional method was examined. The impact of relativistic corrections on these molecular properties was determined utilizing the Douglas-Kroll (cc-pVnZ-DK) and pseudopotential (cc-pVnZ-PP) forms of the correlation consistent basis sets. This work was extended to the characterization of molecular properties of novel chemically bonded krypton species, including HKrCl, FKrCF3, FKrSiF3, FKrGeF3, FKrCCF, and FKrCCKrF, and provided the first evidence of krypton bonding to germanium and the first di-krypton system. For second-row (Al-Ar) species, the construction of the core-valence correlation consistent basis sets, cc-pCVnZ was reexamined, and a revised series, cc-pCV(n+d)Z, was developed as a complement to the augmented tight-d valence series, cc-pV(n+d)Z. Benchmark calculations were performed to show the utility of these new sets for second-row species. Finally, the correlation consistent basis sets were used to study the structural and spectroscopic properties of Au(CO)Cl, providing conclusive evidence that luminescence in the solid-state can be attributed to oligomeric species rather than to the monomer.

Glutathione syntethase (GS) is an enzyme that belongs to the ATP-grasp superfamily and catalyzes the second step in the biosynthesis of glutathione. GS has been purified and sequenced from a variety of biological sources; still, its exact mechanism is not fully understood. Four highly conserved residues were identified in the binding site of human GS. Additionally, the G-loop residues that close the active site during catalysis were found to be conserved. Since these residues are important for catalysis, their function was studied computationally by site-directed mutagenesis. Starting from the reported crystal structure of human GS, different conformations for the wild type and mutants were obtained using molecular dynamics technique. The key interactions between residues and ligands were detected and found to be essential for enzyme activity.

Chloroperoxidase (CPO) is a potential biocatalyst for use in asymmetric synthesis. The mechanisms of CPO catalysis are therefore of interest. The halogenation reaction, one of several chemical reactions that CPO catalyzes, is not fully understood and is the subject of this dissertation. The mechanism by which CPO catalyzes halogenation is disputed. It has been postulated that halogenation of substrates occurs at the active site. Alternatively, it has been proposed that hypochlorous acid, produced at the active site via oxidation of chloride, is released prior to reaction, so that halogenation occurs in solution. The free-solution mechanism is supported by the observation that halogenation of most substrates often occurs non-stereospecifically. On the other hand, the enzyme-bound mechanism is supported by the observation that some large substrates undergo halogenation stereospecifically. The major purpose of this research is to compare chlorination of the substrate beta-cyclopentanedione in the two environments. One study was of the reaction with limited hydration because such a level of hydration is typical of the active site. For this work, a purely quantum mechanical approach was used. To model the aqueous environment, the limited hydration environment approach is not appropriate. Instead, reaction precursor conformations were obtained from a solvated molecular dynamics simulation, and reaction of potentially reactive molecular encounters was modeled with a hybrid quantum mechanical/molecular mechanical approach. Extensive work developing parameters for small molecules was pre-requisite for the molecular dynamics simulation. It is observed that a limited and optimized (active-site-like) hydration environment leads to a lower energetic barrier than the fully solvated model representative of the aqueous environment at room temperature, suggesting that the stable water network near the active site is likely to facilitate the chlorination mechanism. The influence of the solvent environment on the reaction barrier is critical. It is observed that stabilization of the catalytic water by other solvent molecules lowers the barrier for keto-enol tautomerization. Placement of water molecules is more important than the number of water molecules in such studies. The fully-solvated model demonstrates that reaction proceeds when the instantaneous dynamical water environment is close to optimal for stabilizing the transition state.

This dissertation focuses on the assessment and development of heterogeneous catalysts for the deperoxidation of cyclohexyl hydroperoxide and oxidation of cyclohexane, which will be based in metal oxides and gold nanoparticles. For this endeavour a multidisciplinary approach will be used combining theoretical chemistry, kinetic studies and synthesis and characterisation of materials. The starting choice for the catalyst to carry out the process is supported gold nanoparticles. The approach of this dissertation is to first model the mecha- nism of cyclohexyl hydroperoxide decomposition and oxidation of cyclohexane on gold nanoparticles by theoretical calculations, and use these findings to synthesise efficient heterogeneous catalysts which will be subsequently tested and optimised experimentally. But as it will be seen, some metal oxides are active rather than acting as mere supports, which will also be studied both theoretical and experimentally. Each chapter has a specific focus and constitutes a strand of the overall goal: Chapter 1 provides an introductory background on the topics that this dissertation lies upon: oxidation of cyclohexane, heterogeneous catalysis and catalysis by gold and metal oxides. Chapter 2 outlines the objectives of the thesis, formulating the relevant hypotheses of this research and the subsequent validation tests. Chapter 3 exposes the methodology with a brief conceptual background that has been used to carry out this work. Chapter 4 is the first chapter dealing with results. It consists in a theoretical study using density functional theory of the reaction mechanism over different models of gold nanoparticles, in order to study the influence of several parameters on their catalytic activity: the particle size, atom coordination, and presence of additional species like oxygen atoms and water. Chapter 5 uses the findings found in chapter 4 to drive the synthesis of supported gold nanoparticles. It consists in a experimental study of gold-based catalysts, which is combined with a theoretical study which takes into account an additional variable: the support. Chapter 6 exploits one of the findings of chapter 5. One of the supports used for anchoring the gold nanoparticles is active by itself, namely cerium oxide. This chapter comprises an experimental work about its activity, studying parameters like particle size, morphology and the effect of doping. Chapter 7 continues with the catalytic activity of cerium oxide-based materials, but now from a theoretical point of view. It first presents a systematic study of the parameters relevant for the proper quantum mechanical description of cerium oxide, which is followed by a mechanistic study on different models. Chapter 8 outlines the conclusions obtained in this dissertation, present- ing them in a summarised way. Even though each chapter presents its corresponding conclusions at its end, this chapter groups them all in a structured way for the reader's convenience, so a global view of the project can be swiftly grasped. The results herein further the knowledge of heterogeneous catalysis for the oxidation of cyclohexane, one of the most important industrial reactions, and which continues to be a challenge. Although the ultimate goal is to develop an industrial catalyst, the dissertation also aims to show how computational chemistry can drive the design of novel materials, and how it can help to understand catalytic reactions at the atomic level. / El presente trabajo se centra en el estudio y desarrollo de catalizadores heterogéneos para la desperoxidación de ciclohexil hidroperóxido y la oxidación de ciclohexano, basados en óxidos metálicos y nanopartículas de Au. Para lograr tal objetivo se ha usado un enfoque multidisciplinar, que combina química teórica y estudios cinéticos, a la vez que síntesis y caracterización de materiales. El candidato inicial para llevar a cabo el proceso consiste en partículas de Au soportadas. El camino a seguir pasa primero por modelizar el mecanismo de descomposición de ciclohexil hidroperóxido y oxidación de ciclohexano mediante cálculos teóricos, y utilizar el conocimiento generado por este estudio para dictar la síntesis de catalizadores heterogéneos, comprobando y optimizando posteriormente su actividad de forma experimental. Sin embargo, como será visto a lo largo de este trabajo, algunos óxidos metálicos dejan de lado su papel como mero soporte físico para las partículas de Au y son activos por sí mismos. Tal hecho será estudiado tanto teórica como experimentalmente. Cada capítulo tiene un objetivo específico, y es a su vez una parte del objetivo global de esta investigación: El capítulo 1 provee al lector de una breve introducción a los temas sobre los que yace este trabajo: oxidación de ciclohexano, catálisis heterogénea y catálisis mediante Au y óxidos metálicos. El capítulo 2 expone de una forma breve y concisa los objetivos de esta investigación, formulando la hipótesis de partida y los correspondientes experimentos para su validación. El capítulo 3 describe la metodología utilizada junto a una explicación de los fundamentos en los que se basa cada técnica. El capítulo 4 es el primer capítulo que discute los resultados obtenidos en esta investigación. Se trata de un estudio usando la teoria del funcional de densidad para investigar el mecanismo de reacción del proceso sobre diferentes modelos teóricos de Au, con el objetivo de comprender la influencia de diversos factores en la actividad catalítica, tales como el tamaño de partícula, la coordinación de los á'tomos de Au y la presencia de especies adicionales como átomos de O y agua. El capítulo 5 hace uso de los resultados obtenidos en el estudio anterior, y los utiliza para dirigir la síntesis de nanopartículas soportadas de Au. Se trata de un estudio experimental en el que se investigan diversos factores que pueden afectar a su actividad catalítica. Este estudio se combina a su vez con uno de tipo teórico en el que se tiene en cuenta la influencia del soporte en la actividad catalítica de las particulas de Au. El capítulo 6 se basa en uno de los resultados obtenidos en el capítulo 5. Uno de los soportes utilizados para anclar las partículas de Au resulta de por sí activo: el CeO2. Su notable actividad para catalizar este proceso exige un estudio en mayor profundidad, el cual se lleva a cabo en este capítulo. Parámetros como el tamaño de particula, la morfología de superficie y el dopaje entre otros se investigan en este punto. El capítulo 7 sigue la estela del trabajo anterior sobre CeO2, pero ahora desde el punto de vista de la química teórica. Presenta primero un estudio sistemático de parámetros relacionados con la mecánica cuá'ntica que afectan al CeO2, con el objetivo de alcanzar una descripción satisfactoria de los modelos teóricos para este óxido. Tras esto, se lleva a cabo un estudio del mecanismo de reacción en dichos modelos de CeO2, a fin de comprender el origen de su actividad catalítica. El capítulo 8 presenta de forma estructurada y concisa todas las conclusiones que se han sacado a raíz de los resultados obtenidos. Aún a pesar de que cada capítulo presenta sus correspondientes conclusiones al final, aquí se presentan de una forma agrupada a comodidad del lector, para que pueda obtener de forma ágil una visión global de los resultados de esta investigación. / Aquest treball es centra en l'estudi i desenvolupament de catalitzadors hetero- genis per a la desperoxidació de ciclohexil hidroperòxid i la oxidació de ciclohexà, basats en òxids metàl·lics i nanopartícules de Au. Per aconseguir aquest objectiu s'ha utilitzat un enfocament multidisciplinari, en el qual es combinen química teòrica i estudis cinètics amb síntesi i caracterització de materials. El candidat inicial per dur a terme el procés consisteix en partícules de Au suportades. El camí a seguir passa primer per modelitzar el mecanisme de descomposició del ciclohexil hidroperòxid i la oxidació de ciclohexà mitjançant càlculs teòrics, i utilitzar el coneixement generat per aquest estudi per dirigir la síntesi de catalitzadors heterogenis, comprovant i optimitzant posteriorment la seua activitat de forma experimental. No obstant això, com es veurà al llarg d'aquest treball, alguns òxids metàl·lics deixen de costat el seu paper com a suport físic de les partícules de Au y són actius per si mateixos. Aquest fet s'ha estudiat tant teòrica com experimentalment. Cada capítol té un objectiu específic i és al mateix temps una part de l'objectiu global d'aquesta recerca: El capítol 1 proporciona al lector una breu introducció als temes tractats en aquest treball: oxidació de ciclohexà, catàlisi heterogènia i catàlisi mitjançant Au i òxids metàl·lics. El capítol 2 exposa d'una forma breu i concisa els objectius d'aquesta investigació, formulant la hipòtesi inicial i els corresponents experiments per a la seua validació. El capítol 3 descriu la metodologia utilitzada conjuntament amb una explicació dels fonaments en els quals es basa cada tècnica. El capítol 4 és el primer capítol que discuteix els resultats obtinguts en aquesta investigació. Es tracta d'un estudi usant la teoria del funcional de densitat per investigar el mecanisme de reacció del procés en diferents models teòrics de Au, amb l'objectiu de comprendre la influència en l'activitat catalítica de diversos factors, com ara la grandària de partícula, la coordinació dels àtoms de Au i la presencia d'espècies addicionals, com àtoms de O i aigua. El capítol 5 fa ús dels resultats obtinguts en l'estudi anterior, i els utilitza per dirigir la síntesi de nanopartícules suportades de Au. Es tracta d'un estudi experimental en el qual s'investiguen diversos factors que poden afectar a la seua activitat catalítica. Aquest estudi es combina amb un altre de caràcter teòric en el qual es té en compte la influència del suport en la activitat catalítica de les partícules de Au. El capítol 6 es basa en un dels resultats obtinguts en el capítol 5. Un dels suports utilitzats per fixar les partícules de Au resulta de per si actiu: el CeO2. La seua notable activitat per catalitzar aquest procés demana un estudi de major profunditat, el qual es duu a terme en aquest capítol. Paràmetres com la grandària de partícula, la morfologia de superfície i el dopatge, entre altres, s'investiguen en aquest punt. El capítol 7 continua l'estudi anterior sobre el CeO2, però ara des del punt de vista de la química teòrica. Presenta en primer lloc un es- tudi sistemàtic de paràmetres relacionats amb la mecànica quàntica que afecten al CeO2, amb l'objectiu d'aconseguir una descripció satisfactòria pels models teòrics d'aquest òxid. Després, es duu a terme un estudi del mecanisme de reacció en aquests models de CeO2, a fi de com- prendre l'origen de la seua activitat catalítica. El capítol 8 presenta de forma estructurada i concisa totes les conclusions que s'han extret arran dels resultats obtinguts. Encara que cada capí- tol presenta les seues corresponents conclusions al final, ací es presenten d'una forma agrupada per a la comoditat del lector, per què puga obtindre de forma àgil una visió global dels result d'una forma agrupada per a la comoditat del lector, per què puga obtindre de forma à / López Auséns, JT. (2016). From Quantum Mechanics to Catalysis: Studies on the oxidation of alkanes by gold and metal oxides [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/76806 / TESIS

abstract: Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific antibodies, such as IgG, towards epitopes on antigens. Serum IgG molecules carry specific molecular recognition information concerning the antigens that initiated their production. If one could read it, this information can be used to predict B cell epitopes on target antigens in order to design effective epitope driven vaccines, therapies and serological assays. Immunosignature technology captures the specific information content of serum IgG from infected and uninfected individuals on high density microarrays containing ~105 nearly random peptide sequences. Although the sequences of the peptides are chosen to evenly cover amino acid sequence space, the pattern of serum IgG binding to the array contains a consistent signature associated with each specific disease (e.g., Valley fever, influenza) among many individuals. Here, the disease specific but agnostic behavior of the technology has been explored by profiling molecular recognition information for five pathogens causing life threatening infectious diseases (e.g. DENV, WNV, HCV, HBV, and T.cruzi). This was done by models developed using a machine learning algorithm to model the sequence dependence of the humoral immune responses as measured by the peptide arrays. It was shown that the disease specific binding information could be accurately related to the peptide sequences used on the array by the machine learning (ML) models. Importantly, it was demonstrated that the ML models could identify or predict known linear epitopes on antigens of the four viruses. Moreover, the models identified potential novel linear epitopes on antigens of the four viruses (each has 4-10 proteins in the proteome) and of T.cruzi (a eukaryotic parasite which has over 12,000 proteins in its proteome). Finally, the predicted epitopes were tested in serum IgG binding assays such as ELISAs. Unfortunately, the assay results were inconsistent due to problems with peptide/surface interactions. In a separate study for the development of antibody recruiting molecules (ARMs) to combat microbial infections, 10 peptides from the high density peptide arrays were tested in IgG binding assays using sera of healthy individuals to find a set of antibody binding termini (ABT, a ligand that binds to a variable region of the IgG). It was concluded that one peptide (peptide 7) may be used as a potential ABT. Overall, these findings demonstrate the applications of the immunosignature technology ranging from developing tools to predict linear epitopes on pathogens of small to large proteomes to the identification of an ABT for ARMs. / Dissertation/Thesis / Doctoral Dissertation Biochemistry 2020

No description available.

No description available.

The presence of excess free radicals in the body can result in severe health consequences because of oxidative damage to cells. Spin traps may be used as a probe to examine radical reactions in cells, but there is a need for less toxic and more lipid soluble examples. Melatonin is one of the numerous antioxidants used to scavenge free radicals in the body and reportedly one of the most efficient radical scavengers known. It is relatively nontoxic and easily crosses the lipid bilayer in cell membranes. Melatonin is thought to undergo a multistep oxidation process and this work investigates the potential for it to be used as a spin trap. The presence of electron withdrawing or donating groups added to melatonin may stabilize an intermediate and allow it to function as a spin trap. The essence of this study is to conduct a computational inquiry into the relative stability of melatonin, selected derivatives, and the partial oxidation products formed from the scavenging of hydroxyl radical. To determine this, geometries were optimized for each molecule at the DFT/B3LYP/6-31G(d) and HF/6-31G(d) levels of theory.

<div>Data scarcity is intrinsic to many problems in chemical engineering due to physical constraints or cost. This challenge is acute in chemical and materials design applications, where a lack of data is the norm when trying to develop something new for an emerging application. Addressing novel chemical design under these scarcity constraints takes one of two routes: the traditional forward approach, where properties are predicted based on chemical structure, and the recent inverse approach, where structures are predicted based on required properties. Statistical methods such as machine learning (ML) could greatly accelerate chemical design under both frameworks; however, in contrast to the modeling of continuous data types, molecular prediction has many unique obstacles (e.g., spatial and causal relationships, featurization difficulties) that require further ML methods development. Despite these challenges, this work demonstrates how transfer learning and active learning strategies can be used to create successful chemical ML models in data scarce situations.<br></div><div>Transfer learning is a domain of machine learning under which information learned in solving one task is transferred to help in another, more difficult task. Consider the case of a forward design problem involving the search for a molecule with a particular property target with limited existing data, a situation not typically amenable to ML. In these situations, there are often correlated properties that are computationally accessible. As all chemical properties are fundamentally tied to the underlying chemical topology, and because related properties arise due to related moieties, the information contained in the correlated property can be leveraged during model training to help improve the prediction of the data scarce property. Transfer learning is thus a favorable strategy for facilitating high throughput characterization of low-data design spaces.</div><div>Generative chemical models invert the structure-function paradigm, and instead directly suggest new chemical structures that should display the desired application properties. This inversion process is fraught with difficulties but can be improved by training these models with strategically selected chemical information. Structural information contained within this chemical property data is thus transferred to support the generation of new, feasible compounds. Moreover, transfer learning approach helps ensure that the proposed structures exhibit the specified property targets. Recent extensions also utilize thermodynamic reaction data to help promote the synthesizability of suggested compounds. These transfer learning strategies are well-suited for explorative scenarios where the property values being sought are well outside the range of available training data.</div><div>There are situations where property data is so limited that obtaining additional training data is unavoidable. By improving both the predictive and generative qualities of chemical ML models, a fully closed-loop computational search can be conducted using active learning. New molecules in underrepresented property spaces may be iteratively generated by the network, characterized by the network, and used for retraining the network. This allows the model to gradually learn the unknown chemistries required to explore the target regions of chemical space by actively suggesting the new training data it needs. By utilizing active learning, the create-test-refine pathway can be addressed purely in silico. This approach is particularly suitable for multi-target chemical design, where the high dimensionality of the desired property targets exacerbates data scarcity concerns.</div><div>The techniques presented herein can be used to improve both predictive and generative performance of chemical ML models. Transfer learning is demonstrated as a powerful technique for improving the predictive performance of chemical models in situations where a correlated property can be leveraged alongside scarce experimental or computational properties. Inverse design may also be facilitated through the use of transfer learning, where property values can be connected with stable structural features to generate new compounds with targeted properties beyond those observed in the training data. Thus, when the necessary chemical structures are not known, generative networks can directly propose them based on function-structure relationships learned from domain data, and this domain data can even be generated and characterized by the model itself for closed-loop chemical searches in an active learning framework. With recent extensions, these models are compelling techniques for looking at chemical reactions and other data types beyond the individual molecule. Furthermore, the approaches are not limited by choice of model architecture or chemical representation and are expected to be helpful in a variety of data scarce chemical applications.</div>

<div>Organic mixed ion-electron conducting (OMIEC) polymers are capable of transporting both electrons and ions. This unique functionality underpins many emerging applications, including biosensors, electrochemical transistors, and batteries. The fundamental operating principles and structure-function relationships of OMIECs are still being investigated. Computational tools such as coarse-grained molecular dynamics (CGMD), which use simpler representations than in atomistic modeling, are ideal to study OMIECs, as they can explore the slow dynamics and large length scale features of polymers. Nevertheless, methods development is still required for CGMD simulations to accurately describe OMIECs.</div><div><br></div><div>In this thesis, two CGMD simulation approaches have been adopted. One is a so-called "top-down" approach to develop a generic model of OMIECs. Top-down models are phenomenological but capable of exploring a broad space of materials variables, including backbone anisotropy, persistence length, side-chain density, and hydrophilicity. This newly developed model was used to interrogate the effect of side-chain polarity and patterning on OMIEC physics. These studies reproduce experimentally observed polymer swelling while for the first time clarifying several molecular factors affecting charge transport, including the role of trap sites, polaron delocalization, electrolyte percolation, and suggesting side-chain patterning as a potential tool to improve OMIEC performance.</div><div><br></div><div>The second strategy pursued in this thesis is bottom-up CGMD modeling of specific atomistic systems. The bottom-up approach enables CGMD simulations to be quantitatively related to specific materials; yet, the sources of error and methods for addressing them have yet to be systematically established. To address this gap, we have studied the effect of the CG mapping operator, an important CG variable, on the fidelity of atomistic and CGMD simulations. A major observation from this study is that prevailing CGMD methods are underdetermined with respect to atomistic training data. In a separate study, we have proposed a hybrid machine-learning and physics-based CGMD framework that utilizes information from multiple sources and improves on the accuracy of ML-only bottom-up CGMD approaches. </div>