Catalyst design for biorefining

The quest for sustainable resources to meet the demands of a rapidly rising global population while mitigating the risks of rising CO2 emissions and associated climate change, represents a grand challenge for humanity. Biomass offers the most readily implemented and low-cost solution for sustainable transportation fuels, and the only non-petroleum route to organic molecules for the manufacture of bulk, fine and speciality chemicals and polymers. To be considered truly sustainable, biomass must be derived fromresources which do not compete with agricultural land use for food production, or compromise the environment (e.g. via deforestation). Potential feedstocks include waste lignocellulosic or oil-based materials derived from plant or aquatic sources, with the so-called biorefinery concept offering the co-production of biofuels, platform chemicals and energy; analogous to today's petroleum refineries which deliver both high-volume/low-value (e.g. fuels and commodity chemicals) and lowvolume/ high-value (e.g. fine/speciality chemicals) products, thereby maximizing biomass valorization. This article addresses the challenges to catalytic biomass processing and highlights recent successes in the rational design of heterogeneous catalysts facilitated by advances in nanotechnology and the synthesis of templated porous materials, as well as the use of tailored catalyst surfaces to generate bifunctional solid acid/base materials or tune hydrophobicity.

Conformationally homogeneous heterocyclic pseudotetrapeptides as three-dimensional scaffolds for rational drug design: receptor-selective somatostatin analogues

A would-be amide: A 1,4-disubstituted 1,2,3-triazole was used as a surrogate for a trans amide bond to create a library of 16 diastereomeric pseudotetrapeptides as beta-turn mimetics. High-resolution structural analysis indicated that these scaffolds adopt distinct, rigid, conformationally homogeneous beta-turn-like structures (see example), some of which bind somatostatin receptor subtypes selectively, and some of which show broad-spectrum activity.

Rational earthquake design criteria for earth dams,

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Catalyst screening: Refinement of the origin of the volcano curve and its implication in heterogeneous catalysis

Understanding the overall catalytic activity trend for rational catalyst design is one of the core goals in heterogeneous catalysis. In the past two decades, the development of density functional theory (DFT) and surface kinetics make it feasible to theoretically evaluate and predict the catalytic activity variation of catalysts within a descriptor-based framework. Thereinto, the concept of the volcano curve, which reveals the general activity trend, usually constitutes the basic foundation of catalyst screening. However, although it is a widely accepted concept in heterogeneous catalysis, its origin lacks a clear physical picture and definite interpretation. Herein, starting with a brief review of the development of the catalyst screening framework, we use a two-step kinetic model to refine and clarify the origin of the volcano curve with a full analytical analysis by integrating the surface kinetics and the results of first-principles calculations. It is mathematically demonstrated that the volcano curve is an essential property in catalysis, which results from the self-poisoning effect accompanying the catalytic adsorption process. Specifically, when adsorption is strong, it is the rapid decrease of surface free sites rather than the augmentation of energy barriers that inhibits the overall reaction rate and results in the volcano curve. Some interesting points and implications in assisting catalyst screening are also discussed based on the kinetic derivation. Moreover, recent applications of the volcano curve for catalyst design in two important photoelectrocatalytic processes (the hydrogen evolution reaction and dye-sensitized solar cells) are also briefly discussed.

Dissociative adsorption of methane on the Cu and Zn doped (111) surface of CeO2

The development of economical heterogeneous catalysts for the activation of methane is a major challenge for the chemical industry. Screening potential candidates becomes more feasible using rational catalyst design to understand the activity of potential catalysts for CH4 activation. The focus of the present paper is the use of density functional theory to examine and elucidate the properties of doped CeO2. We dope with Cu and Zn transition metals having variable oxidation state (Cu), and a single oxidation state (Zn), and study the activation of methane. Zn is a divalent dopant and Cu can have a +1 or +2 oxidation state. Both Cu and Zn dopants have an oxidation state of +2 after incorporation into the CeO2 (111) surface; however a Hubbard +U correction (+U = 7) on the Cu 3d states is required to maintain this oxidation state when the surface interacts with adsorbed species. Dissociation of methane is found to occur locally at the dopant cations, and is thermodynamically and kinetically more favorable on Zn-doped CeO2 than Cu-doped CeO2. The origins of this lie with the Zn(II) dopant moving towards a square pyramidal geometry in the sub surface layer which facilitates the formation of two-coordinated surface oxygen atoms, that are more beneficial for methane activation on a reducible oxide surface. These findings can aid in rational experimental catalyst design for further exploration in methane activation processes.

Structure-Based, Rational Design of T Cell Receptors.

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1157-165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8(+) T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K D = ∼1 - 5 μM. Beyond the affinity threshold at K D < 1 μM we observed an attenuation in cellular function, in line with the "half-life" model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.

The rational design of vaccines

This review provides an insight into the various opportunities for vaccine intervention, analysis of strategies for vaccine development, vaccine ability to modulate immune responses and resultant rational vaccine design. In addition, wider aspects are considered, such as biotechnological advances, advances in immunological understanding and host-pathogen interactions. The key question addressed here is, with all our research and understanding, have we reached a new echelon in vaccine development, that of rational design? ©2005 Elsevier Ltd. All rights reserved.

EADock : design of a new molecular docking algorithm and some of its applications

3 Summary 3. 1 English The pharmaceutical industry has been facing several challenges during the last years, and the optimization of their drug discovery pipeline is believed to be the only viable solution. High-throughput techniques do participate actively to this optimization, especially when complemented by computational approaches aiming at rationalizing the enormous amount of information that they can produce. In siiico techniques, such as virtual screening or rational drug design, are now routinely used to guide drug discovery. Both heavily rely on the prediction of the molecular interaction (docking) occurring between drug-like molecules and a therapeutically relevant target. Several softwares are available to this end, but despite the very promising picture drawn in most benchmarks, they still hold several hidden weaknesses. As pointed out in several recent reviews, the docking problem is far from being solved, and there is now a need for methods able to identify binding modes with a high accuracy, which is essential to reliably compute the binding free energy of the ligand. This quantity is directly linked to its affinity and can be related to its biological activity. Accurate docking algorithms are thus critical for both the discovery and the rational optimization of new drugs. In this thesis, a new docking software aiming at this goal is presented, EADock. It uses a hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with .the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 R around the center of mass of the ligand position in the crystal structure, and conversely to other benchmarks, our algorithms was fed with optimized ligand positions up to 10 A root mean square deviation 2MSD) from the crystal structure. This validation illustrates the efficiency of our sampling heuristic, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best-ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures in this benchmark could be explained by the presence of crystal contacts in the experimental structure. EADock has been used to understand molecular interactions involved in the regulation of the Na,K ATPase, and in the activation of the nuclear hormone peroxisome proliferatoractivated receptors a (PPARa). It also helped to understand the action of common pollutants (phthalates) on PPARy, and the impact of biotransformations of the anticancer drug Imatinib (Gleevec®) on its binding mode to the Bcr-Abl tyrosine kinase. Finally, a fragment-based rational drug design approach using EADock was developed, and led to the successful design of new peptidic ligands for the a5ß1 integrin, and for the human PPARa. In both cases, the designed peptides presented activities comparable to that of well-established ligands such as the anticancer drug Cilengitide and Wy14,643, respectively. 3.2 French Les récentes difficultés de l'industrie pharmaceutique ne semblent pouvoir se résoudre que par l'optimisation de leur processus de développement de médicaments. Cette dernière implique de plus en plus. de techniques dites "haut-débit", particulièrement efficaces lorsqu'elles sont couplées aux outils informatiques permettant de gérer la masse de données produite. Désormais, les approches in silico telles que le criblage virtuel ou la conception rationnelle de nouvelles molécules sont utilisées couramment. Toutes deux reposent sur la capacité à prédire les détails de l'interaction moléculaire entre une molécule ressemblant à un principe actif (PA) et une protéine cible ayant un intérêt thérapeutique. Les comparatifs de logiciels s'attaquant à cette prédiction sont flatteurs, mais plusieurs problèmes subsistent. La littérature récente tend à remettre en cause leur fiabilité, affirmant l'émergence .d'un besoin pour des approches plus précises du mode d'interaction. Cette précision est essentielle au calcul de l'énergie libre de liaison, qui est directement liée à l'affinité du PA potentiel pour la protéine cible, et indirectement liée à son activité biologique. Une prédiction précise est d'une importance toute particulière pour la découverte et l'optimisation de nouvelles molécules actives. Cette thèse présente un nouveau logiciel, EADock, mettant en avant une telle précision. Cet algorithme évolutionnaire hybride utilise deux pressions de sélections, combinées à une gestion de la diversité sophistiquée. EADock repose sur CHARMM pour les calculs d'énergie et la gestion des coordonnées atomiques. Sa validation a été effectuée sur 37 complexes protéine-ligand cristallisés, incluant 11 protéines différentes. L'espace de recherche a été étendu à une sphère de 151 de rayon autour du centre de masse du ligand cristallisé, et contrairement aux comparatifs habituels, l'algorithme est parti de solutions optimisées présentant un RMSD jusqu'à 10 R par rapport à la structure cristalline. Cette validation a permis de mettre en évidence l'efficacité de notre heuristique de recherche car des modes d'interactions présentant un RMSD inférieur à 2 R par rapport à la structure cristalline ont été classés premier pour 68% des complexes. Lorsque les cinq meilleures solutions sont prises en compte, le taux de succès grimpe à 78%, et 92% lorsque la totalité de la dernière génération est prise en compte. La plupart des erreurs de prédiction sont imputables à la présence de contacts cristallins. Depuis, EADock a été utilisé pour comprendre les mécanismes moléculaires impliqués dans la régulation de la Na,K ATPase et dans l'activation du peroxisome proliferatoractivated receptor a (PPARa). Il a également permis de décrire l'interaction de polluants couramment rencontrés sur PPARy, ainsi que l'influence de la métabolisation de l'Imatinib (PA anticancéreux) sur la fixation à la kinase Bcr-Abl. Une approche basée sur la prédiction des interactions de fragments moléculaires avec protéine cible est également proposée. Elle a permis la découverte de nouveaux ligands peptidiques de PPARa et de l'intégrine a5ß1. Dans les deux cas, l'activité de ces nouveaux peptides est comparable à celles de ligands bien établis, comme le Wy14,643 pour le premier, et le Cilengitide (PA anticancéreux) pour la seconde.

Design and implementation of a multipurpose reactor for scale-up studies

In the theoretical part, the different polymerisation catalysts are introduced and the phenomena related to mixing in the stirred tank reactor are presented. Also the advantages and challenges related to scale-up are discussed. The aim of the applied part was to design and implement an intermediate-sized reactor useful for scale-up studies. The reactor setting was tested making one batch of Ziegler–Natta polypropylene catalyst. The catalyst preparation with a designed equipment setting succeeded and the catalyst was analysed. The analyses of the catalyst were done, because the properties of the catalyst were compared to the normal properties of Ziegler–Natta polypropylene catalyst. The total titanium content of the catalyst was slightly higher than in normal Ziegler–Natta polypropylene catalyst, but the magnesium and aluminium content of the catalyst were in the normal level. By adjusting the siphonation tube and adding one washing step the titanium content of the catalyst could be decreased. The particle size of the catalyst was small, but the activity was in a normal range. The size of the catalyst particles could be increased by decreasing the stirring speed. During the test run, it was noticed that some improvements for the designed equipment setting could be done. For example more valves for the chemical feed line need to be added to ensure inert conditions during the catalyst preparation. Also nitrogen for the reactor needs to separate from other nitrogen line. With this change the pressure in the reactor can be kept as desired during the catalyst preparation. The proposals for improvements are presented in the applied part. After these improvements are done, the equipment setting is ready for start-up. The computational fluid dynamics model for the designed reactor was provided by cooperation with Lappeenranta University of Technology. The experiments showed that for adequate mixing with one impeller, stirring speed of 600 rpm is needed. The computational fluid dynamics model with two impellers showed that there was no difference in the mixing efficiency if the upper impeller were pumping downwards or upwards.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Estudo da seletividade de catalisadores a base de cobre e paládio em transformações de hidrocarbonetos insaturados

Compreender a correlação entre as características de um catalisador particular e seu desempenho catalítico tem sido um dos principais objetos da pesquisa em catálise heterogênea a fim de usar esse conhecimento para o desenho racional de catalisadores mais ativos, seletivos e estáveis. A seletividade é um dos fatores mais importantes a ser controlado pelo desenho de catalisadores, podendo ser alcançada de diversas maneiras, levando-se em consideração mudanças do tipo estrutural, química, eletrônica, de composição, de cinética e de energia. O trabalho descrito nessa tese de doutorado compreende a síntese e caracterização de catalisadores compostos de nanopartículas de óxido de cobre, paládio e cobre-paládio e seu estudo em reações de hidrogenação e oxidação seletivas de hidrocarbonetos insaturados. Os catalisadores foram preparados através da deposição de nanopartículas dos metais cataliticamente ativos sobre suportes magneticamente recuperáveis compostos de nanopartículas de magnetita revestidas por sílica com superfícies funcionalizada com diferentes grupos orgânicos. A natureza magnética do suporte permitiu a fácil separação do catalisador do meio reacional pela simples aproximação de um ímã na parede do reator. O catalisador pôde ser completamente separado da fase líquida, fazendo com que a utilização de outros métodos de separação como filtração e centrifugação, comumente utilizados em sistemas heterogêneos líquidos, fossem completamente dispensados. Os catalisadores foram inicialmente testados em reações de hidrogenação de alquenos e alquinos. As reações de hidrogenação foram realizadas utilizando hidrogênio molecular como agente redutor, dispensando a utilização de agentes redutores mais agressivos. Os catalisadores compostos de NPs de Pd mostram excelente atividade e capacidade de reutilização na hidrogenação de cicloexeno, podendo ser utilizados em até 15 ciclos sem perda de atividade. Nas reações de hidrogenação de alquinos, os catalisadores que contêm cobre mostraram maior seletividade para a obtenção dos produtos de semi-hidrogenação, com destaque para o catalisador composto de NPs de CuPd, que não apresenta nem traços do produto de hidrogenação completa na amostra final. Esse catalisador bimetálico alia as características do paládio (elevada atividade) e do cobre (elevada seletividade) para fornecer um catalisador ativo e seletivo para a transformação desejada. Além disso, os grupos funcionais presentes na superfície do suporte catalítico mostraram influência na atividade e seletividade para a hidrogenação de alquenos e alquinos. Os catalisadores sintetizados também foram testados na reação de oxidação de cicloexeno e mostraram seletividade para a produção do composto carbonílico α,β-insaturado, cicloex-2-en-1-ona, que é um reagente de partida de grande interesse para a síntese de diversos materiais na indústria química. As reações de oxidação foram realizadas utilizando-se apenas O2 como oxidante primário, dispensando o uso de oxidantes tóxicos como cromatos, permanganatos ou compostos halogenados, que não são recomendados do ponto de vista ambiental. Os catalisadores sintetizados puderam ser reutilizados em sucessivos ciclos de oxidação, mostrando seletividade para a formação dos produtos alílicos em todos os ciclos. Os catalisadores foram estáveis sob as condições reacionais e não apresentaram problemas de lixiviação da espécie ativa para o meio reacional, que é comum na catálise heterogênea. Um estudo cinético mostrou que, mesmo no início da reação, o catalisador tem seletividade para a ocorrência de oxidação alílica em detrimento da reação de oxidação direta que dá origem ao epóxidos correspondente, e se mostrou condizente com o mecanismo proposto na literatura para a reação de oxidação de alquenos via radicalar.

A review of advanced catalyst development for Fischer-Tropsch synthesis of hydrocarbons from biomass derived syn-gas

Fischer-Tropsch synthesis (FTS) is a process which converts syn-gas (H2 and CO) to synthetic liquid fuels and valuable chemicals. Thermal gasification of biomass represents a convenient route to produce syn-gas from intractable materials particularly those derived from waste that are not cost effective to process for use in biocatalytic or other milder catalytic processes. The development of novel catalysts with high activity and selectivity is desirable as it leads to improved quality and value of FTS products. This review paper summarises recent developments in FT-catalyst design with regards to optimising catalyst activity and selectivity towards synthetic fuels. © 2014 the Partner Organisations.

A Vaccine Encoding Conserved Promiscuous HIV CD4 Epitopes Induces Broad T Cell Responses in Mice Transgenic to Multiple Common HLA Class II Molecules

Current HIV vaccine approaches are focused on immunogens encoding whole HIV antigenic proteins that mainly elicit cytotoxic CD8+ responses. Mounting evidence points toward a critical role for CD4+ T cells in the control of immunodeficiency virus replication, probably due to cognate help. Vaccine-induced CD4+ T cell responses might, therefore, have a protective effect in HIV replication. In addition, successful vaccines may have to elicit responses to multiple epitopes in a high proportion of vaccinees, to match the highly variable circulating strains of HIV. Using rational vaccine design, we developed a DNA vaccine encoding 18 algorithm-selected conserved, ""promiscuous"" ( multiple HLA-DR-binding) B-subtype HIV CD4 epitopes - previously found to be frequently recognized by HIV-infected patients. We assessed the ability of the vaccine to induce broad T cell responses in the context of multiple HLA class II molecules using different strains of HLA class II-transgenic mice (-DR2, -DR4, -DQ6 and -DQ8). Mice displayed CD4+ and CD8+ T cell responses of significant breadth and magnitude, and 16 out of the 18 encoded epitopes were recognized. By virtue of inducing broad responses against conserved CD4+ T cell epitopes that can be recognized in the context of widely diverse, common HLA class II alleles, this vaccine concept may cope both with HIV genetic variability and increased population coverage. The vaccine may thus be a source of cognate help for HIV-specific CD8+ T cells elicited by conventional immunogens, in a wide proportion of vaccinees.