44 research outputs found
The Polytope Formalism: isomerism and associated unimolecular isomerisation
This thesis concerns the ontology of isomerism, this encompassing the conceptual frameworks and relationships that comprise the subject matter; the necessary formal definitions, nomenclature, and representations that have impacts reaching into unexpected areas such as drug registration and patent specifications; the requisite controlled and precise vocabulary that facilitates nuanced communication; and the digital/computational formalisms that underpin the chemistry software and database tools that empower chemists to perform much of their work.
Using conceptual tools taken from Combinatorics, and Graph Theory, means are presented to provide a unified description of isomerism and associated unimolecular isomerisation spanning both constitutional isomerism and stereoisomerism called the Polytope Formalism. This includes unification of the varying approaches historically taken to describe and understand stereoisomerism in organic and inorganic compounds.
Work for this Thesis began with the synthesis, isolation, and characterisation of compounds not adequately describable using existing IUPAC recommendations. Generalisation of the polytopal-rearrangements model of stereoisomerisation used for inorganic chemistry led to the prescriptions that could deal with the synthesised compounds, revealing an unrecognised fundamental form of isomerism called akamptisomerism.
Following on, this Thesis describes how in attempting to place akamptisomerism within the context of existing stereoisomerism reveals significant systematic deficiencies in the IUPAC recommendations. These shortcomings have limited the conceptualisation of broad classes of compounds and hindered development of molecules for medicinal and technological applications.
It is shown how the Polytope Formalism can be applied to the description of constitutional isomerism in a practical manner. Finally, a radically different medicinal chemistry design strategy with broad application, based upon the principles, is describe
Biochemical complex data generation and integration in genome-scale metabolic models
Dissertação de mestrado em BioinformaticsThe (re-)construction of Genome-Scale Metabolic (GSM) models is highly dependent on
biochemical databases. In fact, the biochemical data within these databases is limited, lacking,
most of the times, in structurally defined compounds’ representations. In order to circumvent
this limitation, compounds are frequently represented by their generic version. Lipids are
paradigmatic cases: given that a multitude of lipid species can occur in nature, not only is
their storage in databases hampered, but also their integration into GSM models. Accordingly,
converting one lipid version, in GSM models, into another can be tricky, as these compounds
possess side chains that are likely to be transferred all across their biosynthetic network.
Hence, converting a lipid implies that all its precursors have to be converted as well, requiring
information on lipid specificity and biosynthetic context.
The present work represents a strategy to tackle this issue. Biochemical cOmplex data
Integration in Metabolic Models at Genome scale (BOIMMG)’s pipeline encompasses the
integration and processing of biochemical data from different sources, aiming at expanding the
current knowledge in lipid biosynthesis, and its integration in GSM models.
Generic reactions retrieved from MetaCyc were handled and transformed into reactions with
structurally defined lipid species. More than 30 generic reactions were fully (and 27 partially)
characterized, allowing to predict over 30000 new lipid structures and their biosynthetic context.
The integration of BOIMMG’s data into GSM models was conducted for electron-transfer
quinones, glycerolipids, and phospholipids metabolism. The validation accounted on the
comparison of models with different versions of these metabolites. BOIMMG’s conversion
modules were applied to Escherichia coli’s iJR904 model [1], generating 53 more matching lipids
and 38 more matching reactions with iJR904 model’s iteration iAF1260b [2, 3], in which the
conversion was performed and curated manually.
To the best of our knowledge, BOIMMG’s database is the only with biosynthetic information
regarding structurally defined lipids. Moreover, there is no other state-of-the-art tool capable
of automatically generating complex lipid-specific networks.A reconstrução de modelos metabólicos à escala genómica (GSM na língua inglesa) depende
grandemente da informaçãoo bioquímica presente em bases de dados. De facto, esta informação
é muitas vezes limitada, podendo não conter representações de compostos estruturalmente
definidos. Como tentativa de contornar esta limitação, os compostos químicos são frequentemente
representados pela sua representação genérica. Os lípidos são casos paradigmáticos,
dado que uma multitude de diferentes espécies químicas de lípidos ocorrem na natureza, dificultando
o seu armazenamento em bases de dados, assim como a sua integração em modelos
GSM. Desta forma, o processo de converter lípidos de uma versão genérica para uma versão
estruturalmente definida não é trivial, dado que estes compostos possuem cadeias laterais que
são transferidas ao longo das suas vias de biossíntese. Consequentemente, essa conversão
implica que todos os precursores desses lípidos também sejam convertidos, requerendo haver
informação relativa a lípidos específicos e às suas relações biossintéticas.
O presente trabalho representa uma estratégia para resolver esse problema. A pipeline do
software desenvolvido no âmbito deste trabalho, Biochemical cOmplex dataIntegration in Metabolic
Models at Genome scale (BOIMMG), engloba a integração e processamento de dados bioquímicos
de diferentes fontes, visando a expansão do conhecimento atual na biossíntese de lípidos, assim
como a sua integração em modelos GSM.
Relativamente à segunda fase, reações genéricas extraídas da base de dados MetaCyc foram
processadas e transformadas em reações com lípidos estruturalmente definidos. Mais de 30
reações genéricas foram completamente (e 27 parcialmente) caracterizadas, permitindo prever
mais de 30000 novas estruturas de lípidos, assim como os seus contextos biossintéticos.
A integração dos dados nos modelos GSM foi conduzido para o metabolismo das quinonas
transportadoras de eletrões, glicerolípidos e fosfolípidos. A validação teve em conta a
comparação entre modelos com diferentes versões destes metabolitos. Os módulos de conversão do BOIMMG foram aplicados ao modelo iJR904 de Escherichia coli [1], gerando mais
53 lípidos e 38 reações que se encontram no modelo iAF1260b [2, 3], uma iteração do modelo
iJR904 cuja conversão de lípidos se procedeu manualmente.
A base de dados gerada pelo método BOIMMG é a única que contém informação biossintética
relata a lípidos estruturalmente definidos. Adicionalmente, BOIMMG é uma ferramenta única
que permite gerar redes complexas de lípidos automaticamente
Biomimetic Based Applications
The interaction between cells, tissues and biomaterial surfaces are the highlights of the book "Biomimetic Based Applications". In this regard the effect of nanostructures and nanotopographies and their effect on the development of a new generation of biomaterials including advanced multifunctional scaffolds for tissue engineering are discussed. The 2 volumes contain articles that cover a wide spectrum of subject matter such as different aspects of the development of scaffolds and coatings with enhanced performance and bioactivity, including investigations of material surface-cell interactions
Towards more rational approaches of membrane protein stabilisation and novel structures of membrane-bound pyrophosphatase
Membrane proteins have a range of crucial biological functions and are targeted by most prescribed drugs despite lagging behind soluble proteins when it comes to their biochemical and biophysical characterisation. A major bottleneck in membrane protein research is protein instability upon extraction. Protein stabilisation strategies are typically expensive and labour-intensive. Therefore, I contributed to the development and evaluation of two new general-purpose tools, both designed for the streamlined and rational stabilisation of membrane proteins. The first tool, the integral membrane protein stability selector (IMPROvER), predicts stabilising point-mutations in membrane proteins using three individual approaches with additive prediction power. The second tool, a novel pre-prepared and easy-to-use screen for the high-throughput identification of stabilising lipids, facilitates the structural and functional analysis of stable and physiologically relevant protein sample. Both tools were successfully employed to stabilise a range of membrane proteins with different folds, topologies and modes of action at significantly reduced cost and work effort.
Moreover, engineered or natively thermostable membrane-bound pyrophosphatases (M-PPase), were studied in more detail using conventional and time-resolved X-ray crystallography. Based on structural data obtained on a pyrophosphate-energised K+-independent H+-pump, I derived an updated model of ion-selectivity that is centred on a glutamate-serine interplay at the ion-gate. This is the first model that explains ion selectivity in all M PPase subclasses when considering functional asymmetry. Indeed, complementary time-resolved structural studies of a K+-dependent Na+-pump revealed asymmetric substrate binding to M-PPase active sites. These findings give valuable mechanistic insights into key processes of M-PPase biochemistry, which are of upmost importance for structure-guided drug discovery. Ultimately, tweaking M-PPase function has the potential to address existing and emerging challenges to human health and global food security as M-PPases play a vital role in the stress resistance of pathogens or salt and drought resistance in plants
Unravelling the structure of glycosylated and deglycosylated immunoglobulin G antibodies
Immunoglobulin G (IgG) is composed of four IgG subclasses, IgG1, IgG2, IgG3 and IgG4, which although differ in function and structure, owing to variability in hinge length, all have a conserved N-linked glycan attached in the Fc region. However, the role of this glycan on the structure, stability and function of these IgG molecules is not fully understood. The focus of this thesis is to investigate the role of the Fc-glycan in respect to IgG1, IgG3 and IgG4 by using a multidisciplinary approach to study both their glycosylated and deglycosylated forms. Primarily by probing the full-length solution structure using small angle X- ray and neutron scattering, as well as analytical ultracentrifugation. Following this extensive computational modelling methods and analysis were used to extract the theoretical models which best fit the solution structure data in order to unpick the role of this glycan. Several studies have investigated the role of the IgG1 Fc-glycan using different structural methods, however, most of these studies investigated the Fc region of IgG rather than the full-length antibody, composed of Fabs, hinge and Fc. In this thesis all experiments are conducted on full-length IgGs, presenting a complete understanding on the effect of the Fc-glycan on the entirety of the IgG structure. Studies of IgG1 and IgG4 indicate that the Fc-glycan plays a role in restricting the flexibility of the Fc. This restriction is less obvious in IgG3; this may be owing to the molecule’s elongated hinge. Of the three antibodies studied, IgG3 has the longest hinge region, composed of 62 amino acids. This long hinge has historically made it difficult to study using other structural techniques, such as X- ray crystallography and NMR, therefore the study of the IgG3 solution structure presented herein is the most complete to date and provides insight into the dynamics of the hinge region
Optimisation of flow chemistry: tools and algorithms
The coupling of flow chemistry with automated laboratory equipment has become increasingly common and used to support the efficient manufacturing of chemicals. A variety of reactors and analytical techniques have been used in such configurations for investigating and optimising the processing conditions of different reactions. However, the integrated reactors used thus far have been constrained to single phase mixing, greatly limiting the scope of reactions for such studies. This thesis presents the development and integration of a millilitre-scale CSTR, the fReactor, that is able to process multiphase flows, thus broadening the range of reactions susceptible of being investigated in this way.
Following a thorough review of the literature covering the uses of flow chemistry and lab-scale reactor technology, insights on the design of a temperature-controlled version of the fReactor with an accuracy of ±0.3 ºC capable of cutting waiting times 44% when compared to the previous reactor are given. A demonstration of its use is provided for which the product of a multiphasic reaction is analysed automatically under different reaction conditions according to a sampling plan. Metamodeling and cross-validation techniques are applied to these results, where single and multi-objective optimisations are carried out over the response surface models of different metrics to illustrate different trade-offs between them. The use of such techniques allowed reducing the error incurred by the common least squares polynomial fitting by over 12%. Additionally, a demonstration of the fReactor as a tool for synchrotron X-Ray Diffraction is also carried out by means of successfully assessing the change in polymorph caused by solvent switching, this being the first synchrotron experiment using this sort of device.
The remainder of the thesis focuses on applying the same metamodeling and cross-validation techniques used previously, in the optimisation of the design of a miniaturised continuous oscillatory baffled reactor. However, rather than using these techniques with physical experimentation, they are used in conjunction with computational fluid dynamics. This reactor shows a better residence time distribution than its CSTR counterparts. Notably, the effect of the introduction of baffle offsetting in a plate design of the reactor is identified as a key parameter in giving a narrow residence time distribution and good mixing. Under this configuration it is possible to reduce the RTD variance by 45% and increase the mixing efficiency by 60% when compared to the best performing opposing baffles geometry
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INSIGHTS INTO POLYMERISATION OF FTSZ AND OTHER CYTOMOTIVE FILAMENTS
Protein filaments are used in different ways to organise other molecules in space and time within cells. Some proteins form filaments that couple hydrolysis of nucleotides to their polymerisation cycle in order to power the directed movement of other molecules, these filaments are termed cytomotive. Only members of the actin and tubulin superfamilies are known to form cytomotive filaments. The protein FtsZ, a homologue of eukaryotic tubulins, forms cytomotive filaments that are used in almost all bacteria and many archaea to organise cell division.
Here I show using X-ray crystallography and electron cryomicroscopy (cryoEM) that FtsZ switches conformation when it polymerises into filaments. I then show using cryoEM that this conformational switch is likely needed for recognition of filaments by the widely conserved filament cross-linking protein ZapA. I also present the development of a high- throughput assay for detection of better FtsZ inhibitors, which uses principles derived from the structural studies. Finally, I demonstrate that the conformational switch upon polymerisation seen in FtsZ is conserved within the tubulin superfamily, that actin superfamily members also exhibit a conserved conformational switch upon polymerisation, and that having such a switch explains the coupling of kinetic and structural polarities required for cytomotivity of the filaments formed by these protein families.I was supported by scholarships from the MRC and the Boehringer Ingelheim Fonds
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Defect-Structure-Property Relations in Complex-Oxide Ferroelectric Thin Films
One role for modern materials science is to provide a foundation upon which scientists and engineers in diverse fields can address the needs of current and future societal challenges through the realization of next-generation technologies. Key to such advances is not only the development of advanced materials with novel or enhanced properties and performance, but also the know-how to synthesize and process such materials in a deterministic manner so that their properties can be effectively and efficiently utilized. Materials science is founded upon the concept that structure, processing, properties, and, ultimately, performance of materials are intimately interconnected. And, as the field has evolved, materials scientists and engineers have increasingly realized that even our best efforts to control these tenets can be remarkably hampered if we do not account for and address the role of material imperfections. Underlying all this is the fact that defects are unavoidable. Even in the most “perfect” materials, there are always finite concentrations of various structural and compositional defects. Although in some material systems defects have been extensively studied and used to engineer and improve properties, the general opinion of defects in ferroelectric community is not a good one – defects are regarded as “bad guys” and thought to be (uniformly) deleterious to material performance. But, armed with advances in our ability to synthesize, characterize, and model these materials, this negative connotation stands poised to be redefined. So can defects really be “good guys” in the ferroelectric world? In this Thesis I aim to view defects in a new light – a positive one – that casts them as another tool to design better ferroelectric materials with emergent properties and functionalities.In the present work, I demonstrate strong defect-structure-property couplings in thin film versions of various complex-oxide ferroelectrics (including BaTiO3, BiFeO3, PbTiO3, PbZrxTi1 xO3) and relaxor ferroelectrics (such as 0.68PbMg1/3Nb2/3O3-0.32PbTiO3) grown via pulsed-laser deposition, and show that such defect-structure-property interplays can be manipulated with deliberate introduction of certain defect types at controlled concentrations and locations which can provide new pathways to enhanced properties and novel functions. Among all defect types that can be present, this work only focuses on point defects as they are the most abundant defects in ionically-bonded solids such as complex-oxide ferroelectrics and play a particularly important role in impacting the properties of these materials. Nevertheless, in surprisingly few cases does one have a detailed understanding of point defects and their impact on the properties due to difficulties in their control and characterization. In the present work, I introduce various in situ and ex situ approaches for on-demand defect creation. The in situ approach relies on the variations of growth parameters (such as laser fluence, laser-repetition rate, target composition, and growth pressure) in order to control defects during the synthesis process of the thin films, while the ex situ approach focuses on the use of energetic ion beams (both defocused high-energy, and focused low-energy ion beams) to introduce defects in already-grown films. This controlled defect production is then used to perform systematic experimental studies on the evolution of various material properties (including transport, dielectric, and ferroelectric properties) as a function of defect type and across many orders of magnitude of defect concentration, which provides valuable understanding regarding the physics of defects in these complex systems. The nature of the induced defects and their impact on the properties are studied using a combination of conventional and advanced characterization techniques including X-ray diffraction, Rutherford backscattering spectrometry, scanning transmission electron microscopy, scanning probe microscopy, and electrical measurements such as traditional dielectric, ferroelectric, and transport measurements, switching kinetics studies, first-order reversal curve analysis, impedance spectroscopy, and deep-level transient spectroscopy. Ultimately, I show that establishing routes to achieve such control and understanding over defects is the key if we desire to use defects as “good guys” and as tools to our advantage for material control and design rather than being limited by them
Enhancing Reaction-based de novo Design using Machine Learning
De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility.
Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable.
The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator.
A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections.
Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources