Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction

Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity. Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results. In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied

Mechanistics of Prothymosin alpha and Nrf2 in the Keap1-Nrf2 mediated Oxidative Stress Response

In an effort to dissect the mechanism of interaction of IDPs, in this thesis we focus on Prothymosin a (ProTa) and nuclear factor erythroid 2-related factor 2 (Nrf2), intrinsically disordered proteins, in the Nrf2 mediated oxidative stress response. Kelch-like ECH-associated protein 1 (Keap1) is an inhibitor of Nrf2, a key transcription factor of cytoprotective genes. Under unstressed conditions, Keap1 interacts with Nrf2 in the cytoplasm via its Kelch domain and suppresses Nrf2 activity. During oxidative stress, Nrf2 is released from Keap1 and is shuttled to the nucleus, where it initiates pro cell survival gene transcription. ProTa also interacts with the Kelch domain and mediates the import of Keap1 into the nucleus to inhibit Nrf2 activity. To gain a molecular basis understanding of the oxidative stress response mechanism, the interaction between ProTa and the Kelch domain of Keap1 has been delineated using nuclear magnetic resonance spectroscopy (NMR), isothermal titration calorimetry (ITC), peptide array analysis, and site-directed mutagenesis. The results revealed that ProTa retains a high level of flexibility, even in the Kelch-bound state. Mutational analysis pinpointed that the region 38NANEENGE45 of ProTa is crucial for the interaction with the Kelch domain, while the flanking residues play relatively minor roles in the affinity of binding. A high yield purification protocol with complete backbone NMR resonance assignment lays the foundation for structural and biophysical studies of the full length-Neh2 domain of the human Nrf2. In this work the full-length Neh2 domain was used to investigate binding to Kelch in the presence of cancer causing somatic mutations. To understand the mechanistic links between Keap1 mutations and cancer pathogenesis, the molecular effects of a series of mutations (G333C, G364C, G379D, G350S, R413L, R415G, A427V, G430C, and G476R on the structural and target recognition properties of Keap1 are investigated. These mutations are found to exert differential effects on the protein stability and target binding. Together with the proposed Hinge-and-Latch mechanism of Nrf2/Keap1 binding, these results provide important insight into the molecular impact of different somatic mutations on Keap1’s function as an Nrf2 repressor

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book

Investigating homeostatic disruption by constitutive signals during biological ageing

PhD ThesisAgeing and disease can be understood in terms of a loss in biological homeostasis. This will often manifest as a constitutive elevation in the basal levels of biological entities. Examples include chronic inflammation, hormonal imbalances and oxidative stress. The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work a computational modelling approach is undertaken to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. A primary outcome of this study is that constitutive signals can give rise to a ‘molecular habituation’ effect that can prime for a gradual loss of biological function. Experimental results obtained highlight the difficulties in testing for this effect in cell lines exposed to oxidative stress. However, further analysis suggests this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and potentially at different levels of biological organisation.Centre for Integrated Research into Musculoskeletal Ageing (CIMA) and through them, Arthritis Research UK and the Medical Research Counc

From building blocks to 2D networks

The aim of this work is to further the understanding of the important parameters in the formation process of 2D nanostructures and therewith pioneer for novel applications. Such 2D nanostructures can be composed of specially designed organic molecules, which are adsorbed on various surfaces. In order to study true 2D structures, monolayers were deposited. Their properties have been investigated by scanning tunneling microscopy (STM) under ultra-high vacuum (UHV) conditions as well as under ambient conditions. The latter is a highly dynamic environment, where several parameters come into play. Complementary surface analysis techniques such as low-energy electron diffraction (LEED), X-Ray photo-emission spectroscopy (XPS), and Raman spectroscopy were used when necessary to characterize these novel molecular networks. In order to conduct this type of experiments, high technical requirements have to be fulfilled, in particular for UHV experiments. Thus, the focus is on a drift-stable STM, which lays the foundation for high resolution STM topographs. Under ambient conditions, the liquid-solid STM can be easily upgraded by an injection add-on due to the highly flexible design. This special extension allows for adding extra solvent without impairing the high resolution of the STM data. Besides the device, also the quality of the tip is of pivotal importance. In order to meet the high requirements for STM tips, an in vacuo ion-sputtering and electron-beam annealing device was realized for the post-preparation of scanning probes within one device. This two-step cleaning process consists of an ion-sputtering step and subsequent thermal annealing of the probe. One study using this STM setup concerned the incorporation dynamics of coronene (COR) guest molecules into pre-existent pores of a rigid 2D supramolecular host networks of trimesic acid (TMA) as well as the larger analogous benzenetribenzoic acid (BTB) at the liquid-solid interface. By means of the injection add-on the additional solution containing the guest molecules was applied to the surface. At the same time the incorporation process was monitored by the STM. The incorporation dynamics into geometrically perfectly matched pores of trimesic acid as well as into the substantially larger pores of benzentribenzoic acid exhibit a clearly different behavior. For the BTB network instantaneous incorporation within the temporal resolution of the experiment was observed; for the TMA network, however, intermediate adsorption states of COR could be visualized before the final adsorption state was reached. A further issue addressed in this work is the generation of metal-organic frameworks (MOFs) under ultra-high vacuum conditions. A suitable building block therefore is an aromatic trithiol, i.e. 1,3,5-tris(4-mercaptophenyl)benzene (TMB). To understand the specific role of the substrate, the surface-mediated reaction has been studied on Cu(111) as well as on Ag(111). Room temperature deposition on both substrates results in densely packed trigonal structures. Yet, heating the Cu(111) with the TMB molecules to moderate temperature (150 °C) yields two different porous metal coordinated networks, depending on the initial surface coverage. For Ag(111) the first structural change occurs after annealing the sample at 300 °C. Here, several disordered structures with partially covalent disulfur bridges were identified. Proceeding further in the scope of increasing interaction strength between the building blocks, covalent organic frameworks (COFs) were studied under ultra-high vacuum conditions as well as under ambient conditions. For this purpose, a promising strategy is covalent coupling through radical addition reactions of appropriate monomers, i.e. halogenated aromatic molecules such as 1,3,5-tris(4-bromophenyl)benzene (TBPB) and 1,3,5-tris(4- iodophenyl)benzene (TIPB). Besides the correct choice of a catalytic surface, the activation energy for the scission of the carbon-halogen bonds is an essential parameter. In the case of ultra-high vacuum experiments, the influence of substrate temperature, material, and crystallographic orientation on the coupling reaction was studied. For reactive Cu(111) and Ag(110) surfaces room temperature deposition of TBPB already leads to a homolysis of the C-Br bond and subsequent formation of proto-polymers. Applying additional heat facilitates the transformation of proto-polymers into 2D covalent networks. In contrast, for Ag(111) just a variety of self-assembled and rather poorly ordered structures composed of intact molecules has emerged. The deposition onto substrates held at 80 K has never resulted in proto-polymers. For ambient conditions, the polymerization reaction of 1,3,5-tri(4-iodophenyl)benzene (TIPB) on Au(111) was studied by STM after drop-casting the monomer onto the substrate held either at room temperature or at 100 °C. For room temperature deposition only poorly ordered non-covalent arrangements were observed. In accordance with the established UHV protocol for halogenated coupling reaction, a covalent aryl-aryl coupling was accomplished for high temperature deposition. Interestingly, these covalent aggregates were not directly adsorbed on the Au(111) surface, but attached on top of a chemisorbed monolayer comprised of iodine and partially dehalogenated TIPB molecules. For a detailed analysis of the processes, the temperature dependent dehalogenation reaction was monitored by X-ray photoelectron spectroscopy under ultra-high vacuum conditions

Introducing biological information in the superparamagnetic clustering algorithm of gene expression data

Tesis (Doctorado en Nanociencias y Nanotecnología)"Los microarreglos proporcionan informaciòn de la actividad a nivel transcripcional de los genes de un organismo, bajo distintas circunstancias. Esto puede llevar al descubrimiento de genes clave en procesos celulares, clasificación molecular de enfermedades o identificar funciones para los genes, entre otras cosas. En el proceso de obtención de esta información, los algoritmos de clustering son una pieza importante al ayudar en la clasificación de los datos provenientes de microarreglos. En este trabajo modificamos el algoritmo de Clustering Superparamagnético añadiendo un peso extra en la fórmula de interacción que aprovecha la información que se tiene sobre los genes regulados por un mismo factor de transcripción. Con este algoritmo modificado, que nombramos SPCTF, analizamos los datos de microarreglos de Spellman et al. para ciclo celular en levadura (Saccharomyces cerevisiae) y encontramos clusters con un número mayor de integrantes, comparando con el algoritmo original SPC. Algunos de los genes que pudimos incorporar no fueron detectados por Spellman et al. en un principio, pero fueron identificados por otros estudios posteriormente. Otros de los genes que fueron incorporados aún no han sido clasificados, por lo que analizamos los clusters compuestos en su mayoría por estos genes sin identificar con el algoritmo MUSA y esto nos permitió seleccionar aquellos cuyos genes contienen sitios de unión a factores de transcripción correspondientes a ciclo celular. Estos clusters pueden ser estudiados ahora de manera experimental para descubrir nuevos genes involucrados en el ciclo celular. La idea de introducir la información biológica ya disponible para optimizar la clasificación de genes puede ser implementada para otros algoritmos de clustering.""Microarray technology allow researchers to examine the transcriptional activity of thousands of genes under different conditions. Microarrays have been used, for example, to discover key genes involved in cellular processes, disease classification, drug development and gene function annotation. Clustering algorithms have become an important step in the microarray data analysis in order to discover biologically relevant information. We modify the superparamagnetic clustering algorithm (SPC) by adding an extra weight to the interaction formula that considers which genes are regulated by the same transcription factor. This combined similarity measure for two genes relies on two types of information: their expression profiles generated by a microarray, and the number of shared transcription factors that have been proved (experimentally) to bind to their promoters. With this modified algorithm which we call SPCTF, we analyze the Spellman et al. microarray data for cell cycle genes in yeast (Saccharomyces cerevisiae), and find clusters with a higher number of elements compared with those obtained with the SPC algorithm. Some of the incorporated genes by using SPCFT were not detected at first by Spellman et al. but were later identified by other studies, whereas several genes still remain unclassified. The clusters composed by unidentified genes were analyzed with MUSA, the motif finding using an unsupervised approach algorithm, and this allow us to select the clusters whose elements contain cell cycle transcription factor binding sites as clusters worthy of further experimental studies because they would probably lead to new cell cycle genes. Our idea of introducing the available information about transcription factors to optimize the gene classification could be implemented for other distance-based clustering algorithms.

Primal Eukaryogenesis:On the Communal Nature of Precellular States, Ancestral to Modern Life

This problem-oriented, exploratory and hypothesis-driven discourse toward the unknown combines several basic tenets: (i) a photo-active metal sulfide scenario of primal biogenesis in the porespace of shallow sedimentary flats, in contrast to hot deep-sea hydrothermal vent conditions; (ii) an inherently complex communal system at the common root of present life forms; (iii) a high degree of internal compartmentalization at this communal root, progressively resembling coenocytic (syncytial) super-cells; (iv) a direct connection from such communal super-cells to proto-eukaryotic macro-cell organization; and (v) multiple rounds of micro-cellular escape with streamlined reductive evolution—leading to the major prokaryotic cell lines, as well as to megaviruses and other viral lineages. Hopefully, such nontraditional concepts and approaches will contribute to coherent and plausible views about the origins and early life on Earth. In particular, the coevolutionary emergence from a communal system at the common root can most naturally explain the vast discrepancy in subcellular organization between modern eukaryotes on the one hand and both archaea and bacteria on the other