Integrated mining of feature spaces for bioinformatics domain discovery

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein\u27s surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized and cured and in which designer drugs are created. With the exponential growth of protein databases and the limitations of experimental protein structure determination, sophisticated computational methods have been developed and applied to search for, detect, and compare protein homology. Most computational tools developed for protein structure prediction are primarily based on sequence similarity searches. These approaches have improved the prediction accuracy of high sequence similarity proteins but have failed to perform well with proteins of low sequence similarity. Data mining offers unique algorithmic computational approaches that have been used widely in the development of automatic protein structure classification and prediction. In this dissertation, we present a novel approach for the integration of physico-chemical properties and effective feature extraction techniques for the classification of proteins. Our approaches overcome one of the major obstacles of data mining in protein databases, the encapsulation of different hydrophobicity residue properties into a much reduced feature space that possess high degrees of specificity and sensitivity in protein structure classification. We have developed three unique computational algorithms for coherent feature extraction on selected scale properties of the protein sequence. When plagued by the problem of the unequal cardinality of proteins, our proposed integration scheme effectively handles the varied sizes of proteins and scales well with increasing dimensionality of these sequences. We also detail a two-fold methodology for protein functional annotation. First, we exhibit our success in creating an algorithm that provides a means to integrate multiple physico-chemical properties in the form of a multi-layered abstract feature space, with each layer corresponding to a physico-chemical property. Second, we discuss a wavelet-based segmentation approach that efficiently detects regions of property conservation across all layers of the created feature space. Finally, we present a unique graph-theory based algorithmic framework for the identification of conserved hydrophobic residue interaction patterns using identified scales of hydrophobicity. We report that these discriminatory features are specific to a family of proteins, which consist of conserved hydrophobic residues that are then used for structural classification. We also present our rigorously tested validation schemes, which report significant degrees of accuracy to show that homologous proteins exhibit the conservation of physico-chemical properties along the protein backbone. We conclude our discussion by summarizing our results and contributions and by listing our goals for future research

Ais-Psmaca: Towards Proposing an Artificial Immune System for Strengthening Psmaca: An Automated Protein Structure Prediction using Multiple Attractor Cellular Automata

Predicting the structure of proteins from their amino acid sequences has gained a remarkable attention in recent years. Even though there are some prediction techniques addressing this problem, the approximate accuracy in predicting the protein structure is closely 75%. An automated procedure was evolved with MACA (Multiple Attractor Cellular Automata) for predicting the structure of the protein. Artificial Immune System (AIS-PSMACA) a novel computational intelligence technique is used for strengthening the system (PSMACA) with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-PSMACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-PSMACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset

Characterisation and Classification of Protein Sequences by Using Enhanced Amino Acid Indices and Signal Processing-Based Methods

Due to copyright reasons, the authors published papers have been removed from this copy of the thesis.Protein sequencing has produced overwhelming amount of protein sequences, especially in the last decade. Nevertheless, the majority of the proteins' functional and structural classes are still unknown, and experimental methods currently used to determine these properties are very expensive, laborious and time consuming. Therefore, automated computational methods are urgently required to accurately and reliably predict functional and structural classes of the proteins. Several bioinformatics methods have been developed to determine such properties of the proteins directly from their sequence information. Such methods that involve signal processing methods have recently become popular in the bioinformatics area and been investigated for the analysis of DNA and protein sequences and shown to be useful and generally help better characterise the sequences. However, there are various technical issues that need to be addressed in order to overcome problems associated with the signal processing methods for the analysis of the proteins sequences. Amino acid indices that are used to transform the protein sequences into signals have various applications and can represent diverse features of the protein sequences and amino acids. As the majority of indices have similar features, this project proposes a new set of computationally derived indices that better represent the original group of indices. A study is also carried out that resulted in finding a unique and universal set of best discriminating amino acid indices for the characterisation of allergenic proteins. This analysis extracts features directly from the protein sequences by using Discrete Fourier Transform (DFT) to build a classification model based on Support Vector Machines (SVM) for the allergenic proteins. The proposed predictive model yields a higher and more reliable accuracy than those of the existing methods. A new method is proposed for performing a multiple sequence alignment. For this method, DFT-based method is used to construct a new distance matrix in combination with multiple amino acid indices that were used to encode protein sequences into numerical sequences. Additionally, a new type of substitution matrix is proposed where the physicochemical similarities between any given amino acids is calculated. These similarities were calculated based on the 25 amino acids indices selected, where each one represents a unique biological protein feature. The proposed multiple sequence alignment method yields a better and more reliable alignment than the existing methods. In order to evaluate complex information that is generated as a result of DFT, Complex Informational Spectrum Analysis (CISA) is developed and presented. As the results show, when protein classes present similarities or differences according to the Common Frequency Peak (CFP) in specific amino acid indices, then it is probable that these classes are related to the protein feature that the specific amino acid represents. By using only the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient, as biologically related features can appear individually either in the real or the imaginary spectrum. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Upon identification of a new protein, it is important to single out amino acid responsible for the structural and functional classification of the protein, as well as the amino acids contributing to the protein's specific biological characterisation. In this work, a novel approach is presented to identify and quantify the relationship between individual amino acids and the protein. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Characterisation and identification problem of the Influenza A virus protein sequences is tackled through a Subgroup Discovery (SD) algorithm, which can provide ancillary knowledge to the experts. The main objective of the case study was to derive interpretable knowledge for the influenza A virus problem and to consequently better describe the relationships between subtypes of this virus. Finally, by using DFT-based sequence-driven features a Support Vector Machine (SVM)-based classification model was built and tested, that yields higher predictive accuracy than that of SD. The methods developed and presented in this study yield promising results and can be easily applied to proteomic fields

Nucleosome Positioning and Its Role in Gene Regulation in Yeast

Nucleosome, composed of a 147-bp segment of DNA helix wrapped around a histone protein octamer, serves as the basic unit of chromatin. Nucleosome positioning refers to the relative position of DNA double helix with respect to the histone octamer. The positioning has an important role in transcription, DNA replication and other DNA transactions since packing DNA into nucleosomes occludes the binding site of proteins. Moreover, the nucleosomes bear histone modifications thus having a profound effect in regulation. Nucleosome positioning and its roles are extensively studied in model organism yeast. In this chapter, nucleosome organization and its roles in gene regulation are reviewed. Typically, nucleosomes are depleted around transcription start sites (TSSs), resulting in a nucleosome-free region (NFR) that is flanked by two well-positioned H2A.Z-containing nucleosomes. The nucleosomes downstream of the TSS are equally spaced in a nucleosome array. DNA sequences, especially 10–11 bp periodicities of some specific dinucleotides, partly determine the nucleosome positioning. Nucleosome occupancy can be determined with high throughput sequencing techniques. Importantly, nucleosome positions are dynamic in different cell types and different environments. Histones depletions, histones mutations, heat shock and changes in carbon source will profoundly change nucleosome organization. In the yeast cells, upon mutating the histones, the nucleosomes change drastically at promoters and the highly expressed genes, such as ribosome genes, undergo more change. The changes of nucleosomes tightly associate the transcription initiation, elongation and termination. H2A.Z is contained in the +1 and −1 nucleosomes and thus in transcription. Chaperon Chz1 and elongation factor Spt16 function in H2A.Z deposition on chromatin. The chapter covers the basic concept of nucleosomes, nucleosome determinant, the techniques of mapping nucleosomes, nucleosome alteration upon stress and mutation, and Htz1 dynamics on chromatin

New methods for automated NMD data analysis and protein structure determination

Die Ermittlung von Proteinstukturen mittels NMR-Spektroskopie ist ein komplexer Prozess, wobei die Resonanzfrequenzen und die Signalintensitäten den Atomen des Proteins zugeordnet werden. Zur Bestimmung der räumlichen Proteinstruktur sind folgende Schritte erforderlich: die Präparation der Probe und 15N/13C Isotopenanreicherung, Durchführung der NMR Experimente, Prozessierung der Spektren, Bestimmung der Signalresonanzen ('Peak-picking'), Zuordnung der chemischen Verschiebungen, Zuordnung der NOESY-Spektren und das Sammeln von konformationellen Strukturparametern, Strukturrechnung und Strukturverfeinerung. Aktuelle Methoden zur automatischen Strukturrechnung nutzen eine Reihe von Computeralgorithmen, welche Zuordnungen der NOESY-Spektren und die Strukturrechnung durch einen iterativen Prozess verbinden. Obwohl neue Arten von Strukturparametern wie dipolare Kopplungen, Orientierungsinformationen aus kreuzkorrelierten Relaxationsraten oder Strukturinformationen, die sich in Gegenwart paramagnetischer Zentren in Proteinen ergeben, wichtige Neuerungen für die Proteinstrukturrechnung darstellen, sind die Abstandsinformationen aus NOESY-Spektren weiterhin die wichtigste Basis für die NMR-Strukturbestimmung. Der hohe zeitliche Aufwand des 'peak-picking' in NOESY-Spektren ist hauptsächlich bedingt durch spektrale Überlagerung, Rauschsignale und Artefakte in NOESY-Spektren. Daher werden für das effizientere automatische 'Peak-picking' zuverlässige Filter benötigt, um die relevanten Signale auszuwählen. In der vorliegenden Arbeit wird ein neuer Algorithmus für die automatische Proteinstrukturrechnung beschrieben, der automatisches 'Peak-picking' von NOESY-Spektren beinhaltet, die mit Hilfe von Wavelets entrauscht wurden. Der kritische Punkt dieses Algorithmus ist die Erzeugung inkrementeller Peaklisten aus NOESY-Spektren, die mit verschiedenen auf Wavelets basierenden Entrauschungsprozeduren prozessiert wurden. Mit Hilfe entrauschter NOESY-Spektren erhält man Signallisten mit verschiedenen Konfidenzbereichen, die in unterschiedlichen Schritten der kombinierten NOE-Zuordnung/Strukturrechnung eingesetzt werden. Das erste Strukturmodell beruht auf stark entrauschten Spektren, die die konservativste Signalliste mit als weitgehend sicher anzunehmenden Signalen ergeben. In späteren Stadien werden Signallisten aus weniger stark entrauschten Spektren mit einer größeren Anzahl von Signalen verwendet. Die Auswirkung der verschiedenen Entrauschungsprozeduren auf Vollständigkeit und Richtigkeit der NOESY Peaklisten wurde im Detail untersucht. Durch die Kombination von Wavelet-Entrauschung mit einem neuen Algorithmus zur Integration der Signale in Verbindung mit zusätzlichen Filtern, die die Konsistenz der Peakliste prüfen ('Network-anchoring' der Spinsysteme und Symmetrisierung der Peakliste), wird eine schnelle Konvergenz der automatischen Strukturrechnung erreicht. Der neue Algorithmus wurde in ARIA integriert, einem weit verbreiteten Computerprogramm für die automatische NOE-Zuordnung und Strukturrechnung. Der Algorithmus wurde an der Monomereinheit der Polysulfid-Schwefel-Transferase (Sud) aus Wolinella succinogenes verifiziert, deren hochaufgelöste Lösungsstruktur vorher auf konventionelle Weise bestimmt wurde. Neben der Möglichkeit zur Bestimmung von Proteinlösungsstrukturen bietet sich die NMR-Spektroskopie auch als wirkungsvolles Werkzeug zur Untersuchung von Protein-Ligand- und Protein-Protein-Wechselwirkungen an. Sowohl NMR Spektren von isotopenmarkierten Proteinen, als auch die Spektren von Liganden können für das 'Screening' nach Inhibitoren benutzt werden. Im ersten Fall wird die Sensitivität der 1H- und 15N-chemischen Verschiebungen des Proteinrückgrats auf kleine geometrische oder elektrostatische Veränderungen bei der Ligandbindung als Indikator benutzt. Als 'Screening'-Verfahren, bei denen Ligandensignale beobachtet werden, stehen verschiedene Methoden zur Verfügung: Transfer-NOEs, Sättigungstransferdifferenzexperimente (STD, 'saturation transfer difference'), ePHOGSY, diffusionseditierte und NOE-basierende Methoden. Die meisten dieser Techniken können zum rationalen Design von inhibitorischen Verbindungen verwendet werden. Für die Evaluierung von Untersuchungen mit einer großen Anzahl von Inhibitoren werden effiziente Verfahren zur Mustererkennung wie etwa die PCA ('Principal Component Analysis') verwendet. Sie eignet sich zur Visualisierung von Ähnlichkeiten bzw. Unterschieden von Spektren, die mit verschiedenen Inhibitoren aufgenommen wurden. Die experimentellen Daten werden zuvor mit einer Serie von Filtern bearbeitet, die u.a. Artefakte reduzieren, die auf nur kleinen Änderungen der chemischen Verschiebungen beruhen. Der am weitesten verbreitete Filter ist das sogenannte 'bucketing', bei welchem benachbarte Punkte zu einen 'bucket' aufsummiert werden. Um typische Nachteile der 'bucketing'-Prozedur zu vermeiden, wurde in der vorliegenden Arbeit der Effekt der Wavelet-Entrauschung zur Vorbereitung der NMR-Daten für PCA am Beispiel vorhandener Serien von HSQC-Spektren von Proteinen mit verschiedenen Liganden untersucht. Die Kombination von Wavelet-Entrauschung und PCA ist am effizientesten, wenn PCA direkt auf die Wavelet-Koeffizienten angewandt wird. Durch die Abgrenzung ('thresholding') der Wavelet-Koeffizienten in einer Multiskalenanalyse wird eine komprimierte Darstellung der Daten erreicht, welche Rauschartefakte minimiert. Die Kompression ist anders als beim 'bucketing' keine 'blinde' Kompression, sondern an die Eigenschaften der Daten angepasst. Der neue Algorithmus kombiniert die Vorteile einer Datenrepresentation im Wavelet-Raum mit einer Datenvisualisierung durch PCA. In der vorliegenden Arbeit wird gezeigt, dass PCA im Wavelet- Raum ein optimiertes 'clustering' erlaubt und dabei typische Artefakte eliminiert werden. Darüberhinaus beschreibt die vorliegende Arbeit eine de novo Strukturbestimmung der periplasmatischen Polysulfid-Schwefel-Transferase (Sud) aus dem anaeroben gram-negativen Bakterium Wolinella succinogenes. Das Sud-Protein ist ein polysulfidbindendes und transferierendes Enzym, das bei niedriger Polysulfidkonzentration eine schnelle Polysulfid-Schwefel-Reduktion katalysiert. Sud ist ein 30 kDa schweres Homodimer, welches keine prosthetischen Gruppen oder schwere Metallionen enthält. Jedes Monomer enhält ein Cystein, welches kovalent bis zu zehn Polysulfid-Schwefel (Sn 2-) Ionen bindet. Es wird vermutet, dass Sud die Polysulfidkette auf ein katalytischen Molybdän-Ion transferiert, welches sich im aktiven Zentrum des membranständigen Enzyms Polysulfid-Reduktase (Psr) auf dessen dem Periplasma zugewandten Seite befindet. Dabei wird eine reduktive Spaltung der Kette katalysiert. Die Lösungsstruktur des Homodimeres Sud wurde mit Hilfe heteronuklearer, mehrdimensionaler NMR-Techniken bestimmt. Die Struktur beruht auf von NOESY-Spektren abgeleiteten Distanzbeschränkungen, Rückgratwasserstoffbindungen und Torsionswinkeln, sowie auf residuellen dipolaren Kopplungen, die für die Verfeinerung der Struktur und für die relative Orientierung der Monomereinheiten wichtig waren. In den NMR Spektren der Homodimere haben alle symmetrieverwandte Kerne äquivalente magnetische Umgebungen, weshalb ihre chemischen Verschiebungen entartet sind. Die symmetrische Entartung vereinfacht das Problem der Resonanzzuordnung, da nur die Hälfte der Kerne zugeordnet werden müssen. Die NOESY-Zuordnung und die Strukturrechnung werden dadurch erschwert, dass es nicht möglich ist, zwischen den Intra-Monomer-, Inter-Monomer- und Co-Monomer- (gemischten) NOESY-Signalen zu unterscheiden. Um das Problem der Symmetrie-Entartung der NOESY-Daten zu lösen, stehen zwei Möglichkeiten zur Verfügung: (I) asymmetrische Markierungs-Experimente, um die intra- von den intermolekularen NOESY-Signalen zu unterscheiden, (II) spezielle Methoden der Strukturrechnung, die mit mehrdeutigen Distanzbeschränkungen arbeiten können. Die in dieser Arbeit vorgestellte Struktur wurde mit Hilfe der Symmetrie-ADR- ('Ambigous Distance Restraints') Methode in Kombination mit Daten von asymetrisch isotopenmarkierten Dimeren berechnet. Die Koordinaten des Sud-Dimers zusammen mit den NMR-basierten Strukturdaten wur- den in der RCSB-Proteindatenbank unter der PDB-Nummer 1QXN abgelegt. Das Sud-Protein zeigt nur wenig Homologie zur Primärsequenz anderer Proteine mit ähnlicher Funktion und bekannter dreidimensionaler Struktur. Bekannte Proteine sind die Schwefeltransferase oder das Rhodanese-Enzym, welche beide den Transfer von einem Schwefelatom eines passenden Donors auf den nukleophilen Akzeptor (z.B von Thiosulfat auf Cyanid) katalysieren. Die dreidimensionalen Strukturen dieser Proteine zeigen eine typische a=b Topologie und haben eine ähnliche Umgebung im aktiven Zentrum bezüglich der Konformation des Proteinrückgrades. Die Schleife im aktiven Zentrum umgibt das katalytische Cystein, welches in allen Rhodanese-Enzymen vorhanden ist, und scheint im Sud-Protein flexibel zu sein (fehlende Resonanzzuordnung der Aminosäuren 89-94). Das Polysulfidende ragt aus einer positiv geladenen Bindungstasche heraus (Reste: R46, R67, K90, R94), wo Sud wahrscheinlich in Kontakt mit der Polysulfidreduktase tritt. Das strukturelle Ergebnis wurde durch Mutageneseexperimente bestätigt. In diesen Experimenten konnte gezeigt werden, dass alle Aminosäurereste im aktiven Zentrum essentiell für die Schwefeltransferase-Aktivität des Sud-Proteins sind. Die Substratbindung wurde früher durch den Vergleich von [15N,1H]-TROSY-HSQC-Spektren des Sud-Proteins in An- und Abwesenheit des Polysulfidliganden untersucht. Bei der Substratbindung scheint sich die lokale Geometrie der Polysulfidbindungsstelle und der Dimerschnittstelle zu verändern. Die konformationellen Änderungen und die langsame Dynamik, hervorgerufen durch die Ligandbindung können die weitere Polysulfid-Schwefel-Aktivität auslösen. Ein zweites Polysulfid-Schwefeltransferaseprotein (Str, 40 kDa) mit einer fünffach höheren nativen Konzentration im Vergleich zu Sud wurde im Bakterienperiplasma von Wolinella succinogenes entdeckt. Es wird angenommen, dass beide Protein einen Polysulfid-Schwefel-Komplex bilden, wobei Str wässriges Polysulfid sammelt und an Sud abgibt, welches den Schwefeltransfer zum katalytischen Molybdän-Ion auf das aktive Zentrum der dem Periplasma zugewandten Seite der Polysulfidreduktase durchführt. Änderungen chemischer Verschiebungen in [15N,1H]-TROSY-HSQC-Spektren zeigen, dass ein Polysulfid-Schwefeltransfer zwischen Str und Sud stattfindet. Eine mögliche Protein-Protein-Wechselwirkungsfläche konnte bestimmt werden. In der Abwesenheit des Polysulfidsubstrates wurden keine Wechselwirkungen zwischen Sud und Str beobachtet, was die Vermutung bestätigt, dass beide Proteine nur dann miteinander wechselwirken und den Polysulfid-Schwefeltransfer ermöglichen, wenn als treibende Kraft Polysulfid präsent ist

Enhancing Single Walled Carbon Nanotube Deposition For The Study Of Extracellular Analytes

Extracellular signaling is a dynamic process responsible for coordinating large scale biological processes. As such, understanding extracellular signaling is important to our determination of normal function and pathophysiological development. High resolution spatial and temporal information are critical to completely understanding these processes. Unfortunately, current methods of detection are lacking in either spatial or temporal resolution of extracellular products, limiting researchers’ ability to understand complex biological processes. A new group of sensors based on fluorescent single walled carbon nanotubes (SWNT) have shown the potential to provide both high quality spatial and temporal resolution for the sensing of analytes. However, while SWNT has already been used extensively as an intracellular probe, it has seldom been used for intercellular monitoring. In the few instances that SWNT has been used to form extracellular sensor arrays the deposition method has relied on electrostatic or non-specific interactions and is not well characterized. Herein a new method of SWNT deposition based on the avidin-biotin bond was developed, where biotin activity was imparted to SWNT via coupling to its DNA wrapping and avidin was covalently immobilized on the surface of a glass slide. The method of SWNT immobilization produced a twofold enhancement in SWNT deposition over the current standard without negatively impacting SWNT spectral properties, distribution, response time, or degradation rates. These results indicate the effectiveness of this method for increasing SWNT deposition and provide a simple pathway for enhancing the deposition of DNA-SWNT complexes. Advisor: Nicole M. Iverso

Integrated glycomics, proteomics, and glycoproteomics of human leukocytes and glioblastoma tissue microarrays

This thesis includes studies on N-, mucin type O-, and glycosaminoglycan (GAG)-linked glycosylation in human biospecimens. Glycosylation plays a central role in biological processes, including protein folding, immune surveillance, and regulation of cell growth. The structures of GAG are regulated in a tissue-specific manner. Heparan sulfate (HS) and chondroitin sulfate (CS) are the two types of GAGs targeted in this thesis. Human leukocytes express both CS and HS GAGs with CS being the more abundant type; however, little is known regarding the properties and structures of GAG chains, their ranges of variability among normal subjects, and changes in structure associated with disease conditions. We measured the relative and absolute disaccharides abundances of HS and CS for purified B, T, NK cells, monocytes, and polymorphonuclear leukocytes (PMNs) using size exclusion chromatography-mass spectrometry (SEC-MS). We found that all leukocytes express HS chains with levels of sulfation more similar to heparin than to organ-derived HS. In addition, CS abundances varied considerably in a leukocyte cell type specific manner. Therefore, our results established the ranges of GAG structures expressed on normal leukocytes as well as necessary for subsequent inquiry into disease conditions. Glioblastoma (GBM) accounts for 30% of human primary brain tumors. It is deadly and highly invasive. In past decades, most GBM research focused on pathophysiological changes in genome. There remains a dearth of knowledge regarding alterations in glycomics, glycoproteomics, and proteomics during GBM tumorigenesis. Therefore, we developed a comprehensive platform for high-throughput sample preparation with surface digestion for tissue microarrays, LC-MS/MS data dependent acquisition, and semi-automated data analysis to integrate glycomics, glycoproteomics, and proteomics for different grade of tumor and different subtypes of GBM. By analyzing GBM tissue microarrays, we found tumor grade and subtype specific changes to the expression of biomolecules. We also identified approximately 100 site-specific N- and mucin type O-glycosylations, the majority of which were previously unreported. Overall, our results improved the fundamental understandings about GBM pathogenesis.2018-11-02T00:00:00

Enhancing Single Walled Carbon Nanotube Deposition For The Study Of Extracellular Analytes

Extracellular signaling is a dynamic process responsible for coordinating large scale biological processes. As such, understanding extracellular signaling is important to our determination of normal function and pathophysiological development. High resolution spatial and temporal information are critical to completely understanding these processes. Unfortunately, current methods of detection are lacking in either spatial or temporal resolution of extracellular products, limiting researchers’ ability to understand complex biological processes. A new group of sensors based on fluorescent single walled carbon nanotubes (SWNT) have shown the potential to provide both high quality spatial and temporal resolution for the sensing of analytes. However, while SWNT has already been used extensively as an intracellular probe, it has seldom been used for intercellular monitoring. In the few instances that SWNT has been used to form extracellular sensor arrays the deposition method has relied on electrostatic or non-specific interactions and is not well characterized. Herein a new method of SWNT deposition based on the avidin-biotin bond was developed, where biotin activity was imparted to SWNT via coupling to its DNA wrapping and avidin was covalently immobilized on the surface of a glass slide. The method of SWNT immobilization produced a twofold enhancement in SWNT deposition over the current standard without negatively impacting SWNT spectral properties, distribution, response time, or degradation rates. These results indicate the effectiveness of this method for increasing SWNT deposition and provide a simple pathway for enhancing the deposition of DNA-SWNT complexes. Advisor: Nicole M. Iverso

MODULATION OF THE RECEPTOR GATING MECHANISM AND ALLOSTERIC COMMUNICATION IN IONOTROPIC GLUTAMATE RECEPTORS

Ionotropic glutamate receptors (iGluRs) found in mammalian brain are primarily known to mediate excitatory synaptic transmission crucial for learning and memory formation. The family of iGluRs consists of AMPA receptors, NMDA receptors and kainate receptors with each member having distinct physiological role. In the recent years, significant progress has been made in understanding the biophysical, and functional properties of iGluRs. The development of Cryo-EM and X-Ray crystallography techniques have further facilitated in the structural understanding of these receptors. However, the multidomain nature, large size of the protein, complex gating mechanism and inadequate knowledge regarding the conformational dynamics of the receptors during channel gating mechanism have been some of the limiting factors in elucidating the structure-function relation of iGluRs. Thus, to understand the conformational dynamics of iGluR family and correlate to its functional behavior, I have utilized single molecule Forster Resonance Energy Transfer (smFRET) and molecular dynamics simulation and specifically investigated the factors influencing gating mechanism and allosteric communication in heteromeric kainate receptor GluK2/K5 and NMDA receptor GluN1/N2A. Some of the major finding in this dissertation includes subunit arrangement of GluK2/K5 and its dynamics involved in resting and desensitized conditions. For the first time we have identified the conformational changes induced at GluK2 and GluK5 subunits in a heteromer GluK2/K5 when bound to different agonists. Utilizing MD simulations in GluN1/N2A NMDA receptors we have identified the structural pathway regarding the mechanism underlying negative cooperativity and how mutation in the receptor leads to abnormal functional behavior. These findings will allow us to understand the conformational control regarding modulation of receptor function and will serve as a basis for developing subunit and conformation-specific therapeutic drugs that can potentially control the abnormal activity of the receptors linked to several neurological diseases