Analysis of class C G-protein coupled receptors using supervised classification methods

G protein-coupled receptors (GPCRs) are cell membrane proteins with a key role in regulating the function of cells. This is the result of their ability to transmit extracellular signals, which makes them relevant for pharmacology and has led, over the last decade, to active research in the field of proteomics. The current thesis specifically targets class C of GPCRs, which are relevant in therapies for various central nervous system disorders, such as Alzheimer’s disease, anxiety, Parkinson’s disease and schizophrenia. The investigation of protein functionality often relies on the knowledge of crystal three dimensional (3-D) structures, which determine the receptor’s ability for ligand binding responsible for the activation of certain functionalities in the protein. The structural information is therefore paramount, but it is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as GPCRs. In the face of the lack of information about the 3-D structure, research is often bound to the analysis of the primary amino acid sequences of the proteins, which are commonly known and available from curated databases. Much research on sequence analysis has focused on the quantitative analysis of their aligned versions, although, recently, alternative approaches using machine learning techniques for the analysis of alignment-free sequences have been proposed. In this thesis, we focus on the differentiation of class C GPCRs into functional and structural related subgroups based on the alignment-free analysis of their sequences using supervised classification models. In the first part of the thesis, the main topic is the construction of supervised classification models for unaligned protein sequences based on physicochemical transformations and n-gram representations of their amino acid sequences. These models are useful to assess the internal data quality of the externally labeled dataset and to manage the label noise problem from a data curation perspective. In its second part, the thesis focuses on the analysis of the sequences to discover subtype- and region-speci¿c sequence motifs. For that, we carry out a systematic analysis of the topological sequence segments with supervised classification models and evaluate the subtype discrimination capability of each region. In addition, we apply different types of feature selection techniques to the n-gram representation of the amino acid sequence segments to find subtype and region specific motifs. Finally, we compare the findings of this motif search with the partially known 3D crystallographic structures of class C GPCRs.Los receptores acoplados a proteínas G (GPCRs) son proteínas de la membrana celular con un papel clave para la regulación del funcionamiento de una célula. Esto es consecuencia de su capacidad de transmisión de señales extracelulares, lo que les hace relevante en la farmacología y que ha llevado a investigaciones activas en la última década en el área de la proteómica. Esta tesis se centra específicamente en la clase C de GPCRs, que son relevante para terapias de varios trastornos del sistema nervioso central, como la enfermedad de Alzheimer, ansiedad, enfermedad de Parkinson y esquizofrenia. La investigación de la funcionalidad de proteínas muchas veces se basa en el conocimiento de la estructura cristalina tridimensional (3-D), que determina la capacidad del receptor para la unión con ligandos, que son responsables para la activación de ciertas funcionalidades en la proteína. El análisis de secuencias de amino ácidos se ha centrado en muchas investigaciones en el análisis cuantitativo de las versiones alineados de las secuencias, aunque, recientemente, se han propuesto métodos alternativos usando métodos de aprendizaje automático aplicados a las versiones no-alineadas de las secuencias. En esta tesis, nos centramos en la diferenciación de los GPCRs de la clase C en subgrupos funcionales y estructurales basado en el análisis de las secuencias no-alineadas utilizando modelos de clasificación supervisados. Estos modelos son útiles para evaluar la calidad interna de los datos a partir del conjunto de datos etiquetados externamente y para gestionar el problema del 'ruido de datos' desde la perspectiva de la curación de datos. En su segunda parte, la tesis enfoca el análisis de las secuencias para descubrir motivos de secuencias específicos a nivel de subtipo o región. Para eso, llevamos a cabo un análisis sistemático de los segmentos topológicos de la secuencia con modelos supervisados de clasificación y evaluamos la capacidad de discriminar entre subtipos de cada región. Adicionalmente, aplicamos diferentes tipos de técnicas de selección de atributos a las representaciones mediante n-gramas de los segmentos de secuencias de amino ácidos para encontrar motivos específicos a nivel de subtipo y región. Finalmente, comparamos los descubrimientos de la búsqueda de motivos con las estructuras cristalinas parcialmente conocidas para la clase C de GPCRs

Analysis of class C G-protein coupled receptors using supervised classification methods

G protein-coupled receptors (GPCRs) are cell membrane proteins with a key role in regulating the function of cells. This is the result of their ability to transmit extracellular signals, which makes them relevant for pharmacology and has led, over the last decade, to active research in the field of proteomics. The current thesis specifically targets class C of GPCRs, which are relevant in therapies for various central nervous system disorders, such as Alzheimer’s disease, anxiety, Parkinson’s disease and schizophrenia. The investigation of protein functionality often relies on the knowledge of crystal three dimensional (3-D) structures, which determine the receptor’s ability for ligand binding responsible for the activation of certain functionalities in the protein. The structural information is therefore paramount, but it is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as GPCRs. In the face of the lack of information about the 3-D structure, research is often bound to the analysis of the primary amino acid sequences of the proteins, which are commonly known and available from curated databases. Much research on sequence analysis has focused on the quantitative analysis of their aligned versions, although, recently, alternative approaches using machine learning techniques for the analysis of alignment-free sequences have been proposed. In this thesis, we focus on the differentiation of class C GPCRs into functional and structural related subgroups based on the alignment-free analysis of their sequences using supervised classification models. In the first part of the thesis, the main topic is the construction of supervised classification models for unaligned protein sequences based on physicochemical transformations and n-gram representations of their amino acid sequences. These models are useful to assess the internal data quality of the externally labeled dataset and to manage the label noise problem from a data curation perspective. In its second part, the thesis focuses on the analysis of the sequences to discover subtype- and region-speci¿c sequence motifs. For that, we carry out a systematic analysis of the topological sequence segments with supervised classification models and evaluate the subtype discrimination capability of each region. In addition, we apply different types of feature selection techniques to the n-gram representation of the amino acid sequence segments to find subtype and region specific motifs. Finally, we compare the findings of this motif search with the partially known 3D crystallographic structures of class C GPCRs.Los receptores acoplados a proteínas G (GPCRs) son proteínas de la membrana celular con un papel clave para la regulación del funcionamiento de una célula. Esto es consecuencia de su capacidad de transmisión de señales extracelulares, lo que les hace relevante en la farmacología y que ha llevado a investigaciones activas en la última década en el área de la proteómica. Esta tesis se centra específicamente en la clase C de GPCRs, que son relevante para terapias de varios trastornos del sistema nervioso central, como la enfermedad de Alzheimer, ansiedad, enfermedad de Parkinson y esquizofrenia. La investigación de la funcionalidad de proteínas muchas veces se basa en el conocimiento de la estructura cristalina tridimensional (3-D), que determina la capacidad del receptor para la unión con ligandos, que son responsables para la activación de ciertas funcionalidades en la proteína. El análisis de secuencias de amino ácidos se ha centrado en muchas investigaciones en el análisis cuantitativo de las versiones alineados de las secuencias, aunque, recientemente, se han propuesto métodos alternativos usando métodos de aprendizaje automático aplicados a las versiones no-alineadas de las secuencias. En esta tesis, nos centramos en la diferenciación de los GPCRs de la clase C en subgrupos funcionales y estructurales basado en el análisis de las secuencias no-alineadas utilizando modelos de clasificación supervisados. Estos modelos son útiles para evaluar la calidad interna de los datos a partir del conjunto de datos etiquetados externamente y para gestionar el problema del 'ruido de datos' desde la perspectiva de la curación de datos. En su segunda parte, la tesis enfoca el análisis de las secuencias para descubrir motivos de secuencias específicos a nivel de subtipo o región. Para eso, llevamos a cabo un análisis sistemático de los segmentos topológicos de la secuencia con modelos supervisados de clasificación y evaluamos la capacidad de discriminar entre subtipos de cada región. Adicionalmente, aplicamos diferentes tipos de técnicas de selección de atributos a las representaciones mediante n-gramas de los segmentos de secuencias de amino ácidos para encontrar motivos específicos a nivel de subtipo y región. Finalmente, comparamos los descubrimientos de la búsqueda de motivos con las estructuras cristalinas parcialmente conocidas para la clase C de GPCRs.Postprint (published version

A computational intelligence analysis of G proteincoupled receptor sequinces for pharmacoproteomic applications

Arguably, drug research has contributed more to the progress of medicine during the past decades than any other scientific factor. One of the main areas of drug research is related to the analysis of proteins. The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. This dependency brings about the challenge of finding robust methods to analyze the complex data they generate. Such challenge invites us to go one step further than traditional statistics and resort to approaches under the conceptual umbrella of artificial intelligence, including machine learning (ML), statistical pattern recognition and soft computing methods. Sound statistical principles are essential to trust the evidence base built through the use of such approaches. Statistical ML methods are thus at the core of the current thesis. More than 50% of drugs currently available target only four key protein families, from which almost a 30% correspond to the G Protein-Coupled Receptors (GPCR) superfamily. This superfamily regulates the function of most cells in living organisms and is at the centre of the investigations reported in the current thesis. No much is known about the 3D structure of these proteins. Fortunately, plenty of information regarding their amino acid sequences is readily available. The automatic grouping and classification of GPCRs into families and these into subtypes based on sequence analysis may significantly contribute to ascertain the pharmaceutically relevant properties of this protein superfamily. There is no biologically-relevant manner of representing the symbolic sequences describing proteins using real-valued vectors. This does not preclude the possibility of analyzing them using principled methods. These may come, amongst others, from the field of statisticalML. Particularly, kernel methods can be used to this purpose. Moreover, the visualization of high-dimensional protein sequence data can be a key exploratory tool for finding meaningful information that might be obscured by their intrinsic complexity. That is why the objective of the research described in this thesis is twofold: first, the design of adequate visualization-oriented artificial intelligence-based methods for the analysis of GPCR sequential data, and second, the application of the developed methods in relevant pharmacoproteomic problems such as GPCR subtyping and protein alignment-free analysis.Se podría decir que la investigación farmacológica ha desempeñado un papel predominante en el avance de la medicina a lo largo de las últimas décadas. Una de las áreas principales de investigación farmacológica es la relacionada con el estudio de proteínas. La farmacología depende cada vez más de los avances en genómica y proteómica, lo que conlleva el reto de diseñar métodos robustos para el análisis de los datos complejos que generan. Tal reto nos incita a ir más allá de la estadística tradicional para recurrir a enfoques dentro del campo de la inteligencia artificial, incluyendo el aprendizaje automático y el reconocimiento de patrones estadístico, entre otros. El uso de principios sólidos de teoría estadística es esencial para confiar en la base de evidencia obtenida mediante estos enfoques. Los métodos de aprendizaje automático estadístico son uno de los fundamentos de esta tesis. Más del 50% de los fármacos en uso hoy en día tienen como ¿diana¿ apenas cuatro familias clave de proteínas, de las que un 30% corresponden a la super-familia de los G-Protein Coupled Receptors (GPCR). Los GPCR regulan la funcionalidad de la mayoría de las células y son el objetivo central de la tesis. Se desconoce la estructura 3D de la mayoría de estas proteínas, pero, en cambio, hay mucha información disponible de sus secuencias de amino ácidos. El agrupamiento y clasificación automáticos de los GPCR en familias, y de éstas a su vez en subtipos, en base a sus secuencias, pueden contribuir de forma significativa a dilucidar aquellas de sus propiedades de interés farmacológico. No hay forma biológicamente relevante de representar las secuencias simbólicas de las proteínas mediante vectores reales. Esto no impide que se puedan analizar con métodos adecuados. Entre estos se cuentan las técnicas provenientes del aprendizaje automático estadístico y, en particular, los métodos kernel. Por otro lado, la visualización de secuencias de proteínas de alta dimensionalidad puede ser una herramienta clave para la exploración y análisis de las mismas. Es por ello que el objetivo central de la investigación descrita en esta tesis se puede desdoblar en dos grandes líneas: primero, el diseño de métodos centrados en la visualización y basados en la inteligencia artificial para el análisis de los datos secuenciales correspondientes a los GPCRs y, segundo, la aplicación de los métodos desarrollados a problemas de farmacoproteómica tales como la subtipificación de GPCRs y el análisis de proteinas no-alineadas

Pattern Discovery from Biosequences

In this thesis we have developed novel methods for analyzing biological data, the primary sequences of the DNA and proteins, the microarray based gene expression data, and other functional genomics data. The main contribution is the development of the pattern discovery algorithm SPEXS, accompanied by several practical applications for analyzing real biological problems. For performing these biological studies that integrate different types of biological data we have developed a comprehensive web-based biological data analysis environment Expression Profiler (http://ep.ebi.ac.uk/)

On the neuroendocrine regulation of reproduction: Functional characterization and kinetic studies of the lamprey gonadotropin -releasing hormone receptor and cloning and analysis of the cDNA encoding lamprey gonadotropin-releasing hormone-III

The vertebrate hypothalamic-pituitary-gonadal (HPG) axis is regulated by gonadotropin-releasing hormone (GnRH), a decapeptide that is produced and released from the hypothalamus. At the anterior pituitary, GnRH action is mediated through high affinity binding with the GnRH receptor, a rhodopsin-like seven transmembrane G-protein coupled receptor (GPCR). Interest in the evolution of reproductive physiology has led scientists to study the lamprey, a member of the oldest extant class of vertebrates, the agnathans. The studies presented herein contribute to the field of reproductive neuroendocrinology through developing our understanding of ancestral, or ancestral-like characteristics and mechanisms of the HPG axis. This dissertation is divided into two major components: (1) functional characterization and kinetic studies of the lamprey GnRH receptor (chapters II and III), and (2) an analysis of the lamprey GnRH-III cDNA (chapter IV). A type II lamprey GnRH receptor was recently identified via cDNA cloning, BLAST analysis and in situ hybridization, however the classification by these homology and expression studies was insufficient. Demonstration of function, through binding capacity or efficacy is a vital and required component of receptor characterization. To this end, a heterologous expression system was developed using COS7 cells transiently transfected with the lamprey GnRH receptor. The lamprey GnRH receptor was shown to be functional as well as lamprey GnRH-III selective based on a series of efficacy and kinetic studies. Ligand dependant internalization was characterized, which was dependant on a motif within the first forty amino acids of the C-terminal tail. Further function and kinetics studies were performed using C-terminal tail truncation mutants. The objective of the second component of this dissertation was to clone and characterize the cDNA encoding lamprey GnRH-III from eight species of lamprey, which were analyzed by phylogenetics methodology to address the molecular evolution of the GnRH family and the lamprey lineage. The lamprey GnRH-III sequences formed three groups, supporting the current view of the lamprey lineage at the family level. Phylogenetic analysis of these sequences together with 64 previously described GnRH sequences suggested that the lamprey GnRHs are unique, as they group together separately from the three previously described paralogous lineages of the GnRH family

Transcriptional Regulatory Logic of Cilium Formation in C. Elegans

[ES] Los cilios son estructuras eucariotas complejas conservadas evolutivamente que, proyectando desde la superficie de las células, desempeñan un gran número de funciones biológicas. Los cilios se clasifican tradicionalmente en móviles o sensoriales y en su composición intervienen cientos de proteínas. Este conjunto de genes que codifican para los componentes ciliares se conoce como cilioma. Las mutaciones en el cilioma subyacen a un grupo cada vez mayor de enfermedades multisistémicas altamente pleiotrópicas denominadas globalmente como ciliopatías. Estas enfermedades se caracterizan, entre otros síntomas, por retraso mental, defectos sensoriales y/o trastornos metabólicos. A pesar de que se estima que 1 de cada 1.000 personas está afectada por estas enfermedades, las bases moleculares de las ciliopatías son todavía poco conocidas. El adecuado ensamblaje y funcionalidad del cilio requieren de la expresión estrechamente coordinada de los componentes del cilio; sin embargo, se sabe poco sobre la lógica reguladora que controla la transcripción del cilioma. La mayoría de los genes del cilioma son compartidos tanto por cilios móviles como sensoriales. Los factores de transcripción (FTs) de la familia RFX tienen un papel evolutivamente conservado en la regulación transcripcional del cilioma tanto móvil como sensorial. En los vertebrados, la transcripción del cilioma móvil también está regulada directamente por FoxJ1, un FT de la familia forkhead (FKH). Sin embargo, hasta la fecha, se desconocen los FTs que actúan junto a RFX en la transcripción del cilioma sensorial en cualquier organismo. En este trabajo, hemos identificado a FKH-8, un FT de la familia FKH, como selector terminal del cilioma sensorial de C. elegans. fkh-8 se expresa de forma consistente en las sesenta neuronas sensoriales ciliadas de C. elegans, se une a las regiones reguladoras de los genes del cilioma sensorial, también es necesario para la correcta expresión de los genes del cilioma y actúa de forma sinérgica con el conocido regulador maestro de la ciliogénesis DAF19/RFX. En consecuencia, los mutantes para fkh-8 muestran una amplia gama de defectos de comportamiento en una plétora de paradigmas sensoriales, incluyendo la olfacción, la gustación y la mecano-sensación. Así, hemos identificado, por primera vez, un FT que actúa junto con los FTs de la familia RFX en la regulación directa del cilioma sensorial. Además, nuestros resultados, junto con trabajos anteriores, muestran que los FTs FKH y RFX actúan conjuntamente en la regulación de los cilios tanto móviles como sensoriales, lo que sugiere que esta lógica reguladora podría ser un rasgo evolutivo antiguo anterior a la subespecialización funcional de los cilios. Finalmente, esperamos que los resultados de nuestro trabajo ayuden a entender mejor las bases biológicas de las ciliopatías huérfanas[CA] Els cilis són estructures eucariotes complexes conservades evolutivament que, projectant des de la superfície de les cèl·lules, exerceixen un gran nombre de funcions biològiques. Els cilis es classifiquen tradicionalment en mòbils o sensorials i en la seua composició intervenen centenars de proteïnes. Aquest conjunt de gens que codifiquen per als components ciliars es coneix com el cilioma. Les mutacions en el cilioma subjauen a un grup cada vegada major de malalties multisistèmiques altament pleiotròpiques denominades globalment com ciliopaties. Aquestes malalties es caracteritzen, entre altres símptomes, per retard mental, defectes sensorials i/o trastorns metabòlics. A pesar que s'estima que 1 de cada 1.000 persones està afectada per aquestes malalties, les bases moleculars de les ciliopaties són encara poc conegudes. L'adequat assemblatge i funcionalitat del cili requereixen de l'expressió estretament coordinada dels components del cili; no obstant això, se sap poc sobre la lògica reguladora que controla la transcripció del cilioma. La majoria dels gens del cilioma són compartits tant per cilis mòbils com sensorials. Els factors de transcripció (FTs) de la família RFX tenen un paper evolutivament conservat en la regulació transcripcional del cilioma tant mòbil com sensorial. En els vertebrats, la transcripció del cilioma mòbil també està regulada directament per FoxJ1, un FT de la família forkhead (FKH). No obstant això, fins hui, es desconeixen els FTs que actuen al costat de RFX en la transcripció del cilioma sensorial en qualsevol organisme. En aquest treball, hem identificat a FKH-8, un FT de la família FKH, com a selector terminal del cilioma sensorial de C. elegans. fkh-8 s'expressa de manera consistent en les seixanta neurones sensorials ciliades de C. elegans, s'uneix a les regions reguladores dels gens del cilioma sensorial, també és necessari per a la correcta expressió dels gens del cilioma i actua de manera sinèrgica amb el conegut regulador mestre de la ciliogènesi DAF-19/RFX. En conseqüència, els mutants per a fkh-8 mostren una àmplia gamma de defectes de comportament en una plètora de paradigmes sensorials, incloent la olfacció, la gustació i la mecano-sensació. Així, hem identificat, per primera vegada, un FT que actua juntament amb els FTs de la família RFX en la regulació directa del cilioma sensorial. A més, els nostres resultats, juntament amb treballs anteriors, mostren que els FTs FKH i RFX actuen conjuntament en la regulació dels cilis tant mòbils com sensorials, la qual cosa suggereix que aquesta lògica reguladora podria ser un tret evolutiu antic anterior a la subespecialització funcional dels cilis. Finalment, esperem que els resultats del nostre treball ajuden a entendre millor les bases biològiques de les ciliopaties òrfenes.[EN] Cilia are complex evolutionary conserved eukaryotic structures that, projecting from cell surfaces, perform a variety of biological roles. Cilia are traditionally classified into motile or sensory and hundreds of proteins take part in their composition. This set of genes coding for ciliary components is known as the ciliome. Mutations in the ciliome underlie an ever-growing group of highly pleiotropic multisystemic diseases globally termed as ciliopathies. These diseases are characterized, among other symptoms, by mental retardation, sensory defects and/or metabolic disorders. Despite an estimated 1 in 1,000 people affected by these diseases, the molecular bases of the ciliopathies are still poorly understood. Proper cilium assembly and functionality requires the tightly co-regulated expression of ciliary components; however, little is known about the regulatory logic controlling ciliome transcription. Most ciliome genes are shared between motile and sensory cilia. RFX transcription factors (TFs) have an evolutionarily conserved role in the transcriptional regulation of both motile and sensory ciliome. In vertebrates, transcription of motile ciliome is also directly regulated by FoxJ1, a Forkhead (FKH) TF. However, to date, TFs working together with RFX in the transcription of the sensory ciliome are unknown in any organism. In this work, we have identified FKH-8, a FKH TF, as a terminal selector of the sensory ciliome in C. elegans. fkh-8 is consistently expressed within the sixty ciliated sensory neurons of C. elegans, it binds the regulatory regions of the sensory ciliome genes, it is also required for correct ciliome gene expression and acts synergistically with the known master regulator of the ciliogenesis DAF-19/RFX. Accordingly, fkh-8 mutants display a wide range of behavioural defects in a plethora of sensory mediated paradigms, including olfaction, gustation, and mechano-sensation. Thus, we have identified, for the first time, a TF that acts together with RFX TFs in the direct regulation of the sensory ciliome. Moreover, our results, together with previous work, show that FKH and RFX TFs act together in the regulation of both motile and sensory cilia, suggesting this regulatory logic could be an ancient trait pre-dating functional sub-specialization of cilia. Finally, we hope our results could help better understand the biological basis of orphan ciliopathies.This thesis project has been made possible thanks to a pre-doctoral fellowship from the FPI Programme (BES-2015-072799) conferred by the (now extinct) Spanish Ministry of Economy & Competitivity. The following grants also provided a funding frame throughout the whole research process: “Estudio de los mecanismos transcripcionales que regulan la diferenciación de las neuronas monoaminérgicas y su conservación evolutiva.” SAF2014-56877-R “Dissecting the gene regulatory mechanisms that generate serotonergic neurons and their link to mental disorders.” ERC-St 281920 “Programas de regulación transcripcional asociados a enfermedades genéticas.” SAF2017-84790-R “Regulatory rules and evolution of neuronal gene expression.” ERC-Co 101002203Brocal Ruiz, R. (2022). Transcriptional Regulatory Logic of Cilium Formation in C. Elegans [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181667TESI

A study of interactions between saccharomyces cerevisiae α-factor and its G protein-coupled receptor, Ste2p

During the life cycle of the yeast Saccharomyces cerevisiae, haploid cells of opposite mating type can fuse during sexual conjugation to form a diploid cell. In preparation for conjugation, haploid cells secrete small diffusible peptide molecules [α-factor, a tridecapeptide pheromone and a-factor, a modified dodecapeptide pheromone] that specifically bind to cell surface receptors found on the opposite mating type cell. The basic structure of the receptors (Ste2p for α-factor and Ste3p for a-factor) is evolutionarily conserved and places them among the 7-transmembrane, G protein-coupled receptors (GPCRs). Part 1 of this dissertation is an overview of the structure and the molecular mechanisms involved in ligand recognition and activation these receptor families with specific emphasis on peptide hormones and α-factor receptors. Part 2 of this dissertation is a study of a-factor analogs in which Tyr13 was replaced with a number of side chains for the design of an iodinatable ligand for affinity labeling studies as a direct iodination at Tyr13 abolished function of α-factor. The result of binding and biological activity assays of these analogs showed the lack of strict requirement for Tyr13 and allowed the design of several multiple replacement analogs in which Phe or p-F-Phe were substituted at position 13 and Tyr was placed in other positions of peptide. One potential receptor ligand [Tyr(125I)1, Nle12, Phe13]α-factor exhibited saturable binding with a Kdof 81 nM and was competed by α-factor for binding. In Part 3, an analysis of the α-factor receptor was carried out using random and site-directed mutagenesis to try to understand pheromone binding and receptor activation mechanisms. Three receptors containing mutations F55V, S219P, and S259P were screened for their altered ligand specificity and analyzed for their biological responses to various α-factor analogs and for their ligand binding profiles. The S259P mutation demonstrated ligand dependent biological response to all peptides tested (α-factor, antagonists and a synergist). The S219P mutation responded to α-factor, some antagonist peptides and the synergist, but not to other antagonists. The F55V mutant receptor responded only to α-factor and the synergist peptide and not to any antagonist analogs. These results confirmed previous findings that the fifth and sixth transmembrane domain of the receptor are important for receptor activation. In addition, changes in binding affinity of α-factor and its analogs indicate that residue 55 of α-factor receptor is involved with ligand binding. Part 4 of this dissertation is a study of identification of the α-factor binding region of Ste2p using site-directed mutagenesis and ligand modification. Affinities and activities of mutant receptors at serine 47 and threonine 48 residues were determined with analogs in which Gln10 of α-factor was replaced with various functional groups. All mutant receptors showed a similar number of binding sites and efficacy but different Kd and EC50 values for a-factor compared to those of wild type receptor. A mutant receptor (S47K, T48K) had dramatically reduced affinity and activity for K10 and Orn10-α-factors while the affinity of S. kluyveri α-factor (E10 with additional four variant residues) was increased over 40-foid compared to that of wild type receptor. In contrast to KK substitution, the affinity of K10- and Orn10-α-factor was greatly increased in a S47E, T48E mutant receptor while the binding of S. kluyveri α-factor was decreased over 100-fold. E10-α-factor showed about two fold higher affinity in this mutant receptor than KK mutant receptor. The affinity of K10- and Orn10-α-factors for the EE mutant, however, dropped 4-6 fold in the presence of 1M NaCI while affinity of α-factor was not affected by this treatment. The results indicate that 10th Gin residue of S. cerevisiae α-factor when bound to the receptor is adjacent to Ser47 and Thr48 residues in the receptor

Low Cost Pathogen Detection with Yeast and Tools for Synthetic Multicellular Systems

We can now manipulate the genetic material of living organism routinely and cheaply. This has inspired a burgeoning field of synthesis based on DNA as a building block. The development of this new synthetic field has mirrored the trajectory of synthetic organic chemistry from small molecular systems to complex macromolecular assemblies. At first, this field of synthetic biology delivered recombinant proteins that enhanced our understanding of the structure-function relationship of biological macromolecules. Now, as the synthetic tools and analysis methods have come of age, synthetic whole-cell and multicellular systems have come within reach. In Chapter 1 we review the significant advances in DNA synthesis and analysis that have brought us to this point. In this work, we first ask what practical applications will benefit most from the unique qualities of synthetic whole-cell system, such as their ability to replicate, sense and respond with molecular specificity. In Chapter 2, we implement a pathogen detection platform based solely on genetically modified yeast. This approach holds the potential to deliver ultra low-cost sensors that can be used and produced at the point-of-care. In Chapter 3, we develop methods to target these yeast-based sensors for the detection of any peptide biomarker of choice. We next look forward to the potential of synthetic multicellular systems. While natural multicellular systems can be directly manipulated, our ability to rationally build multicellular systems from the bottom-up is still in its infancy. There still remain gaps in the available tools to make and analyze such synthetic systems. In Chapter 4, we leverage the explosion of available genomic databases to uncover a highly extensible set of cell-cell signaling modules. In Chapter 5, we implement ratiometric fluorescent tags to track mixed cell populations in multiplex. Together these components will be useful in implementing and analyzing synthetic communication networks that will be key components of advanced synthetic multicellular systems

Profiling patterns of interhelical associations in membrane proteins.

A novel set of methods has been developed to characterize polytopic membrane proteins at the topological, organellar and functional level, in order to reduce the existing functional gap in the membrane proteome. Firstly, a novel clustering tool was implemented, named PROCLASS, to facilitate the manual curation of large sets of proteins, in readiness for feature extraction. TMLOOP and TMLOOP writer were implemented to refine current topological models by predicting membrane dipping loops. TMLOOP applies weighted predictive rules in a collective motif method, to overcome the inherent limitations of single motif methods. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity and 1,392 topological models described in the Swiss-Prot database were refined. The subcellular location (TMLOCATE) and molecular function (TMFUN) prediction methods rely on the TMDEPTH feature extraction method along data mining techniques. TMDEPTH uses refined topological models and amino acid sequences to calculate pairs of residues located at a similar depth in the membrane. Evaluation of TMLOCATE showed a normalized accuracy of 75% in discriminating between proteins belonging to the main organelles. At a sequence similarity threshold of 40%, TMFLTN predicted main functional classes with a sensitivity of 64.1-71.4%) and 70% of the olfactory GPCRs were correctly predicted. At a sequence similarity threshold of 90%, main functional classes were predicted with a sensitivity of 75.6-92.8%) and class A GPCRs were sub-classified with a sensitivity of 84.5%>-92.9%. These results reflect a direct association between the spatial arrangement of residues in the transmembrane regions and the capacity for polytopic membrane proteins to carry out their functions. The developed methods have for the first time categorically shown that the transmembrane regions hold essential information associated with a wide range of functional properties such as filtering and gating processes, subcellular location and molecular function