The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled Receptor subtypes from n-grams of their sequences

König, Caroline; Alquézar, René; Vellido, Alfredo; Giraldo, Jesús

doi:10.1109/ijcnn.2015.7280613

Cited by 4 publications

(10 citation statements)

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“…In previous research, we also analyzed whether the extracellular N-terminus domain of the sequences sufficed to distinguish between class C GPCR subtypes [16]. These experiments revealed that, although the classification models built using the isolated N-terminus domain did not achieve the subtype discrimination capabilities of the entire sequence in full, the observed reduction of classification performance was not too significant.…”

Section: Introductionmentioning

confidence: 99%

Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes

König

Alquézar

Vellido

et al. 2018

Interdiscip Sci Comput Life Sci

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G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.

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Section: Introductionmentioning

confidence: 99%

Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes

König

Alquézar

Vellido

et al. 2018

Interdiscip Sci Comput Life Sci

Self Cite

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“…In this database, receptors are characterized according to subtype labels at different levels of organization. In previous research [12], the analysis of the N-terminus of the extracellular domain provided some preliminary evidence of the potential use of individual domains of complete class C GPCR sequences as the foundation for subtype classification.…”

Section: Discussionmentioning

confidence: 99%

“…Receptor databases are regularly updated. A class C dataset from March 2011 was object of extensive analysis using ML methods in previous research [3,5,12,121,72].…”

Section: Comparison Of Class C Gpcr Datasets 621 Experimental Settingsmentioning

confidence: 99%

“…Work on the 2011 version of the database provided evidence of clearly defined limits to the separability of the different class C subtypes. This evidence was produced using both supervised [12,121] and semi-supervised [72] ML approaches and from different data transformation strategies. Interestingly, the subtypes shown to be most responsible for such lack of complete subtype separability were precisely those which were removed in the 2016 versions of the database (namely vomeronasal, odorant and pheromone receptors).…”

Section: Previous Research On Gpcr Class C From a Data Curation Persp...mentioning

confidence: 99%

“…In the current experiment, the results of the previous experiments are extended by investigating a different classification methodology: we switch from the subtype-vssubtype classification employed in [12,11] to a subtype-vs-all the rest of subtypes procedure. That is, we shift towards the selection of those sequence motifs that distinguish each class C subtype from the rest.…”

Section: Feature Selection Used For the Identification Of Subtype Cha...mentioning

confidence: 99%

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

König

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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.

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Topological Sequence Segments Discriminate Between Class C GPCR Subtypes

König

Alquézar

Vellido

et al. 2017

11th International Conference on Practical Applications of Computational Biology &Amp; Bioinformatics

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The extracellular N-terminal domain suffices to discriminate class C G Protein-Coupled Receptor subtypes from n-grams of their sequences

Cited by 4 publications

References 28 publications

Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes

Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes

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

Topological Sequence Segments Discriminate Between Class C GPCR Subtypes

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