The G protein-coupled receptor heterodimer network (GPCR-HetNet) and its hub components

G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/similar to ismel/GPCR-Nets/index.html

A receptor-based analysis of local ecosystems in the human brain.

BackgroundAs a complex system, the brain is a self-organizing entity that depends on local interactions among cells. Its regions (anatomically defined nuclei and areas) can be conceptualized as cellular ecosystems, but the similarity of their functional profiles is poorly understood. The study used the Allen Human Brain Atlas to classify 169 brain regions into hierarchically-organized environments based on their expression of 100 G protein-coupled neurotransmitter receptors, with no a priori reference to the regions' positions in the brain's anatomy or function. The analysis was based on hierarchical clustering, and multiscale bootstrap resampling was used to estimate the reliability of detected clusters.ResultsThe study presents the first unbiased, hierarchical tree of functional environments in the human brain. The similarity of brain regions was strongly influenced by their anatomical proximity, even when they belonged to different functional systems. Generally, spatial vicinity trumped long-range projections or network connectivity. The main cluster of brain regions excluded the dentate gyrus of the hippocampus. The nuclei of the amygdala formed a cluster irrespective of their striatal or pallial origin. In its receptor profile, the hypothalamus was more closely associated with the midbrain than with the thalamus. The cerebellar cortical areas formed a tight and exclusive cluster. Most of the neocortical areas (with the exception of some occipital areas) clustered in a large, statistically well supported group that included no other brain regions.ConclusionsThis study adds a new dimension to the established classifications of brain divisions. In a single framework, they are reconsidered at multiple scales-from individual nuclei and areas to their groups to the entire brain. The analysis provides support for predictive models of brain self-organization and adaptation

Interactions between the neuromodulatory systems and the amygdala: exploratory survey using the Allen Mouse Brain Atlas.

Neuromodulatory systems originate in nuclei localized in the subcortical region of the brain and control fundamental behaviors by interacting with many areas of the central nervous system. An exploratory survey of the cholinergic, dopaminergic, noradrenergic, and serotonergic receptor expression energy in the amygdala, and in the neuromodulatory areas themselves was undertaken using the Allen Mouse Brain Atlas. The amygdala was chosen because of its importance in cognitive behavior and its bidirectional interaction with the neuromodulatory systems. The gene expression data of 38 neuromodulatory receptor subtypes were examined across 13 brain regions. The substantia innominata of the basal forebrain and regions of the amygdala had the highest amount of receptor expression energy for all four neuromodulatory systems examined. The ventral tegmental area also displayed high receptor expression of all four neuromodulators. In contrast, the locus coeruleus displayed low receptor expression energy overall. In general, cholinergic receptor expression was an order of magnitude greater than other neuromodulatory receptors. Since the nuclei of these neuromodulatory systems are thought to be the source of specific neurotransmitters, the projections from these nuclei to target regions may be inferred by receptor expression energy. The comprehensive analysis revealed many connectivity relations and receptor localization that had not been previously reported. The methodology presented here may be applied to other neural systems with similar characteristics, and to other animal models as these brain atlases become available

딥러닝 기반 단일 거리 공간 내 GPCR 단백질군 계층 구조의 동시적 모델링 기법

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 김선.G 단밸질 연결 수용체(GPCR)은 계층 구조로 형성된 다양한 단백질군으로 구성된다. 단백질 서열을 통한 GPCR에 대한 계산적인 모델링은 군(family), 아군(subfamily), 준아군(sub-subfamily)의 각 계층에서 독립적으로 실행되는 방식으로 이루어져왔다. 하지만 이러한 접근 방식들은 단절된 모델들을 통하여 단백질 내의 정보를 처리하기 때문에 GPCR 종류 사이의 관계는 고려하지 못한다는 한계를 가지고 있다. 본 연구에서는 딥러닝을 이용하여 GPCR의 계층 구조에서 나타나는 특징들을 단일한 모델로 동시적으로 학습하는 방법을 제시한다. 또한 계층적인 관계들을 하나의 벡터 공간에 거리를 통해 표현할 수 있도록 하기 위한 손실함수도 제시한다. 이 연구는 GPCR 수용체들의 여러 계층에서 공통적으로 나타나는 특징들을 학습하고 표현할 수 있도록 하는 방법을 다루고 있다. 여러 심화적인 실험들을 통하여 우리는 기술적인 측면과 생물학적인 측면에서 단백질 간 계층적인 관계가 성공적으로 학습이 되었다는 것을 보였다. 첫번째로, 우리는 임베딩 벡터에 계층적 군집화(hierarchical clustering) 알고리즘을 적용함으로써 계통수(phylogenetic tree)를 만들었고, 군집 알고리즘과 실제 계층 구조와의 수치적인 비교를 통하여 임베딩 벡터를 통해 계통학적 특징에 대한 유추가 가능하다는 것을 보였다. 두번째로, 임베딩 벡터의 군집화 결과에 다중 서열 정렬(multiple sequence alignment)를 적용시킴으로써 생물학적으로 유의미한 서열적 특성들을 찾아낼 수 있다는 것을 보였다. 이는 임베딩 벡터 분석이 GPCR 단백질 연구에 있어 효율적인 첫걸음이 될 수 있다는 것을 보여준다. 이러한 결과는 여러 계층으로 이루어진 단백질군에 대한 동시적인 모델링이 가능하다는 것을 말하고 있다.G protein-coupled receptors (GPCRs) belong to diverse families of proteins that can be defined at multiple levels. Computational modeling of GPCR families from the sequences has been performed separately at each level of family, sub-family, and sub-subfamily. However, relationships between classes are ignored in these approaches as they process the information in the sequences with a group of disconnected models. In this work, we propose a deep learning network to simultaneously learn representations in the GPCR hierarchy with a unified model and a loss term to express hierarchical relations in terms of distances in a single embedding space. The model introduces a method to learn and construct shared representations across hierarchies of the protein family. In extensive experiments, we showed that hierarchical relations between sequences are successfully captured in our model in both of technical and biological aspect. First, we showed that phylogenetic information in the sequences can be inferred from the vectors by constructing phylogenetic tree using hierarchical clustering algorithm and by quantitatively analyzing the quality of clustering results compared to the real label information. Second, inspection on embedding vectors is demonstrated to be a effective first step to-ward an analysis of GPCR proteins by showing that biologically significant sequence features can be revealed from multiple sequence alignments on clustering results on embedding vectors. Our work showed that simultaneous modeling of protein families with multiple hierarchies is possible.Abstract i Chapter Ⅰ. Introduction 1 1.1 Background 1 1.2 Motivation 3 Chapter Ⅱ. Methods 7 2.1 Data Preparation 7 2.1.1 Dataset 7 2.1.2 Data representation 7 2.2 Model architecture 8 2.2.1 Feature extractor with CNN 8 2.2.2 Embedding layer 8 2.2.3 Output layer 9 2.3 Loss function 10 2.3.1 Softmax loss 10 2.3.2 Center loss 10 2.3.3 Overall loss 12 2.4 Training procedure 13 2.5 Evaluation metric 14 2.5.1 Silhouette score 14 2.5.2 Adjusted mutual information score 15 Chapter Ⅲ. Results 17 3.1 Evaluation on hierarchical structure 17 3.1.1 Preservation of distances 17 3.1.2 Phylogenetic tree reconstruction 20 3.1.3 Quantitative evaluation on clustering results 21 3.2 Sequence analysis with embedding vectors 26 3.2.1 Technical analysis 26 3.2.2 Biological analysis 28 3.3 Classification accuracy 30 Chapter Ⅳ. Conclusion 32 References 35Maste

Functional classification of G-Protein coupled receptors, based on their specific ligand coupling patterns

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them re- main as orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 2 subfamilies of Amine GPCRs, a novel method for obtaining fixed-length feature vectors, based on the existence of activating ligand specific patterns, has been developed and utilized for a Support Vector Machine (SVM)-based classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 2 subfamilies of Amine GPCRs with a high predictive accuracy of 97.02% in a ten-fold cross validation test. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization

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

Exploratory visualization of misclassified GPCRs from their transformed unaligned sequences using manifold learning techniques

Class C G-protein-coupled receptors (GPCRs) are cell membrane proteins of great relevance to biology and pharmacology. Previous research has revealed an upper boundary on the accuracy that can be achieved in their classification into subtypes from the unaligned transformation of their sequences. To investigate this, we focus on sequences that have been misclassified using supervised methods. These are visualized, using a nonlinear dimensionality reduction technique and phylogenetic trees, and then characterized against the rest of the data and, particularly, against the rest of cases of their own subtype. This should help to discriminate between different types of misclassification and to build hypotheses about database quality problems and the extent to which GPCR sequence transformations limit subtype discriminability. The reported experiments provide a proof of concept for the proposed method.Postprint (published version

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour