Computational Methods for the Prediction of GPCRs Coupling Selectivity

Computational Methods for the Prediction of GPCRs Coupling Selectivity

Nikolaos G. Sgourakis (Rensselaer Polytechnic Institute, USA), Pantelis G. Bagos (University of Central Greece and University of Athens, Greece) and Stavros J. Hamodrakas (University of Athens, Greece)
Copyright: © 2009 |Pages: 15
DOI: 10.4018/978-1-60566-076-9.ch009
OnDemand PDF Download:


GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of recent genome projects, there is a wealth of information at the sequence level that lacks any functional annotation. These receptors, often quoted as orphan GPCRs, could potentially lead to novel drug targets. However, typical experiments that aim at elucidating their function are hampered by the lack of knowledge on their selective coupling partners at the interior of the cell, the G-proteins. Up-to-date, computational efforts to predict properties of GPCRs have been focused mainly on the ligand-binding specificity, while the aspect of coupling has been less studied. Here, we present the main motivations, drawbacks, and results from the application of bioinformatics techniques to predict the coupling specificity of GPCRs to G-proteins, and discuss the application of the most successful methods in both experimental works that focus on a single receptor and large-scale genome annotation studies.
Chapter Preview

Introduction / Background

G-protein coupled receptors (GPCRs) comprise a very important family of eukaryotic cell-surface membrane proteins. They are characterized by the structural hallmark of seven transmembrane helices, as exemplified by the crystal structure of rhodopsin (Palczewski et al. 2000), that has been extensively used as a homology modeling template for many receptor sequences (Nikiforovich et al. 2001; Becker et al. 2003). GPCRs play a pivotal role in signal transduction of eukaryotic cells, acting as the major sensors at the boundary between a cell and the outside world. Depending on their ligand-binding specificity, GPCRs can be activated by a broad range of external stimuli, from ions and small molecules to larger peptides and proteins, including light (Gether 2000). To perform these functions, GPCRs have evolved to a diversity of sequences that are traditionally classified in six major families, based mainly on shared homology (Horn et al. 2003). GPCRs have known representatives in most eukaryotic organisms, including yeast and plants, such as the recently discovered Arabidopsis thaliana seven-transmembrane (7TM) domain receptor GCR1 (Jones and Assmann 2004).

As signified by their name, upon binding to a ligand, GPCRs exert their role through the specific interaction with a more limited repertoire of intracellular proteins that hydrolyze GTP, namely the G-proteins (Neer and Clapham 1988). G-proteins are heterotrimeric complexes composed of three subunits Gα, Gβ and Gγ. They are classified into four main families, according to the type of their α-subunit, which also possesses Ras-like GTPase activity (Benjamin et al. 1995). These include Gs and Gi/o, which stimulate and inhibit adenylate cyclase, respectively (Johnston and Watts 2003), Gq/11, that activates phospholipase C (Exton 1993) and the less characterized G12/13 family that activates the Na+/H+ exchange pathway (Kurose 2003). At least 16 different subtypes of Gα subunits have been identified and classified in these four families (Downes and Gautam 1999; Kristiansen 2004). Interaction of the G-protein trimer with the activated receptor triggers the exchange of the bound GDP with GTP, and subsequently the dissociation of the complex to Gα and Gβγ moieties, that activate downstream effector molecules. Hydrolysis of GTP to GDP by the α subunit renders the complex to its original, inactive state (Neer 1995). As a result, depending on the selectivity of the GPCR - G-protein interaction, a specific downstream pathway may be activated. Despite extensive experimental and computational studies, the structural basis of this specificity is not well characterized, while the mechanisms that determine the function of the activated GPCR/G-protein complex are yet to be uncovered (Muramatsu and Suwa 2006). Furthermore, the diversity of GPCR-G-protein interactions is enriched by several receptors that may alternatively interact with more than one family of G-proteins, known as promiscuous GPCRs. For instance, the human thyrotropin receptor can couple to all four G-protein families (Laugwitz et al. 1996). In general, promiscuity seems to be a rule rather than an exception for interactions between GPCRs and G-proteins (Wess 1998; Oliveira et al. 1999; Horn et al. 2000). Several lines of evidence indicate the importance of the GPCR intracellular regions, as well as the intracellular boundaries of the transmembrane helices (Gether 2000). It is also established that the regions of interaction on the G-protein are mainly the N-terminus of the Gα and the N- and C-termini of Gγ subunit. However, up to date, these findings have not been incorporated to a high-resolution, systematic model of GPCR – G-protein interactions, while the nature of the underlying mechanism is believed to be specific to the interacting partners (Wess 1998).

Key Terms in this Chapter

G-Protein Coupled Receptors (GPCRs): Also known as seven transmembrane (heptahelical) receptors, due to their characteristic membrane topology (seven transmembrane helices, extracellular N-terminus and intracellular C-terminus). They are transmembrane proteins acting as the sensory component of cellular signalling pathways. GPCRs, are a key class of eukaryotic membrane receptors and roughly 50% of all small molecule therapeutics target GPCRs. Vision, smell and some of taste uses GPCRs. Ligands for GPCRs cover a wide range of organic chemical space, including proteins, peptides, sugars, amines and amino-acids, nucleotides, lipids and more. They transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (a,ß,?). Two GPCRs’ crystal structures are currently available, the structure of Rhodopsin and the recently solved three-dimensional structure of beta-2 Adrenergic Receptor.

Hidden Markov Models (used herein): Probabilistic models widely used for describing features of a protein sequence. Hidden Markov Models introduce a “regular grammar” that characterizes a set of biological sequences. These are generative models, which renders them highly applicable in biological sequence analysis. In general, a HMM is composed of a set of states that form a first order Markovian process, connected by means of the transition probabilities. Each state, has a unique probability distribution for generating (emitting) the symbols of the finite alphabet (nucleotides or amino-acids). The most widely used variant of Hidden Markov Model (HMM) is the profile HMM which models in a probabilistic manner the matches, inserts and deletions occurring in every column of a multiple sequence alignment. However, other variations are also common (i.e. the circular HMM).

Orphan Receptors: GPCRs for which no information on their ligand or coupling specificity is available. These are usually identified as a result of genome sequencing projects and large efforts are undertaken to functionally characterize them.

Coupling Selectivity: G protein trimers are named after their a-subunits, which on the basis of their amino acid similarity and, most importantly by their cellular function, are grouped into four families. These include, Gas and Gai/o, which stimulate and inhibit respectively adenylate cyclase, Gaq/11 which stimulates phospholipase C, and the less characterized Ga12/13 family that activates the Na+/H+ exchanger pathway. The specificity of the interaction of a given GPCR with the pool of available intracellular G-proteins is termed coupling selectivity or specificity. The ability of certain GPCRs to interact with more that one types of G-proteins (i.e. Gas and Gai/o) is known as promiscuous coupling selectivity. GPCRs coupled to members of the Ga12/13 family are all exhibiting promiscuous coupling preferences.

Genome Annotation: The functional characterization (by means of biochemical experiments or computational prediction algorithms) of novel genes in newly sequenced and assembled genomes.

G-Proteins: The term is used to describe GTP-binding proteins. There are two classes of G-proteins, the small cytoplasmic G-proteins (Gh) and the hetero-trimeric G-proteins composed of different subunits (a,ß,?) that mediate the signal of heptahelical receptors (GPCRs). Agonist binding to GPCRs leads to association of the hetero-trimeric G protein with the receptor, GDP-GTP exchange in the G protein a subunit followed by dissociation of the G protein into a-GTP and ß? complexes. The dissociated subunits can activate or inhibit several effectors such as adenylyl cyclase, PLCß, tyrosine kinases, phosphodiesterases, phosphoinositide 3-kinase, GPCR kinases, ion channels, and molecules of the mitogen-activated protein kinase pathway, resulting in a variety of cellular functions. However, there is evidence that some GPCRs transduce their signal through in a way that is not G protein-dependent, and also that hetero-trimeric G proteins are involved in mediating the action of single-spanning membrane receptors.

Complete Chapter List

Search this Book:
Editorial Advisory Board
Table of Contents
Ralf Herwig
Andriani Daskalaki
Andriani Daskalaki
Chapter 1
Peter Ghazal
An increasing number of biological experiments and more recently clinical based studies are being conducted using large-scale genomic, proteomic and... Sample PDF
Pathway Biology Approach to Medicine
Chapter 2
Peter Wellstead, Sree Sreenath, Kwang-Hyun Cho
In this chapter the authors describe systems and control theory concepts for systems biology and the corresponding implications for medicine. The... Sample PDF
Systems and Control Theory for Medical Systems Biology
Chapter 3
S. Nikolov
In this chapter we investigate how the inclusion of time delay alters the dynamic properties of (a) delayed protein cross talk model, (b) time delay... Sample PDF
Mathematical Description of Time Delays in Pathways Cross Talk
Chapter 4
Elisabeth Maschke-Dutz
In this chapter basic mathematical methods for the deterministic kinetic modeling of biochemical systems are described. Mathematical analysis... Sample PDF
Deterministic Modeling in Medicine
Chapter 5
Andrew Kuznetsov
Biologists have used a reductionist approach to investigate the essence of life. In the last years, scientific disciplines have merged with the aim... Sample PDF
Synthetic Biology as a Proof of Systems Biology
Chapter 6
Tuan D. Pham
Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. Given the recent... Sample PDF
Computational Models for the Analysis of Modern Biological Data
Chapter 7
Vanathi Gopalakrishnan
This chapter provides a perspective on 3 important collaborative areas in systems biology research. These areas represent biological problems of... Sample PDF
Computer Aided Knowledge Discovery in Biomedicine
Chapter 8
Thomas Meinel
The function of proteins is a main subject of research in systems biology. Inference of function is now, more than ever, required by the upcoming of... Sample PDF
Function and Homology of Proteins Similar in Sequence: Phylogenetic Profiling
Chapter 9
Nikolaos G. Sgourakis, Pantelis G. Bagos, Stavros J. Hamodrakas
GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of... Sample PDF
Computational Methods for the Prediction of GPCRs Coupling Selectivity
Chapter 10
Pantelis G. Bagos, Stavros J. Hamodrakas
ß-barrel outer membrane proteins constitute the second and less well-studied class of transmembrane proteins. They are present exclusively in the... Sample PDF
Bacterial ß-Barrel Outer Membrane Proteins: A Common Structural Theme Implicated in a Wide Variety of Functional Roles
Chapter 11
L.K. Flack
Clustering methods are used to place items in natural patterns or convenient groups. They can be used to place genes into clusters to have similar... Sample PDF
Clustering Methods for Gene-Expression Data
Chapter 12
George Sakellaropoulos, Antonis Daskalakis, George Nikiforidis, Christos Argyropoulos
The presentation and interpretation of microarray-based genome-wide gene expression profiles as complex biological entities are considered to be... Sample PDF
Uncovering Fine Structure in Gene Expression Profile by Maximum Entropy Modeling of cDNA Microarray Images and Kernel Density Methods
Chapter 13
Wasco Wruck
This chapter describes the application of the BeadArrayTM technology for gene expression profiling. It introduces the BeadArrayTM technology, shows... Sample PDF
Gene Expression Profiling with the BeadArrayTM Platform
Chapter 14
Djork-Arné Clevert, Axel Rasche
Readers shall find a quick introduction with recommendations into the preprocessing of Affymetrix GeneChip® microarrays. In the rapidly growing... Sample PDF
The Affymetrix GeneChip® Microarray Platform
Chapter 15
Jacek Majewski
Eukaryotic genes have the ability to produce several distinct products from a single genomic locus. Recent developments in microarray technology... Sample PDF
Alternative Isoform Detection Using Exon Arrays
Chapter 16
Prerak Desai
The use of systems biology to study complex biological questions is gaining ground due to the ever-increasing amount of genetic tools and genome... Sample PDF
Gene Expression in Microbial Systems for Growth and Metabolism
Chapter 17
Heike Stier
Alternative splicing is an important part of the regular process of gene expression. It controls time and tissue dependent expression of specific... Sample PDF
Alternative Splicing and Disease
Chapter 18
Axel Kowald
Aging is a complex biological phenomenon that practically affects all multicellular eukaryotes. It is manifested by an ever increasing mortality... Sample PDF
Mathematical Modeling of the Aging Process
Chapter 19
Evgenia Makrantonaki
This chapter introduces an in vitro model as a means of studying human hormonal aging. For this purpose, human sebaceous gland cells were maintained... Sample PDF
The Sebaceous Gland: A Model of Hormonal Aging
Chapter 20
R. Seigneuric, N.A.W. van Riel, M.H.W. Starmans, A. van Erk
Complex diseases such as cancer have multiple origins and are therefore difficult to understand and cure. Highly parallel technologies such as DNA... Sample PDF
Systems Biology Applied to Cancer Research
Chapter 21
Matej Orešic, Antonio Vidal-Puig
In this chapter the authors report on their experience with the analysis and modeling of data obtained from studies of animal models related to... Sample PDF
Systems Biology Strategies in Studies of Energy Homeostasis In Vivo
Chapter 22
Axel Rasche
We acquired new computational and experimental prospects to seek insight and cure for millions of afflicted persons with an ancient malady. Type 2... Sample PDF
Approaching Type 2 Diabetes Mellitus by Systems Biology
Chapter 23
Alia Benkahla, Lamia Guizani-Tabbane, Ines Abdeljaoued-Tej, Slimane Ben Miled, Koussay Dellagi
This chapter reports a variety of molecular biology informatics and mathematical methods that model the cell response to pathogens. The authors... Sample PDF
Systems Biology and Infectious Diseases
Chapter 24
Daniela Albrecht, Reinhard Guthke
This chapter describes a holistic approach to understand the molecular biology and infection process of human-pathogenic fungi. It comprises the... Sample PDF
Systems Biology of Human-Pathogenic Fungi
Chapter 25
Jessica Ahmed
Secretases are aspartic proteases, which specifically trim important, medically relevant targets such as the amyloid-precursor protein (APP) or the... Sample PDF
Development of Specific Gamma Secretase Inhibitors
Chapter 26
Paul Wrede
Peptides fulfill many tasks in controlling and regulating cellular functions and are key molecules in systems biology. There is a great demand in... Sample PDF
In Machina Systems for the Rational De Novo Peptide Design
Chapter 27
Ferda Mavituna, Raul Munoz-Hernandez, Ana Katerine de Carvalho Lima Lobato
This chapter summarizes the fundamentals of metabolic flux balancing as a computational tool of metabolic engineering and systems biology. It also... Sample PDF
Applications of Metabolic Flux Balancing in Medicine
Chapter 28
Roberta Alfieri, Luciano Milanesi
This chapter aims to describe data integration and data mining techniques in the context of systems biology studies. It argues that the different... Sample PDF
Multi-Level Data Integration and Data Mining in Systems Biology
Chapter 29
Hendrik Hache
In this chapter, different methods and applications for reverse engineering of gene regulatory networks that have been developed in recent years are... Sample PDF
Methods for Reverse Engineering of Gene Regulatory Networks
Chapter 30
Alok Mishra
This chapter introduces the techniques that have been used to identify the genetic regulatory modules by integrating data from various sources. Data... Sample PDF
Data Integration for Regulatory Gene Module Discovery
Chapter 31
Elizabeth Santiago-Cortés
Biological systems are composed of multiple interacting elements; in particular, genetic regulatory networks are formed by genes and their... Sample PDF
Discrete Networks as a Suitable Approach for the Analysis of Genetic Regulation
Chapter 32
A. Maffezzoli
In this chapter, authors review main methods, approaches, and models for the analysis of neuronal network data. In particular, the analysis concerns... Sample PDF
Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches
Chapter 33
Paolo Vicini
This chapter describes the System for Population Kinetics (SPK), a novel Web service for performing population kinetic analysis. Population kinetic... Sample PDF
The System for Population Kinetics: Open Source Software for Population Analysis
Chapter 34
Julia Adolphs
This chapter introduces the theory of optical spectra and excitation energy transfer of light harvesting complexes in photosynthesis. The light... Sample PDF
Photosynthesis: How Proteins Control Excitation Energy Transfer
Chapter 35
Michael R. Hamblin
Photodynamic therapy (PDT) is a rapidly advancing treatment for multiple diseases. PDT involves the administration of a nontoxic drug or dye known... Sample PDF
Photodynamic Therapy: A Systems Biology Approach
Chapter 36
Andriani Daskalaki
Photodynamic Therapy (PDT) involves administration of a photosensitizer (PS) either systemically or locally, followed by illumination of the lesion... Sample PDF
Modeling of Porphyrin Metabolism with PyBioS
Chapter 37
Alexey R. Brazhe, Nadezda A. Brazhe, Alexey N. Pavlov, Georgy V. Maksimov
This chapter describes the application of interference microscopy and double-wavelet analysis to noninvasive study of cell structure and function.... Sample PDF
Interference Microscopy for Cellular Studies
Chapter 38
Cathrin Dressler, Olaf Minet, Urszula Zabarylo, Jürgen Beuthan
This chapter deals with the mitochondrias’ stress response to heat, which is the central agent of thermotherapy. Thermotherapies function by... Sample PDF
Fluorescence Imaging of Mitochondrial Long-Term Depolarization in Cancer Cells Exposed to Heat-Stress
Chapter 39
Athina Theodosiou, Charalampos Moschopoulos, Marc Baumann, Sophia Kossida
In previous years, scientists have begun understanding the significance of proteins and protein interactions. The direct connection of those with... Sample PDF
Protein Interactions and Diseases
Chapter 40
Bernard de Bono
From a genetic perspective, disease can be interpreted in terms of a variation in molecular sequence or expression (dose) that impairs normal... Sample PDF
The Breadth and Depth of BioMedical Molecular Networks: The Reactome Perspective
Chapter 41
Jorge Numata
Thermodynamics is one of the best established notions in science. Some recent work in biomolecular modeling has sacrificed its rigor in favor of... Sample PDF
Entropy and Thermodynamics in Biomolecular Simulation
Chapter 42
Isabel Reinecke, Peter Deuflhard
In this chapter some model development concepts can be used for the mathematical modeling in physiology as well as a graph theoretical approach for... Sample PDF
Model Development and Decomposition in Physiology
Chapter 43
Mohamed Derouich
Throughout the world, seasonal outbreaks of influenza affect millions of people, killing about 500,000 individuals every year. Human influenza... Sample PDF
A Pandemic Avian Influenza Mathematical Model
Chapter 44
Mohamed Derouich
Dengue fever is a re-emergent disease affecting more than 100 countries. Its incidence rate has increased fourfold since 1970 with nearly half the... Sample PDF
Dengue Fever: A Mathematical Model with Immunization Program
Chapter 45
Ross Foley
The field of histopathology has encountered a key transition point with the progressive move towards use of digital slides and automated image... Sample PDF
Automated Image Analysis Approaches in Histopathology
About the Contributors