GPCR &amp; Company: Databases and Servers for GPCRs and Interacting Partners

Kowalsman, Noga; Niv, Masha Y.

doi:10.1007/978-94-007-7423-0_9

Cited by 9 publications

(7 citation statements)

References 99 publications

(134 reference statements)

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“…Due to the central role of GPCRs in multiple physiological processes, they represent the largest group of targets for drug discovery for a broad spectrum of diseases 3. Interest in these targets has led to the establishment of many freely available databases and servers, recently reviewed in reference 4, which provide useful sources of information.…”

Section: Introductionmentioning

confidence: 99%

Computational Studies of Smell and Taste Receptors

Pizio

Niv

2014

Israel Journal of Chemistry

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Smell and taste are among the basic senses with which we perceive the world around us. In addition to enabling recognition of chemical moieties that provide social or nutritional clues, taste and smell receptors are expressed in many extraoral tissues, including the gastrointestinal, respiratory, and reproductive systems. It is, therefore, likely that taste and smell receptors have additional physiological roles, which are currently under intensive study. Most of the taste modalities, as well as olfaction, are mediated by G‐protein coupled receptors (GPCRs). Recent breakthroughs in crystallography and signaling studies of GPCRs (celebrated by the 2012 Nobel Prize in Chemistry to Robert Lefkowitz and Brian Kobilka) provide excellent opportunities for applying this information towards furthering our understanding of taste and smell signaling. No crystal structures of odorant or taste receptors are currently available. However, computational techniques, many of which stem from the pioneering contributions of the 2013 Nobel Prize in Chemistry laureates, Martin Karplus, Michael Levitt, and Arieh Warshel, can shed light on the function of taste and olfactory GPCRs. In this review, we highlight examples of iterative combinations of simulation and experiment that were successfully applied toward delineating binding modes of tastants and odorants and toward predicting additional ligands. Further studies are required in order to answer remaining questions regarding receptor promiscuity versus selectivity, the details of receptor coupling to G‐proteins, and the roles of oligomerization and of allosteric modulation in taste and smell transduction.

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Section: Introductionmentioning

confidence: 99%

Computational Studies of Smell and Taste Receptors

Pizio

Niv

2014

Israel Journal of Chemistry

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“…1,13 Moreover, around 40 additional online databases and servers are available, which focus on the structural information of GPCRs, relevant receptor-ligand interactions, receptorintracellular partner interactions, and their oligomerization. 14 In particular, ice-breaking progress has been made over the past 10 years on a few high-impact structures including agonist-bound receptors, GPCR-G protein complexes, and elusive chemokine receptors, with the advent of new techniques such as protein-based nanodiscs and fusion protein engineering. [15][16][17][18][19][20][21][22] Multitudes of threedimensional GPCRs structures, including active, inactive, and ligand-binding states are disclosed, revealing the general basis for receptor activation, signaling, and allosteric modulation processes.…”

Section: Introductionmentioning

confidence: 99%

Trends in application of advancing computational approaches in GPCR ligand discovery

Zhu

Huang

et al. 2021

Exp Biol Med (Maywood)

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G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

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“…Aside from humans, GPCRs have been found in many different species including other mammals, insects, fungi, etc. As records of newly discovered GPCRs accumulate over the years, they have been collected in different databases as summarized in the study by Kowalsman and Niv ( 12 ). Some of these databases deal with proteins in general while others are specialized for GPCRs only.…”

Section: Introductionmentioning

confidence: 99%

OUP accepted manuscript

et al. 2020

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G protein-coupled receptors (GPCRs) constitute the largest group of membrane receptor proteins in eukaryotes. Due to their significant roles in various physiological processes such as vision, smell and inflammation, GPCRs are the targets of many prescription drugs. However, the functional and sequence diversity of GPCRs has kept their prediction and classification based on amino acid sequence data as a challenging bioinformatics problem. There are existing computational approaches, mainly using machine learning and statistical methods, to predict and classify GPCRs based on amino acid sequence and sequence derived features. In this paper, we describe a searchable MySQL database, named GPCR-PEnDB (GPCR Prediction Ensemble Database), of confirmed GPCRs and non-GPCRs. It was constructed with the goal of allowing users to conveniently access useful information of GPCRs in a wide range of organisms and to compile reliable training and testing datasets for different combinations of computational tools. This database currently contains 3129 confirmed GPCR and 3575 non-GPCR sequences collected from the UniProtKB/Swiss-Prot protein database, encompassing over 1200 species. The non-GPCR entries include transmembrane proteins for evaluating various prediction programs’ abilities to distinguish GPCRs from other transmembrane proteins. Each protein is linked to information about its source organism, classification, sequence lengths and composition, and other derived sequence features. We present examples of using this database along with its graphical user interface, to query for GPCRs with specific sequence properties and to compare the accuracies of five tools for GPCR prediction. This initial version of GPCR-PEnDB will provide a framework for future extensions to include additional sequence and feature data to facilitate the design and assessment of software tools and experimental studies to help understand the functional roles of GPCRs. Database URL : gpcr.utep.edu/database

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GPCR & Company: Databases and Servers for GPCRs and Interacting Partners

Cited by 9 publications

References 99 publications

Computational Studies of Smell and Taste Receptors

Computational Studies of Smell and Taste Receptors

Trends in application of advancing computational approaches in GPCR ligand discovery

OUP accepted manuscript

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