Background: G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs.Results: Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method.Conclusions: The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/. © 2011 Sanders et al; licensee BioMed Central Ltd.
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Understanding taste is key for optimizing the palatability of seaweeds and other non-animal-based foods rich in protein. The lingual papillae in the mouth hold taste buds with taste receptors for the five gustatory taste qualities. Each taste bud contains three distinct cell types, of which Type II cells carry various G protein-coupled receptors that can detect sweet, bitter, or umami tastants, while type III cells detect sour, and likely salty stimuli. Upon ligand binding, receptor-linked intracellular heterotrimeric G proteins initiate a cascade of downstream events which activate the afferent nerve fibers for taste perception in the brain. The taste of amino acids depends on the hydrophobicity, size, charge, isoelectric point, chirality of the alpha carbon, and the functional groups on their side chains. The principal umami ingredient monosodium l-glutamate, broadly known as MSG, loses umami taste upon acetylation, esterification, or methylation, but is able to form flat configurations that bind well to the umami taste receptor. Ribonucleotides such as guanosine monophosphate and inosine monophosphate strongly enhance umami taste when l-glutamate is present. Ribonucleotides bind to the outer section of the venus flytrap domain of the receptor dimer and stabilize the closed conformation. Concentrations of glutamate, aspartate, arginate, and other compounds in food products may enhance saltiness and overall flavor. Umami ingredients may help to reduce the consumption of salts and fats in the general population and increase food consumption in the elderly.
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Bespreking van onderzoek van Rychard Bouwens in ‘Waar wij trots op zijn. De ontdekkingen van 2011’ van de Universiteit Leiden Faculteit der Wiskunde & Natuurwetenschappen. Het valt goed te begrijpen voor iedereen met een basale kennis van klassieke fotografie: bij weinig licht neem je een lange sluitertijd. En dat is wat Rychard Bouwens deed. Om naar de zogenaamde Dark Ages van het heelal te kijken, hield hij de Hubble-ruimtetelescoop maar liefst 87 uur lang op een plek gericht.
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Bespreking van onderzoek van Erik Danen in ‘Waar wij trots op zijn. De ontdekkingen van 2011’ van de Universiteit Leiden Faculteit der Wiskunde & Natuurwetenschappen. Celbioloog Erik Danen doet onderzoek naar de verwoestende – maar in evolutionaire termen ook wonderlijke – strategieën van de kankercel. Met welke trucs verspreiden kankercellen zich door het lichaam? Hoe overleven ze een aanval van een chemokuur? En hoe wrang is het dat de één procent cellen die de therapie overleeft vervolgens dubbelhard terugslaat.
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Bespreking van onderzoek van Anton Akhmerov in ‘Waar wij trots op zijn. De ontdekkingen van 2011’ van de Universiteit Leiden Faculteit der Wiskunde & Natuurwetenschappen. De Leidse theoretisch natuurkundige Anton Akhmerov promoveerde in mei op een onderzoek naar functionele toepassingen van grafeen, een eenlaags koolstofmateriaal dat de afgelopen jaren volop in de belangstelling staat. Daarnaast werkte hij ook nog aan quantumcomputers, omdat hij tijd over had in zijn onderzoek.
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Bespreking van onderzoek van Todor Stefanov in ‘Waar wij trots op zijn. De ontdekkingen van 2011’ van de Universiteit Leiden Faculteit der Wiskunde & Natuurwetenschappen. De Bulgaar Todor Stefanov onderzoekt methoden en middelen voor het ontwerpen en programmeren van multiprocessorsystemen die zijn geïntegreerd in een enkele chip. Dit om de verwerking van signalen en beelden in bijvoorbeeld smartphones te verbeteren. En dat moet snel, want ieder jaar komt er wel weer een nieuwe generatie op de markt.
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
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