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News Article | December 6, 2016
Site: www.medicalnewstoday.com

Scientists have created a map of all 1,578 licensed drugs and their mechanisms of action - as a means of identifying 'uncharted waters' in the search for future treatments. Their analysis of drugs licensed through the Food and Drug Administration reveals that 667 separate proteins in the human body have had drugs developed against them - just an estimated 3.5% of the 20,000 human proteins. And as many as 70 per cent of all targeted drugs created so far work by acting on just four families of proteins - leaving vast swathes of human biology untouched by drug discovery programmes. The study is the most comprehensive analysis of existing drug treatments across all diseases ever conducted. It was jointly led by scientists at The Institute of Cancer Research, London, which also funded the research. The new map reveals areas where human genes and the proteins they encode could be promising targets for new treatments - and could also be used to identify where a treatment for one disease could be effective against another. The new data, published in a paper in the journal Nature Reviews Drug Discovery, could be used to improve treatments for all human aliments - as diverse as cancer, mental illness, chronic pain and infectious disease. Scientists brought together vast amounts of information from huge datasets including the canSAR database at The Institute of Cancer Research (ICR), the ChEMBL database from the European Bioinformatics Institute (EMBL-EBI) in Cambridge and the University of New Mexico's DrugCentral database. They matched each drug with prescribing information and data from published scientific papers, and built up a comprehensive picture of how existing medicines work - and where the gaps and opportunities for the future lie. The researchers discovered that there are 667 unique human proteins targeted by existing approved drugs, and identified a further 189 drug targets in organisms that are harmful to humans, such as bacteria, viruses and parasites. On average they found there were two drugs for every target in humans - but that a handful of proteins were targeted by many different drugs, such as the glucocorticoid receptor, which is the target of 61 anti-inflammatory drugs. Cancer was found to be the most innovative disease area, with the greatest growth in 'first-in-class' drugs - those that use a new and unique mode of action. Using complex 'Big Data' analytical techniques, the researchers identified four very frequently 'drugged' families of proteins - accounting for 43 per cent of all drug targets, and acting as targets for 70 per cent of all approved small-molecular drugs. The new map of drugs can now be used to identify other proteins with similar properties to these most heavily drugged families - which might be potentially exciting treatment targets for diseases such as cancer. And bringing together complex data from multiple sources within the drug map could predict the best combinations of drugs to give. Targeting two proteins which behave in a similar way is unlikely to be effective against diseases such as cancer, whereas targeting proteins with very different functions could be much more successful. Study co-leader Dr Bissan Al-Lazikani, Head of Data Science at The Institute of Cancer Research, London, said: "Our new study provides a comprehensive map of the current state of medicines for human disease. It identifies areas where drug discovery has been a spectacular success, others where there are major gaps in our armoury of medicines, and opportunities for the future in the form of promising targets and potential drug combinations. By revealing the uncharted waters of drug discovery, it will provide a clear pointer for future exploration and innovation." Professor Paul Workman, Chief Executive of The Institute of Cancer Research, London, said: "We need to do more to innovate in drug discovery if we are really going to tackle the major medical challenges we face, such as cancer's ability to evolve drug resistance in response to treatment. But to help direct future efforts in drug discovery, we first need a very accurate and comprehensive picture of the targets of the medicines that have been created so far, what is currently working, and most importantly where there is the greatest potential for the future. This new map of drugs, created through the latest computational analytical technologies, will enhance our ability to use rational, data-driven approaches to identify the most promising future targets and treatment combinations for the next generation of cancer and other diseases."


Far from just reading the information contained in the human genome, and in order to fully understand how it works, researchers aim to know the ins and outs of all the elements in this tiny regulated gear. Many laboratories, consortia and projects are devoted to get a global view of the functional regions of the genome and to know in which cell types genes are active. Intriguingly, only a small fraction of the human genome (around 2%) contains genes encoding for proteins, which are the building blocks of the cell. The remaining 98% is important for regulation, meaning that it is involved in controlling when and where genes are active. This large portion of the genome produces RNA molecules, called non-coding RNAs, which differ in size, structure and function. As the different types of non-coding RNAs can interact with proteins in different ways, big efforts have been put into investigating them. Until now, there were no computational tools available to handle very long RNA sequences and studying them through experimental methods is at present a huge challenge. In a recent article published in Nature Methods, researchers at the Centre for Genomic Regulation in Barcelona (Spain), in collaboration with scientists at EMBL's site in Monterotondo (Italy) and the California Institute of Technology (US), introduced a new computational tool to predict protein interactions with long non-coding RNAs, which they validated using advanced experimental techniques. "Long non-coding RNAs interact with various proteins to mediate important cellular functions. Trying to identify these interactions can be a good starting point in order to understand the role of these molecules in the normal functioning of the cell but also in disease," explains Gian Gaetano Tartaglia, ICREA research professor at the Centre for Genomic Regulation (CRG) and principal investigator of this article. The new computational tool, which is called Global Score, allows scientists to predict where, along the sequence of a non-coding RNA, a protein will establish a physical contact. To do so, this algorithm integrates not only the global propensity of the protein to bind a particular RNA but also the local features of such a binding. "The structure of the RNA is absolutely important when predicting protein interactions. Our main challenge was to be able to work with RNA sequences regardless of their length in order to keep a complete view of their structural properties when looking for protein partners," adds Davide Cirillo, post-doctoral researcher at the CRG and first author of the paper. "The algorithm we have developed integrates this information and allows us not only to predict protein partners but also to prioritize them for experimental validation. This methodological advance will be crucial to better study long non-coding RNAs and their functions", concludes the researcher. This work highlights, again, the relevant contribution of bioinformatics and computational biology to advance knowledge and their key role boosting and accelerating research in the life sciences. More information: Davide Cirillo et al, Quantitative predictions of protein interactions with long noncoding RNAs, Nature Methods (2016). DOI: 10.1038/nmeth.4100


News Article | February 24, 2017
Site: www.eurekalert.org

New algorithm helps scientists prioritize binding partners for experimental validation, which will contribute to our understanding of the role of long non-coding RNAs in normal cell function and in disease Far from just reading the information contained in the human genome, and in order to fully understand how it works, researchers aim to know the ins and outs of all the elements in this tiny regulated gear. Many laboratories, consortia and projects are devoted to get a global view of the functional regions of the genome and to know in which cell types genes are active. Intriguingly, only a small fraction of the human genome (around 2%) contains genes encoding for proteins, which are the building blocks of the cell. The remaining 98% is important for regulation, meaning that it is involved in controlling when and where genes are active. This large portion of the genome produces RNA molecules, called non-coding RNAs, which differ in size, structure and function. As the different types of non-coding RNAs can interact with proteins in different ways, big efforts have been put into investigating them. Until now, there were no computational tools available to handle very long RNA sequences and studying them through experimental methods is at present a huge challenge. In a recent article published in Nature Methods, researchers at the Centre for Genomic Regulation in Barcelona (Spain), in collaboration with scientists at EMBL's site in Monterotondo (Italy) and the California Institute of Technology (US), introduced a new computational tool to predict protein interactions with long non-coding RNAs, which they validated using advanced experimental techniques. "Long non-coding RNAs interact with various proteins to mediate important cellular functions. Trying to identify these interactions can be a good starting point in order to understand the role of these molecules in the normal functioning of the cell but also in disease," explains Gian Gaetano Tartaglia, ICREA research professor at the Centre for Genomic Regulation (CRG) and principal investigator of this article. The new computational tool, which is called Global Score, allows scientists to predict where, along the sequence of a non-coding RNA, a protein will establish a physical contact. To do so, this algorithm integrates not only the global propensity of the protein to bind a particular RNA but also the local features of such a binding. "The structure of the RNA is absolutely important when predicting protein interactions. Our main challenge was to be able to work with RNA sequences regardless of their length in order to keep a complete view of their structural properties when looking for protein partners," adds Davide Cirillo, post-doctoral researcher at the CRG and first author of the paper. "The algorithm we have developed integrates this information and allows us not only to predict protein partners but also to prioritize them for experimental validation. This methodological advance will be crucial to better study long non-coding RNAs and their functions", concludes the researcher. This work highlights, again, the relevant contribution of bioinformatics and computational biology to advance knowledge and their key role boosting and accelerating research in the life sciences.


News Article | January 12, 2017
Site: www.techtimes.com

New research has found the neural projection responsible for preventing people from acting out their instincts and impulses, with possible impact on schizophrenia and mood disorders such as depression. Prior to this study, researchers were aware that the prefrontal cortex is a key region responsible for validating which instincts to turn into behaviors and which ones to control. However, the exact mechanism was unknown. The brainstem, which is positioned above the spinal chord, is responsible for our instinctual impulses and behaviors, and the prefrontal cortex is the brain area which controls the manifestation of our impulses. As part of the new research published on Jan. 9 in the journal Nature Neuroscience, scientists from the European Molecular Biology Laboratory (EMBL) analyzed the connections between neurons in mice, discovering that the prefrontal cortex is directly connected to the brainstem. Further, the team wanted to confirm the hypothesis as to which instinctive behavior is inhibited by the region of the brain. They observed that, when it comes to mice that were tortured by a bully counterpart, the connection is weaker, and the mice show more signs of being scared. Following this discovery, the team concluded that it is possible to create through drugs the same reaction in mice that have never been aggressed by blocking the connection between the brainstem and the prefrontal cortex. "Social defeat caused a weakening of functional connectivity between these two areas, and selective inhibition of these projections mimicked the behavioral effects of social defeat. These findings define a specific neural projection by which the prefrontal cortex can control and adapt social behavior," noted the research. The research represents an anatomical explanation on how easy it is to control behavior compared with impulses. Should someone feel angry, that person is more likely not to act on that feeling of anger but less likely to stop the very impulse of the feeling. As it turns out, a specific region of the prefrontal cortex is in direct relation with the PAG area in the brainstem, which controls our behavioral responses to instincts. Additionally, this connection is not shared with the hypothalamus, which is responsible for our affective stimuli and the responses to them. This observation provides a stable explanation as to why it is much easier to control our behavioral response to stimuli than to control our affective or emotional responses. "One fascinating implication we're looking at now is that we know the pre-frontal cortex matures during adolescence. Kids are really bad at inhibiting their instincts; they don't have this control, so we're trying to figure out how this inhibition comes about, especially as many mental illnesses like mood disorders are typically adult-onset," noted Cornelius Gross, lead author from EMBL. Various studies have been conducted in an attempt to understand the specific mechanisms of brain activity in humans. Recent research showed that there is a connection between the way we breathe and our judgment of emotions, impacting the way we remember different events. "One of the major findings in this study is that there is a dramatic difference in brain activity in the amygdala and hippocampus during inhalation compared with exhalation," noted the author of that research. This proves that brain activity is governed by a very wide array of cognitive and neural mechanisms, many of which are yet to be defined. © 2017 Tech Times, All rights reserved. Do not reproduce without permission.


News Article | November 3, 2016
Site: phys.org

The plant in this image, however, has gotten its spiral wrong. Instead of several leaves spaced out in a spiral pattern, it has two continuous, spiral-shaped organs developing. Credit: Neha Bhatia/EMBL This flower-like image shows a plant that is not developing quite right. It comes from a study in which scientists at EMBL and the University of Sydney unearthed the molecular feedback loop that creates the spiral pattern of leaves around a stem. The work is published today in Current Biology. For centuries, artists, biologists and mathematicians have been inspired by the recurring patterns of the plant world: the exquisite symmetry of flowers, the sweeping spirals of seeds, spines and leaves. The plant in this image, however, has gotten its spiral wrong. Instead of several leaves spaced out in a spiral pattern, it has two continuous, spiral-shaped organs developing. How do plants create such amazing patterns? Based on mathematical modelling and computer simulations, scientists know that if plant organs like leaves or petals are produced at regular intervals, these complex patterns can automatically emerge. So how do plants produce organs at regular intervals? Biologists knew the answer involved cells in the growing plant coordinating with their neighbours to transport the plant hormone auxin to sites where it accumulates. At each auxin hotspot, a new leaf begins to grow. But how are these hotspots formed and maintained? Neha Bhatia, a PhD student in Marcus Heisler's lab at EMBL, found that if a cell detects a lot of auxin, it makes neighbouring cells transport the hormone towards that cell. This creates a hotspot. At the same time, it depletes auxin levels in the surrounding area, so another hotspot can only form a fair distance away, where that cell's influence is no longer felt. This, the EMBL scientists conclude, is what creates the regular spacing between auxin hotspots, and consequently between leaves. Surprisingly, Bhatia found that this feedback loop has to be active not just in the cells on the surface of the growing plant, but also in the cells below. If only surface cells can respond to auxin, the auxin seems to build up too much and starts to leak sideways. This gives rise to the wonderful spiral shaped organs in the picture. The EMBL scientists speculate that this could be what happens in some species of Cereus cacti, whose leaves are spiral-shaped. Explore further: Is your leaf left-handed? Previously overlooked asymmetry in Arabidopsis and tomato leaves


Letunic I.,Biobyte Solutions GmbH | Doerks T.,EMBL | Bork P.,EMBL
Nucleic Acids Research | Year: 2015

SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de/) providing simple identification and extensive annotation of protein domains and the exploration of protein domain architectures. In the current version, SMART contains manually curated models for more than 1200 protein domains, with ∼200 new models since our last update article. The underlying protein databases were synchronized with UniProt, Ensembl and STRING, bringing the total number of annotated domains and other protein features above 100 million. SMART's 'Genomic' mode, which annotates proteins from completely sequenced genomes was greatly expanded and now includes 2031 species, compared to 1133 in the previous release. SMART analysis results pages have been completely redesigned and include links to several new information sources. A new, vector-based display engine has been developed for protein schematics in SMART, which can also be exported as highresolution bitmap images for easy inclusion into other documents. Taxonomic tree displays in SMART have been significantly improved, and can be easily navigated using the integrated search engine. © The Author(s) 2014.


Maitre J.-L.,EMBL | Heisenberg C.-P.,IST Austria
Current Biology | Year: 2013

Cadherins are transmembrane proteins that mediate cell-cell adhesion in animals. By regulating contact formation and stability, cadherins play a crucial role in tissue morphogenesis and homeostasis. Here, we review the three major functions of cadherins in cell-cell contact formation and stability. Two of those functions lead to a decrease in interfacial tension at the forming cell-cell contact, thereby promoting contact expansion - first, by providing adhesion tension that lowers interfacial tension at the cell-cell contact, and second, by signaling to the actomyosin cytoskeleton in order to reduce cortex tension and thus interfacial tension at the contact. The third function of cadherins in cell-cell contact formation is to stabilize the contact by resisting mechanical forces that pull on the contact. © 2013 Elsevier Ltd.


Letunic I.,EMBL | Doerks T.,EMBL | Bork P.,EMBL
Nucleic Acids Research | Year: 2012

SMART (Simple Modular Architecture Research Tool) is an online resource (http://smart.embl.de/) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 7 contains manually curated models for 1009 protein domains, 200 more than in the previous version. The current release introduces several novel features and a streamlined user interface resulting in a faster and more comfortable workflow. The underlying protein databases were greatly expanded, resulting in a 2-fold increase in number of annotated domains and features. The database of completely sequenced genomes now includes 1133 species, compared to 630 in the previous release. Domain architecture analysis results can now be exported and visualized through the iTOL phylogenetic tree viewer. 'metaSMART' was introduced as a novel subresource dedicated to the exploration and analysis of domain architectures in various metagenomics data sets. An advanced full text search engine was implemented, covering the complete annotations for SMART and Pfam domains, as well as the complete set of protein descriptions, allowing users to quickly find relevant information. © The Author(s) 2011. Published by Oxford University Press.


Letunic I.,EMBL | Bork P.,EMBL
Nucleic Acids Research | Year: 2011

Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. In addition to classical tree viewer functions, iTOL offers many novel ways of annotating trees with various additional data. Current version introduces numerous new features and greatly expands the number of supported data set types. Trees can be interactively manipulated and edited. A free personal account system is available, providing management and sharing of trees in user defined workspaces and projects. Export to various bitmap and vector graphics formats is supported. Batch access interface is available for programmatic access or inclusion of interactive trees into other web services. © 2011 The Author(s).


The present invention relates to unnatural amino acids comprising a cyclooctynyl or trans-cyclooctenyl analog group and having formula (I) or an acid or base addition salt thereof. The invention also relates to the use of said unnatural amino acids, kits and processes for preparation of polypeptides that comprise one or more than one cyclooctynyl or trans-cyclooctenyl analog group. These polypeptides can be covalently modified by in vitro or in vivo reaction with compounds comprising an azide, nitrile oxide, nitrone, diazocarbonyl or 1,2,4,5-tetrazine group.

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