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GPCR Structure: Muscarinic M3 Receptor
Another GPCR structure! - this time the Rat muscarinic M3 receptor, published in Nature, complexed with the drug tiotropium (CHEMBL1201307).
10 20 30 40 50 4dajA ( 64 ) iwqvvfiafltgflAlvTiigNilVivAFkvnkqLktvnnyFllSLAcAD aaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 333 aaaaaaaaaaaaa 60 70 80 90 100 4dajA ( 114 ) liIGviSMnlFttyiimnrWalgnlaÇdlwLSiDYvASNAsVmNLlvISf aaaaa aaaaaaaaaaa aaaaaaaaaaaaaaaaaaaaaaaaaaa 110 120 130 140 150 4dajA ( 164 ) DryfsitrpltyrakrttkrAgvmiglAwviSfvlWApaIlfwqyfvgkr aaaaaaa aaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 160 170 180 190 200 4dajA ( 214 ) tVppgeÇfIqflseptitfgtAiaaFymPvtiMtilywrIyketek lik 333 aaaaaaaaaaa aaaaaaaaaaaaaaaaa 210 220 230 240 250 4dajA ( 485 ) e aqTlsaIllaFiitWtpyNimVlvntfçdsçipktywnlgywlCYiNS aaaaaaaaaaaaaaaaaaaaaaaa aaaaaaaaaaaaaaa 260 270 4dajA ( 537 ) tvNPvcYalcnktFrttfkt aaaaaaaaaa aaaaaaaa
%A A.C. Kruse %A J. Hu, Jianxin %A A.C. Pan %A D.H. Arlow %A D.M. Rosenbaum %A E. Rosemond %A H.F. Green %A T. Liu %A P.S. Chae %A R.O. Dror %A D.E. Shaw %A W.I. Weis %A J. Wess %A B.K. Kobilka %T Structure and dynamics of the M3 muscarinic acetylcholine receptor %J Nature %V 482 %P 552-556 %O http://dx.doi.org/10.1038/nature10867
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Are scientific meetings a great money making opportunity?
There’s a lot of debate on The Internets about the ‘evil’ publishers of scientific journals at the moment - my personal view is that there are too many journals, too many papers, and a lot of stuff probably never gets cited, so why not just have expectations of fewer higher quality publications. Google citations now makes me painfully aware of all the bad choices I made in the past preparing papers for journals/books that were only ever cited in my resume (and it turns out that isn’t a real citation, but maybe if I paid someone it could be ;) ).
A side-show to the discussion about journals is conferences, and thanks to Tudor for first making me think about this - these seem to be going through a transition to where as an academic scientist you’re now seen by conference organisers as an extra revenue source - either as a honeypot if you’re a big name to draw people in, or as a participant in an implicit pay-to-speak business model. I know from my experience in biotech that invariably an invitation to speak at these commercially organised conferences was accompanied with the obligation to book an expensive booth in the expo hall - hardly a route to avoid conflicts of interest in a balanced set of speakers.
Now I’m on the other side of the fence, an academic, and I’m in the lucky current position to be invited to some excellent conferences, and also get invited to quite a few not so excellent ones. I’m happy to pay to go from my grants for some of these, others I get travel and so forth paid.
The sort of invite that starts…
Dear Professor Overington due to your excellence in Molecular Biology and
Transcriptional Control we would like to invite you to give a plenary lectureon any subject you would like, at our forthcoming XXXVIIth International
Meeting on Drug Science and Innovation.
I love the fact that probably the world’s worst mailmerge programme has been used, and all the fonts and colours are all over the place, and also that your area of expertise is essentially random.
I got into the bad habit of replying to some of these, enquiring about travel costs, since these are usually the most material costs for going to a conference, and I have limited funds and time. This sometimes led to exchanges where partial offset of registration was maybe possible (er, I had assumed it would be free, actually) and then a request for a photo of me to be presented before the SAB to decide on how much support they could provide. As if a photo of me is going to encourage any money to be released - at this point, in hindsight I crossed the line into what is know, amongst the youngsters of today as ‘trolling’ - this is probably a crime, so sorry. I then asked what type of photo they would like - face and shoulders, full-body, action, glamour - and often got interesting responses… Seriously, do I want to be any part of a system that uses my physical appearance to help a decision to grant me a bursary to speak at a conference I’ve been invited to?
Another big warning sign is where there are words like “honour”, “prestige”; or worse still, that you have to sign up to a series of one-on-one special meetings - read my lips “I have limited interests and money, I’m not interested in buffers, CRO chemistry or externalisation of my IT functions” - these are just embarassing to sit in, and will be a waste of mine and the other participants time. There’s a clear spectrum of conferences from volunteer interest groups, through professional society organised ones, and then on to out and out commercial ventures. The former are often run using favours from host institutes, and the latter are to make money for somebody. I know the number of rich pharma attendees is falling, but that doesn’t mean that academic speakers are the obvious place to think of creative ways to pad the bottom line.
For invited ‘academic’ speakers at a ‘commercial’ conference I think it is reasonable that…
- Return travel is paid for.
- Registration is waived.
- Accommodation is paid for.
- WiFi costs at the venue are waived (this is soooooo expensive now at loads of places).
- And in return, you will hang around for the entire conference, and not just up for your talk/session.
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ChEMBL RESTful Web Service API Release 1.0.0 - Update
We are pleased to announce that we have updated the ChEMBL RESTful Web Service API (application programming interface) with some more of the features that you - the ChEMBL users - have requested.
In particular, we have added support for the:
- Retrieval of compounds by Canonical SMILES string
- Retrieval of compounds containing a particular substructure, as given by a Canonical SMILES string
- Retrieval of a list of compounds similar, at a given cutoff percentage Tanimoto similarity, to one represented by a given Canonical SMILES string
- Retrieval of compound images, as given by a compound ChEMBLID
- Checking of the API's health status
- Inclusion of standard HTTP response codes in API responses
Sample urls:- Retrieve a compound image - https://www.ebi.ac.uk/chemblws/compounds/CHEMBL192/image
- Retrieve a compound image sized to specific dimensions - https://www.ebi.ac.uk/chemblws/compounds/CHEMBL192/image?dimensions=220
- Retrieve a list of compounds containing a particular substructure - https://www.ebi.ac.uk/chemblws/compounds/substructure/COc1ccc2[C@@H]3[C@H](COc2c1)C(C)(C)OC4=C3C(=O)C(=O)C5=C4OC(C)(C)[C@@H]6COc7cc(OC)ccc7[C@H]56.json
- Retrieve a list of compounds similar, at a given cutoff percentage, to one represented by a given SMILES string - https://www.ebi.ac.uk/chemblws/compounds/similarity/COc1ccc2[C@@H]3[C@H](COc2c1)C(C)(C)OC4=C3C(=O)C(=O)C5=C4OC(C)(C)[C@@H]6COc7cc(OC)ccc7[C@H]56/70.json
In addition to the API changes we have also updated the ChEMBL Java client to take advantage of the new features provided by the API. These updates include:- Methods to invoke the additional API endpoints (searching for compounds based on SMILES matches, common substructures and similarity to a given percentage Tanimoto similarity)
- Method to determine the health status of the API. Whether it is, in fact, running.
- Automated client-side translation of API status codes into developer-friendly exception messages such as TargetNotFoundException, InvalidSmilesException, etc.
The new client can be downloaded from: https://www.ebi.ac.uk/chembl/extra/chemblrestws/chemblRestClient-1.0.0.jar
As always, you're feedback and suggestions for improving the API are most welcome. Please e-mail: chembl-help@ebi.ac.uk. -
ChEMBL Webinars for 2012
We have set up a schedule for the ChEMBL webinars for the first half of 2012. We strongly recommend that people developing against the ChEMBL schema, or performing data-mining attend the relevant webinars, since there are a few gotchas for the unwary, or those who don't like documentation ;)
Times and subjects covered are as follows. Note all times are UK local times, and the dates span the application of Daylight Savings Times, so watch out! Secondly, details of how to participate in each meeting will be made available on the blog about a week before the scheduled webinar.
- 07-Mar-2012 3:30pm Interface and Searching
- 21-Mar-2012 3:30pm Schema and sql querying
- 04-Apr-2012 3:30pm structure based drugEBIlity
- 18-Apr-2012 3:30pm Interface and Searching
- 02-May-2012 3:30pm web services
- 16-May-2012 3:30pm Schema and sql querying
- 30-May-2012 9:00am Interface and Searching (Japanese language)
- 13-Jun-2012 3:30pm Interface and Searching
- 27-Jun-2012 3:30pm Schema and sql querying
- 11-Jul-2012 3:30pm Interface and Searching
The picture is of Will Hay, a great British comic actor of the 1920's and 30's - dressed as one of his classic schoolmaster roles - boys of a certain age (especially tubby 47 year old boys, just like me) will remember the films being on TV during the summer holidays in the 1970s. Anyway, one cool thing about him was that he was a keen amateur astronomer, and discovered the Great White Spot on Saturn (although see the wikipedia article for a fuller description of some earlier observations). -
SciBite - Open Intelligence on Pharmaceutical Discovery & Development
Lee Harland, is a visitor in the ChEMBL group here at EMBL-EBI, and he is collaborating with us on semantic web data integration, text-mining, target ontologies and so forth. I asked him to write a small piece for the ChEMBL-og on one of his personal projects - SciBites, and here is what he wrote......
"SciBite is a new biomedical alerting service tailored to pharmaceutically relevant questions and focused on targets, diseases and drugs. The premise is simple, right now if you're a scientist interested in say, Asthma, how can you stay on top of all the lastest developments? You can of course, set up pubmed searches, patent searches, google news searches (making sure you have remembered every possible synonym as these tools won't do that for you). What you'll get back is a stream of articles. Some relevant, many not, and you'll have to read them to actually find out. We thought there had to be a better way.. What we really wanted was a kind of Twitter for "things", where the drugs, disease and targets could each tweet relevant news, in an easily consumable (and discoverable) way. So we built SciBite.
SciBite works by continually scanning 1000s of sources including literature, patents, news feeds, blogs, databases and more. It looks at every new article and automatically tags the targets, diseases, drugs, companies and 'contexts' (such as "biomaker study", "regulatory approval", "animal model" etc), that it finds. Users can then go to the website and view information by topic, not by source. The lists can be filtered based on source or other criteria such as regulatory approval or biomarkers. Everything is available as an RSS feed so users can stay on top of latest news. Information is presented in a very visual way, making it really easy to scan new articles and identify the key topics. We also have relevancy filters to remove spurious and irrelevant news (although this will never be perfect!). Finally, our "related topics" function allows you to quickly find the targets, diseases or drugs being co-mentioned with the thing you're interested in, which is an incredibly powerful way to spot new and interesting connections.There are a number of major content companies doing this sort of thing, and as a small (tiny) company, we cannot hope to match their levels of curation and resources. However, we believe that this sort of information should be available to everyone and so we hope that by using technology we can provide something that comes close to what the major players offer. We decided early on that as we were using a lot of public data (including ChEMBL), the site itself should be free and support these efforts. We're also making all our data and APIs freely available to any non-profit organisation expressing an interest.We've seen a great growth in user numbers since our very low-key launch at the start of February. Over 2000 people have used the site, and we've done hardly any advertising... Its daunting to see that many so soon, but its a great feeling to know people are finding it useful! What's there now is really just the start. The aim was to build a platform that connected news to things, which we've done. The next stages are to do much more with the data. This is one of the things I'll be exploring as part of my Visitorship with the ChEMBL group, we're looking at some interesting company-centric profiling, tracking whats going on with each organisations drugs. There's a whole lot more planned for 2012 too!Anyone interested can use the system for free now, at http://scibite.com. I tweet as @SciBitely and our blog is at http://about.scibite.com."Wow! Look at what is there. -
Internship Project - Modelling Therapeutic mAbs
We have assembled a large list of antibodies that are, or have been, in clinical development, and have full length sequences for a significant fraction of these. We have available an internship project over the summer to produce a library of structural models for this set of mAbs, and for selected cases to perform docking against their known antigen structures. Applicants should have prior experience of protein structure comparison and analysis, experience of comparative modelling (preferably with Modeller), and ideally experience of protein-protein docking. Additionally experience of antibody structure, CDRs, canonical residues, etc, would be fantastic.
If you are interested in a three to four month internship working on this on our Hinxton campus - please mail us with a full cv. -
Sequence-Structure alignment of the 10 structurally characterised distinct GPCRs
Here is a joy formatted alignment of the (now) 10 sequence distinct rhodopsin-like GPCR structures - I've selected a representative for those for which there are multiple structures known - usually those that are most complete in terms of lack of disordered loops, etc. The alignment is quite unstable in parts, and several regions are open to interpretation.....
The structures are:
- 2rh1 - human beta-2 adrenergic receptor
- 2vt4 - turkey beta-1 adrenergic receptor
- 3pbl - human dopamine D3 receptor
- 3uon - human muscarinic M2 receptor
- 3rze - human histamine H1 receptor
- 3eml - human adenosine A2a receptor
- 3v2w - human sphingosine-1-phosphate receptor
- 3odu - human CCR4 receptor
- 2i35 - bovine rhodopsin
- 2z73 - squid rhodopsin
10 20 30 40 50 2rh1 ( 29 ) devwvvgmgi 2vt4A ( 40 ) weagmsl 3pblA ( 32 ) yal 3uon ( 20 ) tfevvfivl 3rze ( 28 ) mplvv 3eml ( 3 ) imgssvyit 3v2w ( 17 ) sdyvnydIIvrHYnyTgklnisa ltsv 3oduA ( 27 ) pçfre-------------------------enanfnkiflpt 2i35 ( 1 ) mnGtegpnfyVPfsnktgvVrsPfeapQyyLaepwqFsmlAa 2z73A ( 9 ) etwwyNpsIvVhpHWref--------------dqvpdavYyslGi aaaaa 60 70 80 90 100 2rh1 ( 39 ) vmslivlaIvfgNvlVitAIakferLqtvtnyFItsLAcADlvMGlaVVp 2vt4A ( 47 ) lmalVvllIvagNvlViaAigstqrLqtltnlFItsLAcADlvvGllVVp 3pblA ( 35 ) sYcalilaIvfgNglVcmAVlkeraLqtttnyLVvsLAvADllvAtlVMp 3uon ( 29 ) vagslSlvTiigNilVmvSIkvnrhLqtvnnyflfSLAcADliiGvfSMn 3rze ( 33 ) vlsticlvTvglNllVlyAvrserkLhtvGnlYIvsLSvADliVGavVMp 3eml ( 12 ) vElaiavlAilgNvlVcwAvwlnsnLqnvtnyfVvsLAaADiavGvlAIp 3v2w ( 51 ) vfiliCcfIileNifvlltiwktkkFhrpMYyFIgnLAlSDllaGvaYta 3oduA ( 44 ) iYsiIfltGivgNglvilvMgyqkklrsmtdkYRlhLSvADllFVitLpf 2i35 ( 43 ) yMfllimlGfpiNflTlyVTvqHkkLrtpLNyILlNLAvADlfMVfg-GF 2z73A ( 40 ) fIgiCgiiGcggNgiViyLFtktksLqtpanmFiinLAfSDftFSlvNGf aaaaaaaaaaaaaaaaaaaaaa aaaaaaaaaaaaaaaaaa aaa 110 120 130 140 150 2rh1 ( 89 ) fgaahilm-kmWtfgnfwçefWTSiDVlCVTASIeTLcvIAvdryfAIts 2vt4A ( 97 ) fgatlvvr-gtWlwgsflçelWTSlDVlCVTAsIeTLcvIAiDrylaits 3pblA ( 85 ) wvvylevtggvWnfsricÇdvFVTlDVmMcTAsIwNLCaISidRytAVvm 3uon ( 79 ) lytlytvi-gyWplgpvvÇdlWlalDYvVSNAsVmNLliiSfdryfcvtk 3rze ( 83 ) mnilyllm-skwsLgrplÇlfWLSmDYVASTASIfSVfiLCiDryrsvqq 3eml ( 62 ) faitist---gfçaaçhgÇLfiACfvLVLtQSsIfsLlaIAiDryiairi 3v2w ( 101 ) Nlllsga--tTykLtPaqWFlREGsMFvALSASVfSLlaIAieryitmlk 3oduA ( 94 ) WavDAva---nWyfgnflÇkaVHviYTVNlYSSVwILAfISlDRylAiVh 2i35 ( 92 ) ttTlyTSlhGyFvfgptgÇnlEGffATlGGEIALWSLVvLaiERyvvvCk 2z73A ( 90 ) plMtiSCflkkWifgfaaÇkvYGfiGGiFGFMsIMTMAMiSiDrynViGr aaaaaaa aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 160 170 180 190 200 2rh1 ( 138 ) pfkyqSl---ltknkArviilmvwivSgltSflpIqmhwyr-----athq 2vt4A ( 146 ) pfryqsl---mtrarAkviictvwaiSalvSflpImmhwWr-----dedp 3pblA ( 135 ) pvhyqhgtgqsscrrValmitavwvlAfaVSc-pLlfgfNtTg------- 3uon ( 128 ) pltypvk---rttkmAgmmiaaAwvlSfilwapaIlfwqfivg------- 3rze ( 132 ) plrylky---rtktrAsatilgawflSfl-WvipIlgwnh 3eml ( 109 ) plryngl---vtgtrAkgiiaicwvlSfaiGltPmlgwNnÇgq 3v2w ( 156 ) nnfrlfllisacwviSlilGglPimgwn----------- 3oduA ( 141 ) atn----sqrprkllAekvVyvgVwipAlllT-ipDfif-Anvsead--- 2i35 ( 142 ) pmsn----frfgenhAimgvafTwvmAlaCAapPlvgwSrYIPE------ 2z73A ( 140 ) pmaas---kkMshrrAfimiifVwlwSvlwAigPifgwGaYtLE------ aaaaaaaaaaaaaaaaaaa aaa 210 220 230 240 250 2rh1 ( 180 ) eAinÇyae-etçÇdff--------TnqayaiasSivSFyvplviMvfvYs 2vt4A ( 188 ) qAlkçyqd-pgçÇdfv--------TnrayaiasSiiSFyipLliMifval 3pblA ( 177 ) --------dptvÇsIs---------npdFViySSvvSFylPfgvTvlvya 3uon ( 168 ) ----vrtVedgeÇyIqff------snaavtfgtAiaaFylpviiMtvlyw 3rze ( 175 ) rredkÇeTdfy------dvtwfkvmtaiinFylPtllMlwfya 3eml ( 156 ) sqgçgegqvaÇlFedVV-----pmnYMVyfNffacVlvplllMlgvYl 3v2w ( 184 ) ----ÇisalssÇSTVLP-------LYhkhYIlfCTtvFtllllsIvilYc 3oduA ( 182 ) --------dryiÇdrfyp---ndlwvvvfqfqhimvglilPgivIlsCyc 2i35 ( 182 ) -------gMQCSÇGIDYYTpheetnNesFViyMfvvHfiiPlivIffCyg 2z73A ( 181 ) -------GVLCNÇSFdYIsr--dsttrsNIlcMFilGffgPiliiffCyf aaaaaaaaaaa aaaaaaaaaaaa 260 270 280 290 300 2rh1 ( 221 ) rVfqeakrql kfclkeHkaLktlgiIMgtFt 2vt4A ( 229 ) rvyreakeq irehkalktlgiImgvFt 3pblA ( 210 ) rIyvvlkqrrrk-----------------gvplrekkatqMVaiVlgaFi 3uon ( 208 ) hisrasksri pppsrekkvtrtilaIllaFi 3rze ( 212 ) kIykaVrqhc lhmnrerkaakQLgfIMaaFi 3eml ( 199 ) rIflaarrql rstlqkevhaAkSlaiIvglFa 3v2w ( 223 ) riyslvrtr asrssenvaLlkTViiVLsvFi 3oduA ( 221 ) iIisklshs kghqkrkalktTviLilaFf 2i35 ( 225 ) qlvfTvkeaAaq------------qqesattqkaekevtrMViiMviaFl 2z73A ( 222 ) nIvmsvsnhekemaamakrlnakelrkaqaganaemrlAkIsivIVsqFl aaaaaa aaaaaaaaaaaaaaaaa 310 320 330 340 350 2rh1 ( 284 ) lcWlpFFiVNivhviqdn----------lirkevyillNwiGYvNSgfNp 2vt4A ( 301 ) lCWlpFFlvnivnvfnrd----------lvpdwlfvafnwlGYAnSAmnp 3pblA ( 340 ) vCWlpFFltHvlnthçqt--------ç-hvspelysattwlGYvNsalNP 3uon ( 398 ) itWapYNvmVlintfçap--------ç--ipntvwtiGywlCYinstiNp 3rze ( 426 ) lCWipYFiffmviafçkn--------ç--cnehlhmftiWlGYiNStlNP 3eml ( 244 ) lcwlpLHiiNcftffçpd--------çshaplwlMylAivlSHtnSvvnP 3v2w ( 267 ) acwapLFiLLllDvgçkvk------tç--diLfrAeyfLvlAvlNSgtNP 3oduA ( 250 ) acWlpyyigisidsfilleiikqgçefentvhkwisitEAlAFfHCclNp 2i35 ( 263 ) iCWlpYAgvAfyIfthqgsd---------fgPifMTipAFfAKtSAvyNP 2z73A ( 272 ) lSWspYAvvAllAQfgplew---------VtpyaAQlpVMfAKaSaihNP aaaaaaaaaaaaaaa aaaaaaaaaaaaa aaa 360 370 380 390 400 2rh1 ( 324 ) liYc-rspdfriAfqellcl 2vt4A ( 341 ) iiYc-rspdfrkAfkrlla 3pblA ( 381 ) viYttfnieFrkAflkilsc 3uon ( 438 ) acYalcnatFkktfkhllm 3rze ( 466 ) liYplCnenFkktfkrilhi 3eml ( 286 ) fiYayrireFrqTfrkIirshvlrq 3v2w ( 309 ) iiytltNkemrrafiri 3oduA ( 300 ) ilyaflgakfktsaqhalts 2i35 ( 304 ) viYimmnkqFrnCmvtTlccgkn dde 2z73A ( 313 ) miYsvsHpkFreAIsqtfpwvLtccqfddketeddkdaeteipage aaaaaa aaaaaaaaaa
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GPCR Structure: Sphingosine-1-phosphate receptor
Hot on the heals of the M2 receptor comes the 3.35A resolution human Sphingosine-1-Phosphate receptor (S1PR, also known as EDG1) - PDBe3v2w - complexed with the ligand {(3R)-3-amino-4-[(3-hexylphenyl)amino]-4-oxobutyl}phosphoric acid. (and also the entry PDBe3v2y)
10 20 30 40 50 3v2w ( 17 ) sdyvnydIIvrHYnyTgklnisa ltsvvfiliCcfIileNifvlltiwk aaaaaaaaaa aaaaaaaaaaaaaaaaaaaaaaaa 60 70 80 90 100 3v2w ( 73 ) tkkFhrpMYyFIgnLAlSDllaGvaYtaNlllsgatTykLtPaqWFlREG 333 aaaaaaaaaaaaaaaaaaaaaaaaa 3333 aaaaaaaaa 110 120 130 140 150 3v2w ( 123 ) sMFvALSASVfSLlaIAieryitmlk nnfrlfllisacwviSlilGglP aaaaaaaaaaaaaaaaaaaaaaaa aaaaaaaaaaaaaaaaa 160 170 180 190 200 3v2w ( 179 ) imgwnÇisalssÇSTVLPLYhkhYIlfCTtvFtllllsIvilYcriyslv 333 aaaaaaaaaaaaaaaaaaaaaaaaaaaaa 210 220 230 240 250 3v2w ( 229 ) rtr asrssenvaLlkTViiVLsvFiacwapLFiLLllDvgçkvktçdiL aa aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa 33 260 270 280 3v2w ( 291 ) frAeyfLvlAvlNSgtNPiiytltNkemrrafiri 33 aaaaaaaaa333aaaaaaaaa aaaaaaaa
%T Crystal Structure of a Lipid G Protein–Coupled Receptor %A M.A. Hanson %A C.B. Roth %A E. Jo %A M.T. Griffith %A F.L. Scott %A G. Reinhart %A H. Desale %A B. Clemons %A S.M. Cahalan %A S.C. Schuerer %A M.G. Sanna %A G.W. Han %A P. Kuhn %A H. Rosen %A R.C. Stevens %J Science %V 225 %D 2012 %P 851-855
