 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P97412 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MSTDSNSLAREFLIDVNQLCNAVVQRAEAREEEEEETHMATLGQYLVHGR 50
51 GFLLLTKLNSIIDQALTCREELLTLLLSLLPLVWKIPVQEQQATDFNLPL 100
101 SSDIILTKEKNSSLQKSTQGKLYLEGSAPSGQVSAKVNLFRKIRRQRKST 150
151 HRYSVRDARKTQLSTSDSEGNSDEKSTVVSKHRRLHALPRFLTQSPKEGH 200
201 LVAKPDPSATKEQVLSDTMSVENSREVILRQDSNGDILSEPAALSILSNM 250
251 NNSPFDLCHVLLSLLEKVCKFDIALNHNSSLALSVVPTLTEFLAGFGDCC 300
301 NQSDTLEGQLVSAGWTEEPVALVQRMLFRTVLHLMSVDVSTAEAMPESLR 350
351 KNLTELLRAALKIRACLEKQPEPFSPRQKKTLQEVQEGFVFSKYRHRALL 400
401 LPELLEGVLQLLISCLQSAASNPFYFSQAMDLVQEFIQHQGFNLFGTAVL 450
451 QMEWLLTRDGVPSEAAEHLKALINSVIKIMSTVKKVKSEQLHHSMCTRKR 500
501 HRRCEYSHFMQHHRDLSGLLVSAFKNQLSKSPFEETAEGDVQYPERCCCI 550
551 AVCAHQCLRLLQQVSLSTTCVQILSGVHSVGICCCMDPKSVIAPLLHAFK 600
601 LPALKAFQQHILNVLSKLLVDQLGGAELSPRIKKAACNICTVDSDQLAKL 650
651 GETLQGTLCGAGPTSGLPSPSYRFQGILPSSGSEDLLWKWDALEAYQSFV 700
701 FQEDRLHNIQIANHICNLLQKGNVVVQWKLYNYIFNPVLQRGVELVHHCQ 750
751 QLSIPSAQTHMCSQLKQYLPQEVLQIYLKTLPVLLKSRVIRDLFLSCNGV 800
801 NHIIELNYLDGIRSHSLKAFETLIVSLGEQQKDAAVLDVDGLDIQQELPS 850
851 LSVGPSLHKQQASSDSPCSLRKFYASLREPDPKKRKTIHQDVHINTINLF 900
901 LCVAFLCVSKEADSDRESANESEDTSGYDSPPSEPLSHMLPCLSLEDVVL 950
951 PSPECLHHAADIWSMCRWIYMLNSVFQKQFHRLGGFQVCHELIFMIIQKL 1000
1001 FRSHTEDQGRRQGEMSRNENQELIRISYPELTLKGDVSSATAPDLGFLRK 1050
1051 SADSVRGFQSQPVLPTSAEQIVATESVPGERKAFMSQQSETSLQSIRLLE 1100
1101 SLLDICLHSARACQQKMELELPSQGLSVENILCELREHLSQSKVAETELA 1150
1151 KPLFDALLRVALGNHSADLGPGDAVTEKSHPSEEELLSQPGDFSEEAEDS 1200
1201 QCCSLKLLGEEEGYEADSESNPEDVDTQDDGVELNPEAEGFSGSIVSNNL 1250
1251 LENLTHGEIIYPEICMLGLNLLSASKAKLDVLAHVFESFLKIVRQKEKNI 1300
1301 SLLIQQGTVKILLGGFLNILTQTNSDFQACQRVLVDLLVSLMSSRTCSED 1350
1351 LTLLWRIFLEKSPCTEILLLGIHKIVESDFTMSPSQCLTFPFLHTPSLSN 1400
1401 GVLSQKPPGILNSKALGLLRRARISRGKKEADRESFPYRLLSSWHIAPIH 1450
1451 LPLLGQNCWPHLSEGFSVSLVGLMWNTSNESESAAERGKRVKKRNKPSVL 1500
1501 EDSSFEGAEGDRPEVTESINPGDRLIEDGCIHLISLGSKALMIQVWADPH 1550
1551 SGTFIFRVCMDSNDDTKAVSLAQVESQENIFFPSKWQHLVLTYIQHPQGK 1600
1601 KNVHGEISIWVSGQRKTDVILDFVLPRKTSLSSDSNKTFCMIGHCLTSQE 1650
1651 ESLQLAGKWDLGNLLLFNGAKIGSQEAFFLYACGPNYTSIMPCKYGQPVI 1700
1701 DYSKYINKDILRCDEIRDLFMTKKEVDVGLLIESLSVVYTTCCPAQYTIY 1750
1751 EPVIRLKGQVKTQPSQRPFSSKEAQSILLEPSQLKGLQPTECKAIQGILH 1800
1801 EIGGAGTFVFLFARVVELSSCEETQALALRVILSLIKYSQQRTQELENCN 1850
1851 GLSMIHQVLVKQKCIVGFHILKTLLEGCCGEEVIHVSEHGEFKLDVESHA 1900
1901 IIQDVKLLQELLLDWKIWNKAEQGVWETLLAALEVLIRVEHHQQQFNIKQ 1950
1951 LLNAHVVHHFLLTCQVLQEHREGQLTSMPREVCRSFVKIIAEVLGSPPDL 2000
2001 ELLTVIFNFLLAVHPPTNTYVCHNPTNFYFSLHIDGKIFQEKVQSLAYLR 2050
2051 HSSSGGQAFPSPGFLVISPSAFTAAPPEGTSSSNIVPQRMAAQMVRSRSL 2100
2101 PAFPTYLPLIRAQKLAASLGFSVDKLQNIADANPEKQNLLGRPYALKTSK 2150
2151 EEAFISSCESAKTVCEMEALLGAHASANGVSRGSPRFPRARVDHKDVGTE 2200
2201 PRSDDDSPGDESYPRRPDNLKGLASFQRSQSTVASLGLAFPSQNGSAVAS 2250
2251 RWPSLVDRNADDWENFTFSPAYEASYNRATSTHSVIEDCLIPICCGLYEL 2300
2301 LSGVLLVLPDAMLEDVMDRIIQADILLVLVNHPSPAIQQGVIKLLHAYIN 2350
2351 RASKEQKDKFLKNRGFSLLANQLYLHRGTQELLECFVEMFFGRPIGLDEE 2400
2401 FDLEEVKHMELFQKWSVIPVLGLIETSLYDNVLLHNALLLLLQVLNSCSK 2450
2451 VADMLLDNGLLYVLCNTVAALNGLEKNIPVNEYKLLACDIQQLFIAVTIH 2500
2501 ACSSSGTQYFRVIEDLIVLLGYLHNSKNKRTQNMALALQLRVLQAALEFI 2550
2551 RSTANHDSESPVHSPSAHRHSVPPKRRSIAGSRKFPLAQTESLLMKMRSV 2600
2601 ASDELHSMMQRRMSQEHPSQASEAELAQRLQRLTILAVNRIIYQELNSDI 2650
2651 IDILRTPENTSQSKTSVSQTEISEEDMHHEQPSVYNPFQKEMLTYLLDGF 2700
2701 KVCIGSSKTSVSKQQWTKILGSCKETLRDQLGRLLAHILSPTHTVQERKQ 2750
2751 ILEIVHEPAHQDILRDCLSPSPQHGAKLVLYLSELIHNHQDELSEEEMDT 2800
2801 AELLMNALKLCGHKCIPPSAPSKPELIKIIREEQKKYESEESVSKGSWQK 2850
2851 TVNNNQQSLFQRLDFKSKDISKIAADITQAVSLSQGIERKKVIQHIRGMY 2900
2901 KVDLSASRHWQECIQQLTHDRAVWYDPIYYPTSWQLDPTEGPNRERRRLQ 2950
2951 RCYLTIPNKYLLRDRQKSEGVLRPPLSYLFEDKTHSSFSSTVKDKAASES 3000
3001 IRVNRRCISVAPSRETAGELLLGKCGMYFVEDNASDAVESSSLQGELEPA 3050
3051 SFSWTYEEIKEVHRRWWQLRDNAVEIFLTNGRTLLLAFDNNKVRDDVYQS 3100
3101 ILTNNLPNLLEYGNITALTNLWYSGQITNFEYLTHLNKHAGRSFNDLMQY 3150
3151 PVFPFILSDYVSETLDLNDPSIYRNLSKPIAVQYKEKEDRYVDTYKYLEE 3200
3201 EYRKGAREDDPMPPVQPYHYGSHYSNSGTVLHFLVRMPPFTKMFLAYQDQ 3250
3251 SFDIPDRTFHSTNTTWRLSSFESMTDVKELIPEFFYLPEFLVNREGFDFG 3300
3301 VRQNGERVNHVNLPPWARNDPRLFILIHRQALESDHVSQNICHWIDLVFG 3350
3351 YKQKGKASVQAINVFHPATYFGMDVSAVEDPVQRRALETMIKTYGQTPRQ 3400
3401 LFHTAHASRPGAKLNIEGELPAAVGLLVQFAFRETREPVKEVTHPSPLSW 3450
3451 IKGLKWGEYVGSPSAPVPVVCFSQPHGERFGSLQALPTRAICGLSRNFCL 3500
3501 LMTYNKEQGVRSMNNTNIQWSAILSWGYADNILRLKSKQSEPPINFIQSS 3550
3551 QQHQVTSCAWVPDSCQLFTGSKCGVITAYTNRLTSSTPSEIEMESQMHLY 3600
3601 GHTEEITGLCVCKPYSVMISVSRDGTCIVWDLNRLCYVQSLAGHKSPVTA 3650
3651 VSASETSGDIATVCDSAGGGSDLRLWTVNGDLVGHVHCREIICSVAFSNQ 3700
3701 PEGVSINVIAGGLENGIVRLWSTWDLKPVREITFPKSNKPIISLTFSCDG 3750
3751 HHLYTANSEGTVIAWCRKDQQRVKLPMFYSFLSSYAAG 3788
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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