 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P13611 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MFINIKSILWMCSTLIVTHALHKVKVGKSPPVRGSLSGKVSLPCHFSTMP 50
51 TLPPSYNTSEFLRIKWSKIEVDKNGKDLKETTVLVAQNGNIKIGQDYKGR 100
101 VSVPTHPEAVGDASLTVVKLLASDAGLYRCDVMYGIEDTQDTVSLTVDGV 150
151 VFHYRAATSRYTLNFEAAQKACLDVGAVIATPEQLFAAYEDGFEQCDAGW 200
201 LADQTVRYPIRAPRVGCYGDKMGKAGVRTYGFRSPQETYDVYCYVDHLDG 250
251 DVFHLTVPSKFTFEEAAKECENQDARLATVGELQAAWRNGFDQCDYGWLS 300
301 DASVRHPVTVARAQCGGGLLGVRTLYRFENQTGFPPPDSRFDAYCFKPKE 350
351 ATTIDLSILAETASPSLSKEPQMVSDRTTPIIPLVDELPVIPTEFPPVGN 400
401 IVSFEQKATVQPQAITDSLATKLPTPTGSTKKPWDMDDYSPSASGPLGKL 450
451 DISEIKEEVLQSTTGVSHYATDSWDGVVEDKQTQESVTQIEQIEVGPLVT 500
501 SMEILKHIPSKEFPVTETPLVTARMILESKTEKKMVSTVSELVTTGHYGF 550
551 TLGEEDDEDRTLTVGSDESTLIFDQIPEVITVSKTSEDTIHTHLEDLESV 600
601 SASTTVSPLIMPDNNGSSMDDWEERQTSGRITEEFLGKYLSTTPFPSQHR 650
651 TEIELFPYSGDKILVEGISTVIYPSLQTEMTHRRERTETLIPEMRTDTYT 700
701 DEIQEEITKSPFMGKTEEEVFSGMKLSTSLSEPIHVTESSVEMTKSFDFP 750
751 TLITKLSAEPTEVRDMEEDFTATPGTTKYDENITTVLLAHGTLSVEAATV 800
801 SKWSWDEDNTTSKPLESTEPSASSKLPPALLTTVGMNGKDKDIPSFTEDG 850
851 ADEFTLIPDSTQKQLEEVTDEDIAAHGKFTIRFQPTTSTGIAEKSTLRDS 900
901 TTEEKVPPITSTEGQVYATMEGSALGEVEDVDLSKPVSTVPQFAHTSEVE 950
951 GLAFVSYSSTQEPTTYVDSSHTIPLSVIPKTDWGVLVPSVPSEDEVLGEP 1000
1001 SQDILVIDQTRLEATISPETMRTTKITEGTTQEEFPWKEQTAEKPVPALS 1050
1051 STAWTPKEAVTPLDEQEGDGSAYTVSEDELLTGSERVPVLETTPVGKIDH 1100
1101 SVSYPPGAVTEHKVKTDEVVTLTPRIGPKVSLSPGPEQKYETEGSSTTGF 1150
1151 TSSLSPFSTHITQLMEETTTEKTSLEDIDLGSGLFEKPKATELIEFSTIK 1200
1201 VTVPSDITTAFSSVDRLHTTSAFKPSSAITKKPPLIDREPGEETTSDMVI 1250
1251 IGESTSHVPPTTLEDIVAKETETDIDREYFTTSSPPATQPTRPPTVEDKE 1300
1301 AFGPQALSTPQPPASTKFHPDINVYIIEVRENKTGRMSDLSVIGHPIDSE 1350
1351 SKEDEPCSEETDPVHDLMAEILPEFPDIIEIDLYHSEENEEEEEECANAT 1400
1401 DVTTTPSVQYINGKHLVTTVPKDPEAAEARRGQFESVAPSQNFSDSSESD 1450
1451 THPFVIAKTELSTAVQPNESTETTESLEVTWKPETYPETSEHFSGGEPDV 1500
1501 FPTVPFHEEFESGTAKKGAESVTERDTEVGHQAHEHTEPVSLFPEESSGE 1550
1551 IAIDQESQKIAFARATEVTFGEEVEKSTSVTYTPTIVPSSASAYVSEEEA 1600
1601 VTLIGNPWPDDLLSTKESWVEATPRQVVELSGSSSIPITEGSGEAEEDED 1650
1651 TMFTMVTDLSQRNTTDTLITLDTSRIITESFFEVPATTIYPVSEQPSAKV 1700
1701 VPTKFVSETDTSEWISSTTVEEKKRKEEEGTTGTASTFEVYSSTQRSDQL 1750
1751 ILPFELESPNVATSSDSGTRKSFMSLTTPTQSEREMTDSTPVFTETNTLE 1800
1801 NLGAQTTEHSSIHQPGVQEGLTTLPRSPASVFMEQGSGEAAADPETTTVS 1850
1851 SFSLNVEYAIQAEKEVAGTLSPHVETTFSTEPTGLVLSTVMDRVVAENIT 1900
1901 QTSREIVISERLGEPNYGAEIRGFSTGFPLEEDFSGDFREYSTVSHPIAK 1950
1951 EETVMMEGSGDAAFRDTQTSPSTVPTSVHISHISDSEGPSSTMVSTSAFP 2000
2001 WEEFTSSAEGSGEQLVTVSSSVVPVLPSAVQKFSGTASSIIDEGLGEVGT 2050
2051 VNEIDRRSTILPTAEVEGTKAPVEKEEVKVSGTVSTNFPQTIEPAKLWSR 2100
2101 QEVNPVRQEIESETTSEEQIQEEKSFESPQNSPATEQTIFDSQTFTETEL 2150
2151 KTTDYSVLTTKKTYSDDKEMKEEDTSLVNMSTPDPDANGLESYTTLPEAT 2200
2201 EKSHFFLATALVTESIPAEHVVTDSPIKKEESTKHFPKGMRPTIQESDTE 2250
2251 LLFSGLGSGEEVLPTLPTESVNFTEVEQINNTLYPHTSQVESTSSDKIED 2300
2301 FNRMENVAKEVGPLVSQTDIFEGSGSVTSTTLIEILSDTGAEGPTVAPLP 2350
2351 FSTDIGHPQNQTVRWAEEIQTSRPQTITEQDSNKNSSTAEINETTTSSTD 2400
2401 FLARAYGFEMAKEFVTSAPKPSDLYYEPSGEGSGEVDIVDSFHTSATTQA 2450
2451 TRQESSTTFVSDGSLEKHPEVPSAKAVTADGFPTVSVMLPLHSEQNKSSP 2500
2501 DPTSTLSNTVSYERSTDGSFQDRFREFEDSTLKPNRKKPTENIIIDLDKE 2550
2551 DKDLILTITESTILEILPELTSDKNTIIDIDHTKPVYEDILGMQTDIDTE 2600
2601 VPSEPHDSNDESNDDSTQVQEIYEAAVNLSLTEETFEGSADVLASYTQAT 2650
2651 HDESMTYEDRSQLDHMGFHFTTGIPAPSTETELDVLLPTATSLPIPRKSA 2700
2701 TVIPEIEGIKAEAKALDDMFESSTLSDGQAIADQSEIIPTLGQFERTQEE 2750
2751 YEDKKHAGPSFQPEFSSGAEEALVDHTPYLSIATTHLMDQSVTEVPDVME 2800
2801 GSNPPYYTDTTLAVSTFAKLSSQTPSSPLTIYSGSEASGHTEIPQPSALP 2850
2851 GIDVGSSVMSPQDSFKEIHVNIEATFKPSSEEYLHITEPPSLSPDTKLEP 2900
2901 SEDDGKPELLEEMEASPTELIAVEGTEILQDFQNKTDGQVSGEAIKMFPT 2950
2951 IKTPEAGTVITTADEIELEGATQWPHSTSASATYGVEAGVVPWLSPQTSE 3000
3001 RPTLSSSPEINPETQAALIRGQDSTIAASEQQVAARILDSNDQATVNPVE 3050
3051 FNTEVATPPFSLLETSNETDFLIGINEESVEGTAIYLPGPDRCKMNPCLN 3100
3101 GGTCYPTETSYVCTCVPGYSGDQCELDFDECHSNPCRNGATCVDGFNTFR 3150
3151 CLCLPSYVGALCEQDTETCDYGWHKFQGQCYKYFAHRRTWDAAERECRLQ 3200
3201 GAHLTSILSHEEQMFVNRVGHDYQWIGLNDKMFEHDFRWTDGSTLQYENW 3250
3251 RPNQPDSFFSAGEDCVVIIWHENGQWNDVPCNYHLTYTCKKGTVACGQPP 3300
3301 VVENAKTFGKMKPRYEINSLIRYHCKDGFIQRHLPTIRCLGNGRWAIPKI 3350
3351 TCMNPSAYQRTYSMKYFKNSSSAKDNSINTSKHDHRWSRRWQESRR 3396
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.) |
Go back to the NucPred Home Page.