 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q03280 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MVLFTRCEKARKEKLAAGYKPLVDYLIDCDTPTFLERIEAIQEWDRSRDD 50
51 LYVWIPILDRMDGLLLKVAEKYKYKQDPKKECEVKLVEMEAHDVDYCLKM 100
101 LKFTRRLLLNTENRFVYSSGDVLMYLLNCPNFTIKLAVMRILAILGERFV 150
151 IAREKIVAHNIFGDHNLRKKTLKLALSLSSSVMDEDGEHFSLVDLYFDKK 200
201 KVPQKWRKLRFTHYTSNDFKKSSQQKNNINETQTSIKKVTMTTQELCEHS 250
251 LQQIFDKGMALLPAESWFDFSIKASVAKAFSDDSGENIDLRNIIIETKLN 300
301 AIAFVNTIFSPPQVSSKLFELDPYAFNSLTDLISLSETKIPKELRTDALF 350
351 TLECISLKHVWCSDIIRNLGGNISHGLLFQILRYIAKTLREATDEIDEEY 400
401 NVRFFYLISNLADVKPLHESLFAAGLIPTLLEIVSIRNCPYKRTLASATH 450
451 LLETFIDNSETTTEFIENDGFTMLITSVANEIDFTLAHPETWQPPKYSVV 500
501 YYSISFRELAYIRSLLKLVLKLLSTDSGDRIRNLIDSPILVSLKKILENK 550
551 LVFGLTLITYTLDVVQKVINSEPTIYPVLVEAGLIPYVIDNFPKLIGPSA 600
601 ELLSLLPDVVSAICLNPEGLKQVKEKGLINNLFDFLLDADHARILTGGDR 650
651 STEYGTDIDELARHYPDLKANIVEALCNVIRKMPSTFRNEREFLFTSPKD 700
701 QKYFFHRKNEEILTDKEEHEPAYWELLDKGTMLDTFTSVLFGMSLGNGSF 750
751 SQVPQHLEARDFLAIIFMENPPYEYFTSVAISNVTEVLQYLDEKYEDYAF 800
801 MDVMKVLNDQLENLNDFLNSPNDRSFFLERDGENSVRSCHSKLCRLAAIL 850
851 NIVTNVYIDLTTLSCKRIMQIYSYFDKRGFSLIKNLKLLFQKCALEEMYI 900
901 RQHMPDSVITETMPLPIVDVSGDGPPLQIYIDDPKKGDQKGKITSVKTRN 950
951 TLQMRTILYTLQSNTAILFRCFLRLSHSRNMDLEHKDLTTEVHIFENVVE 1000
1001 NVIEMLKATELEGHLPYILVLLNFNTFVFTIPKASPNSTEILQTIPAYIF 1050
1051 YQKGGYLLYLHIIRDLFTRMTKIKDLSSLDNINYIDESNGILTLSCLINA 1100
1101 LTFYNKSMQTETMENVQSIGKYYVSIDDDYNIMKALTVPIKVMALAMILD 1150
1151 LDKSDSLFKTQSRNVPYSVFKQLLSMLKNIFTNVNIYTKELYELHWDLIF 1200
1201 PPIKKISLFEQVGIPGDVAANYLTDTGDDLPADNSIGLFSPEQWEKYKKL 1250
1251 IGEDKSIYYPQPMQAQYYKGCSSKELDELRDTFFNDGLPSRIFTVLPFYP 1300
1301 KLVNAFAKTLLQIFTKYDEPTEVFAGRILDRILETDLDDPATLSSLIHLF 1350
1351 GIFLNEKYIYQKASHLMQRFIEYLEKSLKPEHVNTPWFSKALYVYEIILA 1400
1401 KSELPHLEELSKDVLLRYPLLSMAKVFRIPDPMKQKLFDILIRVSDISNF 1450
1451 YSALATSRILIFYSRDELYANNIARSGILSRLLKVIGSFQKLDKINFLES 1500
1501 SFLLLTRRCFETTENVDALIRAEINRSFTARPLGGGDDAVRELTTILEEK 1550
1551 AHVVMRSPSQFIDVLCETARFHEFDDQGALVDYSLKRFLGEKDKNTQASS 1600
1601 TEKSDIYERTGIMHLLLSQLMAASEKDWLSEPANSSDLPENKKAQLDPSR 1650
1651 NPVCAYMIFLLKLLVELVSSYNQCKFEFLTFSRRNTYAERPRPRTTAINF 1700
1701 FLYRLLDKPVGTDHDKHEAKRREVIGMLARSVIIGFLATVQDDRTTKTDV 1750
1751 KLADPHMNFIRKFAIEAIIKAIRNATSSSKLLESNHLKLDMWFRIITSMV 1800
1801 YVQAPYLRQLLDSNKVEADQYQLCKLVIDLGLPSVITEAMASIDLNYPFS 1850
1851 KKIFNVAVEALNTISSTRNNFSEHFKIEDHDEVEDEVDESDKEEIPDMFK 1900
1901 NSALGMYDVEDIEEDDDDDTSLIGDDDAMAFVDSDNGFEVVFSDEDDDMG 1950
1951 EEDADDARSDSEENELSSEMQSSTADGTDVDYEVDDADGLIINIDQPSGD 2000
2001 DEEMADYDANISHSSHSENEDDASMDVIEVYDDELSSGYDVDLSDYDVDE 2050
2051 SDWDSGLSSLSISDEDSESSEDEPINSTRMGDSRRRWLIAEGVELTDDSQ 2100
2101 GESEEDDRGVFRGIEHIFSNENEPLFRVHDEMRHRNHHRSINRTHFHSAM 2150
2151 SAPSLSLLNRGRRNQSNLINPLGPTGLEQVENDISDQVTVAGSGSRPRSH 2200
2201 HLHFSEVLVSGSFFDEPVLDGIILKSTVSRWKDIFDMFYDSKTYANCIIP 2250
2251 TVINRLYKVSLALQKDLENKREQEKLKNKNLLFNEAKVESHNSSDAISVE 2300
2301 QDDIQESNVTHDDHEPVYVTIQGSEVDIGGTDIDPEFMNALPDDIRADVF 2350
2351 AQHVRERRAEARLNSDHNVHSREIDSDFLEAIPEDIREGILDTEAEEQRM 2400
2401 FGRIGSSADVIRADDDVSNNDEEVENGLDHGNSNDRNNADPEKKKPARIY 2450
2451 FAPLIDRAGIASLMKSVFISKPYIQREIYHELFYRLCSSKQNRNDLMNTF 2500
2501 LFILSEGIIDQHSLEKVYNIISSRAMGHAKTTTVRQLPSDCTPLTVANQT 2550
2551 IEILQSLIDADSRLKYFLIAEHDNLIVNKANNKSRKEALPDKKLRWPLWH 2600
2601 LFSLLDRKLITDESVLMDLLTRILQVCTKTLAVLSTSSNGKENLSKKFHL 2650
2651 PSFDEDDLMKILSIIMLDSCTTRVFQQTLNIIYNLSKLQGCMSIFTKHLV 2700
2701 SLAISIMSKLKSALDGLSREVGTITTGMEINSELLQKFTLPSSDQAKLLK 2750
2751 ILTTVDFLYTHKRKEEERNVKDLQSLYDKMNGGPVWSSLSECLSQFEKSQ 2800
2801 AINTSATILLPLIESLMVVCRRSDLSQNRNTAVKYEDAKLLDFSKTRVEN 2850
2851 LFFPFTDAHKKLLNQMIRSNPKLMSGPFALLVKNPKVLDFDNKRYFFNAK 2900
2901 LKSDNQERPKLPITVRREQVFLDSYRALFFKTNDEIKNSKLEITFKGESG 2950
2951 VDAGGVTREWYQVLSRQMFNPDYALFLPVPSDKTTFHPNRTSGINPEHLS 3000
3001 FFKFIGMIIGKAIRDQCFLDCHFSREVYKNILGRPVSLKDMESLDPDYYK 3050
3051 SLVWILENDITDIIEETFSVETDDYGEHKVINLIEGGKDIIVTEANKQDY 3100
3101 VKKVVEYKLQTSVKEQMDNFLVGFYALISKDLITIFDEQELELLISGLPD 3150
3151 IDVDDWKNNTTYVNYTATCKEVSYFWRAVRSFDAEERAKLLQFVTGTSKV 3200
3201 PLNGFKELSGVNGVCKFSIHRDFGSSERLPSSHTCFNQLNLPPYESYETL 3250
3251 RGSLLLAINEGHEGFGLA 3268
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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