 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q10064 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MEDGFEKSLSSSIELLKAESTEIEEKIKLTKILITKVSSISSDASQLYAE 50
51 LIPLLLDFLRNTEVNLYRNSSVNELKIEALTLIQSCAHRDEFKQYAQSCV 100
101 LSFISLIKADNEEVAVFCLKVIMDIFKTFKFCIESTAQPFFDLVLELSTN 150
151 LPYLIPSIFVENPKSNEEENTTLAFGSYLSTETSIIQQRVNSLAISTQPL 200
201 ELASQSFRVYVECPVIIVLILQAYRQAAFPGVQAIIPCFLKMVQIDVPID 250
251 IASYAMIEKDSSIDFIEFIRNKYQYRNFFMAQVKTLSFLAYILRTHPNTL 300
301 SEKDIIPDIVIKLLRRCPFDMCFARKELLVATRHILSTNLKSLFVKKLDF 350
351 LLDLNILLGNGVGTQKLLRPLAFSTLADLLHHVRDELNETQIRKSIMIYS 400
401 TNMHDLTLSIGLQTMGARLILNMVDRMISLPSIPDAIFLLLSIFDSFVNK 450
451 FSELNDSLDQFFKKKYEEEIKETKSPTRSSPRDLSSFSTSVNDGSFLFKN 500
501 LMFGLRALMYGLRTCKSRCIEIGGEQFSGFLTNIKPFEAVTFQKLFFEVG 550
551 KGFSYFRPEQVYLETFFCCEEESLDRPAISTLPRNKDEKDCLEVFATIFI 600
601 HLEPSIFLKVFETNLPTFFDQLKKNLTLFHIPQFLLSNESTSSKFLNILL 650
651 RFLLSRIEELGSSDIRHGSVLLRLFRLSFVTVSMFATENEPVLRPYVSEI 700
701 IVKCMKLAPNSANSLNYYYLLRALFRGIGGGRFESLYKEVMPLLHALLEA 750
751 FNSLLISARTPKEKDLFTELCLTIPVRLSLLLPYMSYLMRPLVMSLKSSQ 800
801 ELVSQGLRTFELCLDNLTPDFLDPIMAPYIEDLMNALWSHLQPLPYNYNH 850
851 SHTALKILGKLGGRNRKLLDRVQSLKNSPEPNNDFTLLLSIKGVKQPQLL 900
901 HYTQYVDEAVNLLSSPSSDLEVKQQAFTYVCNISKLYVYKSDATNSLASS 950
951 IRCTADKISKSNFDFRRPYSVIPSRMTGRSSFTQLSDDSDETIILASATY 1000
1001 GLFFATTVDELREEAYFWLEKLAVNVIVHDIFYAFDVIQGNHSKFTTNLQ 1050
1051 KEVIISPHYFAHCLSEVVCNDNSKMGDAVKHVLKFMFSFLESIFENPERA 1100
1101 FILPIFEVLLSDFRHKCYDPHWYKKYGGCFGLECLIEQDHSSKWLFDRQV 1150
1151 DILTALFFTLKDTTSEVPTVCKDHVMDVLKQLFRKIYASKDTEIAPGILG 1200
1201 HLVLELSNHNSVVRSSTQKLLSLLSELSNTPIVKLVSPFKERLLSPIFAK 1250
1251 PLRALPFHIQIGHIDAVNYCISLGSELITFSDELIRLIHETTALAEADDD 1300
1301 ALIGIGKTSHFKNAALLIRLRVVCVELLSTCILKIDFNNPQHAHLREGII 1350
1351 VVFFKSLYAKSKELIEAASLGLKNALQEDQKLSKELLQTTLRPILYNISD 1400
1401 YRRLSVAGLEGLGRLLRLLTNYFKVEIGRKLLDHLKALLENVDFQKVSSL 1450
1451 PLFCQTEMKIVRALIDLFHLLPNNANRFMDELLICVVEFSLRLQRTFPNY 1500
1501 FSEPLLKYVNRYPEDAWKFFMVRYDEAAFTSVFVELLRLKDSDPLLSVVK 1550
1551 DNWLFFQTILTNEINTVTANRYSFALDSAIVILQRDPSFFKDKNDFFRGS 1600
1601 MDAVLAISHLVENESILESMVFWNDLLVRTSTFLLEVYDLCIYNYDDGLK 1650
1651 LLSCFHMYKNSLAKNLVSDLTAHLVKKIEEPDLENNVKLILNLILSKDYG 1700
1701 FLLKENLAGILLTYLNQNVSSLEKCNQIFSIFYEVFFQHPSTNVYANDEG 1750
1751 IKIGALQIISFFLKNVPEITVQHQTEMLKMCSLFGNSEDVMIKQLSIYVM 1800
1801 SLFILRSQFPYELVNVVYMALLKSSPIEVRHLVKSSFDNIFSYIFSEEPE 1850
1851 SKKSPIWFELPLQVISSQSQNISQLLNVYDFISSHSDIFIEHRGRYVPIL 1900
1901 IDSLYKFGAIPNPNPEIRALSLGLIKVLLEWNDLQLKVDQKEIFSNNQKR 1950
1951 AILSYLFRFVCLFSEPFTEGLCSEAISLLERLLSSGTWASLGMKLSFFTK 2000
2001 SITHFDATDANSVMFANSLRTLSIVVGHSDSAWIEENLSDLKFLLEKSLE 2050
2051 NESVGVQSAIGNFVSTILTLSNTHPSIAGNPIFNDIWTSIASWTERRLQS 2100
2101 CSQIEVTLPCVECFFKYKKDALHTLLPGFMRCFHKVAKEFLSLGSQPSGN 2150
2151 SLNLQIVNAVDERVSILKSMIELGCSYISYLGDQRRWFLSALVQIIEKSS 2200
2201 SYEICNYLLEIVRGWIMNSPVPVPTVKEKAALLLKMVTFEGRFSQNEQND 2250
2251 LFNKYLSFIADIYEMEPYKNSELTFRLEAVYLLGTRVANKKLKERFIKGL 2300
2301 NSSFPSDLFSRFQFLLGSQHWESLSNTYWIVQLNIFLSRCFDLNQRCQFY 2350
2351 KKPKLFSCFSIYCREFDEDLTSQAQDTEMLHNNLLKYGIIDFNQNSMLVS 2400
2401 DFVLPVLSLQFSNSKIAEYLWRDFFNASVCSFTKDEIPLCIGSIISFLSR 2450
2451 EYHIRLLGKTPNVLETILTSIVSSDMPIPLPPHLLVYLSKTYGLHHYCIL 2500
2501 LLENSLQNNPGLSEDELTVYHKSCLDALSDIYYSLDEHDLYHGLWRRRAN 2550
2551 FLETEVATSHEQCHEWEKAQLVYEHAQLKVCTGSLPYSPTEHGFWLDHWI 2600
2601 LCAQKLNQWDVLFDFSKQEGCAELYLECAWRLSDWSTEQDTLEKATKSLS 2650
2651 PFTSLRRHTADALLYLNKTQRKMGSVTEFSRIIDECMQFSLRRWQQLPKR 2700
2701 VYQSHVSLLHHFQEIVELQEAFGIYSQLNDTNIHNIDNKLRDIKVVLQGW 2750
2751 RERLPNVWDDIDIWSDLIAWRQSVFKSINKVFLPLVSIAQQSTNKSNTNS 2800
2801 VSYLYRGYHELAWIINRFAHVARVHHLPEVCINQLTKIYTLPNIEIQEAF 2850
2851 LKLREQAECHYESPSEMQLGLEVINNTNLMYFRNRQKAEFFTLKGMFQNR 2900
2901 LGEKDEANQAFATAVQIDIGSGKAWSEWGLYHDELFQANPQEIHHACNAV 2950
2951 SCFLQASSLLSSSNSKPLLTRVLWLLSVDDSHGSVSEVVSSFKSEIPTWN 3000
3001 WIPFIPQLLSALSHRESIHARAILIQIAKTYPQSLHFQLRTAYEDFLMLK 3050
3051 KQQAANVLRGNSRLRENDSSSDNKSKDLSPSGSFSSVSQFNSKNGSPSSI 3100
3101 DSSEKHQISTVKPAWELIADVTSILKTAYPLLALTMETMVDQIHTRLKSF 3150
3151 PEEDAYRLIVALLNDGLQYISRLGVVSKNTFQLPMSQANIQRFAENVLPV 3200
3201 SVREAFLRDFVETKLDLLTYVDKLRMWRKKFENILDQRPKFLHLEQCSLY 3250
3251 LSEFQHQKFDEVEIPGQYLLDKNNNNDFVRLERFVPNVDLVRGHTMCYKR 3300
3301 LTLRGYDGKLYPFALQYPATRHSRREERMLQLLGTFNTVLRSKIEIQNRN 3350
3351 FSFQIPSSIPLSSHMRIIADKPSYVTMQTISDEYCKNRGMPLDYGIRFYF 3400
3401 DRLQTGLIQLKRASASMLSNSTVEEKKQIFRQRALQLRMQLLETLNSSVF 3450
3451 PESIYYDYFYKTFERYCDFWFFRRTFTTQYAYMIIMTYVFNIGGRSPQKL 3500
3501 FIVKDSGQVMSQDLLPSMTSNQPVFHNTEAVPFRLTPPIQYLISDLGVEG 3550
3551 LLSGLVMSIAQSLSSPTTDIKQYLSLYVRDEVFWWSKQQRKPIPQGIQLF 3600
3601 ETVKVNVELLFRRISVISHNVPEDLPLNQTLVDLVSQATNPQQLAQMDQL 3650
3651 WQAWL 3655
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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