 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8GY23 from www.uniprot.org...
The NucPred score for your sequence is 0.85 (see score help below)
1 MKLRRRRASEVPSKIKSFINSVTSVPLELIHEPLACFRWEFDKGDFHHWV 50
51 DLFNYFDTFFEKHVQVRKDLHIEENFEESDPPFPKDAVLQVLRVIRVVLE 100
101 NCTNKHFYSSYEQHLSLLLASTDADVVEACLQTLAAFLKRQIGKYSIRDA 150
151 SLNSKLFSLAQGWGGKEEGLGLTSCAAENSCDQVSLQLGRTLHFEFYPSD 200
201 ESPSELPGGLQVIHVPDVSICAESDLELLNKLVIDHNVPPSLRFALLTRM 250
251 RFARAFSSLATRQQFTCIRLYAFVVLVQASGDTENVVSFFNGEPEFVNEL 300
301 VTLVSYEDTVPEKIRILCLLSLVALSQDRTRQPTVLTAVTSGGHRGLLSG 350
351 LMQKAIDSVVCITSKWSLAFAEALLSLVTVLVSSSSGCSAMREAGLIPTL 400
401 VPLIKDTDPQHLHLVSAAVHILEAFMDYSNPAAALFRDLGGLDDTIFRLK 450
451 LEVSRTEDDVKEKNCSSDSNGPDTEQLPYSEALISYHRRLLLKALLRAIS 500
501 LGTYAPGNTNLYGSEESLLPECLCIIFRRAKDFGGGVFSLAATVMSDLIH 550
551 KDPTCFNALDSAGLTSTFLDAISDEVICSAEAITCIPQCLDALCLNNSGL 600
601 QAVKDRNALRCFVKIFTSPSYLRALTGDTPGSLSSGLDELLRHQSSLRTY 650
651 GVDMFIEILNSMLIIGSGMEATTSKSADVPTSAAPVPMEIDVDEKSLAVS 700
701 DEAEPSSDTSPANIELFLPDCVCNVARLFETVLQNAEVCSLFVEKKGIDA 750
751 VLQLFSLPLMPLSTSLGQSFSVAFKNFSPQHSAGLARIVCSYLREHLKKT 800
801 KILLVSIEGTQLLKLESAIQTKILRSLSCLEGMLSLSNFLLKGSASVISE 850
851 LSAADADVLKELGITYKQTIWQMALCNDTKEDEKKSVDRGSDNSVSASSS 900
901 TAERESDEDSSNALAVRYTNPVSIRSSSSQSIWGGDREFLSIVRSGEGIH 950
951 GRTRHAIARMRGGRTRRHLESFNFDSEIPADLPVTSSSHELKKKSTEVLI 1000
1001 AEILNKLNCTLRFFFTALVKGFTSANRRRIDGASLSSASKTLGTALAKVF 1050
1051 LEALNFDGYGAAAGHEKSLSVKCRYLGKVVDDITFLSFDTRRRVCFTAMV 1100
1101 NSFYVHGTFKELLTTFEATSQLLWTVPFSIPASSTENEKPGERNIWSHSK 1150
1151 WLVDTLQNYCRALDYFVNSTYLLSPTSQTQLLVQPASVGLSIGLFPVPRE 1200
1201 PETFVRNLQSQVLDVILPIWNHPMFPDCNPNFVASVTSLVTHIYSGVVDA 1250
1251 RENRSGVTRGINQRALPLQLDESIVGMIVEMGFSRSRAEIALRRVGTNSV 1300
1301 EMAMDWLFTNPEQPVQEDDELAQALALSLGNSSETPKLEDTEKPVDVPQE 1350
1351 EAEPKEPPVDEVIAASVKLFQSDDSMAFPLMDLFVTLCNRNKGEDRPKIV 1400
1401 SYLIQQLKLVQLDFSKDTGALTMIPHILALVLSEDDNTREIAAQDGIVTV 1450
1451 AIGILTDFNLKSESETEILAPKCISALLLVLSMMLQAQTKLSSEYVEGNQ 1500
1501 GGSLVPSDSPQDSTAALKDALSSDVAKGESNQALELIFGKSTGYLTMEEG 1550
1551 HKALLIACGLIKQHVPAMIMQAVLQLCARLTKSHALAIQFLENGGLSSLF 1600
1601 NLPKKCCFPGYDTVASVIVRHLVEDPQTLQIAMETEIRQTLSGKRHIGRV 1650
1651 LPRTFLTTMAPVISRDPVVFMKAVASTCQLESSGGRDFVILSKEKEKPKV 1700
1701 SGSEHGFSLNEPLGISENKLHDVSGKCSKSHRRVPANFIQVIDQLIDLVL 1750
1751 SFPRVKRQEDGETNLISMEVDEPTTKVKGKSKVGEPEKASSSRVGEPEKA 1800
1801 EIPEKSEELARVTFILKLLSDIVLMYSHGTSVILRRDTEISQLRGSNLPD 1850
1851 DSPGNGGLIYHVIHRLLPISLEKFVGPEEWKEKLSEKASWFLVVLCSRSN 1900
1901 EGRKRIINELSRVLSVFASLGRSSSKSVLLPDKRVLAFANLVYSILTKNS 1950
1951 SSSSSNFPGCGCSPDVAKSMMDGGTIQCLTSILHVIDLDHPDAPKLVTLI 2000
2001 LKSLETLTRAANAAEQLKSEVPNEKKNRDSDERHDSHGNSTETEADELNQ 2050
2051 NNSSLQQVTDAAGNGQEQAQVSSQSAGERGSSQTQAMPQDMRIEGDETIL 2100
2101 PEPIQMDFMREEIEGDQIEMSFHVENRADDDVDDDMGDEGEDDEGDDEDA 2150
2151 DLVEDGAGVMSLAGTDVEDPEDTGLGDEYNDDMVDEDDDDFHENRVIEVR 2200
2201 WREALDGLDHFQILGRSGGGNGFIDDITAEPFEGVNVDDLFALRRPLGFE 2250
2251 RRRQTGRSSLDRSGSEVHGFQHPLFSRPSQTGNTASVSASAGSISRHSEA 2300
2301 GSYDVAQFYMFDTPVLPFDQVPVDPFSARLAGGGAPPPLTDYSVVGMDSS 2350
2351 RRGVGDSRWTDIGHPQPSSLSASIAQLIEEHFISNLRASAPVNTVVERET 2400
2401 NTTEIQEQLHPDVPPSVGSETVLGDGNEGGQQSEERELLNNNENVNNPPD 2450
2451 VMAESFAQGQANLASPVSQDTGESLQQLEVMQPLPLNSTPNEIDRMEVGE 2500
2501 GDGAPIDQVDHEAVHLISTAQGQPDTSSIQNVSVTAIAPPVDDPDSNFQP 2550
2551 SVDVDMSSDGAEGNQSVQPSPLDGDNNELSSMEATENVRNDEQVEEGSLD 2600
2601 GRAPEVNAIDPTFLEALPEDLRAEVLASQQAQSVQPPTYEPPPVDDIDPE 2650
2651 FLAALPPDIQTEVLAQQRAQRMVQQSQGQAVDMDNASIIATLPADLREEV 2700
2701 LLTSSEAVLAALPSPLLAEAQMLRDRAMSHYQARSSVFGSSHRLNNRRNG 2750
2751 LGYNRLTGMDRGVGVTIGQRAVSSSADGLKVKEIEGDPLVNADALKSLIR 2800
2801 LLRLAQPLGKGLLQRLLLNLCAHSFTRANLVQLLLDMIRPEMETSPSELA 2850
2851 ITNPQRLYGCQSNVVYGRSQLLNGLPPLVFRRVLEVLTYLATNHSAVADM 2900
2901 LFYFDSSLLSQLSSRKGKEKVTHVTDSRDLEIPLVVFLKLLNRPQLLQST 2950
2951 SHLGLVMGLLQVVVYTAASRIEGWSPSSGVPEKLENKPVGEEASSETRKD 3000
3001 AESELVGEADLSVARRKNCAEIYNIFLQLPQSDLCNLCILLGYEGLSDKI 3050
3051 YSLAGEVLKKLAAVDVAHRKFFTKELSELASSLSSSTVRELATLSSKQKM 3100
3101 SRSTGSMAGASILRVLQVLSSLTSPIDESNVGTERETEQEEQNIMQRLNV 3150
3151 ALEPLWHELSQCISMTELQLDHTAAASNINPGDHVLGISPTSSLSPGTQR 3200
3201 LLPLIEAFFVLCEKIQTPSMLQQDTNVTAGEVKESSAHGSSSKTSVDSQK 3250
3251 KTDGSVTFSKFAEKHRRLLNSFIRQNPSLLEKSLSMMLKAPRLIDFDNKK 3300
3301 AYFRSRIRHQHDQHISGPLRISVRRAYVLEDSYNQLRMRSPQDLKGRLNV 3350
3351 QFQGEEGIDAGGLTREWYQLLSRVIFDKGALLFTTVGNDATFQPNPNSVY 3400
3401 QTEHLSYFKFVGRMVAKALFDGQLLDVYFTRSFYKHILGVKVTYHDIEAV 3450
3451 DPDYYKNLKWLLENDVSDILDLTFSMDADEEKHILYEKTEVTDYELKPGG 3500
3501 RNIRVTEETKHEYVDLVAGHILTNAIRPQINAFLEGFNELIPRELVSIFN 3550
3551 DKELELLISGLPEIDFDDLKANTEYTSYTAGSPVIHWFWEVVKAFSKEDM 3600
3601 ARFLQFVTGTSKVPLEGFKALQGISGPQRLQIHKAYGAPERLPSAHTCFN 3650
3651 QLDLPEYQSKEQLQERLLLAIHEASEGFGFA 3681
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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