 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8H0T4 from www.uniprot.org...
The NucPred score for your sequence is 0.88 (see score help below)
1 MKLRRRRASEVPTKISLFINSVTSVPLELIQEPLASFRWEFDKGDFHHWV 50
51 DLFYHFDTFFEKHVKVRKDLRIEEEFDESDPPFPKDAVLQVLRVIRLVLE 100
101 NCTNKQFYTSYEQHLSLLLASTDADVVEACLQTLAAFLKRPTGKYSIRDA 150
151 SLNLKLFSLAQGWGGKEEGLGLTSCATEHSCDQLFLQLGCTLLFEFYASD 200
201 ESPSELPGGLQVIHVPDVSMRSESDLELLNKLVIDHNVPPSLRFALLTRL 250
251 RFARAFSSLATRQQYTCIRLYAFIVLVQASGDTENVVSFFNGEPEFVNEL 300
301 VTLVSYEDTVPAKIRILCLQSLVALSQDRTRQPTVLTAVTSGGHRGLLSG 350
351 LMQKAIDSVICNTSKWSLAFAEALLSLVTVLVSSSSGCSAMREAGLIPTL 400
401 VPLIKDTDPQHLHLVSTAVHILEVFMDYSNPAAALFRDLGGLDDTIFRLK 450
451 QEVSRTEDDVKEIVCCSGSNGPEDDTEQLPYSEALISYHRRLLLKALLRA 500
501 ISLGTYAPGNTNLYGSEESLLPECLCIIFRRAKDFGGGVFSLAATVMSDL 550
551 IHKDPTCFNALDSAGLTSAFLDAISDEVICSAEAITCIPQCLDALCLNNS 600
601 GLQAVKDRNALRCFVKIFSSPSYLKALTSDTPGSLSSGLDELLRHQSSLR 650
651 TYGVDMFIEILNSILIIGSGMEATTSKSADVPTDAAPVPMEIDVDEKSLA 700
701 VSDEAEPSSDTSPANIELFLPDCVCNVARLFETVLQNAEVCSLFVEKKGI 750
751 DTVLQLFSLPLMPLSTSLGQSFSVAFKNFSPQHSAGLARILCSYLREHLK 800
801 KTNNLLVSIEGTQLLKLESAVQTKILRSLSCLEGMLSLSNFLLKGSASVI 850
851 SELSAANADVLKELGITYKQTIWQMALCNDTKEDEKKSVDRASDNSVSAS 900
901 SSTAERESDEDSSNALAVRYTNPVSIRSSSSQSIWGGHREFLSVVRSGRG 950
951 VHGHTRHAIARMRGGRTRRHLESFNFDSEIPADLPVTSSSHELKKKSTEV 1000
1001 LIAEILNKLNCTLRFFFTSLVKGFTSANRRRIDGPSLSSASKTLGTALAK 1050
1051 VFLEALNFQGYGAAAGPDTSLSLKCRYLGKVVDDITFLTFDTRRRVCFTA 1100
1101 MVNSFYVHGTFKELLTTFEATSQLLWKVPFSIRASSTENEKSGERNLWSH 1150
1151 SKWLVDTLQNYCRALDYFVNSTYLLSPTSQTQLLVQPASVDLSIGLFPVP 1200
1201 REPETFVRNLQSQVLEVILPIWNHPMFPDCNPNFVASVTSLVTHIYSGVV 1250
1251 DTRENRSGATQGTNQRALPLQPDEAIVGMIVEMGFSRSRAEDALRRVGTN 1300
1301 SVEMAMDWLFTNPEDPVQEDDELAQALALSLGNSSETPKLEDTEKPVDVP 1350
1351 QEEAEPKEPPVDEVIAASVKLFQSDDSIAFPLVDLFVTLCNRNKGEDRPK 1400
1401 IVFYLIQQLKLVQLDFSKDTGALTMIPHILALVLSEDDNTREIAAQDGIV 1450
1451 AVAIGILTDFNLKSESETDILAPKCISALLLVLSMMLQAQTRLSSEYVEG 1500
1501 NQGGSLVLSDSPQDSTAALKDALSSDVAKGESNQALESMFGKSTGYLTME 1550
1551 ESSKVLLIACGLIKQRVPAMIMQAVLQLCARLTKSHALAIQFLENGGLSS 1600
1601 LFNLPKKCFFPGYDTVASVIVRHLVEDPQTLQIAMETEIRQTLSGKRHIG 1650
1651 RVLPRTFLTTMAPVISRDPVVFMKAVASTCQLESSGGTDFVILTKEKEKP 1700
1701 KVSGSEHGFSLNEPLGISENKLHDGSGKCSKSHRRVPTNFIQVIDQLIDI 1750
1751 VLSFPGLKRQEGEAANLISMDVDEPTTKVKGKSKVGEPEKAELGSEKSEE 1800
1801 LARVTFILKLLSDIVLMYLHGTSVILRRDTEISQLRGSNLPDDSPGNGGL 1850
1851 IYHVIHRLLPISLEKFVGPEEWKEKLSEKASWFLVVLCSRSNEGRKRIIN 1900
1901 ELTRVLSVFASLGRSSSQSVLLPDKRVLAFANLVYSILTKNSSSSNFPGC 1950
1951 GCSPDVAKSMIDGGTIQCLTSILNVIDLDHPDAPKLVTLILKSLETLTRA 2000
2001 ANAAEQLKSEVPNEQKNTDSDERHDSHGTSTSTEVDELNQNNSSLQQVTD 2050
2051 AVDNGQEQPQVSSQSEGERGSSLTQAMLQEMRIEGDETILPEPIQMDFFR 2100
2101 EEIEGDQIEMSFHVEDRADDDVDDDMDDEGEDDEGDDEDADSVEDGAGVM 2150
2151 SIAGTDVEDPEDTGLGDEYNDDMVDEDEEDEDEYNDDMVDEDEDDEDEYN 2200
2201 DDMVDEDEDDFHETRVIEVRWREALDGLDHFQIVGRSGGGNGFIDDITAE 2250
2251 PFEGVNVDDLFALRRSLGFERRRQTGRSSFDRSGSEVHGFQHPLFSRPSQ 2300
2301 TGNTASVSASAGSISRHSEAGSYDVAQFYMFDSPVLPFDQVPVDPFSDRL 2350
2351 GGGGAPPPLTDYSVVGMDSSRRGVGDSRWTDVGHPQPSSLSASIAQLIEE 2400
2401 HFITNLRASAPVDTVVERETNTTEVQEQQQPDVPPSVGSETVLGDGNEGG 2450
2451 EQSEEHELLNNNEVMHPLPLNSTPNEIDRMEVGEGGGAPIEQVDREAVHL 2500
2501 ISSAQGQSDTSGIQNVSVTAIPPPVDDPDSNFQPSVDVDMSSDGAEGNQS 2550
2551 VQPSPLDGDNNELSSMEATQDVRNDEQVDEGSLDGRAPEVNAIDPTFLEA 2600
2601 LPEDLRAEVLASQQAQSVQPPTYEPPSVDDIDPEFLAALPPEIQREVLAQ 2650
2651 QRAQRMLQQSQGQPVDMDNASIIATLPADLREEVLLTSSEAVLAALPPPL 2700
2701 LAEAQMLRDRAMRHYQARSRVFGSSHRLNNRRNGLGYRLTGMERGVGVTI 2750
2751 GQRDVSSSADGLKVKEIEGDPLVNADALKSLIRLLRLAQPLGKGLLQRLL 2800
2801 LNLCAHSFTRANLVQLLLDMIRPEMETLPSELALTNPQRLYGCQLNVVYG 2850
2851 RSQLLNGLPPLVFRRVLEVLTYLATNHSAVADMLFYFDSSLLSQLSSRKG 2900
2901 KEKVTHETDSRDLEIPLVVFLKLLNRPQLLQSTSHLALVMGLLQVVVYTA 2950
2951 ASRIEGWSPSSGVPEKLENKPVGEEASSETQKDAESELSVARRKNCAELY 3000
3001 NIFLQLPQSDLCNLCMLLGYEGLSDKIYSLAGEVLKKLAAVDVTHRKFFT 3050
3051 KELSELASGLSSSTVRVLATLSTTQKMSQNTCSMAGASILRVLQVLSSLT 3100
3101 STIDDSNVGTDKETDQEEQNIMQGLKVALEPLWQELGQCISMTELQLDHT 3150
3151 AATSNVNPGDHVLGISPTSSLSPGTQSLLPLIEAFFVLCEKIQTPSMLQQ 3200
3201 DATVTAGEVKESSTHGSSSKTIVDSQKKIDGSVTFSKFVEKHRRLLNSFV 3250
3251 RQNPSLLEKSFSMMLKAPRLIDFDNKKAYFRSRIRHQHDQHISGPLRISV 3300
3301 RRAYVLEDSYNQLRMRSPQDLKGRLNVQFQGEEGIDAGGLTREWYQLLSR 3350
3351 VIFDKGALLFTTVGNDATFQPNPNSVYQTEHLSYFKFVGRMVAKALFDGQ 3400
3401 LLDVYFTRSFYKHILGVKVTYHDIEAVDPDYYKNLKWLLENDVSDILDLT 3450
3451 FSMDADEEKHILYEKTEVTDYELKPGGRNIRVTEETKHEYVDLVADHILT 3500
3501 SAIRPQINAFLEGLNELIPRELVSIFNDKELELLISGLPEIDFDDLKANT 3550
3551 EYTSYTVGSPVIRWFWEVVKAFSKEDMARFLQFVTGTSKVPLEGFKALQG 3600
3601 ISGPQRLQIHKAYGSPERLPSAHTCFNQLDLPEYQSKEQVQERLLLAIHE 3650
3651 ANEGFGFA 3658
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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