 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02388 from www.uniprot.org...
The NucPred score for your sequence is 0.22 (see score help below)
1 MTLRLLVAALCAGILAEAPRVRAQHRERVTCTRLYAADIVFLLDGSSSIG 50
51 RSNFREVRSFLEGLVLPFSGAASAQGVRFATVQYSDDPRTEFGLDALGSG 100
101 GDVIRAIRELSYKGGNTRTGAAILHVADHVFLPQLARPGVPKVCILITDG 150
151 KSQDLVDTAAQRLKGQGVKLFAVGIKNADPEELKRVASQPTSDFFFFVND 200
201 FSILRTLLPLVSRRVCTTAGGVPVTRPPDDSTSAPRDLVLSEPSSQSLRV 250
251 QWTAASGPVTGYKVQYTPLTGLGQPLPSERQEVNVPAGETSVRLRGLRPL 300
301 TEYQVTVIALYANSIGEAVSGTARTTALEGPELTIQNTTAHSLLVAWRSV 350
351 PGATGYRVTWRVLSGGPTQQQELGPGQGSVLLRDLEPGTDYEVTVSTLFG 400
401 RSVGPATSLMARTDASVEQTLRPVILGPTSILLSWNLVPEARGYRLEWRR 450
451 ETGLEPPQKVVLPSDVTRYQLDGLQPGTEYRLTLYTLLEGHEVATPATVV 500
501 PTGPELPVSPVTDLQATELPGQRVRVSWSPVPGATQYRIIVRSTQGVERT 550
551 LVLPGSQTAFDLDDVQAGLSYTVRVSARVGPREGSASVLTVRREPETPLA 600
601 VPGLRVVVSDATRVRVAWGPVPGASGFRISWSTGSGPESSQTLPPDSTAT 650
651 DITGLQPGTTYQVAVSVLRGREEGPAAVIVARTDPLGPVRTVHVTQASSS 700
701 SVTITWTRVPGATGYRVSWHSAHGPEKSQLVSGEATVAELDGLEPDTEYT 750
751 VHVRAHVAGVDGPPASVVVRTAPEPVGRVSRLQILNASSDVLRITWVGVT 800
801 GATAYRLAWGRSEGGPMRHQILPGNTDSAEIRGLEGGVSYSVRVTALVGD 850
851 REGTPVSIVVTTPPEAPPALGTLHVVQRGEHSLRLRWEPVPRAQGFLLHW 900
901 QPEGGQEQSRVLGPELSSYHLDGLEPATQYRVRLSVLGPAGEGPSAEVTA 950
951 RTESPRVPSIELRVVDTSIDSVTLAWTPVSRASSYILSWRPLRGPGQEVP 1000
1001 GSPQTLPGISSSQRVTGLEPGVSYIFSLTPVLDGVRGPEASVTQTPVCPR 1050
1051 GLADVVFLPHATQDNAHRAEATRRVLERLVLALGPLGPQAVQVGLLSYSH 1100
1101 RPSPLFPLNGSHDLGIILQRIRDMPYMDPSGNNLGTAVVTAHRYMLAPDA 1150
1151 PGRRQHVPGVMVLLVDEPLRGDIFSPIREAQASGLNVVMLGMAGADPEQL 1200
1201 RRLAPGMDSVQTFFAVDDGPSLDQAVSGLATALCQASFTTQPRPEPCPVY 1250
1251 CPKGQKGEPGEMGLRGQVGPPGDPGLPGRTGAPGPQGPPGSATAKGERGF 1300
1301 PGADGRPGSPGRAGNPGTPGAPGLKGSPGLPGPRGDPGERGPRGPKGEPG 1350
1351 APGQVIGGEGPGLPGRKGDPGPSGPPGPRGPLGDPGPRGPPGLPGTAMKG 1400
1401 DKGDRGERGPPGPGEGGIAPGEPGLPGLPGSPGPQGPVGPPGKKGEKGDS 1450
1451 EDGAPGLPGQPGSPGEQGPRGPPGAIGPKGDRGFPGPLGEAGEKGERGPP 1500
1501 GPAGSRGLPGVAGRPGAKGPEGPPGPTGRQGEKGEPGRPGDPAVVGPAVA 1550
1551 GPKGEKGDVGPAGPRGATGVQGERGPPGLVLPGDPGPKGDPGDRGPIGLT 1600
1601 GRAGPPGDSGPPGEKGDPGRPGPPGPVGPRGRDGEVGEKGDEGPPGDPGL 1650
1651 PGKAGERGLRGAPGVRGPVGEKGDQGDPGEDGRNGSPGSSGPKGDRGEPG 1700
1701 PPGPPGRLVDTGPGAREKGEPGDRGQEGPRGPKGDPGLPGAPGERGIEGF 1750
1751 RGPPGPQGDPGVRGPAGEKGDRGPPGLDGRSGLDGKPGAAGPSGPNGAAG 1800
1801 KAGDPGRDGLPGLRGEQGLPGPSGPPGLPGKPGEDGKPGLNGKNGEPGDP 1850
1851 GEDGRKGEKGDSGASGREGRDGPKGERGAPGILGPQGPPGLPGPVGPPGQ 1900
1901 GFPGVPGGTGPKGDRGETGSKGEQGLPGERGLRGEPGSVPNVDRLLETAG 1950
1951 IKASALREIVETWDESSGSFLPVPERRRGPKGDSGEQGPPGKEGPIGFPG 2000
2001 ERGLKGDRGDPGPQGPPGLALGERGPPGPSGLAGEPGKPGIPGLPGRAGG 2050
2051 VGEAGRPGERGERGEKGERGEQGRDGPPGLPGTPGPPGPPGPKVSVDEPG 2100
2101 PGLSGEQGPPGLKGAKGEPGSNGDQGPKGDRGVPGIKGDRGEPGPRGQDG 2150
2151 NPGLPGERGMAGPEGKPGLQGPRGPPGPVGGHGDPGPPGAPGLAGPAGPQ 2200
2201 GPSGLKGEPGETGPPGRGLTGPTGAVGLPGPPGPSGLVGPQGSPGLPGQV 2250
2251 GETGKPGAPGRDGASGKDGDRGSPGVPGSPGLPGPVGPKGEPGPTGAPGQ 2300
2301 AVVGLPGAKGEKGAPGGLAGDLVGEPGAKGDRGLPGPRGEKGEAGRAGEP 2350
2351 GDPGEDGQKGAPGPKGFKGDPGVGVPGSPGPPGPPGVKGDLGLPGLPGAP 2400
2401 GVVGFPGQTGPRGEMGQPGPSGERGLAGPPGREGIPGPLGPPGPPGSVGP 2450
2451 PGASGLKGDKGDPGVGLPGPRGERGEPGIRGEDGRPGQEGPRGLTGPPGS 2500
2501 RGERGEKGDVGSAGLKGDKGDSAVILGPPGPRGAKGDMGERGPRGLDGDK 2550
2551 GPRGDNGDPGDKGSKGEPGDKGSAGLPGLRGLLGPQGQPGAAGIPGDPGS 2600
2601 PGKDGVPGIRGEKGDVGFMGPRGLKGERGVKGACGLDGEKGDKGEAGPPG 2650
2651 RPGLAGHKGEMGEPGVPGQSGAPGKEGLIGPKGDRGFDGQPGPKGDQGEK 2700
2701 GERGTPGIGGFPGPSGNDGSAGPPGPPGSVGPRGPEGLQGQKGERGPPGE 2750
2751 RVVGAPGVPGAPGERGEQGRPGPAGPRGEKGEAALTEDDIRGFVRQEMSQ 2800
2801 HCACQGQFIASGSRPLPSYAADTAGSQLHAVPVLRVSHAEEEERVPPEDD 2850
2851 EYSEYSEYSVEEYQDPEAPWDSDDPCSLPLDEGSCTAYTLRWYHRAVTGS 2900
2901 TEACHPFVYGGCGGNANRFGTREACERRCPPRVVQSQGTGTAQD 2944
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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