 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P78509 from www.uniprot.org...
The NucPred score for your sequence is 0.61 (see score help below)
1 MERSGWARQTFLLALLLGATLRARAAAGYYPRFSPFFFLCTHHGELEGDG 50
51 EQGEVLISLHIAGNPTYYVPGQEYHVTISTSTFFDGLLVTGLYTSTSVQA 100
101 SQSIGGSSAFGFGIMSDHQFGNQFMCSVVASHVSHLPTTNLSFIWIAPPA 150
151 GTGCVNFMATATHRGQVIFKDALAQQLCEQGAPTDVTVHPHLAEIHSDSI 200
201 ILRDDFDSYHQLQLNPNIWVECNNCETGEQCGAIMHGNAVTFCEPYGPRE 250
251 LITTGLNTTTASVLQFSIGSGSCRFSYSDPSIIVLYAKNNSADWIQLEKI 300
301 RAPSNVSTIIHILYLPEDAKGENVQFQWKQENLRVGEVYEACWALDNILI 350
351 INSAHRQVVLEDSLDPVDTGNWLFFPGATVKHSCQSDGNSIYFHGNEGSE 400
401 FNFATTRDVDLSTEDIQEQWSEEFESQPTGWDVLGAVIGTECGTIESGLS 450
451 MVFLKDGERKLCTPSMDTTGYGNLRFYFVMGGICDPGNSHENDIILYAKI 500
501 EGRKEHITLDTLSYSSYKVPSLVSVVINPELQTPATKFCLRQKNHQGHNR 550
551 NVWAVDFFHVLPVLPSTMSHMIQFSINLGCGTHQPGNSVSLEFSTNHGRS 600
601 WSLLHTECLPEICAGPHLPHSTVYSSENYSGWNRITIPLPNAALTRNTRI 650
651 RWRQTGPILGNMWAIDNVYIGPSCLKFCSGRGQCTRHGCKCDPGFSGPAC 700
701 EMASQTFPMFISESFGSSRLSSYHNFYSIRGAEVSFGCGVLASGKALVFN 750
751 KDGRRQLITSFLDSSQSRFLQFTLRLGSKSVLSTCRAPDQPGEGVLLHYS 800
801 YDNGITWKLLEHYSYLSYHEPRIISVELPGDAKQFGIQFRWWQPYHSSQR 850
851 EDVWAIDEIIMTSVLFNSISLDFTNLVEVTQSLGFYLGNVQPYCGHDWTL 900
901 CFTGDSKLASSMRYVETQSMQIGASYMIQFSLVMGCGQKYTPHMDNQVKL 950
951 EYSTNHGLTWHLVQEECLPSMPSCQEFTSASIYHASEFTQWRRVIVLLPQ 1000
1001 KTWSSATRFRWSQSYYTAQDEWALDSIYIGQQCPNMCSGHGSCDHGICRC 1050
1051 DQGYQGTECHPEAALPSTIMSDFENQNGWESDWQEVIGGEIVKPEQGCGV 1100
1101 ISSGSSLYFSKAGKRQLVSWDLDTSWVDFVQFYIQIGGESASCNKPDSRE 1150
1151 EGVLLQYSNNGGIQWHLLAEMYFSDFSKPRFVYLELPAAAKTPCTRFRWW 1200
1201 QPVFSGEDYDQWAVDDIIILSEKQKQIIPVINPTLPQNFYEKPAFDYPMN 1250
1251 QMSVWLMLANEGMVKNETFCAATPSAMIFGKSDGDRFAVTRDLTLKPGYV 1300
1301 LQFKLNIGCANQFSSTAPVLLQYSHDAGMSWFLVKEGCYPASAGKGCEGN 1350
1351 SRELSEPTMYHTGDFEEWTRITIVIPRSLASSKTRFRWIQESSSQKNVPP 1400
1401 FGLDGVYISEPCPSYCSGHGDCISGVCFCDLGYTAAQGTCVSNVPNHNEM 1450
1451 FDRFEGKLSPLWYKITGAQVGTGCGTLNDGKSLYFNGPGKREARTVPLDT 1500
1501 RNIRLVQFYIQIGSKTSGITCIKPRTRNEGLIVQYSNDNGILWHLLRELD 1550
1551 FMSFLEPQIISIDLPQDAKTPATAFRWWQPQHGKHSAQWALDDVLIGMND 1600
1601 SSQTGFQDKFDGSIDLQANWYRIQGGQVDIDCLSMDTALIFTENIGKPRY 1650
1651 AETWDFHVSASTFLQFEMSMGCSKPFSNSHSVQLQYSLNNGKDWHLVTEE 1700
1701 CVPPTIGCLHYTESSIYTSERFQNWKRITVYLPLSTISPRTRFRWIQANY 1750
1751 TVGADSWAIDNVVLASGCPWMCSGRGICDAGRCVCDRGFGGPYCVPVVPL 1800
1801 PSILKDDFNGNLHPDLWPEVYGAERGNLNGETIKSGTSLIFKGEGLRMLI 1850
1851 SRDLDCTNTMYVQFSLRFIAKSTPERSHSILLQFSISGGITWHLMDEFYF 1900
1901 PQTTNILFINVPLPYTAQTNATRFRLWQPYNNGKKEEIWIVDDFIIDGNN 1950
1951 VNNPVMLLDTFDFGPREDNWFFYPGGNIGLYCPYSSKGAPEEDSAMVFVS 2000
2001 NEVGEHSITTRDLNVNENTIIQFEINVGCSTDSSSADPVRLEFSRDFGAT 2050
2051 WHLLLPLCYHSSSHVSSLCSTEHHPSSTYYAGTMQGWRREVVHFGKLHLC 2100
2101 GSVRFRWYQGFYPAGSQPVTWAIDNVYIGPQCEEMCNGQGSCINGTKCIC 2150
2151 DPGYSGPTCKISTKNPDFLKDDFEGQLESDRFLLMSGGKPSRKCGILSSG 2200
2201 NNLFFNEDGLRMLMTRDLDLSHARFVQFFMRLGCGKGVPDPRSQPVLLQY 2250
2251 SLNGGLSWSLLQEFLFSNSSNVGRYIALEIPLKARSGSTRLRWWQPSENG 2300
2301 HFYSPWVIDQILIGGNISGNTVLEDDFTTLDSRKWLLHPGGTKMPVCGST 2350
2351 GDALVFIEKASTRYVVSTDVAVNEDSFLQIDFAASCSVTDSCYAIELEYS 2400
2401 VDLGLSWHPLVRDCLPTNVECSRYHLQRILVSDTFNKWTRITLPLPPYTR 2450
2451 SQATRFRWHQPAPFDKQQTWAIDNVYIGDGCIDMCSGHGRCIQGNCVCDE 2500
2501 QWGGLYCDDPETSLPTQLKDNFNRAPSSQNWLTVNGGKLSTVCGAVASGM 2550
2551 ALHFSGGCSRLLVTVDLNLTNAEFIQFYFMYGCLITPNNRNQGVLLEYSV 2600
2601 NGGITWNLLMEIFYDQYSKPGFVNILLPPDAKEIATRFRWWQPRHDGLDQ 2650
2651 NDWAIDNVLISGSADQRTVMLDTFSSAPVPQHERSPADAGPVGRIAFDMF 2700
2701 MEDKTSVNEHWLFHDDCTVERFCDSPDGVMLCGSHDGREVYAVTHDLTPT 2750
2751 EGWIMQFKISVGCKVSEKIAQNQIHVQYSTDFGVSWNYLVPQCLPADPKC 2800
2801 SGSVSQPSVFFPTKGWKRITYPLPESLVGNPVRFRFYQKYSDMQWAIDNF 2850
2851 YLGPGCLDNCRGHGDCLREQCICDPGYSGPNCYLTHTLKTFLKERFDSEE 2900
2901 IKPDLWMSLEGGSTCTECGILAEDTALYFGGSTVRQAVTQDLDLRGAKFL 2950
2951 QYWGRIGSENNMTSCHRPICRKEGVLLDYSTDGGITWTLLHEMDYQKYIS 3000
3001 VRHDYILLPEDALTNTTRLRWWQPFVISNGIVVSGVERAQWALDNILIGG 3050
3051 AEINPSQLVDTFDDEGTSHEENWSFYPNAVRTAGFCGNPSFHLYWPNKKK 3100
3101 DKTHNALSSRELIIQPGYMMQFKIVVGCEATSCGDLHSVMLEYTKDARSD 3150
3151 SWQLVQTQCLPSSSNSIGCSPFQFHEATIYNSVNSSSWKRITIQLPDHVS 3200
3201 SSATQFRWIQKGEETEKQSWAIDHVYIGEACPKLCSGHGYCTTGAICICD 3250
3251 ESFQGDDCSVFSHDLPSYIKDNFESARVTEANWETIQGGVIGSGCGQLAP 3300
3301 YAHGDSLYFNGCQIRQAATKPLDLTRASKIMFVLQIGSMSQTDSCNSDLS 3350
3351 GPHAVDKAVLLQYSVNNGITWHVIAQHQPKDFTQAQRVSYNVPLEARMKG 3400
3401 VLLRWWQPRHNGTGHDQWALDHVEVVLVSTRKQNYMMNFSRQHGLRHFYN 3450
3451 RRRRSLRRYP 3460
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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