SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q9NYQ6 from www.uniprot.org...

The NucPred score for your sequence is 0.41 (see score help below)

   1  MAPPPPPVLPVLLLLAAAAALPAMGLRAAAWEPRVPGGTRAFALRPGCTY    50
  51  AVGAACTPRAPRELLDVGRDGRLAGRRRVSGAGRPLPLQVRLVARSAPTA   100
 101  LSRRLRARTHLPGCGARARLCGTGARLCGALCFPVPGGCAAAQHSALAAP   150
 151  TTLPACRCPPRPRPRCPGRPICLPPGGSVRLRLLCALRRAAGAVRVGLAL   200
 201  EAATAGTPSASPSPSPPLPPNLPEARAGPARRARRGTSGRGSLKFPMPNY   250
 251  QVALFENEPAGTLILQLHAHYTIEGEEERVSYYMEGLFDERSRGYFRIDS   300
 301  ATGAVSTDSVLDRETKETHVLRVKAVDYSTPPRSATTYITVLVKDTNDHS   350
 351  PVFEQSEYRERVRENLEVGYEVLTIRASDRDSPINANLRYRVLGGAWDVF   400
 401  QLNESSGVVSTRAVLDREEAAEYQLLVEANDQGRNPGPLSATATVYIEVE   450
 451  DENDNYPQFSEQNYVVQVPEDVGLNTAVLRVQATDRDQGQNAAIHYSILS   500
 501  GNVAGQFYLHSLSGILDVINPLDFEDVQKYSLSIKAQDGGRPPLINSSGV   550
 551  VSVQVLDVNDNEPIFVSSPFQATVLENVPLGYPVVHIQAVDADSGENARL   600
 601  HYRLVDTASTFLGGGSAGPKNPAPTPDFPFQIHNSSGWITVCAELDREEV   650
 651  EHYSFGVEAVDHGSPPMSSSTSVSITVLDVNDNDPVFTQPTYELRLNEDA   700
 701  AVGSSVLTLQARDRDANSVITYQLTGGNTRNRFALSSQRGGGLITLALPL   750
 751  DYKQEQQYVLAVTASDGTRSHTAHVLINVTDANTHRPVFQSSHYTVSVSE   800
 801  DRPVGTSIATLSANDEDTGENARITYVIQDPVPQFRIDPDSGTMYTMMEL   850
 851  DYENQVAYTLTIMAQDNGIPQKSDTTTLEILILDANDNAPQFLWDFYQGS   900
 901  IFEDAPPSTSILQVSATDRDSGPNGRLLYTFQGGDDGDGDFYIEPTSGVI   950
 951  RTQRRLDRENVAVYNLWALAVDRGSPTPLSASVEIQVTILDINDNAPMFE  1000
1001  KDELELFVEENNPVGSVVAKIRANDPDEGPNAQIMYQIVEGDMRHFFQLD  1050
1051  LLNGDLRAMVELDFEVRREYVLVVQATSAPLVSRATVHILLVDQNDNPPV  1100
1101  LPDFQILFNNYVTNKSNSFPTGVIGCIPAHDPDVSDSLNYTFVQGNELRL  1150
1151  LLLDPATGELQLSRDLDNNRPLEALMEVSVSDGIHSVTAFCTLRVTIITD  1200
1201  DMLTNSITVRLENMSQEKFLSPLLALFVEGVAAVLSTTKDDVFVFNVQND  1250
1251  TDVSSNILNVTFSALLPGGVRGQFFPSEDLQEQIYLNRTLLTTISTQRVL  1300
1301  PFDDNICLREPCENYMKCVSVLRFDSSAPFLSSTTVLFRPIHPINGLRCR  1350
1351  CPPGFTGDYCETEIDLCYSDPCGANGRCRSREGGYTCECFEDFTGEHCEV  1400
1401  DARSGRCANGVCKNGGTCVNLLIGGFHCVCPPGEYERPYCEVTTRSFPPQ  1450
1451  SFVTFRGLRQRFHFTISLTFATQERNGLLLYNGRFNEKHDFIALEIVDEQ  1500
1501  VQLTFSAGETTTTVAPKVPSGVSDGRWHSVQVQYYNKPNIGHLGLPHGPS  1550
1551  GEKMAVVTVDDCDTTMAVRFGKDIGNYSCAAQGTQTGSKKSLDLTGPLLL  1600
1601  GGVPNLPEDFPVHNRQFVGCMRNLSVDGKNVDMAGFIANNGTREGCAARR  1650
1651  NFCDGRRCQNGGTCVNRWNMYLCECPLRFGGKNCEQAMPHPQLFSGESVV  1700
1701  SWSDLNIIISVPWYLGLMFRTRKEDSVLMEATSGGPTSFRLQILNNYLQF  1750
1751  EVSHGPSDVESVMLSGLRVTDGEWHHLLIELKNVKEDSEMKHLVTMTLDY  1800
1801  GMDQNKADIGGMLPGLTVRSVVVGGASEDKVSVRRGFRGCMQGVRMGGTP  1850
1851  TNVATLNMNNALKVRVKDGCDVDDPCTSSPCPPNSRCHDAWEDYSCVCDK  1900
1901  GYLGINCVDACHLNPCENMGACVRSPGSPQGYVCECGPSHYGPYCENKLD  1950
1951  LPCPRGWWGNPVCGPCHCAVSKGFDPDCNKTNGQCQCKENYYKLLAQDTC  2000
2001  LPCDCFPHGSHSRTCDMATGQCACKPGVIGRQCNRCDNPFAEVTTLGCEV  2050
2051  IYNGCPKAFEAGIWWPQTKFGQPAAVPCPKGSVGNAVRHCSGEKGWLPPE  2100
2101  LFNCTTISFVDLRAMNEKLSRNETQVDGARALQLVRALRSATQHTGTLFG  2150
2151  NDVRTAYQLLGHVLQHESWQQGFDLAATQDADFHEDVIHSGSALLAPATR  2200
2201  AAWEQIQRSEGGTAQLLRRLEGYFSNVARNVRRTYLRPFVIVTANMILAV  2250
2251  DIFDKFNFTGARVPRFDTIHEEFPRELESSVSFPADFFRPPEEKEGPLLR  2300
2301  PAGRRTTPQTTRPGPGTEREAPISRRRRHPDDAGQFAVALVIIYRTLGQL  2350
2351  LPERYDPDRRSLRLPHRPIINTPMVSTLVYSEGAPLPRPLERPVLVEFAL  2400
2401  LEVEERTKPVCVFWNHSLAVGGTGGWSARGCELLSRNRTHVACQCSHTAS  2450
2451  FAVLMDISRRENGEVLPLKIVTYAAVSLSLAALLVAFVLLSLVRMLRSNL  2500
2501  HSIHKHLAVALFLSQLVFVIGINQTENPFLCTVVAILLHYIYMSTFAWTL  2550
2551  VESLHVYRMLTEVRNIDTGPMRFYYVVGWGIPAIVTGLAVGLDPQGYGNP  2600
2601  DFCWLSLQDTLIWSFAGPIGAVIIINTVTSVLSAKVSCQRKHHYYGKKGI  2650
2651  VSLLRTAFLLLLLISATWLLGLLAVNRDALSFHYLFAIFSGLQGPFVLLF  2700
2701  HCVLNQEVRKHLKGVLGGRKLHLEDSATTRATLLTRSLNCNTTFGDGPDM  2750
2751  LRTDLGESTASLDSIVRDEGIQKLGVSSGLVRGSHGEPDASLMPRSCKDP  2800
2801  PGHDSDSDSELSLDEQSSSYASSHSSDSEDDGVGAEEKWDPARGAVHSTP  2850
2851  KGDAVANHVPAGWPDQSLAESDSEDPSGKPRLKVETKVSVELHREEQGSH  2900
2901  RGEYPPDQESGGAARLASSQPPEQRKGILKNKVTYPPPLTLTEQTLKGRL  2950
2951  REKLADCEQSPTSSRTSSLGSGGPDCAITVKSPGREPGRDHLNGVAMNVR  3000
3001  TGSAQADGSDSEKP                                      3014

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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