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

NucPred

Fetching P51111 from www.uniprot.org...

The NucPred score for your sequence is 0.81 (see score help below)

   1  MKAFESLKSFQQQQQQQQPPPQPPPPPPPPPQPPQPPPQGQPPPPPPLPG    50
  51  PAEEPLHRPKKELSATKKDRVNHCLTICENIVAQSLRNSPEFQKLLGIAM   100
 101  ELFLLCSDDASRRRMVADECLNKVIKALMDSNLPRLQLELYKEIKKNGAP   150
 151  RSLRAALWRFAELAHLVRPQKCRPYLVNLLPCLTRTSKRPEESVQETLAA   200
 201  AVPKIMASFGNFANDNEIKVLLKAFIANLKSSSPTVRRTAAGSAVSICQH   250
 251  SRRTQYFYNWLLNVLLGLLVPMEEDHPTLLILGVLLTLRCLVPLLQQQVK   300
 301  DTSLKGSFGVTRKEMEVSPSAEQLVQVYELTLHHTQHQDHNVVTGALELL   350
 351  QQLFRTPPPELLQALTTPGGLGQLTLVREEAGGRGRSGSIVELLAGGGSS   400
 401  CSPVLSRKQKGKVLLGEEEALEDDSESRSDVSSSAFAASVKSEIGGELAA   450
 451  SSSGVSTPGSVGHDIITEQPRSQHTLQADSVDLSGCDLTSAATDGDEEDI   500
 501  LSHSSSQFSAVPSDPAMDLNDGTQASSPISDSSQTTTEGPDSAVTPSDSS   550
 551  EIVLDGADSQYLGVQIGQPQEEDREAAGVLSGEVSDVFRNSSLALQQAHL   600
 601  LERMGHSRQPSDSSVDKFVSKDEVAEAGDPESKPCRIKGDIGQPNDDDSA   650
 651  PLVHCVRLLSASFLLTGEKKALVPDRDVRVSVKALALSCIGAAVALHPES   700
 701  FFSKLYKVPLSTMESTEEQYVSDILNYIDHGDPQVRGATAILCGTLVYSI   750
 751  LSRSRLRVGDWLGTIRALTGNTFSLVDCIPLLQKTLKDESSVTCKLACTA   800
 801  VRHCVLSLCSSSYSDLGLQLLIDMLPLKNSSYWLVRTELLETLAEIDFRL   850
 851  VSFLEAKAESLHRGPHHYTGFLKLQERVLNNVVIYLLGDEDPRVRHVAAT   900
 901  TLTRLVPKLFYKCDQGQADPVEAVARDQSSVYLKLLMHETQPPSHFSVST   950
 951  ITRIYRGYSLLPSVTDVTMENNLSRVVAAVSHELITSTTRALTFGCCEAL  1000
1001  CVLSAAFPVCTWSLGWHCGVPPLSASDESRKSCTVGMASMILTLLSSAWF  1050
1051  PLDLSAIQDALILAGNLLAASAPKSLRSSWASEEEGSSAATRQEEIWPAL  1100
1101  GDRTLVPMVEQLFSHLLKVINICAHVLDDETPGPAIKAALPSLTNPPSLS  1150
1151  PIRRKGKEKEPGEQTSTPMSPKKGGEASTASRQSDTSGPVTASKSSSLGS  1200
1201  FYHLPSYLRLHDVLKATHANYKVTLDLQNSTEKFGGFLRSALDVLSQILE  1250
1251  LATLQDIGKCVEEVLGYLKSCFSREPMMATVCVQQLLKTLFGTNLASQFD  1300
1301  GLSSNPSKSQCRAQRLGSSSVRPGLYHYCFMAPYTHFTQALADASLRNMV  1350
1351  QADQEHDASGWFDVLQKVSAQLKTNLTSVTKNRADKNAIHNHIRLFEPLV  1400
1401  IKALKQYTTTTSVQLQKQVLDLLAQLVQLRVNYCLLDSDQVFIGFVLKQF  1450
1451  EYIEVGQFRESEAIIPNIFFFLVLLSYERYHSKQIIGIPKIIQLCDGIMA  1500
1501  SGRKAVTHAIPALQPIVHDLFVLRGTNKADAGKELETQKEVVVSMLLRLI  1550
1551  QYHQVLEMFILVLQQCHKENEDKWKRLSRQVADIILPMLAKQQMHIDSHE  1600
1601  ALGVLNTLFEILAPSSLRPVDMLLRSMFITPSTMASVSTVQLWISGILAI  1650
1651  LRVLISQSTEDIVLSRIQELSFSPYLISCPVINRLRDGDSNPTLGERSRG  1700
1701  KQVKNLPEDTFSRFLLQLVGILLEDIVTKQLKVDMSEQQHTFYCQELGTL  1750
1751  LMCLIHIFKSGMFRRITAAATRLFTSDGCEGSFYTLDSLNARVRAMVPTH  1800
1801  PALVLLWCQILLLINHTDHRWWAEVQQTPKRHSLSCTKSLNPQISAEEDS  1850
1851  GSAAQLGMCNREIVRRGALILFCDYVCQNLHDSEHLTWLIVNHIQDLISL  1900
1901  SHEPPVQDFISAIHRNSAASGLFIQAIQSRCENLSTPTTLKKTLQCLEGI  1950
1951  HLSQSGAVLTLYVDRLLGTPFRALARMVDTLACRRVEMLLAANLQSSMAQ  2000
2001  LPEEELNRIQEHLQNTGLAQRHQRLYSLLDRFRLSTVQDSLSPLPPVTSH  2050
2051  PLDGDGHTSLETVNPDKDWYLQLVRSQCWTRSDSALLEGAELVNRIPAED  2100
2101  MSDFMMSSEFNLSLFAPCLSLGMSEIAGSQKSPLFEAARRVTLDRVTNVV  2150
2151  QQLPAVHQVFQPFLPTEPTAYWSKLNDLFGDTTSYQSLTTLARALAQYLV  2200
2201  VLSKVPAPLHLPPEKEGHTVKFVVMTLEALSWHLIHEQIPLSLDLQAGLD  2250
2251  CCCLALQVPGLWGVLSSPEYVTHTCSLIHCVRFILEAIAVQPGDQLLGPE  2300
2301  SRSHTPRAVRKEEVDSDIQNLSHITSACEMVADMVESLQSVLALGHKRNS  2350
2351  TLPSFLTAVLKNIVVSLARLPLVNSYTRVPPLVWKLGWSPKPGGDFGTVF  2400
2401  PEIPVEFLQEKEVLKEFIYRINTLGWTSRTQFEETWATLLGVLVTQPLVM  2450
2451  EQEESPPEEDTERTQIHVLAVQAITSLVLSAMAVPVAGNPAVSCLEQQPR  2500
2501  NKPLKALDTRFGRKLSMIRGIVEQEIQEMVSQRENTATHHSHQAWDPVPS  2550
2551  LLPATTGALISHDKLLLQINSEREPGNMSYKLGQVSIHSVWLGNNITPLR  2600
2601  EEEWDEEEEEEADAPAPTSPPVSPVNSRKHRAGVDIHSCSQFLLELYSRW  2650
2651  ILPSSAARRTPVILISEVVRSLLVVSDLFTDVPQFEMMYLTLTELRRVHP  2700
2701  SEDEILIQYLVPATCKAAAVLGMDKTVAEPVSRLLESTLRSTHLPSQIGA  2750
2751  LHGILYVLECDLLDDTVKQLIPVVSDYLLSNLKGIAHCVNIHSQQHVLVM  2800
2801  CATAFYLMENYPLDVGPEFSASVIQMCGVMLSGSEESTPSVIYHCALRGL  2850
2851  ERLLLSEQLSRLDTESLVKLSVDRVNVQSPHRAMAALGLMLTCMYTGKEK  2900
2901  ASPGRASDPSPATPDSESVIVAMERVSVLFDRIRKGFPCEARVVARILPQ  2950
2951  FLDDFFPPQDVMNKVIGEFLSNQQPYPQFMATVVYKVFQTLHSAGQSSMV  3000
3001  RDWVMLSLSNFTQRTPVAMAMWSLSCFLVSASTSPWVSAILPHVISRMGK  3050
3051  LEQVDVNLFCLVATDFYRHQIEEEFDRRAFQSVFEVVAAPGSPYHRLLAC  3100
3101  LQNVHKVTAC                                          3110

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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