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

NucPred

Fetching P42858 from www.uniprot.org...

The NucPred score for your sequence is 0.79 (see score help below)

   1  MATLEKLMKAFESLKSFQQQQQQQQQQQQQQQQQQQQQPPPPPPPPPPPQ    50
  51  LPQPPPQAQPLLPQPQPPPPPPPPPPGPAVAEEPLHRPKKELSATKKDRV   100
 101  NHCLTICENIVAQSVRNSPEFQKLLGIAMELFLLCSDDAESDVRMVADEC   150
 151  LNKVIKALMDSNLPRLQLELYKEIKKNGAPRSLRAALWRFAELAHLVRPQ   200
 201  KCRPYLVNLLPCLTRTSKRPEESVQETLAAAVPKIMASFGNFANDNEIKV   250
 251  LLKAFIANLKSSSPTIRRTAAGSAVSICQHSRRTQYFYSWLLNVLLGLLV   300
 301  PVEDEHSTLLILGVLLTLRYLVPLLQQQVKDTSLKGSFGVTRKEMEVSPS   350
 351  AEQLVQVYELTLHHTQHQDHNVVTGALELLQQLFRTPPPELLQTLTAVGG   400
 401  IGQLTAAKEESGGRSRSGSIVELIAGGGSSCSPVLSRKQKGKVLLGEEEA   450
 451  LEDDSESRSDVSSSALTASVKDEISGELAASSGVSTPGSAGHDIITEQPR   500
 501  SQHTLQADSVDLASCDLTSSATDGDEEDILSHSSSQVSAVPSDPAMDLND   550
 551  GTQASSPISDSSQTTTEGPDSAVTPSDSSEIVLDGTDNQYLGLQIGQPQD   600
 601  EDEEATGILPDEASEAFRNSSMALQQAHLLKNMSHCRQPSDSSVDKFVLR   650
 651  DEATEPGDQENKPCRIKGDIGQSTDDDSAPLVHCVRLLSASFLLTGGKNV   700
 701  LVPDRDVRVSVKALALSCVGAAVALHPESFFSKLYKVPLDTTEYPEEQYV   750
 751  SDILNYIDHGDPQVRGATAILCGTLICSILSRSRFHVGDWMGTIRTLTGN   800
 801  TFSLADCIPLLRKTLKDESSVTCKLACTAVRNCVMSLCSSSYSELGLQLI   850
 851  IDVLTLRNSSYWLVRTELLETLAEIDFRLVSFLEAKAENLHRGAHHYTGL   900
 901  LKLQERVLNNVVIHLLGDEDPRVRHVAAASLIRLVPKLFYKCDQGQADPV   950
 951  VAVARDQSSVYLKLLMHETQPPSHFSVSTITRIYRGYNLLPSITDVTMEN  1000
1001  NLSRVIAAVSHELITSTTRALTFGCCEALCLLSTAFPVCIWSLGWHCGVP  1050
1051  PLSASDESRKSCTVGMATMILTLLSSAWFPLDLSAHQDALILAGNLLAAS  1100
1101  APKSLRSSWASEEEANPAATKQEEVWPALGDRALVPMVEQLFSHLLKVIN  1150
1151  ICAHVLDDVAPGPAIKAALPSLTNPPSLSPIRRKGKEKEPGEQASVPLSP  1200
1201  KKGSEASAASRQSDTSGPVTTSKSSSLGSFYHLPSYLKLHDVLKATHANY  1250
1251  KVTLDLQNSTEKFGGFLRSALDVLSQILELATLQDIGKCVEEILGYLKSC  1300
1301  FSREPMMATVCVQQLLKTLFGTNLASQFDGLSSNPSKSQGRAQRLGSSSV  1350
1351  RPGLYHYCFMAPYTHFTQALADASLRNMVQAEQENDTSGWFDVLQKVSTQ  1400
1401  LKTNLTSVTKNRADKNAIHNHIRLFEPLVIKALKQYTTTTCVQLQKQVLD  1450
1451  LLAQLVQLRVNYCLLDSDQVFIGFVLKQFEYIEVGQFRESEAIIPNIFFF  1500
1501  LVLLSYERYHSKQIIGIPKIIQLCDGIMASGRKAVTHAIPALQPIVHDLF  1550
1551  VLRGTNKADAGKELETQKEVVVSMLLRLIQYHQVLEMFILVLQQCHKENE  1600
1601  DKWKRLSRQIADIILPMLAKQQMHIDSHEALGVLNTLFEILAPSSLRPVD  1650
1651  MLLRSMFVTPNTMASVSTVQLWISGILAILRVLISQSTEDIVLSRIQELS  1700
1701  FSPYLISCTVINRLRDGDSTSTLEEHSEGKQIKNLPEETFSRFLLQLVGI  1750
1751  LLEDIVTKQLKVEMSEQQHTFYCQELGTLLMCLIHIFKSGMFRRITAAAT  1800
1801  RLFRSDGCGGSFYTLDSLNLRARSMITTHPALVLLWCQILLLVNHTDYRW  1850
1851  WAEVQQTPKRHSLSSTKLLSPQMSGEEEDSDLAAKLGMCNREIVRRGALI  1900
1901  LFCDYVCQNLHDSEHLTWLIVNHIQDLISLSHEPPVQDFISAVHRNSAAS  1950
1951  GLFIQAIQSRCENLSTPTMLKKTLQCLEGIHLSQSGAVLTLYVDRLLCTP  2000
2001  FRVLARMVDILACRRVEMLLAANLQSSMAQLPMEELNRIQEYLQSSGLAQ  2050
2051  RHQRLYSLLDRFRLSTMQDSLSPSPPVSSHPLDGDGHVSLETVSPDKDWY  2100
2101  VHLVKSQCWTRSDSALLEGAELVNRIPAEDMNAFMMNSEFNLSLLAPCLS  2150
2151  LGMSEISGGQKSALFEAAREVTLARVSGTVQQLPAVHHVFQPELPAEPAA  2200
2201  YWSKLNDLFGDAALYQSLPTLARALAQYLVVVSKLPSHLHLPPEKEKDIV  2250
2251  KFVVATLEALSWHLIHEQIPLSLDLQAGLDCCCLALQLPGLWSVVSSTEF  2300
2301  VTHACSLIYCVHFILEAVAVQPGEQLLSPERRTNTPKAISEEEEEVDPNT  2350
2351  QNPKYITAACEMVAEMVESLQSVLALGHKRNSGVPAFLTPLLRNIIISLA  2400
2401  RLPLVNSYTRVPPLVWKLGWSPKPGGDFGTAFPEIPVEFLQEKEVFKEFI  2450
2451  YRINTLGWTSRTQFEETWATLLGVLVTQPLVMEQEESPPEEDTERTQINV  2500
2501  LAVQAITSLVLSAMTVPVAGNPAVSCLEQQPRNKPLKALDTRFGRKLSII  2550
2551  RGIVEQEIQAMVSKRENIATHHLYQAWDPVPSLSPATTGALISHEKLLLQ  2600
2601  INPERELGSMSYKLGQVSIHSVWLGNSITPLREEEWDEEEEEEADAPAPS  2650
2651  SPPTSPVNSRKHRAGVDIHSCSQFLLELYSRWILPSSSARRTPAILISEV  2700
2701  VRSLLVVSDLFTERNQFELMYVTLTELRRVHPSEDEILAQYLVPATCKAA  2750
2751  AVLGMDKAVAEPVSRLLESTLRSSHLPSRVGALHGVLYVLECDLLDDTAK  2800
2801  QLIPVISDYLLSNLKGIAHCVNIHSQQHVLVMCATAFYLIENYPLDVGPE  2850
2851  FSASIIQMCGVMLSGSEESTPSIIYHCALRGLERLLLSEQLSRLDAESLV  2900
2901  KLSVDRVNVHSPHRAMAALGLMLTCMYTGKEKVSPGRTSDPNPAAPDSES  2950
2951  VIVAMERVSVLFDRIRKGFPCEARVVARILPQFLDDFFPPQDIMNKVIGE  3000
3001  FLSNQQPYPQFMATVVYKVFQTLHSTGQSSMVRDWVMLSLSNFTQRAPVA  3050
3051  MATWSLSCFFVSASTSPWVAAILPHVISRMGKLEQVDVNLFCLVATDFYR  3100
3101  HQIEEELDRRAFQSVLEVVAAPGSPYHRLLTCLRNVHKVTTC          3142

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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