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

NucPred

Fetching P49792 from www.uniprot.org...

The NucPred score for your sequence is 0.76 (see score help below)

   1  MRRSKADVERYIASVQGSTPSPRQKSMKGFYFAKLYYEAKEYDLAKKYIC    50
  51  TYINVQERDPKAHRFLGLLYELEENTDKAVECYRRSVELNPTQKDLVLKI   100
 101  AELLCKNDVTDGRAKYWLERAAKLFPGSPAIYKLKEQLLDCEGEDGWNKL   150
 151  FDLIQSELYVRPDDVHVNIRLVEVYRSTKRLKDAVAHCHEAERNIALRSS   200
 201  LEWNSCVVQTLKEYLESLQCLESDKSDWRATNTDLLLAYANLMLLTLSTR   250
 251  DVQESRELLQSFDSALQSVKSLGGNDELSATFLEMKGHFYMHAGSLLLKM   300
 301  GQHSSNVQWRALSELAALCYLIAFQVPRPKIKLIKGEAGQNLLEMMACDR   350
 351  LSQSGHMLLNLSRGKQDFLKEIVETFANKSGQSALYDALFSSQSPKDTSF   400
 401  LGSDDIGNIDVREPELEDLTRYDVGAIRAHNGSLQHLTWLGLQWNSLPAL   450
 451  PGIRKWLKQLFHHLPHETSRLETNAPESICILDLEVFLLGVVYTSHLQLK   500
 501  EKCNSHHSSYQPLCLPLPVCKQLCTERQKSWWDAVCTLIHRKAVPGNVAK   550
 551  LRLLVQHEINTLRAQEKHGLQPALLVHWAECLQKTGSGLNSFYDQREYIG   600
 601  RSVHYWKKVLPLLKIIKKKNSIPEPIDPLFKHFHSVDIQASEIVEYEEDA   650
 651  HITFAILDAVNGNIEDAVTAFESIKSVVSYWNLALIFHRKAEDIENDALS   700
 701  PEEQEECKNYLRKTRDYLIKIIDDSDSNLSVVKKLPVPLESVKEMLNSVM   750
 751  QELEDYSEGGPLYKNGSLRNADSEIKHSTPSPTRYSLSPSKSYKYSPKTP   800
 801  PRWAEDQNSLLKMICQQVEAIKKEMQELKLNSSNSASPHRWPTENYGPDS   850
 851  VPDGYQGSQTFHGAPLTVATTGPSVYYSQSPAYNSQYLLRPAANVTPTKG   900
 901  PVYGMNRLPPQQHIYAYPQQMHTPPVQSSSACMFSQEMYGPPALRFESPA   950
 951  TGILSPRGDDYFNYNVQQTSTNPPLPEPGYFTKPPIAAHASRSAESKTIE  1000
1001  FGKTNFVQPMPGEGLRPSLPTQAHTTQPTPFKFNSNFKSNDGDFTFSSPQ  1050
1051  VVTQPPPAAYSNSESLLGLLTSDKPLQGDGYSGAKPIPGGQTIGPRNTFN  1100
1101  FGSKNVSGISFTENMGSSQQKNSGFRRSDDMFTFHGPGKSVFGTPTLETA  1150
1151  NKNHETDGGSAHGDDDDDGPHFEPVVPLPDKIEVKTGEEDEEEFFCNRAK  1200
1201  LFRFDVESKEWKERGIGNVKILRHKTSGKIRLLMRREQVLKICANHYISP  1250
1251  DMKLTPNAGSDRSFVWHALDYADELPKPEQLAIRFKTPEEAALFKCKFEE  1300
1301  AQSILKAPGTNVAMASNQAVRIVKEPTSHDNKDICKSDAGNLNFEFQVAK  1350
1351  KEGSWWHCNSCSLKNASTAKKCVSCQNLNPSNKELVGPPLAETVFTPKTS  1400
1401  PENVQDRFALVTPKKEGHWDCSICLVRNEPTVSRCIACQNTKSANKSGSS  1450
1451  FVHQASFKFGQGDLPKPINSDFRSVFSTKEGQWDCSACLVQNEGSSTKCA  1500
1501  ACQNPRKQSLPATSIPTPASFKFGTSETSKTLKSGFEDMFAKKEGQWDCS  1550
1551  SCLVRNEANATRCVACQNPDKPSPSTSVPAPASFKFGTSETSKAPKSGFE  1600
1601  GMFTKKEGQWDCSVCLVRNEASATKCIACQNPGKQNQTTSAVSTPASSET  1650
1651  SKAPKSGFEGMFTKKEGQWDCSVCLVRNEASATKCIACQNPGKQNQTTSA  1700
1701  VSTPASSETSKAPKSGFEGMFTKKEGQWDCSVCLVRNEASATKCIACQCP  1750
1751  SKQNQTTAISTPASSEISKAPKSGFEGMFIRKGQWDCSVCCVQNESSSLK  1800
1801  CVACDASKPTHKPIAEAPSAFTLGSEMKLHDSSGSQVGTGFKSNFSEKAS  1850
1851  KFGNTEQGFKFGHVDQENSPSFMFQGSSNTEFKSTKEGFSIPVSADGFKF  1900
1901  GISEPGNQEKKSEKPLENGTGFQAQDISGQKNGRGVIFGQTSSTFTFADL  1950
1951  AKSTSGEGFQFGKKDPNFKGFSGAGEKLFSSQYGKMANKANTSGDFEKDD  2000
2001  DAYKTEDSDDIHFEPVVQMPEKVELVTGEEDEKVLYSQRVKLFRFDAEVS  2050
2051  QWKERGLGNLKILKNEVNGKLRMLMRREQVLKVCANHWITTTMNLKPLSG  2100
2101  SDRAWMWLASDFSDGDAKLEQLAAKFKTPELAEEFKQKFEECQRLLLDIP  2150
2151  LQTPHKLVDTGRAAKLIQRAEEMKSGLKDFKTFLTNDQTKVTEEENKGSG  2200
2201  TGAAGASDTTIKPNPENTGPTLEWDNYDLREDALDDSVSSSSVHASPLAS  2250
2251  SPVRKNLFRFGESTTGFNFSFKSALSPSKSPAKLNQSGTSVGTDEESDVT  2300
2301  QEEERDGQYFEPVVPLPDLVEVSSGEENEQVVFSHRAKLYRYDKDVGQWK  2350
2351  ERGIGDIKILQNYDNKQVRIVMRRDQVLKLCANHRITPDMTLQNMKGTER  2400
2401  VWLWTACDFADGERKVEHLAVRFKLQDVADSFKKIFDEAKTAQEKDSLIT  2450
2451  PHVSRSSTPRESPCGKIAVAVLEETTRERTDVIQGDDVADATSEVEVSST  2500
2501  SETTPKAVVSPPKFVFGSESVKSIFSSEKSKPFAFGNSSATGSLFGFSFN  2550
2551  APLKSNNSETSSVAQSGSESKVEPKKCELSKNSDIEQSSDSKVKNLFASF  2600
2601  PTEESSINYTFKTPEKAKEKKKPEDSPSDDDVLIVYELTPTAEQKALATK  2650
2651  LKLPPTFFCYKNRPDYVSEEEEDDEDFETAVKKLNGKLYLDGSEKCRPLE  2700
2701  ENTADNEKECIIVWEKKPTVEEKAKADTLKLPPTFFCGVCSDTDEDNGNG  2750
2751  EDFQSELQKVQEAQKSQTEEITSTTDSVYTGGTEVMVPSFCKSEEPDSIT  2800
2801  KSISSPSVSSETMDKPVDLSTRKEIDTDSTSQGESKIVSFGFGSSTGLSF  2850
2851  ADLASSNSGDFAFGSKDKNFQWANTGAAVFGTQSVGTQSAGKVGEDEDGS  2900
2901  DEEVVHNEDIHFEPIVSLPEVEVKSGEEDEEILFKERAKLYRWDRDVSQW  2950
2951  KERGVGDIKILWHTMKNYYRILMRRDQVFKVCANHVITKTMELKPLNVSN  3000
3001  NALVWTASDYADGEAKVEQLAVRFKTKEVADCFKKTFEECQQNLMKLQKG  3050
3051  HVSLAAELSKETNPVVFFDVCADGEPLGRITMELFSNIVPRTAENFRALC  3100
3101  TGEKGFGFKNSIFHRVIPDFVCQGGDITKHDGTGGQSIYGDKFEDENFDV  3150
3151  KHTGPGLLSMANQGQNTNNSQFVITLKKAEHLDFKHVVFGFVKDGMDTVK  3200
3201  KIESFGSPKGSVCRRITITECGQI                            3224

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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