 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UPX8 from www.uniprot.org...
The NucPred score for your sequence is 0.95 (see score help below)
1 MPRSPTSSEDEMAQSFSDYSVGSESDSSKEETIYDTIRATAEKPGGARTE 50
51 ESQGNTLVIRVVIHDLQQTKCIRFNPDATVWVAKQRILCTLTQSLKDVLN 100
101 YGLFQPASNGRDGKFLDEERLLREYPQPVGEGVPSLEFRYKKRVYKQASL 150
151 DEKQLAKLHTKTNLKKCMDHIQHRLVEKITKMLDRGLDPNFHDPETGETP 200
201 LTLAAQLDDSVEVIKALKNGGAHLDFRAKDGMTALHKAARARNQVALKTL 250
251 LELGASPDYKDSYGLTPLYHTAIVGGDPYCCELLLHEHATVCCKDENGWH 300
301 EIHQACRYGHVQHLEHLLFYGADMSAQNASGNTALHICALYNQDSCARVL 350
351 LFRGGNKELKNYNSQTPFQVAIIAGNFELAEYIKNHKETDIVPFREAPAY 400
401 SNRRRRPPNTLAAPRVLLRSNSDNNLNASAPDWAVCSTATSHRSLSPQLL 450
451 QQMPSKPEGAAKTIGSYVPGPRSRSPSLNRLGGAGEDGKRPQPLWHVGSP 500
501 FALGANKDSLSAFEYPGPKRKLYSAVPGRLFVAVKPYQPQVDGEIPLHRG 550
551 DRVKVLSIGEGGFWEGSARGHIGWFPAECVEEVQCKPRDSQAETRADRSK 600
601 KLFRHYTVGSYDSFDTSSDCIIEEKTVVLQKKDNEGFGFVLRGAKADTPI 650
651 EEFTPTPAFPALQYLESVDEGGVAWQAGLRTGDFLIEVNNENVVKVGHRQ 700
701 VVNMIRQGGNHLVLKVVTVTRNLDPDDTARKKAPPPPKRAPTTALTLRSK 750
751 SMTSELEELVDKASVRKKKDKPEEIVPASKPSRAAENMAVEPRVATIKQR 800
801 PSSRCFPAGSDMNSVYERQGIAVMTPTVPGSPKAPFLGIPRGTMRRQKSI 850
851 DSRIFLSGITEEERQFLAPPMLKFTRSLSMPDTSEDIPPPPQSVPPSPPP 900
901 PSPTTYNCPKSPTPRVYGTIKPAFNQNSAAKVSPATRSDTVATMMREKGM 950
951 YFRRELDRYSLDSEDLYSRNAGPQANFRNKRGQMPENPYSEVGKIASKAV 1000
1001 YVPAKPARRKGMLVKQSNVEDSPEKTCSIPIPTIIVKEPSTSSSGKSSQG 1050
1051 SSMEIDPQAPEPPSQLRPDESLTVSSPFAAAIAGAVRDREKRLEARRNSP 1100
1101 AFLSTDLGDEDVGLGPPAPRTRPSMFPEEGDFADEDSAEQLSSPMPSATP 1150
1151 REPENHFVGGAEASAPGEAGRPLNSTSKAQGPESSPAVPSASSGTAGPGN 1200
1201 YVHPLTGRLLDPSSPLALALSARDRAMKESQQGPKGEAPKADLNKPLYID 1250
1251 TKMRPSLDAGFPTVTRQNTRGPLRRQETENKYETDLGRDRKGDDKKNMLI 1300
1301 DIMDTSQQKSAGLLMVHTVDATKLDNALQEEDEKAEVEMKPDSSPSEVPE 1350
1351 GVSETEGALQISAAPEPTTVPGRTIVAVGSMEEAVILPFRIPPPPLASVD 1400
1401 LDEDFIFTEPLPPPLEFANSFDIPDDRAASVPALSDLVKQKKSDTPQSPS 1450
1451 LNSSQPTNSADSKKPASLSNCLPASFLPPPESFDAVADSGIEEVDSRSSS 1500
1501 DHHLETTSTISTVSSISTLSSEGGENVDTCTVYADGQAFMVDKPPVPPKP 1550
1551 KMKPIIHKSNALYQDALVEEDVDSFVIPPPAPPPPPGSAQPGMAKVLQPR 1600
1601 TSKLWGDVTEIKSPILSGPKANVISELNSILQQMNREKLAKPGEGLDSPM 1650
1651 GAKSASLAPRSPEIMSTISGTRSTTVTFTVRPGTSQPITLQSRPPDYESR 1700
1701 TSGTRRAPSPVVSPTEMNKETLPAPLSAATASPSPALSDVFSLPSQPPSG 1750
1751 DLFGLNPAGRSRSPSPSILQQPISNKPFTTKPVHLWTKPDVADWLESLNL 1800
1801 GEHKEAFMDNEIDGSHLPNLQKEDLIDLGVTRVGHRMNIERALKQLLDR 1849
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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