 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9W517 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MSRWGKNIVVPLDSLCKEKENTNRPTVARSVGTVGKWGKMGFTSTRTYTL 50
51 PAIHPMAAAAAAAAAAASPSQSPASTQDHDPNDLSVSVPEPPKPKKFFKS 100
101 RNTAPPEVIAQIIQQLPHCGAGASPMRDHFSSAGAGAGGLTPTSGAQEAG 150
151 GVKLKPGKGASSAERKRKSPKKKAATTSASTPSTPGAFYGASDRDGDGLS 200
201 DPASEQPEQPSSASGKQKQKKPKEEKKLKPEAPPSRVLGRARKAVNYREV 250
251 DEDERYPTPTKDLIIPKAGRQPAEVAATATLAAASSEAFISSTFGSPGSE 300
301 PSLPPPTSAPSASASTSSQLPSASGSASNPPSASRTPEHPPIVLRISKGT 350
351 SRLVSTDSEEPPSSSPAHQNQLNQLSVTEEEPAERSGDETVPASTPKITV 400
401 KPLRPPTAADSVDGSSAAVGGASAGDSFEERKSQSLEPNEDEEEEEEEED 450
451 EEEEPPEINYCTVKISPDKPPKERLKLIIKTDVIRNAIAKAAAAAESRSE 500
501 KKSRSKKHKHKQLLAAGSGAAPASGATPAEINSEFKTPSPHLALSEANSQ 550
551 QAQHTPSHLHQLHQLHPQRGSAVISPTTRSDHDFDSQSSVLGSISSKGNS 600
601 TPQLLAQAVQEDSCVIRSRGSSVITSDLETSQHSSLVAPPSDIESRLESM 650
651 MMTIDGAGTGAASAVPETPLQEDILAVLRGEVPRLNGNTDPEPTEEEDQQ 700
701 QQPKRATRGRGRKANNNVDVTPPATETRTRGRAKGADATTAAISPPTGKR 750
751 NTRGTRGSRKAEQEVDMEVDETAMTTVPANEEQLEQATLPPRRGRNAAAR 800
801 ANNNNLASVNNNINKIAANLSAKAEASRLAEGGVAGGAARSYGRKRKNQQ 850
851 VTQVLQQEPVPEEQETPDAEEEQPTPAKIPHTDHREHSPDHDPDPDPDEL 900
901 SNNSNNSSLQHDGSSSSPPPRDFKFKDKFKRTLTLDTQGAANAGAGGAAA 950
951 AAPPESSGEQRGAVKLVISKKKGSIFKSRALVPSDQAEQATVAKRHLYKH 1000
1001 SWDAALEANGGGTNSDASNASASGVGVAGAKDHLHHLAAGKSDGDFGDSP 1050
1051 SSNNNGSSSACSSASTLRGDSPALGKISRLAGKQGVPATSTSSDAFDLDL 1100
1101 EPIAGELDLERSAAGASAGGTGATTGGGGATGGGGPVRVDRKTKDYYPVV 1150
1151 RNVKTAHQIQEIGEYQEMDDDVEYILDALQPHNPPATRCLSALQLAAKCM 1200
1201 MPAFRMHVRAHGVVTKFFKALSDANKDLSLGLCTSAIMYILSQEGLNMDL 1250
1251 DRDSLELMINLLEADGVGGSTETGHPDRAGYDRNKQKVRELCEEIKAQGK 1300
1301 GTHLNVDSLTVGTLAMETLLSLTSKRAGEWFKEDLRKLGGLEHIIKTISD 1350
1351 FCRPVIACDTEIDWQPTLLDNMQTVARCLRVLENVTQHNETNQRYMLTSG 1400
1401 QGKAVETLCQLYRLCSRQIMLHPSDGGGSNKEHPGVAMRELLVPVLKVLI 1450
1451 NLTHTFNEAQPSLGAELLGQRGDVVETSFRLLLLSANYIPDQCVFELSIL 1500
1501 VLTLLINLCMHTVPNRAALMQAAAPAEYVADNPPAQGSVSALQALLEYFY 1550
1551 KCEELARLVEKNTDAFLESNEKGKKKQEEVEETVNNLVQRAGHHMEHTLK 1600
1601 GSYAAILVGNLIADNELYESVVRRQLRGNSFKEIIGVLEKYHTFMNLTSS 1650
1651 LEAAFVAHMKSTKRIIDNFKKRDYIYEHSDEHDNPLPLNLETTAQVLAVG 1700
1701 ADASHAATSSTTVGSGSAPSSTSATGTTRAPRVYKTYSSHR 1741
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.) |
Go back to the NucPred Home Page.