 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q92538 from www.uniprot.org...
The NucPred score for your sequence is 0.79 (see score help below)
1 MVDKNIYIIQGEINIVVGAIKRNARWSTHTPLDEERDPLLHSFGHLKEVL 50
51 NSITELSEIEPNVFLRPFLEVIRSEDTTGPITGLALTSVNKFLSYALIDP 100
101 THEGTAEGMENMADAVTHARFVGTDPASDEVVLMKILQVLRTLLLTPVGA 150
151 HLTNESVCEIMQSCFRICFEMRLSELLRKSAEHTLVDMVQLLFTRLPQFK 200
201 EEPKNYVGTNMKKLKMRAGGMSDSSKWKKQKRSPRPPRHMTKVTPGSELP 250
251 TPNGTTLSSNLTGGMPFIDVPTPISSASSEAASAVVSPSTDSGLEFSSQT 300
301 TSKEDLTDLEQPGSPGYSTATEPGSSELGVPEQPDLQQEGTHVEKSQSAS 350
351 VESIPEVLEECTSPADHSDSASVHDMDYVNPRGVRFTQSSQKEGTALVPY 400
401 GLPCIRELFRFLISLTNPHDRHNSEVMIHMGLHLLTVALESAPVAQCQTL 450
451 LGLIKDEMCRHLFQLLSIERLNLYAASLRVCFLLFESMREHLKFQMEMYI 500
501 KKLMEIITVENPKMPYEMKEMALEAIVQLWRIPSFVTELYINYDCDYYCS 550
551 NLFEELTKLLSKNAFPVSGQLYTTHLLSLDALLTVIDSTEAHCQAKVLNS 600
601 LTQQEKKETARPSCEIVDGTREASNTERTASDGKAVGMASDIPGLHLPGG 650
651 GRLPPEHGKSGCSDLEEAVDSGADKKFARKPPRFSCLLPDPRELIEIKNK 700
701 KKLLITGTEQFNQKPKKGIQFLQEKGLLTIPMDNTEVAQWLRENPRLDKK 750
751 MIGEFVSDRKNIDLLESFVSTFSFQGLRLDEALRLYLEAFRLPGEAPVIQ 800
801 RLLEAFTERWMNCNGSPFANSDACFSLAYAVIMLNTDQHNHNVRKQNAPM 850
851 TLEEFRKNLKGVNGGKDFEQDILEDMYHAIKNEEIVMPEEQTGLVRENYV 900
901 WNVLLHRGATPEGIFLRVPTASYDLDLFTMTWGPTIAALSYVFDKSLEET 950
951 IIQKAISGFRKCAMISAHYGLSDVFDNLIISLCKFTALSSESIENLPSVF 1000
1001 GSNPKAHIAAKTVFHLAHRHGDILREGWKNIMEAMLQLFRAQLLPKAMIE 1050
1051 VEDFVDPNGKISLQREETPSNRGESTVLSFVSWLTLSGPEQSSVRGPSTE 1100
1101 NQEAKRVALECIKQCDPEKMITESKFLQLESLQELMKALVSVTPDEETYD 1150
1151 EEDAAFCLEMLLRIVLENRDRVGCVWQTVRDHLYHLCVQAQDFCFLVERA 1200
1201 VVGLLRLAIRLLRREEISAQVLLSLRILLLMKPSVLSRVSHQVAYGLHEL 1250
1251 LKTNAANIHSGDDWATLFTLLECIGSGVKPPAALQATARADAPDAGAQSD 1300
1301 SELPSYHQNDVSLDRGYTSDSEVYTDHGRPGKIHRSATDADVVNSGWLVV 1350
1351 GKDDVDNSKPGPSRPGPSPLINQYSLTVGLDLGPHDTKSLLKCVESLSFI 1400
1401 VRDAAHITPDNFELCVKTLRIFVEASLNGGCKSQEKRGKSHKYDSKGNRF 1450
1451 KKKSKEGSMLRRPRTSSQHASRGGQSDDDEDEGVPASYHTVSLQVSQDLL 1500
1501 DLMHTLHTRAASIYSSWAEEQRHLETGGQKIEADSRTLWAHCWCPLLQGI 1550
1551 ACLCCDARRQVRMQALTYLQRALLVHDLQKLDALEWESCFNKVLFPLLTK 1600
1601 LLENISPADVGGMEETRMRASTLLSKVFLQHLSPLLSLSTFAALWLTILD 1650
1651 FMDKYMHAGSSDLLSEAIPESLKNMLLVMDTAEIFHSADARGGGPSALWE 1700
1701 ITWERIDCFLPHLRDELFKQTVIQDPMPMEPQGQKPLASAHLTSAAGDTR 1750
1751 TPGHPPPPEIPSELGACDFEKPESPRAASSSSPGSPVASSPSRLSPTPDG 1800
1801 PPPLAQPPLILQPLASPLQVGVPPMTLPIILNPALIEATSPVPLLATPRP 1850
1851 TDPIPTSEVN 1860
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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