 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P15368 from www.uniprot.org...
The NucPred score for your sequence is 0.42 (see score help below)
1 MRPEVEQELAHTLLVELLAYQFASPVRWIETQDVILAEQRTERIVEIGPA 50
51 DTLGGMARRTLASKYEAYDAATSVQRQILCYNKDAKEIYYDVDPVEEEPE 100
101 ATEPAPSATPAAPAAAPAAGAPPPPPSAGPAASVEDIPVTAVDILRTLVA 150
151 QKLKKSLADVPLSKAIKDLVGGKSTLQNEILGDLGKEFGSTPEKPEDVPL 200
201 DELGASMQATFNGQLGKQSSSLIARMVSSKMPGGFNITSVRKYLETRWGL 250
251 GSGRQDGVLLLALTMEPAARLGSEVDAKAYLDDVTNKYAASAGVNLSAPV 300
301 AGGDSGGAGGGMVMDPAAIDALTKDQRALFKQQLEIIARYLKMDLRGGEK 350
351 AHVISQETQKALQAQLDLWQAEHGDFYASGIEPSFDQLKARVYDSSWNWA 400
401 RQDALSMYYDIIFGRLQVVDREIVSQCIRIMNRSNPLLLDFMQYHIDNCP 450
451 TERGETYQLAKELGQQLIENCREVLEVAPVYKDVAVPTGPQTTIDARGNI 500
501 SYKETPRTSARKLEHYVKHMAEGGPISEYSNRTKVQNDLKSVYKLIRKQH 550
551 RLSKSSQLQFDALYKDVVHALGMNESQIIPQENGHSKKGGRSAAKRNTPT 600
601 RPGKVETIPFLHLKKKTEHGWDYNKKLTGIYLNVTESAAKDGLSFQGKNV 650
651 LMTGAGAGSIGAEVLQGLISGGAQVIVTTSRFSREVTEYYQAMYARYGAR 700
701 GSQLVVVPFNQGSKQDVEALVEYIYDTKKGLGWDLDFVVPFAAIPENGRE 750
751 IDSIDSKSELAHRIMLTNLLRLLGSVKTQKQAHGFETRPAQVILPLSPNH 800
801 GTFGNDGLYSESKLALETLFNRWYSENWGHYLTICGAVIGWTRGTGLMSG 850
851 NNMVAEGVEKLGVRTFSQQEMAFNLLGLMSPAIVNLCQLDPVFADLNGGL 900
901 QFIPDLKGLMTKLRTDIMETSDVRQAVMKETAIEHNIVNGEDSGVLYKKV 950
951 IAEPRANIKFEFPNLPDWEKEVKPLNENLKGMVNLDKVVVVTGFSEVGPW 1000
1001 GNSRTRWEMESKGKFSLEGCVEMAWIMGLIKHHNGPLKGQAYSGWVDAKT 1050
1051 GEPVDDKDVKPKYEKHILEHTGIRLIEPELFKGYDPKKKQLLQEIVIQED 1100
1101 LEPFEASKETAEEFKREHGDKVEIFEIPESGEYTVRLCKGATMLIPKALQ 1150
1151 FDRLVAGQVPTGWDASRYGIPDDIISQVDPVTLFVLVCTAEAMLSAGVTD 1200
1201 PYEFYKYVHLSEVGNCIGSGIGGTHRLRGMYKDRFLDKPLQKDILQESFI 1250
1251 NTMSAWVNMLLLSSTGPIKTPVGCCATAVESVDIGYETIVEGKARVCFVG 1300
1301 GFDDFQEEGSYEFANMKATSNAEDEFAHGRTPQEMSRPTTTTRAGFMESQ 1350
1351 GCGMQLIMTAQLALDMGVPIHGIIALTTTATDKIGRSVRSVPAPGQGVLT 1400
1401 TARENPGKFPSPLLDIKYRRRQLDLRKKQINEWQEAELLYLQEEAEAMKA 1450
1451 QSDETFNEAEYMQERAQHIEREAIRQEKDAQYSLGNNFWKQDSRIAPLRG 1500
1501 AMATWGLTVDDIDVASFHGTSTVANDKNESDVICQQMKHLGRSKGNAVMG 1550
1551 IFQKYLTGHPKGAAGAWMFNGCLQVLDSGLVPGNRNADNVDKVMEKFDYI 1600
1601 VYPSRSIQTDGVKAFSVTSFGFGQKGAQVIGIHPKYLYATLDQAQYEAYK 1650
1651 TKVEARQKKAYRYFHNGLINNSIFVAKSKAPYEDEQQSKVFLNPDYRVSV 1700
1701 DKKTSELKFSTTAPEAKQSESTRQTLESLAKANATENSKIGVDVEHIDSV 1750
1751 NIENETFVERNFTQSEQDYCRKAASPQSSFAGRWSAKEAVFKSLGVSSKG 1800
1801 AGAALKDIEIGVDANGAPVVNLHGAAAAAAKQAGVKQVSVSISHSDSQAV 1850
1851 AVAVSQF 1857
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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