 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P59894 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MAKTGAEDHREALSQSSLSLLTEAMEVLQQSSPEGTLDGNTVNPIYKYIL 50
51 NDLPREFMSSQAKAVIKTTDDYLQSQFGPNRLVHSAAVSEGSGLQDCSTH 100
101 QTASDHSHDEISDLDSYKSNSKNNSCSISASKRNRPVSAPVGQLRVAEFS 150
151 SLKFQSARNWQKLSQRHKLQPRVIKVTAYKNGSRTVFARVTVPTITLLLE 200
201 ECTEKLNLNMAARRVFLADGKEALEPEDIPHEADVYVSTGEPFLNPFKKI 250
251 KDHLLLIKKVTWTMNGLMLPTDIKRRKTKPVLSIRMKKLTERTSVRILFF 300
301 KNGMGQDGHEITVGKETMKKVLDTCTIRMNLNLPARYFYDLYGRKIEDIS 350
351 KVPLLEKCLQNSITPLRGLLWVSKGEGFSPSGAKMYIQGVLLALYQRLKS 400
401 AKKYYKQLNLVMNEQKEKITEKVILSMTAKEHHKEQEEVSRLIDELQTAI 450
451 KSNIGHLCKLGPQLQAEQEQFSSYVYQHIKSLPANTLVPGGLQLKVFENG 500
501 KNTGEISVGISKKDLGSDSPIQTDHMMERLLLKIHQRLQGSSINPPGLNY 550
551 SSMRLFDENGQEIKNPLSLKNEQKIWVSYGRAYRSPLNLALGLTFDRVSA 600
601 FARGDIMVAYKTFLDPNAVLLPGCGNWEVCEGFPINFNCTSQQIPDQFEK 650
651 VDLENHFLQNKVDPNIVLHASVSIGKWSFSGSEASSRSQIAPSILWPVAS 700
701 VWLITKTGMILSRAITQGCLAIGHPIRVKAAEGTSLEGYKLILQKRHSGD 750
751 DSQKWVFGTDGCIYSKAYPQFVLTYLEELNAQVDVTQTEYHIHHGAWTTA 800
801 HQEHGRNLAEEVLQESASNLGLKQLPEPSDTHLMPEGSLEETGELTVALV 850
851 RKLEEKHPKASAQRWAIKHEGTSKPGQWKHSRVENPLWNKLTYMWPVLPS 900
901 GQLNEEFDWPIQGLLVPSSPPMKKPICKTTEPYAPVRLRVLQNGEKNKNR 950
951 SVTILGPDISPGRKTQCTEILNLPSAARRLYNEKGKEIFALKDLQRDELV 1000
1001 YVSCGELWINPDLSIAQQKKQIFLRNLESDIAKIQIFCSTHKIEALVLEV 1050
1051 QSDIVSGSKLAVHKPVAIFGEEKQVTEPEEKQMQEDPLTTENASSEILDS 1100
1101 HVRAHLRMKACHTLPRYAWQETSHDFDEDDSLPKKTEKGLFENVEPQKKH 1150
1151 SCSPKHSKLHKHCHQQFEYRDGQIISHAAPQLVLGVQGPNLRSGMEVVLV 1200
1201 EKKSDGSHQRWIHQEDSRTFHLVSNPDLVLAVSMTKTRNEVCGYPVIVQK 1250
1251 YKPYNNGAANQKWHYMKNIKALVAFHSTALDKEITSANYAGVCTSSVIKE 1300
1301 ENIDQPGYCYLSPDGKRKTMLCLACGQSMRTEKGLKQLLPGVPFLCISGT 1350
1351 KTQKPFLQGPFKVISVAEVDLSCDKAEKTLSYYQARLLSLRMKTCTQAAS 1400
1401 HSGMAATHQKAVKIIAYKNGDGYRNGKLIVAGTFPMLLTECTEQLGLARA 1450
1451 ASKVYTKDGTPIFTLRDLVLWALDESFLQRDSEKQKQDAAPVGKEQIIVE 1500
1501 KNPRMKVKNRLFAKSVTSDSLDGIDKSLLTLILRNPIAIWVSCGEPFLPP 1550
1551 NALQKAEKLEKQNWLKKDRILADLDTMRHKMRQLKGRRVAACQPATMVPT 1600
1601 KSPVQPVVVEGGWTEQTQQEIKLMELIRHTEAHLSEIQEMESKINFPIAT 1650
1651 KRIAVKPSNLYKQPNTKRVWIYLNGGRPEDGTYAWGKTISELLQDCSSRL 1700
1701 KMTHPARALYTPSGEPIQSWDDIERDMVICVSMGHGFKTPKELKQLMEIR 1750
1751 ANYARIRRQQGPQATDIVVSPSTKLLSLAHLHN 1783
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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