 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UT02 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MTDLEIETVSRVSSNPDEVKGSSVVEEEPDSESTIKSRVSDEIDEHDSIP 50
51 EQSNTEKSIEINDKNLEAEKDIESNLSLRPPEDDLDSRSIESEQTGTLSK 100
101 QTTSTSEAPQDLKIWKNISNASNQNSGKLNPQLFVIEAFKHMSKAKATKR 150
151 HKKLREAINNVQIELQKQPFLLPEVILEPLVMACQTNSTTLLTITLDCFA 200
201 KLIDYNYFDSPTLNPSDITLMERVVNTIASCFCGESTPERVQLQIVKALL 250
251 AAITSERTIIRHSFLLTAVRQTYNIFLLCKDSTTQAIAQVALLQMVDSVF 300
301 QRLSTVLNHEREFSTINMNKSSSNGTPDRANSPIPSQLSENKLTLESFEH 350
351 RKSFDQVREEAPLEEDSLEQQLLRDAFLLIRALCKLSIKNIPYEHEYDLK 400
401 SQSMRSKLMSLHLIYHILRTYMNILSDINVKIRSPTSTPTPLIDAVKQYI 450
451 CLALAKNVVSHVLPVFEISCEIFWLILSELKNFFKSELEVFFTEIFFPIL 500
501 EMRTSSNQQKIVLLNIFHRMCEEPQTLIELYLNYDCISGNTENIYERAIV 550
551 TLSRIASQSTSDPPPSFVFRDDQLVIDKPGFVYHTLNDIPQLNSSTIGSY 600
601 VHSHNPPYFDYQIRLKSYRCLISTLSSLFTWCNQTFAPTVEITAKDDETE 650
651 STSKGEEPQKSKSEPPSAGINSTSMDNLESSGQALATDDPSQFENLKHRK 700
701 KQLQEAIQKFNYKPKEGIKILLSSHFIASKTPTDIAKFLISTEGLDKAVL 750
751 GEYLGEGNDENIAIMHSFVDHMSFNDIPFVNALRSFLQKFRLPGEAQKID 800
801 RFMLKFAEKYIDDNLGVFKNADTAYILAYSIIMLNTDLHSPQVKNRMTCQ 850
851 DFIKNNRGVDDGANLSDSFLTEVYEEIQKNEIVLKDEQDPTSNFPEIPGT 900
901 SNLSFAANISNALATVGRDLQREAYYMASNKMANKTEALFKDLIREQRER 950
951 GKLSGNDIYYTARHFEHVCPMFEAVWMPILAAFSEPLQLSSDPALIQLSL 1000
1001 DGFRLAMNVIFFFSMDLPRNAFMQTLTKFTHLNNTSELKWTNMHALKTLL 1050
1051 EISLAHGDKLRDSWKDVLLCISQLERVQLISAGVDINSLPDVSTTKPLRK 1100
1101 SLDKNIRQSRSGSISLKHSKSFQSASTHSTKSSSVEIVREYSSREVVMAV 1150
1151 DMLFSNTRNLGSEGIYDFVKALIEVSWEEIECSLELSNPRLFSLQKLVEI 1200
1201 SYYNMRRIRMEWSSIWSLLGTYFTQVSCHENSIIASFALDSLRQFSMQFL 1250
1251 EIEELSHFKFQKDFLQPFSHAMENSQDLKIKDLVLRCIDQMIKARYQNIR 1300
1301 SGWRTIFHILAYASKIENLLVLQCAISVVSSLGHEHISCVLTQGAYIDLI 1350
1351 SCITKFAKLNGNQKFCLSCVDMLKNLEHELIKHLKHMKKESVYSKKLEEE 1400
1401 YWLPFLLSFNEIICEASDLEVRSKALKVLFDCLYRHADDFDEEFWETVSN 1450
1451 KALLSIFSILSITNSQRLYLAKNTEETEVWMLTTMVEALKAFIELIKNLF 1500
1501 ERLHFLLPKALNLLEKCICQENSMISKVGLSCFSQFVLKNKNQFKDVDWD 1550
1551 EIINSINQLLQMTLPIELRDPSLYPQVNSDSSLEDVKENSFRPHEISRFN 1600
1601 SQSVFKSKKHHLKSIVVKCTLQLLMLNCLWELFHSDNMLTNIPKRKMVKL 1650
1651 LDILKQSWEFAESFNSDFEIRAKILSSGIVEHMPNLLSQEALCAKLYFYT 1700
1701 AFECMSSLKSDSHDTEEYNDLMDVFQKKIYLASQLVLHGFQRVIGDNPVK 1750
1751 GVAAFQPVIAALVSYINSLDEIQFSRGKSEFYQLLCAIVACGHIDQQLGT 1800
1801 SLSNAFLRYAC 1811
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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