SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q9UKX2 from www.uniprot.org...

The NucPred score for your sequence is 0.94 (see score help below)

   1  MSSDSELAVFGEAAPFLRKSERERIEAQNRPFDAKTSVFVAEPKESFVKG    50
51 TIQSREGGKVTVKTEGGATLTVKDDQVFPMNPPKYDKIEDMAMMTHLHEP 100
101 AVLYNLKERYAAWMIYTYSGLFCVTVNPYKWLPVYKPEVVTAYRGKKRQE 150
151 APPHIFSISDNAYQFMLTDRENQSILITGESGAGKTVNTKRVIQYFATIA 200
201 VTGEKKKEEITSGKIQGTLEDQIISANPLLEAFGNAKTVRNDNSSRFGKF 250
251 IRIHFGTTGKLASADIETYLLEKSRVVFQLKAERSYHIFYQITSNKKPEL 300
301 IEMLLITTNPYDYPFVSQGEISVASIDDQEELMATDSAIDILGFTNEEKV 350
351 SIYKLTGAVMHYGNLKFKQKQREEQAEPDGTEVADKAAYLQSLNSADLLK 400
401 ALCYPRVKVGNEYVTKGQTVEQVSNAVGALAKAVYEKMFLWMVARINQQL 450
451 DTKQPRQYFIGVLDIAGFEIFDFNSLEQLCINFTNEKLQQFFNHHMFVLE 500
501 QEEYKKEGIEWTFIDFGMDLAACIELIEKPMGIFSILEEECMFPKATDTS 550
551 FKNKLYDQHLGKSANFQKPKVVKGKAEAHFALIHYAGVVDYNITGWLEKN 600
601 KDPLNETVVGLYQKSAMKTLAQLFSGAQTAEGEGAGGGAKKGGKKKGSSF 650
651 QTVSALFRENLNKLMTNLRSTHPHFVRCIIPNETKTPGAMEHELVLHQLR 700
701 CNGVLEGIRICRKGFPSRILYADFKQRYKVLNASAIPEGQFIDSKKASEK 750
751 LLASIDIDHTQYKFGHTKVFFKAGLLGLLEEMRDDKLAQLITRTQARCRG 800
801 FLARVEYQRMVERREAIFCIQYNIRSFMNVKHWPWMKLFFKIKPLLKSAE 850
851 TEKEMATMKEEFQKIKDELAKSEAKRKELEEKMVTLLKEKNDLQLQVQAE 900
901 AEGLADAEERCDQLIKTKIQLEAKIKEVTERAEDEEEINAELTAKKRKLE 950
951 DECSELKKDIDDLELTLAKVEKEKHATENKVKNLTEEMAGLDETIAKLTK 1000
1001 EKKALQEAHQQTLDDLQAEEDKVNTLTKAKIKLEQQVDDLEGSLEQEKKL 1050
1051 RMDLERAKRKLEGDLKLAQESIMDIENEKQQLDEKLKKKEFEISNLQSKI 1100
1101 EDEQALGIQLQKKIKELQARIEELEEEIEAERASRAKAEKQRSDLSRELE 1150
1151 EISERLEEAGGATSAQIEMNKKREAEFQKMRRDLEEATLQHEATAATLRK 1200
1201 KHADSVAELGEQIDNLQRVKQKLEKEKSEMKMEIDDLASNVETVSKAKGN 1250
1251 LEKMCRTLEDQLSELKSKEEEQQRLINDLTAQRGRLQTESGEFSRQLDEK 1300
1301 EALVSQLSRGKQAFTQQIEELKRQLEEEIKAKNALAHALQSSRHDCDLLR 1350
1351 EQYEEEQESKAELQRALSKANTEVAQWRTKYETDAIQRTEELEEAKKKLA 1400
1401 QRLQAAEEHVEAVNAKCASLEKTKQRLQNEVEDLMLDVERTNAACAALDK 1450
1451 KQRNFDKILAEWKQKCEETHAELEASQKEARSLGTELFKIKNAYEESLDQ 1500
1501 LETLKRENKNLQQEISDLTEQIAEGGKRIHELEKIKKQVEQEKCELQAAL 1550
1551 EEAEASLEHEEGKILRIQLELNQVKSEVDRKIAEKDEEIDQLKRNHIRIV 1600
1601 ESMQSTLDAEIRSRNDAIRLKKKMEGDLNEMEIQLNHANRMAAEALRNYR 1650
1651 NTQGILKDTQIHLDDALRSQEDLKEQLAMVERRANLLQAEIEELRATLEQ 1700
1701 TERSRKIAEQELLDASERVQLLHTQNTSLINTKKKLETDISQMQGEMEDI 1750
1751 LQEARNAEEKAKKAITDAAMMAEELKKEQDTSAHLERMKKNMEQTVKDLQ 1800
1801 LRLDEAEQLALKGGKKQIQKLEARVRELEGEVESEQKRNAEAVKGLRKHE 1850
1851 RRVKELTYQTEEDRKNILRLQDLVDKLQAKVKSYKRQAEEAEEQSNTNLA 1900
1901 KFRKLQHELEEAEERADIAESQVNKLRVKSREVHTKVISEE 1941

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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