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

NucPred

Fetching Q7PCJ6 from www.uniprot.org...

The NucPred score for your sequence is 0.98 (see score help below)

   1  MNIFFQRKLVKYFGNIKKKKHYLTVSEECNHYVFVKKTYVSSVFLKSLNK    50
  51  VNKTCNTNSGILYNQYSLGYINMHKQLPSLIRNKSKTIGNYGVAHPSRLV   100
 101  KCLVTKNATAPFFNFTFDKGRLKNLVSWTLENYGQYKTVELLEQLKKTGF   150
 151  EYATKAGISLGLDDLKIPPKKKILLLEAEQLTKLTIHQYQRGDITAVERF   200
 201  QRLIDTWHRTSEQLKQEVINYFEETDILNPVYMMAFSGARGNISQVRQLV   250
 251  GMRGLMSDPQGQIIDFPIQSNFREGLTLTEYIISSYGARKGIVDTALRTA   300
 301  NAGYLTRRLVDVAQHVIISHYDCGTHKGIFLTDMKEGNKTIVSAQSRIIG   350
 351  RVLARDIYKPNSTITIAKRNQEISTDVAFEIGKVTNRIFVRSALTCNTTK   400
 401  LLCQLCYGWSLAQGNLVSVGEAVGVIAAQSIGEPGTQLTMRTFHTGGVFS   450
 451  GDVSDEIRAPYNGFVYYDNKIPGILIRTLDGKILFLTKSEGTLIFTADPN   500
 501  FNKPQALTDSFLNSGHGEKEKYEIKKYKIPAYTLLFIRNGESVLQKQVLA   550
 551  QITMISTKPNMRDTAELVIKAELEGLFYAKNLQVQKKILGPKPKFIGEGK   600
 601  QNVLLDPKAMEIIVKARGWNFAWVLSGKRYEFPLLLKSFASSGDLITPNT   650
 651  IMAKHNLQLSSPFLNVGTALQVGNSTDIPKLPLLGATSKFRAMSGVLTSS   700
 701  IVRRYPTKINSSSRPKVNQFGKSNLFYTQVLQNKVNALNGEVDSYSETAK   750
 751  LPYWKNLNLKFVQKLNLSKFFKQYNLKNKTKTKLAFYSLVQANALQASKL   800
 801  DSFKANTFQSKQLGRPIFIYRHCLLTKESNKKTVKLIHNIQLQQSVLFLK   850
 851  IKKIKFYKMGYFQLVNSNKTAFLVSLSHNKNRLTLYHNKFNYNNSYNDII   900
 901  VLPTSLTQDISSKKQIALKRFKPAYNLFQWFYPSLIKPSSKEAQNLTVQQ   950
 951  VEFFNAREQMHFNSRYSKTDFVQLLQNPFKLDFNKALHRPLCISESYKEI  1000
1001  PTGEVSISPQNSVNLPMNTFFNTDGVPDKKPNYTNMYAFWLKQQVLKSYK  1050
1051  KRYKKYKLTSILKSHLSDKIYLPASVELQHQSQQNNLLSLTKEFKALNSS  1100
1101  KYLKNNQLQSRPLLYKQEKLLKTLVLKKWFKSNLLYVNSAISKEENMDGS  1150
1151  STQMSAHSLPKRQRKGAKVSKINRFLKIDNYIKQLTYFKTSKIMRIKHSF  1200
1201  NSLRVDFKFTSSKLVCPKRKRNLQIVCTLRGEEQTSVRESSVTHVNNLIC  1250
1251  SSKYKRSIRLQNDSHLFTTTKKAKVPFTGYSDSHQAGKVLQSSQISSTKP  1300
1301  KTQSSFTFFEYFKLKLAQSGTKAILKHFKSLKAVNISKVSMDQSRNKGFN  1350
1351  NWGKSSSECLNTSLGDKQALYFKTKTEANLQLLTLVLENFYRKKQFANLL  1400
1401  AKNLTILNKNLKLKKNHVHFLLYYYNKLSVQKPNAIFLLTHMLAKANKIQ  1450
1451  ISYSYVVPTSPKSKLNLDRGNLTTTNQKNVASLGGYQPRSAGTKYYSEGI  1500
1501  RNRTRKNNKSSMTKITKIFNQLKLDTQFVIILLSPHKLLHHSHQLPQNTL  1550
1551  GIYDKFNSNFEMAKVTLLTHPLWFNQFQMKSIKEVGNKFINTRNNSLLEN  1600
1601  YVILLLSNNYVFNFLSIKSEQINLPEEQESNLSLQEYKSPANCQEKALPS  1650
1651  DNKKIIFDLKKLQHIVLTEQYKNNLRWVKNKKPRFLPKDIDINTLEVNLD  1700
1701  RIKPKNKLSNLNPTQQLQLSNSLLEFVKTIKINPTDLAKVYSGSANTVMS  1750
1751  IENCQPSLEVSNTFFLKTQKLLKLINKTKHSIALNQTKFINTSLLKGHLV  1800
1801  AYARPVFIITNKAEPFIAKQKKDLLLLPKNATINVLIKPQQEKNSLNKAI  1850
1851  FPLGGNAANNHSIFVSPNTNKLANPNVSYLKKHIYFNQMPFFDSPFRNAS  1900
1901  YLFSYTENSIKPLTKVDFMHSFAQKNHKLLPESEREKRLQFKALHIPKQP  1950
1951  CLNICFKSNSNLIFNSKTTNSLVIRKTTTNYKYNIDLSEGVDFKKLKTNM  2000
2001  FSARKCNNFKLDTALESKLFKGRPTLLNYKNVVTQTNYFSPFEGELLATK  2050
2051  TYKNYLDLNPYQSLKVGLMQLNTSSALATLVPKTKRAGNKSSKQKIKLNQ  2100
2101  GELSTELGSTIPQSGKHKTKTEKMRMAMFKYYLKTINSQKIIGNKGWSRF  2150
2151  NLILTKKDFITLKYNNTLYPNFTIFSEIQKHWQPRIQMTKPIRPISYDEV  2200
2201  CLNYLFSEEITNQVKLQTLAKLNKEIHFNKSYHFNLKNRWLKQKLVINAS  2250
2251  TSKSTSSLLTNIHMDQGEDKKTSVGVSLKLPAIYLCEEEGLHTNLFIHKA  2300
2301  QRLLYKIKIILSEKALNVEHYSWNKNSSLQKTFGYQNGIIQAKSLLHTLP  2350
2351  TNLNYNLKWINYKQKNIFTTNKVGFFFLKGNTFFNTSQKLFNKKITKQTT  2400
2401  FLNATIYNFGRNNKNNLISYNNSNFLENTHFVDLSLCLELQKIYLNKNYL  2450
2451  NLKPILDKTIHCQKPTKVLFKKSGFSKKQHYYLEFLNTKNHRRLIGLKEF  2500
2501  NDYHMSYSKSQTKEMSNFIDSYYFVKPINMDCAHYIKHELVLYNDLITHF  2550
2551  ASLNLYISREHGLKSLSAFFINILKIFITSNQSQISLAPIGIDKYTNIYI  2600
2601  PEGEGEKDMTKNVFQVIKKSGQLIQMNKEKMTLRLGQPLVISPRSTIHAT  2650
2651  HGDFIRYKTPVVTLTYQQLKTGDIVQGIPKIEQLFEARTTKRGRLFRDNV  2700
2701  TNLLTGLFLKYFIKSTYLLRKTMIGFSKKRWKKSIKYTLPVNKQPNMPRV  2750
2751  NHTLNTTVGTELGRQSKTKVDKNKHSIAINKNLNYSNFINNKQNQTIILA  2800
2801  LALQWAVKQSFYKIQQIIVDGILRVYRSQGVSIADKHVEIVVKQMTSKVR  2850
2851  IINSNASKMSEYMFSLDTIKAGEMPETDLPEEEVSLQQNKAVSKQNVVAQ  2900
2901  TGKKRKKRLRKSKLSERDVITTKRTEGIDSSKIPSSNIPEGKVTQNNKRK  2950
2951  STRKNVSLADRELKTRNTLSNTTKPIQISQVFEHKVLNQLLSNNLDGPTG  3000
3001  LFPGEIVDIDFVENINTFLLKTASVDRASRETLSTDPLNPNNQVAFAIEP  3050
3051  IKYEPIVLGITRASLEVESFLSAASFQQTTRVLSQAALYKKKDFLKGLKE  3100
3101  NIIIGNLIPAGTGFLSSLNI                                3120

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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