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

NucPred

Fetching Q32065 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MTFLNHYTYLFSIPEKQADKVSGILRLAQARPIETLQNERINKQLNAFLK    50
  51  TYKFEKLITNYKKMQSFIPNNSLNGNKTNSSTNKLYATSLNVFPENPPLM   100
 101  VRKAVSDEADKFSKFTYSKVQVVTNNLNNGMNSKEFIKANNLKPSLRAAE   150
 151  SLVLNHLTYNKFKENLYFKTNNIQPTKSKSTSLFFLNILSNSKPRTCSDF   200
 201  LSSPKIRKTWFRNTAWSLQTQQHRSSNGINLSLQLPYALGPSVPAGASGQ   250
 251  NMYELPVAQSSSRFGTYYFLQKLLSKYLDVWNASADNGSVLSNSENIKLN   300
 301  FSMVSLLDSKMAIQTPNSLYFVFTQLNQKTFLSYWLLPVAGLALLTPTLL   350
 351  TLTGQSVSVQKFNSFINKKTDMMVLSNTEMPSKSFGTPTLFGTSVEIYLP   400
 401  NSYMPKGEGESGINRVNSSINAVKKNTVTANLVLDSESQEVATSFQNDLI   450
 451  SIKYCFNNLYNYISNKTALSTKNLFLFSAIKSNATKHKRTQSFFSVENTT   500
 501  TLGNNSNFVKGHFKSSINAFSSYLPSTNVHSMIPLTSLPYLKAISPLYSK   550
 551  FMIDHSLKFITPKTTLKLLQHKLNKSPKQMYTKTQNFTGLRDLRALNSFS   600
 601  FGQVNFRTNHFLHSNSRPLNHYNQALKLINGYEQYKNNLQINCNKTLDLN   650
 651  TKNKLVYQVHKSHLFNQKCSQIVYKQSLYNRDLCTIRGTGTKVVDYFSHG   700
 701  DKLSNKNGIVLDYFVYSNLLFDNKTNTIINKDGKQNITKLKLNLTKTTVP   750
 751  FKTLIKKYTSINSLVANEQTRNNLNLGLIHFNGHLSVVSNANLLTGRPVK   800
 801  FIYYKFDKRLNSYLIYVNQNLKKFIQLNNNFLKPKPLSHQKNKPVEDFNQ   850
 851  YATNNSSPPKTNVFEKSFVEDSSLRKPLTSLRGSKQFLNSLTILFKHQKM   900
 901  FKKKTLKAHKWHSDTQGIFRKHTNSSFGSANFSNGPEESSLSTRLHIQKK   950
 951  RKAKKQRLETRRQKKRTRFFPRPVWLRSRMFLNFLTERNKYYLNSTITKQ  1000
1001  GFSLPSKDVVTTKLDWLKEDMRPSSLGAYQYKSLLTQKAGNKFQRQSFTE  1050
1051  VVSTMEYINGIHKALNNSIFNKIVRKSLLSSSQNPLKLRLVANYSKMQFM  1100
1101  HRVKLPFYRTLKHSEGTKNLANKKQNLRDIKIKANYNNFKSQKANNQPQQ  1150
1151  NDKDKDKDTMFRDFWVWSYNNTQTNAFNQNLWWLLPNLTTKQSNLEFLTS  1200
1201  TYPTAKETQRAKEEIHGNSIPTASKNQIALIRLNWALNKTNINTFTDYSK  1250
1251  RNNLWTTQKLRNQSKNNKTKSLEKQFITNWEKFFLNKNLNIFSKKIISKV  1300
1301  KQKKQKLNYMTSYLNVQSEHNVKIFHNSWWTHLNIKNLVNNQDMVIPVRE  1350
1351  GYFSVGNFNSEFINSAIIKSINNKTLVENYVYSPSSEKETMQLLLMSSSI  1400
1401  LLHLCAIISLVSISQVRCFVKFHLILLYKLSNVYNAILNQLSNKLQKNLP  1450
1451  IYNNINKLNSRYFYMNHQKSQIKQRKKLLTYFSLTLLKKQFVTVKPLQIR  1500
1501  NFASIKNQSSNNSNLTYTDMLPLSLRANKFRGSKYDISIREEEGQSAHIK  1550
1551  PSKSMYAKLNILSLKTIFLKQLLMNKKPSALPSNVGLKSNRETQKSQLIQ  1600
1601  RIKTKELQISLKKNIIGFSKVTKNHILKILFNVIEVFQTAVRNISSFFEK  1650
1651  PAEFTTTWIAYGFLVEWSSDFITIIPENVDIYIWNVFSKIYRTIPLSFIS  1700
1701  TTLGPASTVFDPVTNSTIPIQMGNFNYQKMVAFPILLSLSHLLHRRILYL  1750
1751  FDTLFSTITQPDTDLIARQEKGTLFWDIWADFLVTAADYYNVNVAALSTI  1800
1801  KAEQNSLIENISNDFDNLTMSSKKPFFMPNKGVSNIKNIFWIKKLKEPQL  1850
1851  PESIVQNREVFVRERKRTLKGLFNIYAPQEETLWNNPTSPKNLSDEKISF  1900
1901  KLFNQLNLQLFAEKNKIKPYFEAYFSTTQQKTNIMQSAFPEANLNRWSVN  1950
1951  QFITYQSWHSHNGSNNSNGDLFIDYHPPKTFSHIPALKYNSILQQPIGSL  2000
2001  VCQIYSGLFNKQISKNILLVNPKTTSNNLVDYNVLLIQALAGETEMKIIT  2050
2051  DNAQRYALVNRGFAIGIKLLREVFDAIALNTPCIFLLEDIHAIGERRPML  2100
2101  ISDFGGGMSDDNGSFKEDFFGSQRDEVHEKNQVVYQLTRHAITHYKKPFK  2150
2151  GDYSLAIPTNLYVTDLFLKLPTQSISNLTNVENHNLSIKNKIQHNGTQSL  2200
2201  TETKRNLGGDINKNSYLQLTQFTKTLAPPSTSPFSVLLLKEEKRLKPNKI  2250
2251  VEELPWTSLPGEQLATKPRTSYSVRAKVAMLAELSLSNLSAKLDMITDLL  2300
2301  VIIDSVRSNKGFVVFATTDIPHVLDPALRRPGRLDETICLPNIHTSNILN  2350
2351  FTKNYEIFKSAKDTSNFGKKIILNEMQNLTTTSTQRDMYLSCLPTNNQTH  2400
2401  KTKREGVLTMNLKDYNILLNQVYFAEGTGGILNSQMHKDSLQKSLNFALI  2450
2451  SHSKKLKELNVSKLIGSNGTVSQGNVDQLGVFAGQIVNKQKKSLQQHLPN  2500
2501  SKKSFKKKYKDKAIIYYEVGKFVLNYFLNNQLTQSSIIDKPVSVTNKQTN  2550
2551  DITIFGNDFLNLKTINYLSLYNSKNKILLQLMLIFGGKISQLLSSKNLVK  2600
2601  SLKQASINSYMVEEESGSISSAGMPLGQTHLLPKALSVLAKPMIFSDGYN  2650
2651  NQNLKTATTLLLSFIHKRYLYRKNLIVPKLLSFADGNILDEPPSPPFSSL  2700
2701  LIPAKRFENYKRFFRDTLTGDKMGQRKSQITLLEKLQYHMQLRSIKQLNA  2750
2751  TFSSQENLDFQSNAALTSQKLDTLMSLSTNNLLQNPTNINWYYQNRILKR  2800
2801  HGQYLTNQWWNGQLSEHNAETVFLSDIDWRSSFIKNKNINITKSKNLYRL  2850
2851  TQQKNNTDGLDVLLDFPDTDQYYNPKRRRWLLNNGSWNFWFNFDKLYSEE  2900
2901  IVTTWILESLIQTYKYLHKNTELLDFVTNKFITLGYIAPENANLQNISGF  2950
2951  PSQSELLSTKEIILTNSFKRF                               2971

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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