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

NucPred

Fetching P04932 from www.uniprot.org...

The NucPred score for your sequence is 0.82 (see score help below)

   1  MKIIFFLCSFLFFIINTQCVTHESYQELVKKLEALEDAVLTGYSLFHKEK    50
  51  MILNEEEITTKGASAQSGTSGTSGTSGPSGPSGTSPSSRSNTLPRSNTSS   100
 101  GASPPADASDSDAKSYADLKHRVRNYLLTIKELKYPQLFDLTNHMLTLCD   150
 151  NIHGFKYLIDGYEEINELLYKLNFYFDLLRAKLNDVCANDYCQIPFNLKI   200
 201  RANELDVLKKLVFGYRKPLDNIKDNVGKMEDYIKKNKKTIENINELIEES   250
 251  KKTIDKNKNATKEEEKKKLYQAQYDLSIYNKQLEEAHNLISVLEKRIDTL   300
 301  KKNENIKELLDKINEIKNPPPANSGNTPNTLLDKNKKIEEHEKEIKEIAK   350
 351  TIKFNIDSLFTDPLELEYYLREKNKNIDISAKVETKESTEPNEYPNGVTY   400
 401  PLSYNDINNALNELNSFGDLINPFDYTKEPSKNIYTDNERKKFINEIKEK   450
 451  IKIEKKKIESDKKSYEDRSKSLNDITKEYEKLLNEIYDSKFNNNIDLTNF   500
 501  EKMMGKRYSYKVEKLTHHNTFASYENSKHNLEKLTKALKYMEDYSLRNIV   550
 551  VEKELKYYKNLISKIENEIETLVENIKKDEEQLFEKKITKDENKPDEKIL   600
 601  EVSDIVKVQVQKVLLMNKIDELKKTQLILKNVELKHNIHVPNSYKQENKQ   650
 651  EPYYLIVLKKEIDKLKVFMPKVESLINEEKKNIKTEGQSDNSEPSTEGEI   700
 701  TGQATTKPGQQAGSALEGDSVQAQAQEQKQAQPPVPVPVPEAKAQVPTPP   750
 751  APVNNKTENVSKLDYLEKLYEFLNTSYICHKYILVSHSTMNEKILKQYKI   800
 801  TKEEESKLSSCDPLDLLFNIQNNIPVMYSMFDSLNNSLSQLFMEIYEKEM   850
 851  VCNLYKLKDNDKIKNLLEEAKKVSTSVKTLSSSSMQPLSLTPQDKPEVSA   900
 901  NDDTSHSTNLNNSLKLFENILSLGKNKNIYQELIGQKSSENFYEKILKDS   950
 951  DTFYNESFTNFVKSKADDINSLNDESKRKKLEEDINKLKKTLQLSFDLYN  1000
1001  KYKLKLERLFDKKKTVGKYKMQIKKLTLLKEQLESKLNSLNNPKHVLQNF  1050
1051  SVFFNKKKEAEIAETENTLENTKILLKHYKGLVKYYNGESSPLKTLSEES  1100
1101  IQTEDNYASLENFKVLSKLEGKLKDNLNLEKKKLSYLSSGLHHLIAELKE  1150
1151  VIKNKNYTGNSPSENNTDVNNALESYKKFLPEGTDVATVVSESGSDTLEQ  1200
1201  SQPKKPASTHVGAESNTITTSQNVDDEVDDVIIVPIFGESEEDYDDLGQV  1250
1251  VTGEAVTPSVIDNILSKIENEYEVLYLKPLAGVYRSLKKQLENNVMTFNV  1300
1301  NVKDILNSRFNKRENFKNVLESDLIPYKDLTSSNYVVKDPYKFLNKEKRD  1350
1351  KFLSSYNYIKDSIDTDINFANDVLGYYKILSEKYKSDLDSIKKYINDKQG  1400
1401  ENEKYLPFLNNIETLYKTVNDKIDLFVIHLEAKVLNYTYEKSNVEVKIKE  1450
1451  LNYLKTIQDKLADFKKNNNFVGIADLSTDYNHNNLLTKFLSTGMVFENLA  1500
1501  KTVLSNLLDGNLQGMLNISQHQCVKKQCPQNSGCFRHLDEREECKCLLNY  1550
1551  KQEGDKCVENPNPTCNENNGGCDADAKCTEEDSGSNGKKITCECTKPDSY  1600
1601  PLFDGIFCSSSNFLGISFLLILMLILYSFI                      1630

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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