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

NucPred

Fetching Q17353 from www.uniprot.org...

The NucPred score for your sequence is 0.93 (see score help below)

   1  MSASSTSSSSTSCPEGGEPSGSCKSSDEGESTLKKRMQQYGIASGYANSS    50
  51  ISTLDRSQYQSLPLNGTRRVTVQFGRMKIVVPWKESDQTVGQLADAALLR   100
 101  YKKARGMANEDRIHVHRLECASDGGILDMDDVLEEVFDLNYDQILAITDE   150
 151  ANGGSTTPTYSQIQKQQHHYAQPLPYARKFDGGPSTPIASAFGSVTVNHQ   200
 201  AHRAASPYNVGFARSNSRDFAPQPTHSKERRDSVVEVSSFDQIPQSGLRV   250
 251  STPKPSRQSEDVIDGKPMNQPILRSSLRTEASGSRTEEATPVKQSRVTLS   300
 301  PEVEKKLAEQDERKSERRKHYDKNPGRFARGSDRKSRITDALLDARDRIA   350
 351  DQLESQNPAEETKSQMIRVKIDQGPMPGTSLVTFPPIPEKSENEKQLGIE   400
 401  VNAVFDESSELPGTSEPTKLSSVQIMKIEDGGRIAKDGRIRVGDCIVAID   450
 451  GKPVDQMSIIRVRASISDLAAVTSRPVTLIINRSLESFLEQESSAKPIQS   500
 501  ALQQANTQYIGHTTVVELIKSSNGFGFTVTGRETAKGERLFYIGTVKPYG   550
 551  VALGHLKSGDRLLEINGTPTGQWTQSEIVEKLKETMVGEKIKFLVSRVSQ   600
 601  SAIMSTSASSENKENEETLKVVEEEKIPQKLPLPALMTPPVPKDTPALSP   650
 651  SGASRFEIVIPFINGSSSAGLGVSLKARVSKKSNGSKVDCGIFIKNVMHG   700
 701  GAAFKEGGLRVDDRIVGVEDIDLEPLDNREAQAALAKKLKEVGMISSNVR   750
 751  LTISRYNECNPGQISRDLSRITVDASSPSPSSRMSSHTAPDSLLPSPATR   800
 801  GTSSSGADSSHSRQSSASSAVPAVPARLTERDSIVSDGTSRNDESELPDS   850
 851  ADPFNREGLGRKSLSEKRGMGAAADPQHIKLFQDIKHQRQNSAPTSSTQK   900
 901  RSKSQPRSSSQRNYRSPMKLVDLPTTAAASASTNSQNLDDSDMLNRRSQS   950
 951  MESINRPVESILRGTGQIPTGSSSKVQFMQAASPDQHPFPPGAALLRLKN  1000
1001  EESRSRDKSRRKSMGNPFSAMRNFFGFGSKSRDASPEKTPTESVQLRSVE  1050
1051  RPKSIIDERNNGSSERAPPPLPPHQSQRRGSGGNVFVDYGEPYGLIPQYP  1100
1101  HNTTSGYESYADSELYDRYAAHRYHPRGGPIIDEDEYIYRQQSTSGNSPI  1150
1151  NTSSYVNYGLPASNAYHVGSRIPPQTSSGSISKTSGAMRRVYPAEYDEDV  1200
1201  AYHQQIPQQSTRYQQGSGSGRGNADYHHMFNSWFAYTGGGAVGAAPVIKS  1250
1251  SYGSSPVRIAAASAIERGESFVVEPVSGSSASATDRRGRSTSSGAVASGS  1300
1301  SSTGFQYAAKEKYADARSGKFNGGSTRLFIPRHGGGLSAAAFATNFGGEA  1350
1351  YETRGGGAGGSPSQYRRRDQGPPHRFPQY                       1379

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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