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

NucPred

Fetching Q98936 from www.uniprot.org...

The NucPred score for your sequence is 0.71 (see score help below)

   1  MRRLLQPCWWIFFLKITSSVLHDVVCFPALTEGYVGSLHESRHGSSVQIR    50
  51  RRKASGDPYWGYSGTYGPEHWVTSSEKCGGSHQSPIDIVDHQAHVLYEYQ   100
 101  ELQLDGFDNESSNKTWMKNTGKTVAILLKDDYFVSGAGLPGRFKAEKVEF   150
 151  HWGQSNGSAGSEHSINGKRFPVEMQIYFYNPDDFDSFGTAVLENREVGAM   200
 201  AVFFQVSQRDNSALDPIIRGLKGVVHHEKETFLDPFVLRDLLPTSLGSYY   250
 251  RYTGSLTTPPCSEIVEWIIFRKPVPISYHQLEAFYSIFTTEQQDHVKSVE   300
 301  YLRNNFRPQQRLNNRKVSKSAVKDAWSQDMTDILENPLGTEASKACSTPP   350
 351  VNMKVQPVNRTALLVTWNQPETIYHPPIMNYMISYSWTKNEDEKEKTFTK   400
 401  DSDKDLKAIISHVSPDILYLFRVQAVCRNEMRSDFSQTMLFQANTTRIFE   450
 451  GTRIVKTGVPTASPASSADMAPISSGSSTWTSSGLPFSFVSMATGMGPSS   500
 501  SGSQATVASVVTSTLLAGLGFSGSSISSFPSSVWPTRLPTAAAPTKQAGR   550
 551  PVVATTEPAAASPGPERDSALTKDGEGAEEGEKDEKSESEDGEREHEEED   600
 601  EKEAEKKEKSRATAAAEARNSTEPSVATASPNWTAEEEGNKTVSGEEPNQ   650
 651  NVVPKAGRPEEESFTDADTQPQPLPSTQVPPAFTDELYLEKIPRRPETTR   700
 701  KPLPKDNRFLEEYPSDNKFITINPADKNSSSMATRPSPGKMEWIIPLIVV   750
 751  SALTFVCLILLIAVLVYWRKCFQTAHFYVEDSSSPRVVPNESIPIIPIPD   800
 801  DMEAIPVKQFVKHISELYSNNQHGFSEDFEEVQRCTADMNITAEHSNHPD   850
 851  NKHKNRYINILAYDHSRVKLRPLPGKDSKHSDYINANYVSGYNKAKAYIA   900
 901  TQGPLKSTFEDFWRMIWAQHTGIIVMITNLVEKGRRKCDQYWPTENSEEY   950
 951  GNIIVTLKSTNIHACYTVRPLHGQEHKDEKGSERKPKGRQNERTVIQYHY  1000
1001  TQWPDMGVPEYALPVLTFVRRSSAARTPHMGPVVVHCSAGVGRTGTYIVI  1050
1051  DSMLQQIKDKSTVNVLGFLKHIRTQRNYLVQTEEQYIFIHDALLEAILGK  1100
1101  ETEVSANQLHSYVNSILIPGIGGKTRLEKQFKLVTQCNAKYVECFSAQKD  1150
1151  CNKEKNRNSSVVPSERARVGLAPLPGMKGTDYINASYIMGYYRSNENVIT  1200
1201  QHPLPHTTKDFWRMIWDHNAQIIVMLPDNQSLAEDEFVYWPSREESMNCE  1250
1251  AFTVTLISKDRLCLSNEEQIIIHDFILEATQDDYVLEVRHFQCPKWPNPD  1300
1301  APISSTFELINVIKEEALTRDGPTIVHDEYGAVSAGTLCALTTLSQQLEN  1350
1351  ENAVDVFQVAKMINLMRPGVFTDIEQYQFLYKAMLSLVSTKENGNGPMTL  1400
1401  DKNGAVMASDESDPAESMESLV                              1422

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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