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

NucPred

Fetching Q99683 from www.uniprot.org...

The NucPred score for your sequence is 0.54 (see score help below)

   1  MSTEADEGITFSVPPFAPSGFCTIPEGGICRRGGAAAVGEGEEHQLPPPP    50
  51  PGSFWNVESAAAPGIGCPAATSSSSATRGRGSSVGGGSRRTTVAYVINEA   100
 101  SQGQLVVAESEALQSLREACETVGATLETLHFGKLDFGETTVLDRFYNAD   150
 151  IAVVEMSDAFRQPSLFYHLGVRESFSMANNIILYCDTNSDSLQSLKEIIC   200
 201  QKNTMCTGNYTFVPYMITPHNKVYCCDSSFMKGLTELMQPNFELLLGPIC   250
 251  LPLVDRFIQLLKVAQASSSQYFRESILNDIRKARNLYTGKELAAELARIR   300
 301  QRVDNIEVLTADIVINLLLSYRDIQDYDSIVKLVETLEKLPTFDLASHHH   350
 351  VKFHYAFALNRRNLPGDRAKALDIMIPMVQSEGQVASDMYCLVGRIYKDM   400
 401  FLDSNFTDTESRDHGASWFKKAFESEPTLQSGINYAVLLLAAGHQFESSF   450
 451  ELRKVGVKLSSLLGKKGNLEKLQSYWEVGFFLGASVLANDHMRVIQASEK   500
 501  LFKLKTPAWYLKSIVETILIYKHFVKLTTEQPVAKQELVDFWMDFLVEAT   550
 551  KTDVTVVRFPVLILEPTKIYQPSYLSINNEVEEKTISIWHVLPDDKKGIH   600
 601  EWNFSASSVRGVSISKFEERCCFLYVLHNSDDFQIYFCTELHCKKFFEMV   650
 651  NTITEEKGRSTEEGDCESDLLEYDYEYDENGDRVVLGKGTYGIVYAGRDL   700
 701  SNQVRIAIKEIPERDSRYSQPLHEEIALHKHLKHKNIVQYLGSFSENGFI   750
 751  KIFMEQVPGGSLSALLRSKWGPLKDNEQTIGFYTKQILEGLKYLHDNQIV   800
 801  HRDIKGDNVLINTYSGVLKISDFGTSKRLAGINPCTETFTGTLQYMAPEI   850
 851  IDKGPRGYGKAADIWSLGCTIIEMATGKPPFYELGEPQAAMFKVGMFKVH   900
 901  PEIPESMSAEAKAFILKCFEPDPDKRACANDLLVDEFLKVSSKKKKTQPK   950
 951  LSALSAGSNEYLRSISLPVPVLVEDTSSSSEYGSVSPDTELKVDPFSFKT  1000
1001  RAKSCGERDVKGIRTLFLGIPDENFEDHSAPPSPEEKDSGFFMLRKDSER  1050
1051  RATLHRILTEDQDKIVRNLMESLAQGAEEPKLKWEHITTLIASLREFVRS  1100
1101  TDRKIIATTLSKLKLELDFDSHGISQVQVVLFGFQDAVNKVLRNHNIKPH  1150
1151  WMFALDSIIRKAVQTAITILVPELRPHFSLASESDTADQEDLDVEDDHEE  1200
1201  QPSNQTVRRPQAVIEDAVATSGVSTLSSTVSHDSQSAHRSLNVQLGRMKI  1250
1251  ETNRLLEELVRKEKELQALLHRAIEEKDQEIKHLKLKSQPIEIPELPVFH  1300
1301  LNSSGTNTEDSELTDWLRVNGADEDTISRFLAEDYTLLDVLYYVTRDDLK  1350
1351  CLRLRGGMLCTLWKAIIDFRNKQT                            1374

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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