  |  Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. | 
NucPred
Fetching  P40480  from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
   1  MNETTTKQPLKKRSLSSYLSNVSTRREELEKISKQETSEEEDTAGKHEQR    50
  51  ETLSEEVSDKFPENVASFRSQTTSVHQATQNNLNAKESEDLAHKNDASSH   100
 101  EGEVNGDSRPDDVPETNEKISQAIRAKISSSSSSPNVRNVDIQNHQPFSR   150
 151  DQLRAMLKEPKRKTVDDFIEEEGLGAVEEEDLSDEVLEKNTTEPENVEKD   200
 201  IEYSDSDKDTDDVGSDDPTAPNSPIKLGRRKLVRGDQLDATTSSMFNNES   250
 251  DSELSDIDDSKNIALSSSLFRGGSSPVKETNNNLSNMNSSPAQNPKRGSV   300
 301  SRSNDSNKSSHIAVSKRPKQKKGIYRDSGGRTRLQIACDKGKYDVVKKMI   350
 351  EEGGYDINDQDNAGNTALHEAALQGHIEIVELLIENGADVNIKSIEMFGD   400
 401  TPLIDASANGHLDVVKYLLKNGADPTIRNAKGLTAFESVDDESEFDDEED   450
 451  QKILREIKKRLSIAAKKWTNRAGIHNDKSKNGNNAHTIDQPPFDNTTKAK   500
 501  NEKAADSPSMASNIDEKAPEEEFYWTDVTSRAGKEKLFKASKEGHLPYVG   550
 551  TYVENGGKIDLRSFFESVKCGHEDITSIFLAFGFPVNQTSRDNKTSALMV   600
 601  AVGRGHLGTVKLLLEAGADPTKRDKKGRTALYYAKNSIMGITNSEEIQLI   650
 651  ENAINNYLKKHSEDNNDDDDDDDNNNETYKHEKKREKTQSPILASRRSAT   700
 701  PRIEDEEDDTRMLNLADDDFNNDRDVKESTTSDSRKRLDDNENVGTQYSL   750
 751  DWKKRKTNALQDEEKLKSISPLSMEPHSPKKAKSVEISKIHEETAAEREA   800
 801  RLKEEEEYRKKRLEKKRKKEQELLQKLAEDEKKRIEEQEKQKVLEMERLE   850
 851  KATLEKARKMEREKEMEEISYRRAVRDLYPLGLKIINFNDKLDYKRFLPL   900
 901  YYFVDEKNDKFVLDLQVMILLKDIDLLSKDNQPTSEKIPVDPSHLTPLWN   950
 951  MLKFIFLYGGSYDDKKNNMENKRYVVNFDGVDLDTKIGYELLEYKKFVSL  1000
1001  PMAWIKWDNVVIENHAKRKEIEGNMIQISINEFARWRNDKLNKAQQPTRK  1050
1051  QRSLKIPRELPVKFQHRMSISSVLQQTSKEPFW                   1083
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
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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