  |  Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. | 
NucPred
Fetching  P24339  from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
   1  MAPRVAPGGSQQFLGKQGLKAKNPVSTPNSHFRSASNPRKRREPPTIDTG    50
  51  YPDRSDTNSPTDHALHDENETNINVVVRVRGRTDQEVRDNSSLAVSTSGA   100
 101  MGAELAIQSDPSSMLVTKTYAFDKVFGPEADQLMLFENSVAPMLEQVLNG   150
 151  YNCTIFAYGQTGTGKTYTMSGDLSDSDGILSEGAGLIPRALYQLFSSLDN   200
 201  SNQEYAVKCSYYELYNEEIRDLLVSEELRKPARVFEDTSRRGNVVITGIE   250
 251  ESYIKNAGDGLRLLREGSHRRQVAATKCNDLSSRSHSIFTITLHRKVSSG   300
 301  MTDETNSLTINNNSDDLLRASKLHMVDLAGSENIGRSGAENKRARETGMI   350
 351  NQSLLTLGRVINALVEKAHHIPYRESKLTRLLQDSLGGKTKTSMIVTVSS   400
 401  TNTNLEETISTLEYAARAKSIRNKPQNNQLVFRKVLIKDLVLDIERLKND   450
 451  LNATRKKNGVYLAESTYKELMDRVQNKDLLCQEQARKLEVLDLNVKSSRE   500
 501  QLQYVSKSNQEHKKEVEALQLQLVNSSTELESVKSENEKLKNELVLEIEK   550
 551  RKKYETNEAKITTVATDLSQYYRESKEYIASLYEKLDRTERNNKENENNF   600
 601  WNLKFNLLTMLRSFHGSFTDETNGYFTLLNDFNASMEELLNTHSNQLLIS   650
 651  MTKITEHFQSLDEALQSARSSCAVPNSSLDLIVSELKDSKNSLLDALEHS   700
 701  LQDISMSSQKLGNGISSELIELQKDMKESYRQLVQELRSLYNLQHTHEES   750
 751  QKELMYGVRNDIDALVKTCTTSLNDADIILSDYISDQKSKFESKQQDLIA   800
 801  NIGKIVSNFLQEQNESLYTKADILHSHLNDTNSNIRKANEIMNNRSEEFL   850
 851  RNAASQAEIVGANKERIQKTVENGSQLLDSKSKAIHSNSRSMYDHCLALA   900
 901  ESQKQGVNLEVQTLDRLLQKVKEHSEDNTKEKHQQLLDLLESLVGNNDNL   950
 951  IDSIKTPHTELQKITDHVLKGTTSLANHTNELLGLGDESLCNLETTIEDT  1000
1001  SLVKLETTGDTPSKRELPATPSWTRDSSLIKETTNLNLDSDKKFVRETYT  1050
1051  SSNQTNEPDVYDKPSNSSRTSLLRSSRSAYSKMKR                 1085
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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