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

NucPred

Fetching P40450 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MDSSPNKKTYRYPRRSLSLHARDRVSEARKLEELNLNDGLVAAGLQLVGV    50
  51  ALEKQGTGSHIYMKQKNFSANDVSSSPMVSEEVNGSEMDFNPKCMPQDAS   100
 101  LVERMFDELLKDGTFFWGAAYKNLQNISLRRKWLLICKIRSSNHWGKKKV   150
 151  TSSTTYSTHLATNELAENAHFLDGLVRNLSTGGMKLSKALYKLEKFLRKQ   200
 201  SFLQLFLKDEIYLTTLIEKTLPLISKELQFVYLRCFKILMNNPLARIRAL   250
 251  HSEPLIRWFTELLTDQNSNLKCQLLSMELLLLLTYVEGSTGCELIWDQLS   300
 301  ILFTDWLEWFDKILADDIAIHSSLYLNWNQLKIDYSTTFLLLINSILQGF   350
 351  NNKTALEILNFLKKNNIHNTITFLELAYKDDPNSVVIMEQIKQFKSKESA   400
 401  IFDSMIKTTNDTNSLHPTKDIARIESEPLCLENCLLLKAKDSPVEAPINE   450
 451  IIQSLWKILDSQKPYSESIKLLKLINSLLFYLIDSFQVSTNPSFDETLES   500
 501  AENVDYVFQDSVNKLLDSLQSDEIARRAVTEIDDLNAKISHLNEKLNLVE   550
 551  NHDKDHLIAKLDESESLISLKTKEIENLKLQLKATKKRLDQITTHQRLYD   600
 601  QPPSLASSNLSIAGSIIKNNSHGNIIFQNLAKKQQQQQKISLPKRSTSLL   650
 651  KSKRVTSLSSYLTDANNENESQNESEDKSKDSLFQRSTSTINFNIPSMKN   700
 701  ITNMQNVSLNSILSELEFSNSLGTQPNYQSSPVLSSVSSSPKLFPRLSSD   750
 751  SLDNGIQLVPEVVKLPQLPPPPPPPPPPPLPQSLLTEAEAKPDGVSCIAA   800
 801  PAPPPLPDLFKTKTCGAVPPPPPPPPLPESLSMNKGPSNHDLVTPPAPPL   850
 851  PNGLLSSSSVSINPTTTDLKPPPTEKRLKQIHWDKVEDIKDTLWEDTFQR   900
 901  QETIKELQTDGIFSQIEDIFKMKSPTKIANKRNAESSIALSSNNGKSSNE   950
 951  LKKISFLSRDLAQQFGINLHMFSQLSDMEFVMKVLNCDNDIVQNVNILKF  1000
1001  FCKEELVNIPKSMLNKYEPYSQGKDGKAVSDLQRADRIFLELCINLRFYW  1050
1051  NARSKSLLTLSTYERDYYDLIFKLQKIDDAISHLNRSPKFKSLMFIITEI  1100
1101  GNHMNKRIVKGIKLKSLTKLAFVRSSIDQNVSFLHFIEKVIRIKYPDIYG  1150
1151  FVDDLKNIEDLGKISLEHVESECHEFHKKIEDLVTQFQVGKLSKEENLDP  1200
1201  RDQIIKKVKFKINRAKTKSELLIGQCKLTLIDLNKLMKYYGEDPKDKESK  1250
1251  NEFFQPFIEFLAMFKKCAKENIEKEEMERVYEQRKSLLDMRTSSNKKSNG  1300
1301  SDENDGEKVNRDAVDLLISKLREVKKDPEPLRRRKSTKLNEIAINVHEGD  1350
1351  VKTRKDEDHVLLERTHAMLNDIQNI                           1375

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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