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

NucPred

Fetching Q09541 from www.uniprot.org...

The NucPred score for your sequence is 0.32 (see score help below)

   1  MIVIRNWQRISYLSFCRFSTRSLSIQQFQKTPLLLVVRQQGSARLKMTSS    50
  51  PPEIVPQQPLDALLLNKTDTEQEIFLTKYPNYDGRDILIAILDTGVDPSL   100
 101  PGMQVTTTGERKMFDVIDCSGAGDVDTSITRTVKDGVIEGISGRKLAIPE   150
 151  KWKCPTGQYHVGLKPIFELYTKGVKSRVISERKEDVVGPSHNIAASEALK   200
 201  QLTEHEKVVGGTSEKTSDKWAREDFACKVDFLKSMASVADVGPVADVVTW   250
 251  HDGEMWRVCIDTSFRGRLGLGNVLGTFRETGDYAYLTNKDSVVYTVRVSP   300
 301  DGNLTEIVVPSGAHGSHVAGIAAANYPDNPQKNGLAPGAKILSLNIGDHR   350
 351  LGAMETGQAMTRAFNMCAELNVDIINMSFGEGTHLPDVGRVIEEARRLIN   400
 401  RRGVIYVCSAGNQGPALSTVGAPGGTTTGVIGIGAYLTSESADTLYGVYK   450
 451  PVESSIYPWSSRGPCQDGKLGVSLVAPAAAFAGVPQYCRQSMQMMNGTSM   500
 501  SSPNAAGNVACMLSGLKQNNLKWTPYTVRMALENTAYMLPHIESFSQGQG   550
 551  MIKIATAYEKLSEILVNKVFPPRLTHFEINVSNHCKKSKGVYVREPNWNG   600
 601  PQEFTIGVEPIFQNHLSDNNLPAISFEKQIILQSTAPWVSHPQTMFVVAQ   650
 651  ERTMVVTVDASKAPKGANYTEIVGIDTADPSLGPIFRIPVTVINPEKVAV   700
 701  DQYTSRLVGKSGVTERRFVEVPSWATSAKITLRSTNKDEMDRFTLHTVYI   750
 751  EDDKCSRNTETQKIQGPIGNEWSKSITVQGGKTLEACVVRAWSRGKNPVD   800
 801  VDMTIDFFGVKKPTSISLIHGATNTPIRFQAAPTKSIDVSPSISLKSLVV   850
 851  SLKPQSAKVEPLGPRDMFLTSGLQINRLLLTYQLKVQKPSEVQLQLAGLT   900
 901  PYLYESPVDCVLFQIFGANKSFVGASSSYPDRWTQKLEKGDYTIQAQIRY   950
 951  PDDQVLQGMKELPLLVHVKLGNKISVDLAASASDATLGKECKFAGKALLP  1000
1001  NQEMTVYAMNIADDKLPKTIVPTSGSFLAGTFSALKDSDLSDVDKSEVIY  1050
1051  FLSEYSTRPTKGLSMVTTKKDTNQNQEMTDAIRDLEVSWVQKLTDEKAAK  1100
1101  EFFEACLQKYPDHLPLLQNRVKQLMQAKLVDQTPENVQKIIELCGQILQI  1150
1151  TKPNETLQFSSVKQEHDDDLLTVDKWLALTGGSEDQRKDVVKLISQFEER  1200
1201  KKSIILALQALSSLEQDIEVRKSKFDVPASLRFGGITPLIFGGKQGEVIN  1250
1251  KKSEGYEALKSKSEQIDATVSEELKKLDSNWTGNQFYVKLLVWLSADDTK  1300
1301  TALISAKHAAALGQFGRCAKLLNKAGDELKSSATDSQAVDTSLAEVCESL  1350
1351  EWNHLATHFKNSALIKNRTSYRLF                            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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