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

NucPred

Fetching Q96J66 from www.uniprot.org...

The NucPred score for your sequence is 0.31 (see score help below)

   1  MTRKRTYWVPNSSGGLVNRGIDIGDDMVSGLIYKTYTLQDGPWSQQERNP    50
  51  EAPGRAAVPPWGKYDAALRTMIPFRPKPRFPAPQPLDNAGLFSYLTVSWL   100
 101  TPLMIQSLRSRLDENTIPPLSVHDASDKNVQRLHRLWEEEVSRRGIEKAS   150
 151  VLLVMLRFQRTRLIFDALLGICFCIASVLGPILIIPKILEYSEEQLGNVV   200
 201  HGVGLCFALFLSECVKSLSFSSSWIINQRTAIRFRAAVSSFAFEKLIQFK   250
 251  SVIHITSGEAISFFTGDVNYLFEGVCYGPLVLITCASLVICSISSYFIIG   300
 301  YTAFIAILCYLLVFPLAVFMTRMAVKAQHHTSEVSDQRIRVTSEVLTCIK   350
 351  LIKMYTWEKPFAKIIEDLRRKERKLLEKCGLVQSLTSITLFIIPTVATAV   400
 401  WVLIHTSLKLKLTASMAFSMLASLNLLRLSVFFVPIAVKGLTNSKSAVMR   450
 451  FKKFFLQESPVFYVQTLQDPSKALVFEEATLSWQQTCPGIVNGALELERN   500
 501  GHASEGMTRPRDALGPEEEGNSLGPELHKINLVVSKGMMLGVCGNTGSGK   550
 551  SSLLSAILEEMHLLEGSVGVQGSLAYVPQQAWIVSGNIRENILMGGAYDK   600
 601  ARYLQVLHCCSLNRDLELLPFGDMTEIGERGLNLSGGQKQRISLARAVYS   650
 651  DRQIYLLDDPLSAVDAHVGKHIFEECIKKTLRGKTVVLVTHQLQYLEFCG   700
 701  QIILLENGKICENGTHSELMQKKGKYAQLIQKMHKEATSDMLQDTAKIAE   750
 751  KPKVESQALATSLEESLNGNAVPEHQLTQEEEMEEGSLSWRVYHHYIQAA   800
 801  GGYMVSCIIFFFVVLIVFLTIFSFWWLSYWLEQGSGTNSSRESNGTMADL   850
 851  GNIADNPQLSFYQLVYGLNALLLICVGVCSSGIFTKVTRKASTALHNKLF   900
 901  NKVFRCPMSFFDTIPIGRLLNCFAGDLEQLDQLLPIFSEQFLVLSLMVIA   950
 951  VLLIVSVLSPYILLMGAIIMVICFIYYMMFKKAIGVFKRLENYSRSPLFS  1000
1001  HILNSLQGLSSIHVYGKTEDFISQFKRLTDAQNNYLLLFLSSTRWMALRL  1050
1051  EIMTNLVTLAVALFVAFGISSTPYSFKVMAVNIVLQLASSFQATARIGLE  1100
1101  TEAQFTAVERILQYMKMCVSEAPLHMEGTSCPQGWPQHGEIIFQDYHMKY  1150
1151  RDNTPTVLHGINLTIRGHEVVGIVGRTGSGKSSLGMALFRLVEPMAGRIL  1200
1201  IDGVDICSIGLEDLRSKLSVIPQDPVLLSGTIRFNLDPFDRHTDQQIWDA  1250
1251  LERTFLTKAISKFPKKLHTDVVENGGNFSVGERQLLCIARAVLRNSKIIL  1300
1301  IDEATASIDMETDTLIQRTIREAFQGCTVLVIAHRVTTVLNCDHILVMGN  1350
1351  GKVVEFDRPEVLRKKPGSLFAALMATATSSLR                    1382

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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