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

NucPred

Fetching P78357 from www.uniprot.org...

The NucPred score for your sequence is 0.18 (see score help below)

   1  MMHLRLFCILLAAVSGAEGWGYYGCDEELVGPLYARSLGASSYYSLLTAP    50
  51  RFARLHGISGWSPRIGDPNPWLQIDLMKKHRIRAVATQGSFNSWDWVTRY   100
 101  MLLYGDRVDSWTPFYQRGHNSTFFGNVNESAVVRHDLHFHFTARYIRIVP   150
 151  LAWNPRGKIGLRLGLYGCPYKADILYFDGDDAISYRFPRGVSRSLWDVFA   200
 201  FSFKTEEKDGLLLHAEGAQGDYVTLELEGAHLLLHMSLGSSPIQPRPGHT   250
 251  TVSAGGVLNDQHWHYVRVDRFGRDVNFTLDGYVQRFILNGDFERLNLDTE   300
 301  MFIGGLVGAARKNLAYRHNFRGCIENVIFNRVNIADLAVRRHSRITFEGK   350
 351  VAFRCLDPVPHPINFGGPHNFVQVPGFPRRGRLAVSFRFRTWDLTGLLLF   400
 401  SRLGDGLGHVELTLSEGQVNVSIAQSGRKKLQFAAGYRLNDGFWHEVNFV   450
 451  AQENHAVISIDDVEGAEVRVSYPLLIRTGTSYFFGGCPKPASRWDCHSNQ   500
 501  TAFHGCMELLKVDGQLVNLTLVEGRRLGFYAEVLFDTCGITDRCSPNMCE   550
 551  HDGRCYQSWDDFICYCELTGYKGETCHTPLYKESCEAYRLSGKTSGNFTI   600
 601  DPDGSGPLKPFVVYCDIRENRAWTVVRHDRLWTTRVTGSSMERPFLGAIQ   650
 651  YWNASWEEVSALANASQHCEQWIEFSCYNSRLLNTAGGYPYSFWIGRNEE   700
 701  QHFYWGGSQPGIQRCACGLDRSCVDPALYCNCDADQPQWRTDKGLLTFVD   750
 751  HLPVTQVVIGDTNRSTSEAQFFLRPLRCYGDRNSWNTISFHTGAALRFPP   800
 801  IRANHSLDVSFYFRTSAPSGVFLENMGGPYCQWRRPYVRVELNTSRDVVF   850
 851  AFDVGNGDENLTVHSDDFEFNDDEWHLVRAEINVKQARLRVDHRPWVLRP   900
 901  MPLQTYIWMEYDQPLYVGSAELKRRPFVGCLRAMRLNGVTLNLEGRANAS   950
 951  EGTSPNCTGHCAHPRLPCFHGGRCVERYSYYTCDCDLTAFDGPYCNHDIG  1000
1001  GFFEPGTWMRYNLQSALRSAAREFSHMLSRPVPGYEPGYIPGYDTPGYVP  1050
1051  GYHGPGYRLPDYPRPGRPVPGYRGPVYNVTGEEVSFSFSTSSAPAVLLYV  1100
1101  SSFVRDYMAVLIKDDGTLQLRYQLGTSPYVYQLTTRPVTDGQPHSINITR  1150
1151  VYRNLFIQVDYFPLTEQKFSLLVDSQLDSPKALYLGRVMETGVIDPEIQR  1200
1201  YNTPGFSGCLSGVRFNNVAPLKTHFRTPRPMTAELAEALRVQGELSESNC  1250
1251  GAMPRLVSEVPPELDPWYLPPDFPYYHDEGWVAILLGFLVAFLLLGLVGM  1300
1301  LVLFYLQNHRYKGSYHTNEPKAAHEYHPGSKPPLPTSGPAQVPTPTAAPN  1350
1351  QAPASAPAPAPTPAPAPGPRDQNLPQILEESRSE                  1384

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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