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

NucPred

Fetching P25172 from www.uniprot.org...

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

   1  MHLQNTHNTMESNAAMDAASPASRDVRQFHDLITCRLCRGYMIDPTTVDY    50
  51  CYHTYCRSCILKHLLRAVYCPECKASGGKEINELNLKSDDTLRSLIYKLV   100
 101  PGLYQRECKELADFKEQHDLVDEQTTDEPEFFTTTELISLSLEYHPAMLH   150
 151  QCGPGEVPPTIYLQCAAGLPVELLKRFLCSKYNIETDNGLVEVEVTYKDE   200
 201  VLPTNFTLMDVAYCYNWSRESPMAFCYRILLYDNEQTKNDENNLSRINQD   250
 251  IEPEHSVRRSKSAKSVTFAEDLESEIDSGSPRSKVRCKTPPKVSPSSKNK   300
 301  RLTSSKREAEPESPVSNFKSLRSNDMRYSDYAVSKVKSEPEQEQFLLPRE   350
 351  REQQPLEANTNIVVSIPPSQLRKSYVDAEDFELKTANRKGVGHLPKLKIE   400
 401  LNSMKSKLSMPLSAGPRLEDTSCSLSCSAQQLDLETYAKNIGLKPIEQPL   450
 451  QQSASNPDSKYSPNASPMSSCSSSTNGSSSSLGTADASTSTSTSSSHRKR   500
 501  KKKHSKEPKDANGKRKKLHAEISSQTDGKMKVKITAKPNHKLDFKRSHSL   550
 551  ASGELDLQKLKLDSTSTSEALNRTLGEEARSINSLVVGGAPTPPPTPTAE   600
 601  PEQQQQQQQQQQQPQQQQQQQQQQQQQQQFVVLPKIKDLTLPTSPPLPPS   650
 651  LFKAYTPSTTPTAPHTVAGGKPKQQQQQMPQQPQAVLQQSLAKTNPAKPP   700
 701  LSSNNNRKPNSGHFAVPQAPTHRNMYHMQRYQSTPSSIASAANKMPKRSM   750
 751  SLDESHPAKQARLSQAQAMASSYAAKLHMQTSNQAKSQAAAFLPNPQMRS   800
 801  YGLPDLGSKPTLPMLCPASSSSQVTITPRPRATPSIYSFSEPNIHVPALE   850
 851  IVRLPVNKQSAGGKGLTMPPLSPPATSSARLMGPPAALPKHAGHGHGAAK   900
 901  RSCQMPTMPMPLPLPLPMPMTTIPAIVKSPPLSVALSGQRNNKGNSSNSN   950
 951  AYRTSPPALINLRNTAAPQHSFPSKSSPKVEANSKKSPPAAGCQGKTNGT  1000
1001  AALDKSKTSLREFRPAVQSAVTATATTSVTTAAGAGAGAGTGTGTALAKD  1050
1051  ADILDLSANPGRSNNDAKLAPNSPPAGNNNNNNNNNNNNNNNNNNNNNNS  1100
1101  TSNSLEAALNKIKQNISANSNGGPSTTSGSNSNGTTNGDDLQNLHMLSES  1150
1151  ATAREKISIKAASSGNGSGSTSSSSAKPKNANALVRPQNASVRSIPNPSA  1200
1201  LAFRNQPAAASTAASISKPLTVRAEEKPKVSTSNPGSLSPTNTSSSSSSS  1250
1251  SSGSSGCSAATSPRAMTKKPTTIDQVAANLNIRAEAKAAALAEEAPPVLS  1300
1301  SNAAKSPELAKTTTAVALRPEPKETPITVSAASTLLTIPSAVSSVSAVPE  1350
1351  TMAKPPVQIANAPVASSA                                  1368

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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