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

NucPred

Fetching P52179 from www.uniprot.org...

The NucPred score for your sequence is 0.47 (see score help below)

   1  MSLPFYQRCHQHYDLSYRNKDVRSTVSHYQREKKRSAVYTQGSTAYSSRS    50
  51  SAAHRRESEAFRRASASSSQQQASQHALSSEVSRKAASAYDYGSSHGLTD   100
 101  SSLLLDDYSSKLSPKPKRAKHSLLSGEEKENLPSDYMVPIFSGRQKHVSG   150
 151  ITDTEEERIKEAAAYIAQRNLLASEEGITTSKQSTASKQTTASKQSTASK   200
 201  QSTASKQSTASRQSTASRQSVVSKQATSALQQEETSEKKSRKVVIREKAE   250
 251  RLSLRKTLEETETYHAKLNEDHLLHAPEFIIKPRSHTVWEKENVKLHCSI   300
 301  AGWPEPRVTWYKNQVPINVHANPGKYIIESRYGMHTLEINGCDFEDTAQY   350
 351  RASAMNVKGELSAYASVVVKRYKGEFDETRFHAGASTMPLSFGVTPYGYA   400
 401  SRFEIHFDDKFDVSFGREGETMSLGCRVVITPEIKHFQPEIQWYRNGVPL   450
 451  SPSKWVQTLWSGERATLTFSHLNKEDEGLYTIRVRMGEYYEQYSAYVFVR   500
 501  DADAEIEGAPAAPLDVKCLEANKDYIIISWKQPAVDGGSPILGYFIDKCE   550
 551  VGTDSWSQCNDTPVKFARFPVTGLIEGRSYIFRVRAVNKMGIGFPSRVSE   600
 601  PVAALDPAEKARLKSRPSAPWTGQIIVTEEEPSEGIVPGPPTDLSVTEAT   650
 651  RSYVVLSWKPPGQRGHEGIMYFVEKCEAGTENWQRVNTELPVKSPRFALF   700
 701  DLAEGKSYCFRVRCSNSAGVGEPSEATEVTVVGDKLDIPKAPGKIIPSRN   750
 751  TDTSVVVSWEESKDAKELVGYYIEASVAGSGKWEPCNNNPVKGSRFTCHG   800
 801  LVTGQSYIFRVRAVNAAGLSEYSQDSEAIEVKAAIGGGVSPDVCPALSDE   850
 851  PGGLTASRGRVHEASPPTFQKDALLGSKPNKPSLPSSSQNLGQTEVSKVS   900
 901  ETVQEELTPPPQKAAPQGKSKSDPLKKKTDRAPPSPPCDITCLESFRDSM   950
 951  VLGWKQPDKIGGAEITGYYVNYREVIDGVPGKWREANVKAVSEEAYKISN  1000
1001  LKENMVYQFQVAAMNMAGLGAPSAVSECFKCEEWTIAVPGPPHSLKCSEV  1050
1051  RKDSLVLQWKPPVHSGRTPVTGYFVDLKEAKAKEDQWRGLNEAAIKNVYL  1100
1101  KVRGLKEGVSYVFRVRAINQAGVGKPSDLAGPVVAETRPGTKEVVVNVDD  1150
1151  DGVISLNFECDKMTPKSEFSWSKDYVSTEDSPRLEVESKGNKTKMTFKDL  1200
1201  GMDDLGIYSCDVTDTDGIASSYLIDEEELKRLLALSHEHKFPTVPVKSEL  1250
1251  AVEILEKGQVRFWMQAEKLSGNAKVNYIFNEKEIFEGPKYKMHIDRNTGI  1300
1301  IEMFMEKLQDEDEGTYTFQLQDGKATNHSTVVLVGDVFKKLQKEAEFQRQ  1350
1351  EWIRKQGPHFVEYLSWEVTGECNVLLKCKVANIKKETHIVWYKDEREISV  1400
1401  DEKHDFKDGICTLLITEFSKKDAGIYEVILKDDRGKDKSRLKLVDEAFKE  1450
1451  LMMEVCKKIALSATDLKIQSTAEGIQLYSFVTYYVEDLKVNWSHNGSAIR  1500
1501  YSDRVKTGVTGEQIWLQINEPTPNDKGKYVMELFDGKTGHQKTVDLSGQA  1550
1551  YDEAYAEFQRLKQAAIAEKNRARVLGGLPDVVTIQEGKALNLTCNVWGDP  1600
1601  PPEVSWLKNEKALASDDHCNLKFEAGRTAYFTINGVSTADSGKYGLVVKN  1650
1651  KYGSETSDFTVSVFIPEEEARMAALESLKGGKKAK                 1685

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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