 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9R244 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MLMSRTDSKSGKNRSGVRMFKDGDFLTPASGESWDRLRLTCSQPFTRHQS 50
51 FGLAFLRVRSSLGSLADPVVDPSAPGSSGLNQNSTDVLESDPRPWLTNPS 100
101 IRRTFFPDPQTSTKEISELKGMLKQLQPGPLGRAARMVLSAARKAPPASV 150
151 VSPNNSHGEPGPSRAESAEPRAEEPNRKTAVGRRKRRKVQEPRRSLSNSS 200
201 SQPNRRTGRTRQRQHRPQTKSDDGGVQAAGQCPICAGFFSIETLPQHAAT 250
251 CGESPPPQPASPASLSSSESVLRRHHVALTPVPLVPKPQPNWTEIVNKKL 300
301 KFPPTLLRAIQEGQLGLVQQLLESSSDASGAGPGGPLRNVEESEDRSWRE 350
351 ALNLAIRLGHEVITDVLLANVKFDFRQIHEALLVAVDTNQPAVVRRLLAR 400
401 LEREKGRKVDTKSFSLAFFDSSIDGSRFAPGVTPLTLACQKDLYEIAQLL 450
451 MDQGHTIARPHPVSCACLECSNARRYDLLKFSLSRINTYRGIASRAHLSL 500
501 ASEDAMLAAFQLSRELRRLARKEPEFKPQYIALESLCQDYGFELLGMCRN 550
551 QSEVTAVLNDLGEDSETEPEAEGLGQAFEEGIPNLARLRLAVNYNQKQFV 600
601 AHPICQQVLSSIWCGNLAGWRGSTTIWKLFVAFLIFLTMPFLCIGYWLAP 650
651 KSQLGRLLKIPVLKFLLHSASYLWFLIFLLGESLVMETQLSTFKGRSQSV 700
701 WETSLHMIWVTGFLWFECKEVWIEGLRSYLLDWWNFLDVVILSLYLASFA 750
751 LRLLLAGLAYMHCRDASDSTTCRYFTTAERSEWRTEDPQFLAEVLFAVTS 800
801 MLSFTRLAYILPAHESLGTLQISIGKMIDDMIRFMFILMIILTAFLCGLN 850
851 NIYVPYQESEKLGNFNETFQFLFWTMFGMEEHTVVDMPQFLVPEFVGRAM 900
901 YGIFTIVMVIVLLNMLIAMITNSFQKIEDDADVEWKFARSKLYLSYFREG 950
951 LTLPVPFNILPSPKAAFYLVRRIFRFLCCGSSCCKAKKSDYPPIGTFTNP 1000
1001 GARAGSAGEGERVSYRLRVIKALVQRYIETARREFEETRRKDLGNRLTEL 1050
1051 TKTVSRLQSEVASVQKNLAAGGAPRPPDGASILSRYITRVRNSFQNLGPP 1100
1101 TSDTPAELTMPGIVETEVSLGDGLDGTGEAGAPAPGEPGSSSSAHVLVHR 1150
1151 EQEAEGSGDLLLEGDLETKGES 1172
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
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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