 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P47068 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 MSEPEVPFKVVAQFPYKSDYEDDLNFEKDQEIIVTSVEDAEWYFGEYQDS 50
51 NGDVIEGIFPKSFVAVQGSEVGKEAESSPNTGSTEQRTIQPEVEQKDLPE 100
101 PISPETKKETLSGPVPVPAATVPVPAATVPVPAATAVSAQVQHDSSSGNG 150
151 ERKVPMDSPKLKARLSMFNQDITEQVPLPKSTHLDLENIPVKKTIVADAP 200
201 KYYVPPGIPTNDTSNLERKKSLKENEKKIVPEPINRAQVESGRIETENDQ 250
251 LKKDLPQMSLKERIALLQEQQRLQAAREEELLRKKAKLEQEHERSAVNKN 300
301 EPYTETEEAEENEKTEPKPEFTPETEHNEEPQMELLAHKEITKTSREADE 350
351 GTNDIEKEQFLDEYTKENQKVEESQADEARGENVAEESEIGYGHEDREGD 400
401 NDEEKEEEDSEENRRAALRERMAKLSGASRFGAPVGFNPFGMASGVGNKP 450
451 SEEPKKKQHKEKEEEEPEQLQELPRAIPVMPFVDPSSNPFFRKSNLSEKN 500
501 QPTETKTLDPHATTEHEQKQEHGTHAYHNLAAVDNAHPEYSDHDSDEDTD 550
551 DHEFEDANDGLRKHSMVEQAFQIGNNESENVNSGEKIYPQEPPISHRTAE 600
601 VSHDIENSSQNTTGNVLPVSSPQTRVARNGSINSLTKSISGENRRKSINE 650
651 YHDTVSTNSSALTETAQDISMAAPAAPVLSKVSHPEDKVPPHPVPSAPSA 700
701 PPVPSAPSVPSAPPVPPAPPALSAPSVPPVPPVPPVSSAPPALSAPSIPP 750
751 VPPTPPAPPAPPAPLALPKHNEVEEHVKSSAPLPPVSEEYHPMPNTAPPL 800
801 PRAPPVPPATFEFDSEPTATHSHTAPSPPPHQNVTASTPSMMSTQQRVPT 850
851 SVLSGAEKESRTLPPHVPSLTNRPVDSFHESDTTPKVASIRRSTTHDVGE 900
901 ISNNVKIEFNAQERWWINKSAPPAISNLKLNFLMEIDDHFISKRLHQKWV 950
951 VRDFYFLFENYSQLRFSLTFNSTSPEKTVTTLQERFPSPVETQSARILDE 1000
1001 YAQRFNAKVVEKSHSLINSHIGAKNFVSQIVSEFKDEVIQPIGARTFGAT 1050
1051 ILSYKPEEGIEQLMKSLQKIKPGDILVIRKAKFEAHKKIGKNEIINVGMD 1100
1101 SAAPYSSVVTDYDFTKNKFRVIENHEGKIIQNSYKLSHMKSGKLKVFRIV 1150
1151 ARGYVGW 1157
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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