 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q09996 from www.uniprot.org...
The NucPred score for your sequence is 0.34 (see score help below)
1 MSKINKERKKVAQLQEIEKSIQELWESKKAFEADARDDGKPKYLVTFPFP 50
51 YMNGRLHLGHTFSASKCEFAAGFQRLQGKEVLFPFGFHCTGMPIKACADK 100
101 LKREMQDFGYPPNFPEDVVEEVKEEVSAVDEIIKDKSKGKKSKLVAKTGN 150
151 AKYQWQIMKSLGLCDEEIKEFSDPNHWLYYFPPHCIADLKKMGLKADWRR 200
201 SFITTDVNPYFDSFVRWQFNLLRAAKKIDFGKRYTIYSPKDGQPCMDHDR 250
251 ASGEGVGPQEYTLIKLKVLDPKPQALSHIKEDIYLVAATLRPETMYGQTN 300
301 CYLHPDIQYSVFYATENEKQVFVATARSARIMSYQGLTKENGKVNYVLGL 350
351 EKIAGSKILGAPLSAPLAKYERVYALPMLTIKDDKGTGVVTSVPSDSPDD 400
401 FAALSDLKKKKPLREKYGLTDEMVLPFEPVPIIKIEGLGDLAAVEMCSRL 450
451 KIESQNEKDKLEEAKKEVYLKGFYDGVMLVGKYAGKKTADVKKVIQDDLT 500
501 AEGLATKYVEPEKKVMSRSGDECVVALCDQWYLNYGEAEWKAAAKKVLEP 550
551 LRTFNDETRRGLETTVDWLHEYACSRSYGLGTKLPWDTQYLIESLSDSTI 600
601 YNAYYTVAHLLQQGAFDGSVVGPAGIKADQMTDASWSYVFLGEIYDSKTM 650
651 PVEEEKLKSLRKEFMYWYPIDMRASGKDLIGNHLTYLLFNHAAIWPTDTS 700
701 KWPKGIRANGHLLLNNEKMSKSTGNFMTLEEAIEKFSADGMRLSLADAGD 750
751 GLEDANFVYAMADAAILRLFTMIEWIKEMIEQRDAGLLRKDAKKFADRVF 800
801 ANEMNSLIAATEQNYEATNFKDALKTGFFEYQAIRDTYRELCAGIDEPMS 850
851 ESLVFRFIETQMLILSPICPHIAEYIWQLLGKDGLIVNAPWPTVDPVDEK 900
901 LAIGARFITESLAEFRARLKTYMTPKKKALKEIPEVPTEAVIYVAKEYPP 950
951 WQKTILDILEKQAKANNGALPDNKAISQLIGKEESLKKFAKKAMPFVQMI 1000
1001 KERFEQKGVSALASSSPVDQTSILNENIDFIMNALDLDRVTIRHTDEEGI 1050
1051 DANIVETTVPLVPMLNFTPNRPTIKLVARNVQICNAMFDVDVPIVNGDSV 1100
1101 SMVIRKMRRISKAIKPKFEVSLWRYKNAVWGDRQMISYRNPFEENIQLSD 1150
1151 ADIFNFEADNKISVTSGSEKFDLGRTIVYKANVPEN 1186
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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