 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q95168 from www.uniprot.org...
The NucPred score for your sequence is 0.68 (see score help below)
1 MEELIWEQYTVTLQKDSKRGFGIAVSGGRDNPHFENGETSIVISDVLPGG 50
51 PADGLLQENDRVVMVNGTPMEDVLHSFAVQQLRKSGKIAAIVVKRPRKVQ 100
101 LAPPQGSLPVDEDDRAFEVMDEFDGRSARSGYSERSRRSSHGGRSRSWED 150
151 SPERGRPHERAWSQERERSRGRSLERGLDHDDDYRRPRERSRGRSLERGL 200
201 DHDDDYGRPGERSHGMSTDRGYDRGYDRGYDRGYDRTYSPEAEYGRRTQP 250
251 DARHAGSRSRSREHLRSRSPSPELRGRPDHAGQPDSDRPIGVLLMKSKAN 300
301 EEYGLRLGSQIFIKQMTRTALATKDGNLHEGDIILKINGTVTENMSLTDA 350
351 RKLIEKSRGKLQLVVLRDSKQTLINIPSLNDSDSEIEDISEIESNRSFSP 400
401 EERRQQYSDYDYHSSNEKLKERPNSREDMQNRWSRMGATPTPFKSMGDIA 450
451 SVVGTENSKEPRYQEEPPAPQPKAAPRTFLRPSPEDEAIYGPNTKMVRFK 500
501 KGDSVGLRLAGGNDVGIFVAGIQEGTSAEQEGLQEGDQILKVNTQDFRGL 550
551 VREDAVLYLLEIPKGEMVTILAQSRADVYRDILACGRGDSFFIRSHFECE 600
601 KETPQSLAFSRGEVFRVVDTLYDGKLGHWLAVRIGNELEKGLIPNKSRAE 650
651 QMASVQNAQRDNAGDRADFWRMRGQRSGMKKNLRKSREDLTAAVSVSTKF 700
701 PAYERVLLREAGFKRPVVLFGPIADIALEKLANELPDLFQTAKTEPKDAG 750
751 SEKSSGVVRLNTVRQIIEQDKHALLDVTPKAVDLLNYTQWFPIVIFFNPD 800
801 SRQGVKTMRQRLNPTSNKSSRKLYDQANKLKKTCAHLFTATINLNSANDS 850
851 WFGSLKDTIQHQQGEAVWVSEGKMEGMDDDPEDRMSYLTAMGADYLSCDS 900
901 RLISDFEDTDGEGGAYTDNELDEPAEEPLVSSITRSSEPVQHEESIRKPS 950
951 PEPRAQMRRAASRDQLRDSSPPPAFKPEPPKAKTQNREESFDISRSHDYK 1000
1001 SNPSAVAGNEVSGASTRSCPPPIAAKPSFGRSILKPSTPVPSPESEEVGE 1050
1051 GSEEQEGAPKSVLGKVKIFEKMDHKARLQRMQELQEAQNARIEIAQKHPD 1100
1101 IYAVPIKTHKPDPGLSQHTSSRPPEPQKGPSRLYQDPRGSYGSDAEEEEY 1150
1151 RQQLSEHSKRGYYSQPSRYRDTEL 1174
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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