 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q4IL82 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MDQNGYNSSALQRPPRRGDEGCEEDRDSRPHHHHRHHHHHHHHRRDGDLP 50
51 AGAVAGEAATASSNAGGANAHQHSTFSLRSPKPEYRPPPFSSPNGHNHSH 100
101 HNTSTSSANHSLQSPPRPALPNPYMSSSTGAPGGPVAPALPPPVGINSSS 150
151 SPGSSAAGLHQRHHQPGAPAHQHRAAPPPVSPLHPPVAYYPPGTNTDIYI 200
201 PPPEPKPASRGFYDPTTDTTKERRISDAATPGASWHNANANAPPAGTPKT 250
251 RDPYSYSQTADQHTPSYYNGGSYTSPRGPSYNRPRSPLSHSHQNPPAGSL 300
301 SPPGQQPLLASPSVRHGTTANMNPTTNGASAIPPFKSDLAAPSPPKPAPS 350
351 STTSRANPMSFDSILSSSEPAPKPKEPSPIIAREPEIKEEREPRRDRESK 400
401 RDSREPKQTKRSLEPELDHDTEVEKDVETEPEPLPSREKEKEPAPKKRGA 450
451 RKSTKGRASDIRDAATPKNGRRLSVKKESPTPRLPAKRQANGQPKPKTWS 500
501 AEMEKKIQNAESDIENRAANLDADEFDEQQYKERAQKRRRVMSELDVEYG 550
551 LSRRDALANTISKKLVLHAELGKRRYDDVFYDEALHEVREQEVYAEKERK 600
601 KDMQRKRRREKSMAVTMEQKEAALARAEAAEDETERQKHLRDAERASKKA 650
651 QQTKLILQKGIKGPARNLEINLEGGTMSSFQASDVESGEAGTPSGKRKGK 700
701 GRSGPRLKKSKEQKQAEKDSAEAAQAALDAGEELPTKEENRVRIKIKKTK 750
751 KDVAVDSEKDKDEAEKTEEEVVEKKTKKSKDKDKEKVDDIPDNEKRFMSK 800
801 GYNQIYDQIWRDMARKDVNKTFKLAVDSYATKASNLKKTAILASKEAKRW 850
851 QLRTNKGTKDLQARAKRVMRDMMGFWKRNEREERDLRKAAEKQEIENARK 900
901 EEADREAARQKRKLNFLISQTELYSHFIGKKIKTDEVERSTDNPEIAKDA 950
951 HQTDQKMLDIDEPTGPVIGKVTNFENLDFEEGSDEALRAAAMANAQNAIA 1000
1001 EAQKKARDFNNQGLDMDDEGEMNFQNPTGLGDVEIEQPKLINAQLKEYQL 1050
1051 KGLNWLVNLYEQGINGILADEMGLGKTVQSISVMAYLAEKHDIWGPFLVV 1100
1101 APASTLHNWQQEIAKFVPEFKILPYWGGASDRKVLRKFWDRKHTTYRKDA 1150
1151 PFHVCVTSYQLVVSDVAYFQKMRWQYMILDEAQAIKSSQSSRWKALLNFH 1200
1201 CRNRLLLTGTPIQNNMQELWALLHFIMPSLFDSHDEFSEWFSKDIESHAQ 1250
1251 SNTKLNEDQLKRLHMILKPFMLRRVKKHVQKELGDKIELDIFCDLTYRQR 1300
1301 AYYSNLRNQINIMDLVEKATMGDDQDSGTLMNLVMQFRKVCNHPDLFERA 1350
1351 EVNSPFACAYFAETASFVREGNDVAVGYSSRNLIEYELPRLVWRDGGRVH 1400
1401 KAGPDSQVAGWKNRTLNHLMNIWSPDNIRDSSDGSKAFSWLRFADTSPNE 1450
1451 AYQATHQSLIARAAKELQKRDRLGYMNVAYSDTEDANFTPAHALFQIRPR 1500
1501 QNRKPLADITNEGILSRLMNVAQGDYDESGLGRLEPAGRPRASAPPIQVS 1550
1551 CRSWASEFERSEVLFNAPIRKILYGPTVFEEKALVEKKLPMELWPTRQML 1600
1601 PKPDHEKKGFTNISIPSMQRFVTDSGKLAKLDDLLFKLKSEGHRVLLYFQ 1650
1651 MTRMIDMMEEYLTYRNYKYCRLDGSTKLEDRRDTVHDFQTRPEIFIFLLS 1700
1701 TRAGGLGINLTTADTVIFYDSDWNPTIDSQAMDRAHRLGQTKQVTVYRLI 1750
1751 TRGTIEERIRKRAMQKEEVQRVVIQGGGASVDFSGRRAPENRNRDIAMWL 1800
1801 ADDEQAEMIERREKELLESGELEKQQKKKGGKRRKAENSASLDEMYHEGE 1850
1851 GNFDDGSKGVSGTATPATAATPADSDSKGKKGRKGTKRAKTAKQRLAIAD 1900
1901 GMME 1904
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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