 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q03297 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 NKDKSRKKKKPKCIALATATAVSLEGTRESPLPASGSCEKVLQELQDTQQ 50
51 LGEPLVVTETQLSEQLLETEQNEDQNKSEQLAQFPLPTPIVTTLSPGIGP 100
101 GHDCVGGASGGAVAGGCLVVGAGTDKTSELIPGKLESAGTKPSQERPKEE 150
151 SFCCVISMHDGIVLYTTPSISDVLGFPRDMWLGRSFVDFVHHKDRATFAS 200
201 QITTGIPIAESRGCMPKDARSTFCVMLRRYRGLNSGGFGVIGRAVNYEPF 250
251 RLGLTFREAPEEARPDNYMVSNGTNMLLVICATPIKSSYKVPDEILSQKS 300
301 PKFAIRHTATGIISHVDSAAVSALGYLPQDLIGRSIMDFYHHEDLSVMKD 350
351 TYETVMKKGQTAGASFCSKPYRFLIQNGCFVLLETEWTSFVNPWSRKLEF 400
401 VVGHHRVFQGPKLCNVFETSVSAKPKISEEAQNRNARIKEDIVKLLAETV 450
451 SRPSDTVKQEVSRRCQALANFMETLMDEITRADLKLDLPHENELTVSERD 500
501 SVMLGEISPHHDYYDSKSSTETPPSYNQLNYNENLLRFFNSKPVTAPVEL 550
551 DPPKVESSYVSSARGEDARSTLSPVQGFEGSGGSGSSGNFTTGSNLHMSS 600
601 VTNTSNAGTGTSGTGNSGDGGGGGGADGTGSGAAPPVTLTESLLNKHNDE 650
651 MEKFMLKKHRESRGRSGDKNKKSANEAMKMLEYSGPGPGHGHGIKRGGSH 700
701 SWEGEANKPKQQLTLNTGGGGGGGGGGGGGGGGGLPLFLDVTHTSSSSQN 750
751 KGPTGVAAGGAGGGVGGGGGSCSGLGGNGNVGSGNGNNSQPSTNQYTQSG 800
801 LPCTQNINLWPPFSVGITTPTSVLSSHTAVPPSSFSPQHSLFPTFYYIPA 850
851 SIAASSPSSTNTNPNRPHKHAHVHSSSEKPSTSQAAAATMPLQYMTGVMY 900
901 PHPSLFYTHPAAAAATAMVYQPVPFAGVANPMQLPEQASKNVYTTQPVMV 950
951 APPTATNKTQGAFHSITPAPPQRPSSQATSVKAETGSNVAPSDTSKKEVP 1000
1001 DSPITPTMGDFTLDQPCNNNATTLKKYTDSNGNSDDMDGSSFSSFYSSFI 1050
1051 KTTDGSESPPENDKDAKHRKLKSLDQSDNKIVEHPEEDQTQHG 1093
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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