| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q00722 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MSLLNPVLLPPKVKAYLSQGERFIKWDDETTVASPVILRVDPKGYYLYWT 50
51 YQSKEMEFLDITSIRDTRFGKFAKMPKSQKLRDVFNMDFPDNSFLLKTLT 100
101 VVSGPDMVDLTFHNFVSYKENVGKAWAEDVLALVKHPLTANASRSTFLDK 150
151 ILVKLKMQLNSEGKIPVKNFFQMFPADRKRVEAALSACHLPKGKNDAINP 200
201 EDFPEPVYKSFLMSLCPRPEIDEIFTSYHAKAKPYMTKEHLTKFINQKQR 250
251 DSRLNSLLFPPARPDQVQGLIDKYEPSGINAQRGQLSPEGMVWFLCGPEN 300
301 SVLAQDKLLLHHDMTQPLNHYFINSSHNTYLTAGQFSGLSSAEMYRQVLL 350
351 SGCRCVELDCWKGKPPDEEPIITHGFTMTTDIFFKEAIEAIAESAFKTSP 400
401 YPIILSFENHVDSPRQQAKMAEYCRTIFGDMLLTEPLEKFPLKPGVPLPS 450
451 PEDLRGKILIKNKKNQFSGPTSSSKDTGGEAEGSSPPSAPAGEGTVWAGE 500
501 EGTELEEEEVEEEEEEESGNLDEEEIKKMQSDEGTAGLEVTAYEEMSSLV 550
551 NYIQPTKFVSFEFSAQKNRSYVISSFTELKAYDLLSKASVQFVDYNKRQM 600
601 SRIYPKGTRMDSSNYMPQMFWNAGCQMVALNFQTMDLPMQQNMAVFEFNG 650
651 QSGYLLKHEFMRRPDKQFNPFSVDRIDVVVATTLSITVISGQFLSERSVR 700
701 TYVEVELFGLPGDPKRRYRTKLSPSTNSINPVWKEEPFVFEKILMPELAS 750
751 LRVAVMEEGNKFLGHRIIPINALNSGYHHLCLHSESNMPLTMPALFIFLE 800
801 MKDYIPGAWADLTVALANPIKFFSAHDTKSVKLKEAMGGLPEKPFPLASP 850
851 VASQVNGALAPTSNGSPAARAGAREEAMKEAAEPRTASLEELRELKGVVK 900
901 LQRRHEKELRELERRGARRWEELLQRGAAQLAELGPPGVGGVGACKLGPG 950
951 KGSRKKRSLPREESAGAAPGEGPEGVDGRVRELKDRLELELLRQGEEQYE 1000
1001 CVLKRKEQHVAEQISKMMELAREKQAAELKALKETSENDTKEMKKKLETK 1050
1051 RLERIQGMTKVTTDKMAQERLKREINNSHIQEVVQVIKQMTENLERHQEK 1100
1101 LEEKQAACLEQIREMEKQFQKEALAEYEARMKGLEAEVKESVRACLRTCF 1150
1151 PSEAKDKPERACECPPELCEQDPLIAKADAQESRL 1185
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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