 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O89040 from www.uniprot.org...
The NucPred score for your sequence is 0.93 (see score help below)
1 MSLLNPVLLPPKVKAYLSQGERFIKWDDETSIASPVILRVDPKGYYLYWT 50
51 HQSKEMEFLDVTSIRDTRFGKFAKIPKSQKLREVFNMDFPDNHFLLKTFT 100
101 VVSGPDMVGLTFHNFVSYKENVGKDWAEDVLALAKHPMTANASRSTFLDK 150
151 ILVKLKMQLSPEGKIPVKNFFQMFPADRKRVEAALSACHLAKGKNDAINP 200
201 EDFPESVYKSFLMSLCPRPEIDEIFTSYHAKAKPYMTKEHLTKFINQKQR 250
251 DPRLNSLLFPPARPEQVQALIDKYEPSGINVQRGQLSPEGMVWFLCGPEN 300
301 SVLAHDTLRIHQDMTQPLNHYFINSSHNTYLTAGQFSGPSSAEMYRQVLL 350
351 SGCRCVELDCWKGKPPDEEPIITHGFTMTTDILFKEAVEAIAESAFKTSP 400
401 YPVILSFENHVDSPRQQAKMAEYCRTMFGETLLTEPLENFPLKPGMPLPS 450
451 PEDLRGKILIKNKKNQFSGPASPNKKPDGVSEGGFPSSVPVEEDTGWTAE 500
501 DRTEVEEGEEEEEVEEEEEEESGNLDEEEIKKMQSDEGTAGLEVTAYEEM 550
551 SSLVNYIQPTKFISFEFSAQKNRSYLVSSFTELKAYELLSKASMQFVDYN 600
601 KRQMSRVYPKGTRMDSSNYMPQMFWNAGCQMVALNFQTMDLPMQQNMALF 650
651 EFNGQSGYLLKHEFMRRQDKQFNPFSVDRIDVVVATTLSITVISGQFLSE 700
701 RSVRTYVEVELFGLPGDPKRRYRTKLSPTANSINPVWKEEPFIFEKILVP 750
751 ELASLRIAVMEEGGKFIGHRIIPINALHSGYHHLCLRSESNMPLTMPALF 800
801 VFLEMKDYVPDTWADLTVALANPIKYFSAHDKKSVKLKEVTGSLPEKLFS 850
851 GIPVASQSNGAPVSAGNGSTAPGTKAKEEATKEVAEPQTTSLEELRELKG 900
901 VVKLQRRHEKELRELERRGARRWEELLQRGAAQLAELQDPAASCKLRPGK 950
951 GSRKKRIVPCEETIVVPREVLEGPDPRVQDLKDRLEQELQQQGEEQYRSV 1000
1001 LKRKEQHVTEQIAKMMELAREKQAAELKSFKETSETDTKEMKKKLEAKRL 1050
1051 ERIQAMTKVTTDKVAQERLKREINNSHIQEVVQAVKQMTETLERHQEKLE 1100
1101 EKQTACLEQIQAMEKQFQEKALAEYEAKMKGLEAEVKESMRACFKACFPT 1150
1151 EAEEKPERPCEASEESCPQEPLVNKTDTQESRL 1183
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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