 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P79621 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MNHFQAILAQVQTLLSSQKPRQVRALLDGLLEEELLSREYHCALLHEPDG 50
51 DALARKISLTLLEKGDLDLTFLSWVCNSLQAPTVERGTSYRDHGDHSLCA 100
101 TMDLGSPEGSYLELLNSDADPLHLYHLYDQMDLAGEEEIELSSEPDTDTI 150
151 NCDQFSKLLQDMELDEETREAYANIAELDQYVFQDTQLEGLSKDLFIEHI 200
201 GAEEGFGENIEIPVEAGQKPQKRRFPEEHAMDSKHRKLVPTSRTSLNYLD 250
251 LPTGHIQIFTTLPQGLWQISGAGTGLSSVLIYHGEMPQVNQVLPSSSLSI 300
301 PSLPESPDRPGSTSPFTPSAADLPSMPEPALTSRVNETEDTSPSPCQEGP 350
351 ESSIKLPKWPEAVERFQHSLQDKYKALPQSPRGPLVAVELVRARLERGSN 400
401 KSQERELATPDWTERQLAHGGLAEVLQVVSDCRRPGETQVVAVLGKAGQG 450
451 KSHWARTVSHTWACGQLLQYDFVFYVPCHCLDRPGDTYHLRDLLCPPSLQ 500
501 PLAMDDEVLDYIVRQPDRVLLILDAFEELEAQDGLLHGPCGSLSPEPCSL 550
551 RGLLAGIFQRKLLRGCTLLLTARPRGRLAQSLSKADAIFEVPSFSTKQAK 600
601 TYMRHYFENSGTAGNQDKALGLLEGQPLLCSYSHSPVVCRAVCQLSKALL 650
651 EQGTEAQLPCTLTGLYVSLLGPAAQNSPPGALVELAKLAWELGRRHQSTL 700
701 QETRFSSVEVKTWAVTQGLMQQTLETTEAQLAFSSFLLQCFLGAVWLAQC 750
751 NEIKDKELPQYLALTPRKKRPYDNWLEGVPRFLAGLVFQPRAHCLGALVE 800
801 PAVAAVADRKQKVLTRYLKRLKLGTLRAGRLLELLHCAHETQQPGIWEHV 850
851 AHQLPGHLSFLGTRLTPPDVYVLGRALETASQDFSLDLRQTGVEPSGLGN 900
901 LVGLSCVTSFRASLSDTMALWESLQQQGEAQLLQAAEEKFTIEPFKAKSP 950
951 KDVEDLDRLVQTQRLRNPSEDAAKDLPAIRDLKKLEFALGPILGPQAFPT 1000
1001 LAKILPAFSSLQHLDLDSLSENKIGDKGVSKLSATFPQLKALETLNLSQN 1050
1051 NITDVGACKLAEALPALAKSLLRLSLYNNCICDKGAKSLAQVLPDMVSLR 1100
1101 VMDVQFNKFTAAGAQQLASSLQKCPQVETLAMWTPTIPFGVQEHLQQLDA 1150
1151 RISLR 1155
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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