 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8R5K4 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MPSAGERPAVKMGPAPAGEQHRRATEDPEVMELAFEGMDKEKAPSRKRAR 50
51 TEPPAEGLLQPVNLSREELYKEPTNEELNRLRETEILFHSTLLRLQVEEL 100
101 LKEVRLSEKKKERIDNFLKEVTKRIQKVPPVPEAELTDQSWLPAGVRVPL 150
151 HQVPYAVKGSFRFRPPSQITVVGSYLLDTCMRPDINVDVAVTMPREILQD 200
201 KDGLNQRYFRKRALYLAHLAYHLAQDPLFSSVRFSYMSGCHLKPSLLLRP 250
251 HGKDERLVTVRLLPCPPLDFFRPCRLLPTKNNVRSAWYRGQSCPDYEPPT 300
301 PHYNTWILQDVALETHMHLLASVLGSAQGLKDGVALLKVWLRQRELDKGL 350
351 GGFNGFIISMLVAFLVSKRKIHTTMSGYQVLRSVLQFLATTDLTINGISF 400
401 SLSSDPSLPTLAEFHQLFAVVFVDPSGRLNLCADVTASTYNQVQYEAELS 450
451 MALLDSKADDGFQLLLMTPKPMIQAFDHVVHLHPLSRLQASCHQLKLWPE 500
501 LQDNGGDYVSAALGPLTNILVQGLGCRLHLLAHSRPPVPEWSINQDPPKH 550
551 KDAGTLTLGFLFRPEGLTSVIDLGPEADKPEAADFRQFWGTRSELRRFQD 600
601 GAIREAVVWEAESLFEKRLIPHQVVTHLLALHADIPDTCIQYVGGFLDAL 650
651 IQNPKEISSTGEEALALAVRCYDDLSRLLWGLEGLPLTVSAVQGAHPVLR 700
701 YTEVFPPAPVRPAYSFYNRLQELASLLPRPDKPCPAYVEPMTVVCHLEGS 750
751 GQWPQDAEAVQRVRAAFQLRLAEVLTQEHRLQCCATATHTDVLKDGFVFR 800
801 IRVAYQREPQILKEVRSPEGMVSLRDTPASLRLERDTKLLPLLTSALHGL 850
851 QQQYPAYSGVARLAKRWVRAQLLGEGFTDESLDLLAASLFLHPEPFTPPS 900
901 VPQVGFLRFLYLVSTFDWKNNPLIVNLNGELTAEEQVGIRSSFLAARTQL 950
951 PVMVIITPQDRRSSVWTQDGPSAQILQQLVSLAAEALPILEKQLMDPRGP 1000
1001 GDIRTVFRPPFDMYDVLIHLTPRHIPRHRQAVDPPVASFCRGLLAEPGPS 1050
1051 SLMPVLGYDPPQLYLAQLREAFEDLALFFYDQHGGEVIGVLWKPSSFQPQ 1100
1101 PFKASSIKGRMVVSQGGELVMLPNIEAILEDFAVLGEGLVQAVEARSERW 1150
1151 TV 1152
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.) |
Go back to the NucPred Home Page.