 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O54705 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MACPWNFLWKLKSSRYDLTEEKDINNNVGKASHLYSPEIQDDPKYCSPGK 50
51 HQNGSSQSLTGTAKKVPESQSKPHKPSPTCSQHMKIKNWGNGMILQDTLH 100
101 TKAKTNFTCKPKSCLGSVMNPRSMTRGPRDTPIPPDELLPQAIEFVNQYY 150
151 DSFKEAKIEEYLARVETVTKEIETTGTYQLTGDELIFATKLAWRNAPRCI 200
201 GRIQWSNLQVFDARSCHTAQEMFEHICRHVRYSTNNGNIRSAITVFPQRT 250
251 DGKHDFRVWNAQLIRYAGYQMPDGTIQGDPANLEFTQLCIDLGWKPRYGR 300
301 FDVLPLILQADGRDPELFEIPPDLVLEVPMEHPKYEWFQDLGLKWYALPA 350
351 VANMLLEVGGLEFPACPFNGWYMGTEIGVRDFCDAQRYNILEEVGRRMGL 400
401 ETHTLASLWKDRAVTEINVAVLHSFQKQNVTIMDHHSAAESFMKHMQNEY 450
451 RARGGCPADWIWLVPPISGSITPVFHQEMLNYILSPFYYYQVEAWKTHVW 500
501 QDETRRPKRREIPFRVLAKATLFASLLMRKMMASRVRATILFATETGKSE 550
551 ALAQDLGALFSCAFNPKVLCMDQYQLSSLEEEKLLLVVTSTFGNGDCPGN 600
601 GETLKKSLFVLKKLTNTFRYAVFGLGSSMYPRFCAFAHDIDIKLSQLGAS 650
651 QLTPVGEGDELSGQEDAFCTWAVQTFQAACAAFDVRGRHHITIPKRYTSS 700
701 VTWEPYHYRLVQDSQPLDLNKALSRMHATDVFTMRLKSQKNLQSPKSSRT 750
751 TLLMELSCDDSRSLAYLPGEHLGVFPCNQPALVQGILECVVDNPGPHHTV 800
801 CLEVLDDSGSYWAKDKRLPPCSLSQALTYFLDITTPPTQLQLQKLARLAT 850
851 EQAERLRLESLSQPSEYNKWKFTNSPTFLEVLEEFPSLRVPAAFLLSQLP 900
901 ILKPRYYSISSSLDHTPAEVHLTVAVVTYRTRDGRGPLHHGVCSTWFSGL 950
951 KPQDPVPCLVRSVNSFQLPKDPSQPCILIGPGTGIAPFRSFWQQRLHNLK 1000
1001 HTGLQGGRMTLLFGCRHPEEDHIYKEEMQEMVQKGVLHEVHTAYSRLPGK 1050
1051 PKAYVQDILRQQLAREVLRVLHEEPGHLYVCGNVLMAQDVACTLKQLLAA 1100
1101 KLNLNEEQVEDYFFQLKSQKRYHEDIFGAVFPHGVKKDRAERPPGDDKL 1149
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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