 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q28314 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MACPWQFLFKIKSQKVDLTTELDINNNVRKGHQPPSSPVTQDDPKRHSPG 50
51 KHGNESPQPLTGTVKTSPDPLGKLDAPPSACPRHVRIKNWGSGVTFQDTL 100
101 HQKAKGDLSCKSKSCLASIMNPKSLTIGPRDKPTPPDELLPQAIEFVNQY 150
151 YGSFKEAKIEEHLARVEAVTKEIETTGTYQLTGDELIFATKQAWRNAPRC 200
201 IGRIQWSNLQVFDARSCSTAQEMFEHICRHVRYATNNGNIRSAITVFPQR 250
251 SDGKHDFRVWNAQLIRYAGYQMPDGSIRGDPASVEFTQLCIDLGWKPKFS 300
301 RFDVLPLVLQADGRDPELFEIPPDLVLEVPMEHPRYEWFQELELKWYALP 350
351 AVANMLLEVGGLEFPGCPFNGWYMGTEVGVRDFCDVQRYNILEEVGRRMG 400
401 LETHKLASLWKDRAVVEINVAVLHSFQKQNVTIMDHHSAAESFMKYMQNE 450
451 YRSRGGCPADWIWLVPPISGSITPVFHQEMLNYILSPFYYYQVEAWKTHV 500
501 WQDERRRPQRREIRFKVLIKAVFFASVLMQKAMSSRVRATILFATETGRS 550
551 ETLAQDLGALFSCAFNPKVLCMDQYQLSHLEEEQLLLVVTSTFGNGDSPG 600
601 NGEKLKKSLLMLKELTNKFRYAVFGLGSSMYPQFCAFAHDIDQKLSQLGA 650
651 SQLAPTGEGDELSGQEEAFRSWAVQTFKAACEIFDVSGKHHIEIPKLYTS 700
701 SVTWDPHHYRLVQDSEPLDLNKALSSMHAKPVFTMRLKSQQNLQSPKSSR 750
751 TTLLVELSCEGSQAPSYLPGEHLGVFPCNQPALVQGILERVVDGPAPHQP 800
801 VRLETLCENGSYWVKDKRLPPCSLSQALTHFLDITTPPTQLLLRKLAQLA 850
851 TEEAERQRLETLCQPSDYNKWKFTNSPTFLEVLEEFPSLRVSASFLLSQL 900
901 PILKPRYYSISSSRDLRPTEIHLTVAVLTYRTRDGQGPLHHGVCSTWLSS 950
951 LKPQDPVPCFVRSASGFQLPEDRSRPCILIGPGTGIAPFRSFWQQRLHEA 1000
1001 EHKGLQGGRMTLVFGCRRPEEDHLYWEEMLEMARKGVLHEVHTAYSRLPD 1050
1051 QPKVYVQDILRQRLAGEVLRVLHEEQGHLYVCGDVRMARDVAHSLKQLMA 1100
1101 TALSLDEEQVEDYFFQLKSQKRYHEDIFGAVFPYEMKKDGAAPSDPRAPG 1150
1151 ANRS 1154
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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