 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P55013 from www.uniprot.org...
The NucPred score for your sequence is 0.18 (see score help below)
1 MEPAFPASSAGVQSQSGPEPGAGQQEPPPPATPLRPVASQSRFQVDLVTE 50
51 GGGGGDGQKGQTAAQPAAAAKDKDRGDGGAAAPSPASPAAAAEPPAAAAE 100
101 EAKGRFRVNFVDPASDEPPLSSQQQPPPPSSASSAHGGHQPPSESMNGYP 150
151 QNGDTMMSEGSLHSSGTGAHHYYDTHTNTYYLRTFGHNTIDAVPRIDHYR 200
201 HTVAQLGEKLIRPSLAELHDELDKEPFEDGYVNGEESSPAEEAVSKHVAD 250
251 NKGVVKFGWIKGVLVRCMLNIWGVMLFIRLSWIVGHAGIGLALLVIGTAT 300
301 VVTTITGLSTSAITTNGFVRGGGAYYLISRSLGPEFGGAIGLIFAFANAV 350
351 AVAMYVVGFAETVRDLLVEHNALMIDEMSDIRIIGSVTIVVLFGISVAGM 400
401 EWEAKAQIVLLGILLLAIVNFTVGTFIPANDKRAKGFFNYRGEIFSENFV 450
451 PDFRDGEDFFSVFAIFFPAATGILAGANISGDLADPQLAIPKGTLLAILI 500
501 TTIVYAGAAVSVGSCIVREATGNLTDAIIPGTVTNCTNVACKLGFNFSSC 550
551 ATNKCSYGLMNDFQVMSLVSGFGPLITAGIFSATLSSALASLVSAPKIFQ 600
601 ALCKDNIYPGLHVFSVGYGKNNEPLRGYVLTFFIGLGFILIAELNVIAPI 650
651 ISNFFLASYALINFSVFHASLAKSPGWRPAFRFYNMWISLIGAILCCGVM 700
701 FVINWWAALLTNVIVLALYIYVTYKKPDVNWGSSTQALTYLNALQHAIRL 750
751 TGVEDHVKNFRPQCLLMTGAPTSRPALLHLVHAFTKNVGLVVCGHVHTGP 800
801 RRQALKEISTDQAKYQRWLIKNKMKAFYAPVYAEDLREGTQFLLQAVGLG 850
851 RMRPNTLVFGFKKDWRQALMKDVENYINAIHDAFDYQYGVVVIRLKEGFN 900
901 ISHLQAQEELCTSQEKSAHPKDIVVNLEHSDADSSKPSSKSVSETNSPAV 950
951 CQDQKDEEDDGKASTQPLLKKEVKDPSVPLNMTDQKLLQASSQFQKKQGK 1000
1001 GTIDVWWLFDDGGLTLLIPYLLTTKKKWKDCKIRVFIGGKINRIDHDRRT 1050
1051 MATLLSKFRIDFSDITVLGDMNTKPSKDNITAFEEMIEPFRLHEDDKEQE 1100
1101 ASEKMKEEEPWRITDNELEIYRMKTYRQIRLNELLRENSGTANLIVMSLP 1150
1151 VARKGAVSSALYMAWIETLSKDLPPILLVRGNHQSVLTFYS 1191
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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