 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JI03 from www.uniprot.org...
The NucPred score for your sequence is 0.49 (see score help below)
1 MDVHTRWKAPRPGAPLLSSPLLLLLLLLWAPPPSRAAQPTDLLEMLDFHN 50
51 LPSGVTKTTGFCATRRSSKEPDVAYRVSKDAQLSMPTKQLYPESDFPEDF 100
101 SILTTVKAKKGSQAFLVSVYNEQGIQQLGLELGRSPVFLYEDHTGKPGPE 150
151 EYPLFPGINLSDGKWHRIAISVYKKNVTLILDCKKKITKFLNRGDHPIID 200
201 VNGIIMFGSRILDDEIFEGDIQQLLFVSDHRAAYDYCEHYSPDCDTAVPD 250
251 TPQSQDPNPDEYYPEGEGETYYYEYPYYEDPEDPGKEPAPSQKPVEAARE 300
301 TTEVPEEQTQPPSEAPTVPETSDTAGKEDNPGIGDYDYVPTDDYYTTSPY 350
351 EDFGYGEGVENPDQPTNPDSGAEIPTSTSVTSNSSNPAPSPEEGKDDLGG 400
401 EFTEETIKNLDENYYDPYFDPDSDSNVSPSEIGPGMPANQDTIYEGIGGP 450
451 RGEKGQKGEPAIIEPGMLIEGPPGPEGPAGLPGPPGTTGPTGQMGDPGER 500
501 GPPGRPGLPGADGLPGPPGTMLMLPFRFGGGGDAGSKGPMVSAQESQAQA 550
551 ILQQARLALRGPAGPMGLTGRPGPMGPPGSGGLKGEPGDMGPQGPRGVQG 600
601 PPGPTGKPGRRGRAGSDGARGMPGQTGPKGDRGFDGLAGLPGEKGHRGDP 650
651 GPSGPPGLPGDDGERGDDGEVGPRGLPGEPGPRGLLGPKGPPGPPGPPGV 700
701 TGMDGQPGPKGNVGPQGEPGPPGQQGNPGAQGLPGPQGAIGPPGEKGPLG 750
751 KPGLPGMPGADGPPGHPGKEGPPGEKGGQGPPGPQGPIGYPGPRGVKGAD 800
801 GIRGLKGTKGEKGEDGFPGFKGDMGIKGDRGEIGPPGPRGEDGPEGPKGR 850
851 GGPNGDPGPLGPTGEKGKLGVPGLPGYPGRQGPKGSIGFPGFPGANGEKG 900
901 GRGTPGKPGPRGQRGPTGPRGERGPRGITGKPGPKGNSGGDGPAGPPGER 950
951 GPNGPQGPTGFPGPKGPPGPPGKDGLPGHPGQRGETGFQGKTGPPGPPGV 1000
1001 VGPQGPTGETGPMGERGHPGPPGPPGEQGLPGAAGKEGTKGDPGPAGLPG 1050
1051 KDGPPGLRGFPGDRGLPGPVGALGLKGSEGPPGPPGPAGSPGERGPAGAA 1100
1101 GPIGIPGRPGPQGPPGPAGEKGVPGEEGPQGPAGRDGLQGPVGLPGPAGP 1150
1151 VGPPGEDGDKGEIGEPGQKGSKGDKGEQGPPGPTGPQGPTGQPGPSGADG 1200
1201 EPGPRGQQGLFGQKGDEGSRGFPGPPGPVGLQGLPGPPGEKGETGDVGQM 1250
1251 GPPGPPGPRGPSGAPGADGPQGPPGGIGNPGAVGEKGEPGEAGEPGLPGE 1300
1301 GGPLGPKGERGEKGEAGPSGAAGPPGPKGPPGDDGPKGSPGPVGFPGDPG 1350
1351 PPGEPGPAGQDGPPGDKGDDGEPGQTGSPGPTGEPGPSGPPGKRGPPGPA 1400
1401 GPEGRQGEKGAKGEAGLEGPPGKTGPIGPQGAPGKPGPDGLRGIPGPVGE 1450
1451 QGLPGSPGPDGPPGPMGPPGLPGLKGDSGPKGEKGHPGLIGLIGPPGEQG 1500
1501 EKGDRGLPGPQGSSGPKGEQGITGPSGPLGPPGPPGLPGPPGPKGAKGSS 1550
1551 GPTGPKGEAGHPGLPGPPGPPGEVIQPLPIQASRTRRNIDASQLLDDGAG 1600
1601 ESYVDYADGMEEIFGSLNSLKLEIEQMKRPLGTQQNPARTCKDLQLCHPD 1650
1651 FPDGEYWVDPNQGCSRDSFKVYCNFTAGGSTCVFPDKKSEGARITSWPKE 1700
1701 NPGSWFSEFKRGKLLSYVDAEGNPVGVVQMTFLRLLSASAQQNITYNCYQ 1750
1751 SVAWQDAATGSYDKAIRFLGSNDEEMSYDNNPYIRALVDGCATKKGYQKT 1800
1801 VLEIDTPKVEQVPIVDIMFTDFGEASQKFGFEVGPACFLG 1840
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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