 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P17139 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MSRLSLLGLTAAVVLLSSFCQDRIHVDAAAACKGCAPPCVCPGTKGERGN 50
51 PGFGGEPGHPGAPGQDGPEGAPGAPGMFGAEGDFGDMGSKGARGDRGLPG 100
101 SPGHPGLQGLDGLPGLKGEEGIPGCNGTDGFPGMPGLAGPPGQSGQNGNP 150
151 GRPGLSGPPGEGGVNSQGRKGVKGESGRSGVPGLPGNSGYPGLKGAKGDP 200
201 GPYGLPGFPGVSGLKGRMGVRTSGVKGEKGLPGPPGPPGQPGSYPWASKP 250
251 IEMEVLQGPVGPAGVKGEKGRDGPVGPPGMLGLDGPPGYPGLKGQKGDLG 300
301 DAGQRGKRGKDGVPGNYGEKGSQGEQGLGGTPGYPGTKGGAGEPGYPGRP 350
351 GFEGDCGPEGPLGEGTGEAGPHGAQGFDGVQGGKGLPGHDGLPGPVGPRG 400
401 PVGAPGAPGQPGIDGMPGYTEKGDRGEDGYPGFAGEPGLPGEPGDCGYPG 450
451 EDGLPGYDIQGPPGLDGQSGRDGFPGIPGDIGDPGYSGEKGFPGTGVNKV 500
501 GPPGMTGLPGEPGMPGRIGVDGYPGPPGNNGERGEDCGYCPDGVPGNAGD 550
551 PGFPGMNGYPGPPGPNGDHGDCGMPGAPGKPGSAGSDGLSGSPGLPGIPG 600
601 YPGMKGEAGEIVGPMENPAGIPGLKGDHGLPGLPGRPGSDGLPGYPGGPG 650
651 QNGFPGLQGEPGLAGIDGKRGRQGSLGIPGLQGPPGDSFPGQPGTPGYKG 700
701 ERGADGLPGLPGAQGPRGIPAPLRIVNQVAGQPGVDGMPGLPGDRGADGL 750
751 PGLPGPVGPDGYPGTPGERGMDGLPGFPGLHGEPGMRGQQGEVGFNGIDG 800
801 DCGEPGLDGYPGAPGAPGAPGETGFGFPGQVGYPGPNGDAGAAGLPGPDG 850
851 YPGRDGLPGTPGYPGEAGMNGQDGAPGQPGSRGESGLVGIDGKKGRDGTP 900
901 GTRGQDGGPGYSGEAGAPGQNGMDGYPGAPGDQGYPGSPGQDGYPGPSGI 950
951 PGEDGLVGFPGLRGEHGDNGLPGLEGECGEEGSRGLDGVPGYPGEHGTDG 1000
1001 LPGLPGADGQPGFVGEAGEPGTPGYRGQPGEPGNLAYPGQPGDVGYPGPD 1050
1051 GPPGLPGQDGLPGLNGERGDNGDSYPGNPGLSGQPGDAGYDGLDGVPGPP 1100
1101 GYPGITGMPGLKGESGLPGLPGRQGNDGIPGQPGLEGECGEDGFPGSPGQ 1150
1151 PGYPGQQGREGEKGYPGIPGENGLPGLRGQDGQPGLKGENGLDGQPGYPG 1200
1201 SAGQLGTPGDVGYPGAPGENGDNGNQGRDGQPGLRGESGQPGQPGLPGRD 1250
1251 GQPGPVGPPGDDGYPGAPGQDIYGPPGQAGQDGYPGLDGLPGAPGLNGEP 1300
1301 GSPGQYGMPGLPGGPGESGLPGYPGERGLPGLDGKRGHDGLPGAPGVPGV 1350
1351 EGVPGLEGDCGEDGYPGAPGAPGSNGYPGERGLPGVPGQQGRSGDNGYPG 1400
1401 APGQPGIKGPRGDDGFPGRDGLDGLPGRPGREGLPGPMAMAVRNPPGQPG 1450
1451 ENGYPGEKGYPGLPGDNGLSGPPGKAGYPGAPGTDGYPGPPGLSGMPGHG 1500
1501 GDQGFQGAAGRTGNPGLPGTPGYPGSPGGWAPSRGFTFAKHSQTTAVPQC 1550
1551 PPGASQLWEGYSLLYVQGNGRASGQDLGQPGSCLSKFNTMPFMFCNMNSV 1600
1601 CHVSSRNDYSFWLSTDEPMTPMMNPVTGTAIRPYISRCAVCEVPTQIIAV 1650
1651 HSQDTSVPQCPQGWSGMWTGYSFVMHTAAGAEGTGQSLQSPGSCLEEFRA 1700
1701 VPFIECHGRGTCNYYATNHGFWLSIVDQDKQFRKPMSQTLKAGGLKDRVS 1750
1751 RCQVCLKNR 1759
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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