 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P27393 from www.uniprot.org...
The NucPred score for your sequence is 0.19 (see score help below)
1 MSSRLRIPLWLLLPTTALVYFVTTVSTQITCRDCTNRGCFCVGEKGSMGI 50
51 PGPQGPPGAQGIRGFPGPEGLPGPKGQKGSQGPPGPQGIKGDRGIIGVPG 100
101 FPGNDGANGRPGEPGPPGAPGWDGCNGTDGAPGVPGLPGPPGMPGFPGPP 150
151 GVPGMKGEPAIGYAGAPGEKGDAGMPGMPGLPGPPGRDGFPGEKGDRGDV 200
201 GQAGPRGPPGEAGPPGNPGIGSIGPKGDPGEQGPRGPQGPPGPVPSTGAK 250
251 GTIIGPEGAPGMKGEKGDPGEAGPRGFPGTPGVAGQPGLPGMKGEKGLSG 300
301 PAGPRGKEGRPGLPGPPGFKGDRGLDGLPGVPGLPGQKGEAGFPGRDGAK 350
351 GARGPPGPPGGGEFSDGPPGPPGLPGREGQPGPPGADGYPGPPGPQGPQG 400
401 LPGGPGLPGLPGLEGLPGPKGEKGDSGIPGAPGVQGPPGLAGPPGAKGEP 450
451 GPRGVDGQSIPGLPGKDGRPGLDGLPGRKGEMGLPGVRGPPGDSLNGLPG 500
501 PPGPRGPQGPKGYDGRDGAPGLPGIPGPKGDRGGTCAFCAHGAKGEKGDA 550
551 GYAGLPGPQGERGLPGIPGATGAPGDDGLPGAPGRPGPPGPPGQDGLPGL 600
601 PGQKGEPTQLTLRPGPPGYPGQKGETGFPGPRGQEGLPGKPGIVGAPGLP 650
651 GPPGPKGEPGLTGLPEKPGKDGIPGLPGLKGEPGYGQPGMPGLPGMKGDA 700
701 GLPGLPGLPGAVGPMGPPVPESQLRPGPPGKDGLPGLPGPKGEAGFPGAP 750
751 GLQGPAGLPGLPGMKGNPGLPGAPGLAGLPGIPGEKGIAGKPGLPGLTGA 800
801 KGEAGYPGQPGLPGPKGEPGPSTTGPPGPPGFPGLKGKDGIPGAPGLPGL 850
851 EGQRGLPGVPGQKGEIGLPGLAGAPGFPGAKGEPGLPGLPGKEGPQGPPG 900
901 QPGAPGFPGQKGDEGLPGLPGVSGMKGDTGLPGVPGLAGPPGQPGFPGQK 950
951 GQPGFPGVAGAKGEAGLPGLPGAPGQKGEQGLAGLPGIPGMKGAPGIPGA 1000
1001 PGQDGLPGLPGVKGDRGFNGLPGEKGEPGPAARDGEKGEPGLPGQPGLRG 1050
1051 PQGPPGLPGLPGLKGDEGQPGYGAPGLMGEKGLPGLPGKPGRPGAPGPKG 1100
1101 LDGAPGFPGLKGEAGLPGAPGLPGQDGLPGLPGQKGESGFPGQPGLVGPP 1150
1151 GLPGKMGAPGIRGEKGDAGLPGLPGERGLDGLPGQKGEAGFPGAPGLPGP 1200
1201 VGPKGSAGAPGFPGLKGEPGLPGLEGQPGPRGMKGEAGLPGAPGRDGLPG 1250
1251 LPGMKGEAGLPGLPGQPGKSITGPKGNAGLPGLPGKDGLPGLPGLKGEPG 1300
1301 KPGYAGAAGIKGEPGLPGIPGAKGEPGLSGIPGKRGNDGIPGKPGPAGLP 1350
1351 GLPGMKGESGLPGPQGPAGLPGLPGLKGEPGLPGFPGQKGETGFPGQPGI 1400
1401 PGLPGMKGDSGYPGAPGRDGAPGKQGEPGPMGPPGAQPIVQRGEKGEMGP 1450
1451 MGAPGIRGEKGLPGLDGLPGPSGPPGFAGAKGRDGFPGQPGMPGEKGAPG 1500
1501 LPGFPGIEGIPGPPGLPGPSGPPGPPGPSYKDGFLLVKHSQTSEVPQCPP 1550
1551 GMVKLWDGYSLLYIEGNEKSHNQDLGHAGSCLSRFSTMPFLFCDVNNVCN 1600
1601 YASRNDKSYWLSTTAPIPMMPVSEGGIEPYISRCAVCEAPANVIAVHSQT 1650
1651 IQIPNCPNGWNSLWIGYSFAMHTGAGAEGGGQSLSSPGSCLEDFRATPFI 1700
1701 ECNGARGTCHYFANKFSFWLTTIEDDQQFRIPESETLKAGSLRTRVSRCQ 1750
1751 VCIRSPDVQPYRG 1763
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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