 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q32S24 from www.uniprot.org...
The NucPred score for your sequence is 0.39 (see score help below)
1 MERCSRCHHLLLLVLLLLWLSAAPAWAGTAPVDVLRALRFPALPDGVRRA 50
51 RGICPADVAYRVSRPAQLSAPTRQLFPGGFPKDFSLLTAVRARPGLQAPL 100
101 LTLYSAQGVRQLGLELGRPVRFLYEDQTGRPQPPAQPVFRGLSLADGKWH 150
151 RVAVAVKGQSVTLIIDCKKRVTRPLPRSARPVLDTRGVIIFGARILDEEV 200
201 FEGDIQELSIIPGVQAAYESCDQKELECEGGWRERPQRQPSHRTQRSPKQ 250
251 QPPRLHRPQNQEPQAQSTESLYYDYEPPYYDVMTTGTTPDYQDPTPGEEE 300
301 GILESSPLPPPEEEQTDLQVPPTADRFLTEEYGEGGTEPPAGPYDYTYAY 350
351 GDDYHEETELGPALSAETARSEAAARGPRGLKGEKGEPAVLEPGMLVEGP 400
401 PGPEGPAGFPGPPGIQGNPGPVGDPGERGPPGRAGLPGSDGAPGPPGTSL 450
451 MLPFRFGSGGGDKGPVVAAQEAQAQAILQQARVALRGPPGPMGYTGRPGP 500
501 LGQPGSPGMKGESGDLGPQGPRGPQGLMGPPGKAGRRGRAGADGARGMPG 550
551 EPGVKGDRGFDGLPGLPGEKGHRGDTGAQGLPGPPGEDGERGDDGEIGPR 600
601 GLPGESGPRGLLGPKGPPGIPGPPGVRGMDGPHGPKGSLGPQGEPGPPGQ 650
651 QGTPGTQGLPGPQGAIGPHGEKGPRGKPGLPGMPGSDGPPGHPGKEGPPG 700
701 TKGNQGPSGPQGPLGYPGPRGIKGVDGIRGLKGHKGEKGEDGFPGFKGDM 750
751 GVKGDRGEVGVPGSRGEDGPEGPKGRTGPTGDPGPPGLMGEKGKLGVPGL 800
801 PGYPGRQGPKGSLGFPGFPGASGEKGARGLSGKSGPRGERGPTGPRGQRG 850
851 PRGATGKSGAKGTSGGDGPHGPPGERGLPGPQGPNGFPGPKGPPGPPGKD 900
901 GLPGHPGQRGEVGFQGKTGPPGPPGVVGPQGAAGETGPMGERGHPGPPGP 950
951 PGEQGLTGTAGKEGTKGDPGPPGAPGKDGPAGLRGFPGERGLPGTAGGPG 1000
1001 LKGNEGPAGPPGPAGSPGERGSAGSGGPIGPPGRPGPQGPPGAAGEKGVP 1050
1051 GEKGPIGPTGRDGVQGPVGLPGPAGPPGVAGEDGDKGEVGDPGQKGAKGN 1100
1101 KGEHGPPGPPGPIGPVGQPGAAGADGEPGARGPQGHFGAKGDEGTRGFNG 1150
1151 PPGPIGLQGLPGPSGEKGETGDVGPMGPPGPPGPRGPAGPNGADGPQGPP 1200
1201 GGVGNLGPPGEKGEPGESGSPGVQGEPGVKGPRGERGEKGETGQAGEAGP 1250
1251 PGPKGPTGDDGPKGNPGPVGFPGDPGPPGEVGPRGQDGAKGDRGEDGEPG 1300
1301 QPGSPGPTGENGPPGPLGKRGPAGTPGPEGRQGEKGAKGDPGAVGAPGKT 1350
1351 GPVGPAGPAGKPGPDGLRGLPGSVGQQGRPGATGQAGPPGPVGPPGLPGL 1400
1401 RGDTGAKGEKGHPGLIGLIGPPGEQGEKGDRGLPGPQGSTGQKGETGIPG 1450
1451 ASGPIGPGGPPGLPGPAGPKGAKGATGPAGPKGEKGVQGPPGHPGPPGEV 1500
1501 IQPLPIQMPKKTRRSVDGSRLMQEDEAVPTGGAPGSPGGLEEIFGSLDSL 1550
1551 REEIEQMRRPMGTQDSPARTCQDLKLCHPELPDGEYWVDPNQGCARDAFR 1600
1601 VFCNFTAGGETCVTPRDDVTQFSYVDSEGAPVGVVQLTFLRLLSVSARQN 1650
1651 ISYPCSGEAQDSPLKLRGANEDELSPETSPYIKEIRDGCQTQQGRTVLEV 1700
1701 RTPVLEQLPVLDASFSELGAPPRRGGVLLGPVCFMG 1736
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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