 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P20909 from www.uniprot.org...
The NucPred score for your sequence is 0.52 (see score help below)
1 MEPWSRWKTKRWIWDLTISTLVLTFLFQAREVRGAAPVDILKALDFHNSP 50
51 VGISKTTGFCTSRKNSKDPDIAYRVTEEAQISAPTKQLFPGGIFPQDFSI 100
101 LFTIKPKKGTQAFLLSLYNEHGIQQLGVEVGRSPVFLFEDHTGKPTPENY 150
151 PLFSTVNIADGKWHRVAISVEKKTVTMIVDCKKKITKPLDRSERSIVDTN 200
201 GIMVFGTRILETDVFQGDIQQFLITGDPKAAYDYCDHYSPDCDLTSKAAQ 250
251 AQEPHIDEYAPEDIIEYDYEYGETDYKEAESVTEMPTVTEETVAQTEANI 300
301 VDDFQDYNYGTMETYQTESPRRVSGSNEPNPVEEGFTEEYLTGEDYDVQR 350
351 NISEDILYGNKGIDGRDSDLLVDGDLGEYDFYEYKEYEERTTTSPNEEFG 400
401 PGVPAETDFTETSINGHGAYGEKGQKGEPAVVEPGMLVEGPPGPAGPAGL 450
451 MGPPGLQGPSGLPGDPGDRGPPGRPGLPGADGLPGPPGTMLMLPFRYGGD 500
501 GSKGPTISAQEAQAQAILQQARIALRGPPGPMGLTGRPGPVGGPGSAGAK 550
551 GESGDPGPQGPRGVQGPPGPTGKPGKRGRPGADGGRGMPGEPGSKGDRGF 600
601 DGLPGLPGDKGHRGERGPQGPPGLPGDDGMRGEDGEIGPRGLPGEAGPRG 650
651 LLGPRGTPGPPGQPGIGGIDGPQGPKGNMGPQGEPGPPGQQGNPGPQGLP 700
701 GPQGPIGPPGEKGPQGKPGLAGLPGADGPPGHPGKEGQSGEKGALGPPGP 750
751 QGPIGYPGPRGVKGADGVRGLKGSKGEKGEDGFPGFKGDMGLKGDRGEVG 800
801 QVGPRGEDGPEGPKGRAGPTGDPGPSGQAGEKGKLGVPGLPGYPGRQGPK 850
851 GSTGFPGFPGANGEKGARGIAGKPGPRGQRGPTGPRGSRGARGPTGKPGP 900
901 KGTSGGDGPPGPPGERGPQGPQGPVGFPGPKGPPGPAGKDGLPGHPGQRG 950
951 ETGFQGKTGPPGPGGVVGPQGPTGETGPIGERGHPGTPGPPGEQGLPGAA 1000
1001 GKEGAKGDPGPQGISGKDGPAGIRGFPGERGLPGAQGAPGLKGGEGPQGP 1050
1051 QGPIGSPGERGSAGTAGPIGLPGRPGPQGPPGPAGEKGAPGEKGPQGPAG 1100
1101 RDGVQGPVGLPGPAGPAGSPGEDGDKGEIGEPGQKGSKGDKGENGPPGPP 1150
1151 GLQGPVGAPGIAGGDGEAGPRGQQGMFGQKGDEGARGFPGPPGPIGLQGL 1200
1201 PGPPGEKGENGDVGPMGPPGPPGPRGPQGPNGADGPQGPPGSIGSVGGVG 1250
1251 EKGEPGEAGNPGPPGEAGSGGPKGERGEKGEAGPPGAAGPPGIKGPPGDD 1300
1301 GPKGNPGPVGFPGDPGPPGEPGPAGQDGVGGDKGEDGDPGQPGPPGPSGE 1350
1351 AGPPGPPGKRGPPGASGSEGRQGEKGAKGEAGAEGPPGKTGPVGPQGPSG 1400
1401 KPGPEGLRGIPGPVGEQGLPGAAGQDGPPGPLGPPGLPGLKGDPGSKGEK 1450
1451 GHPGLIGLIGPPGEQGEKGDRGLPGTQGSPGAKGDGGIPGPAGPIGPPGP 1500
1501 PGLPGPAGPKGNKGSSGPTGQKGDSGMPGPPGPPGPPGEVIQPLPILSPK 1550
1551 KTRRHTESIQADAGDNILDYSDGMEEIFGSLNSLKQDIEHMKFPMGTQTN 1600
1601 PARTCKDLQLSHPDFPDGEYWIDPNQGCSGDSFKVYCNFTAGGETCIYPD 1650
1651 KKSEGVRLSSWPKEKPGSWYSEFKRGKLLSYLDVEGNSINMVQMTFLKLL 1700
1701 TASARQNFTYNCHQSTAWYDVLSGSYDKALRFLGSNDEEMSYENNPHIKA 1750
1751 LYDGCASRKGYEKTVIEINTPKIDQVPIIDVMINDFGDQNQKFGFEVGPA 1800
1801 CFLG 1804
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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