 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P35084 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MAAFFPPSSAELRKVKRVQFGILSPDEIRNMSVARVEHPETYENGKPKAG 50
51 GLLDPAMGTIDKTQRCQTCSGTMAECPGHFGHIELAKPVFHIGFIDTVLK 100
101 ILRCVCYHCSKLLTDTNEHSFRQALKIRNQKHRLNAVVDCCKNKKVCAIG 150
151 GEEEEEHDLSKTDEELDKPVKHGGCGNVLPKITKEDLKIIVEFKDVTDES 200
201 IEKKSVLSAERVLNILKRIKDEDSRAMGINPDWARADWMIATVLPVPPPP 250
251 VRPSIMMDTSTRGEDDLTHKLADIVKANRELQRQEKNGAPAHIIAEATQF 300
301 LQFHVATYVDNEIPGLPQAQQRSGRPLKSIRQRLKGKEGRIRGNLMGKRV 350
351 DFSARTVITADPNLSIDQVGVPRSIALNLTYPETVTPFNIDKMRELIRNG 400
401 PSEHPGAKYIIREDGTRFDLRFVKKVSDTHLECGYKVERHINDGDVVIFN 450
451 RQPSLHKMSMMGHRIKVMPYSTFRLNLSVTSPYNADFDGDEMNLHVPQTL 500
501 ETRAEVIEIMMVPRQIVSPQSNRPVMGIVQDTLLGSRLFTKRDCFMEKDL 550
551 VMNILMWLPSWDGKVPPPAILKPKQLWTGKQLFSLIIPDINLIRFTSTHN 600
601 DKEPNECSAGDTRVIIERGELLAGILCKRSLGAANGSIIHVVMNEHGHDT 650
651 CRLFIDQTQTVVNHWLINRGFTMGIGDTIADSATMAKVTLTISSAKNQVK 700
701 ELIIKAQNKQFECQPGKSVIETFEQKVNQVLNKARDTAGSSAQDSLSEDN 750
751 NLKAMVTAGSKGSFINISQMMACVGQQNVEGKRIPFGFQSRTLPHFTKDD 800
801 YGPESRGFVENSYLRGLTPQEFFFHAMGGREGLIDTAVKTSETGYIQRRL 850
851 VKAMEDVSIKYDATVRNSLGDVIQFAYGEDGIDGCFVENQSIDSLRKDNT 900
901 ELERMYRHQVDKPDYGDGWMDPLVIEHVRNDSLTRDTLEKEFERIKSDRS 950
951 LLRNEIIPSGEANWPLPVNLRRLINNAQKLFNIDIRRVSDLNPAVVVLEI 1000
1001 EKLVARLKIIATADTTEDDENFNRAWAEVYFNATMLFSILVRSTFASKRV 1050
1051 LTEFRLTEKAFLWVCGEIESKFLQALAHPGEMVGALAAQSIGEPATQMTL 1100
1101 NTFHYAGVSSKNVTLGVPRLKEIINIAKQVKTPSLTIYLKPHMARDMDRA 1150
1151 KIVKSQLEYTTLANVTSATEIYYDPDPQNTIISEDAEFVNSYFELPDEEI 1200
1201 DVHSMSPWLLRIELDRGMVTDKKLTMADITQCVVRDFGLSLNCIFSDDNA 1250
1251 EKLILRIRMVESQETKGTDNDDDDQFLRRIESNMLSEMVLRGIKGIKKVF 1300
1301 MRTDDKIPKVTENGGFGVREEWILDTDGVSLLEVMSHPDVDHTRTTSNDI 1350
1351 VEIIQVLGIEAVRNALLKELRAVISFDGSYVNYRHLAILADVMTYRGHLM 1400
1401 AITRHGINRVETGPLMRCSFEETVEILMDAAMFSETDDVKGVTENIILGQ 1450
1451 LPPLGTGSFEVFLNQDMIKNAHSIALPEPSNVSYPDTPGSQTPSYSYGDG 1500
1501 STTPFHNPYDAPLSPFNETFRGDFSPSAMNSPGYNANKSYGSSYQYFPQS 1550
1551 PTYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSP 1600
1601 FYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPS 1650
1651 YSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPTSPSYSPSSPSYSPSSPSY 1700
1701 SPSSPSYSPSSPTFTNKYNYQPNNKKK 1727
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.) |
Go back to the NucPred Home Page.