 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q63755 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MHQNTESVAATETLAEVPEHVLRGLPEEVRLFPSAVDKTRIGVWATKPIL 50
51 KGKKFGPFVGDKKKRSQVRNNVYMWEVYYPNLGWMCIDATDPEKGNWLRY 100
101 VNWACSGEEQNLFPLEINRAIYYKTLKPIAPGEELLVWYNGEDNPEIAAA 150
151 IEEERASARSKRSSPKSRRGKKKSHENKNKGIRTHPTQLKASELDSTFAN 200
201 MRGSAEGPKEEDERPLASAPEQPAPLPEVGNQDAVPQVAIPLPACEPQPE 250
251 VDGKQEVTDCEVNDVEEEELEEEEELEEEEEEELGEDGVEEADMPNESSA 300
301 KEPEIRCEEKPEDLLEEPQSMSNEAREDSPDVTPPPHTPRAREEANGDVL 350
351 ETFMFPCQHCERKFATKQGLERHMHIHISTINHAFKCKYCGKRFGTQINR 400
401 RRHERRHETGLKRRPSMTLQSSEDPDDGKGENVTSKDESSPPQLGQDCLI 450
451 LNSEKTSQEVLNSSFVEENGEVKELHPCKYCKKVFGTHTNMRRHQRRVHE 500
501 RHLIPKGVRRKGGLLEEPQPPAEQAPPSQNVYVPSTEPEEEGETDDVYIM 550
551 DISSNISENLNYYIDGKIQTNSSTSNCDVIEMESNSAHLYGIDCLLTPVT 600
601 VEITQNIKSTQVSVTDDLLKDSPSSTNCESKKRRTASPPVLPKIKTETES 650
651 DSTAPSCSLSLPLSISTAEVVSFHKEKGVYLSSKLKQLLQTQDKLTLPAG 700
701 FSAAEIPKLGPVCASAPASMLPVTSSRFKRRTSSPPSSPQHSPALRDFGK 750
751 PNDGKAAWTDTVLTSKKPKLESRSDSPAWSLSGRDERETGSPPCFDEYKI 800
801 SKEWAASSTFSSVCNQQPLDLSSGVKQKSEGTGKTPVPWESVLDLSVHKK 850
851 PCDSEGKEFKENHLAQPAAKKKKPTTCMLQKVLLNEYNGVSLPTETTPEV 900
901 TRSPSPCKSPDTQPDPELGPDSSCSVPTAESPPEVVGPSSPPLQTASLSS 950
951 GQLPPLLTPTEPSSPPPCPPVLTVATPPPPLLPTVPLSHPSSDASPQQCP 1000
1001 SPFSNTTAQSPLPILSPTVSPSPSPIPPVEPLMSAASPGPPTLSSSSSSS 1050
1051 SSFPSSSCSSTSPSPPPLSAVSSVVSSGDNLEASLPAVTFKQEESESEGL 1100
1101 KPKEEAPPAGGQSVVQETFSKNFICNVCESPFLSIKDLTKHLSVHAEEWP 1150
1151 FKCEFCVQLFKVKTDLSEHRFLLHGVGNIFVCSVCKKEFAFLCNLQQHQR 1200
1201 DLHPDEVCTHHEFESGTLRPQNFTDPSKANVEHMPSLPEEPLETSREEEL 1250
1251 NDSSEELYTTIKIMASGIKTKDPDVRLGLNQHYPSFKPPPFQYHHRNPMG 1300
1301 IGVTATNFTTHNIPQTFTTAIRCTKCGKGVDNMPELHKHILACASASDKK 1350
1351 RYTPKKNPVPLKQTVQPKNGVVVLDNSGKNAFRRMGQPKRLSFNVELGKM 1400
1401 SPNKLKLSALKKKNQLVQKAILQKNRAAKQKADLRDTSEASSHICPYCDR 1450
1451 EFTYIGSLNKHAAFSCPKKPLSPSKRKVSHSSKKGGHASSSSSDRNSSCH 1500
1501 PRRRTADTEIKMQSTQAPLGKTRARSTGPAQASLPSSSFRSRQNVKFAAS 1550
1551 VKSKKASSSSLRNSSPIRMAKITHVEGKKPKAVAKSHSAQLSSKSSRGLH 1600
1601 VRVQKSKAVIQSKTALASKRRTDRFIVKSRERSGGPITRSLQLAAAADLS 1650
1651 ESRREDSSARHELKDFSYSLRLASRCGSSTASYITRQCRKVKAAAATPFQ 1700
1701 GPFLKE 1706
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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