 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P55268 from www.uniprot.org...
The NucPred score for your sequence is 0.62 (see score help below)
1 MELTSRERGRGQPLPWELRLGLLLSVLAATLAQAPAPDVPGCSRGSCYPA 50
51 TGDLLVGRADRLTASSTCGLNGPQPYCIVSHLQDEKKCFLCDSRRPFSAR 100
101 DNPHSHRIQNVVTSFAPQRRAAWWQSENGIPAVTIQLDLEAEFHFTHLIM 150
151 TFKTFRPAAMLVERSADFGRTWHVYRYFSYDCGADFPGVPLAPPRHWDDV 200
201 VCESRYSEIEPSTEGEVIYRVLDPAIPIPDPYSSRIQNLLKITNLRVNLT 250
251 RLHTLGDNLLDPRREIREKYYYALYELVVRGNCFCYGHASECAPAPGAPA 300
301 HAEGMVHGACICKHNTRGLNCEQCQDFYRDLPWRPAEDGHSHACRKCECH 350
351 GHTHSCHFDMAVYLASGNVSGGVCDGCQHNTAGRHCELCRPFFYRDPTKD 400
401 LRDPAVCRSCDCDPMGSQDGGRCDSHDDPALGLVSGQCRCKEHVVGTRCQ 450
451 QCRDGFFGLSISDRLGCRRCQCNARGTVPGSTPCDPNSGSCYCKRLVTGR 500
501 GCDRCLPGHWGLSHDLLGCRPCDCDVGGALDPQCDEGTGQCHCRQHMVGR 550
551 RCEQVQPGYFRPFLDHLIWEAEDTRGQVLDVVERLVTPGETPSWTGSGFV 600
601 RLQEGQTLEFLVASVPKAMDYDLLLRLEPQVPEQWAELELIVQRPGPVPA 650
651 HSLCGHLVPKDDRIQGTLQPHARYLIFPNPVCLEPGISYKLHLKLVRTGG 700
701 SAQPETPYSGPGLLIDSLVLLPRVLVLEMFSGGDAAALERQATFERYQCH 750
751 EEGLVPSKTSPSEACAPLLISLSTLIYNGALPCQCNPQGSLSSECNPHGG 800
801 QCLCKPGVVGRRCDLCAPGYYGFGPTGCQACQCSHEGALSSLCEKTSGQC 850
851 LCRTGAFGLRCDRCQRGQWGFPSCRPCVCNGHADECNTHTGACLGCRDHT 900
901 GGEHCERCIAGFHGDPRLPYGGQCRPCPCPEGPGSQRHFATSCHQDEYSQ 950
951 QIVCHCRAGYTGLRCEACAPGHFGDPSRPGGRCQLCECSGNIDPMDPDAC 1000
1001 DPHTGQCLRCLHHTEGPHCAHCKPGFHGQAARQSCHRCTCNLLGTNPQQC 1050
1051 PSPDQCHCDPSSGQCPCLPNVQGPSCDRCAPNFWNLTSGHGCQPCACHPS 1100
1101 RARGPTCNEFTGQCHCRAGFGGRTCSECQELHWGDPGLQCHACDCDSRGI 1150
1151 DTPQCHRFTGHCSCRPGVSGVRCDQCARGFSGIFPACHPCHACFGDWDRV 1200
1201 VQDLAARTQRLEQRAQELQQTGVLGAFESSFWHMQEKLGIVQGIVGARNT 1250
1251 SAASTAQLVEATEELRREIGEATEHLTQLEADLTDVQDENFNANHALSGL 1300
1301 ERDRLALNLTLRQLDQHLDLLKHSNFLGAYDSIRHAHSQSAEAERRANTS 1350
1351 ALAVPSPVSNSASARHRTEALMDAQKEDFNSKHMANQRALGKLSAHTHTL 1400
1401 SLTDINELVCGAPGDAPCATSPCGGAGCRDEDGQPRCGGLSCNGAAATAD 1450
1451 LALGRARHTQAELQRALAEGGSILSRVAETRRQASEAQQRAQAALDKANA 1500
1501 SRGQVEQANQELQELIQSVKDFLNQEGADPDSIEMVATRVLELSIPASAE 1550
1551 QIQHLAGAIAERVRSLADVDAILARTVGDVRRAEQLLQDARRARSWAEDE 1600
1601 KQKAETVQAALEEAQRAQGIAQGAIRGAVADTRDTEQTLYQVQERMAGAE 1650
1651 RALSSAGERARQLDALLEALKLKRAGNSLAASTAEETAGSAQGRAQEAEQ 1700
1701 LLRGPLGDQYQTVKALAERKAQGVLAAQARAEQLRDEARDLLQAAQDKLQ 1750
1751 RLQELEGTYEENERALESKAAQLDGLEARMRSVLQAINLQVQIYNTCQ 1798
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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