| Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P59997 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MEPEEERIRYSQRLRGTMRRRYEDDGISDDEIEGKRTFDLEEKLQTNKYN 50
51 ANFVTFMEGKDFNVEYIQRGGLRDPLIFKNSDGLGIKMPDPDFTVNDVKM 100
101 CVGSRRMVDVMDVNTQKGIEMTMAQWTRYYETPEEEREKLYNVISLEFSH 150
151 TRLENMVQWPSTVDFIDWVDNMWPRHLKESQTESTNAILEMQYPKVQKYC 200
201 LISVRGCYTDFHVDFGGTSVWYHIHQGGKVFWLIPPTAHNLELYENWLLS 250
251 GKQGDIFLGDRVSDCQRIELKQGYTFVIPSGWIHAVYTPTDTLVFGGNFL 300
301 HSFNIPMQLKIYSIEDRTRVPNKFRYPFYYEMCWYVLERYVYCITNRSHL 350
351 TKDFQKESLSMDMELNELESGNGDEEGVDREARRMNNKRSVLTSPVANGV 400
401 NLDYDGLGKACRSLPSLKKTLSGDSSSDSTRGSHNGQVWDPQCSPKKDRQ 450
451 VHLTHFELEGLRCLVDKLESLPLHKKCVPTGIEDEDALIADVKILLEELA 500
501 SSDPKLALTGVPIVQWPKRDKLKFPTRPKVRVPTIPITKPHTMKPAPRLT 550
551 PVRPAAASPIVSGARRRRVRCRKCKACVQGECGVCHYCRDMKKFGGPGRM 600
601 KQSCVLRQCLAPRLPHSVTCSLCGEVDQNEETQDFEKKLMECCICNEIVH 650
651 PGCLQMDGEGLLNEELPNCWECPKCYQEDSSDKAQKRKIEESDEEAVQAK 700
701 VLRPLRSCEEPLTPPPHSPTSMLQLIHDPVSPRGMVTRSSPGAGPSDHHS 750
751 ASRDERFKRRQLLRLQATERTMVREKENNPSGKKELSEVEKAKIRGSYLT 800
801 VTLQRPTKELHGTSIVPKLQAITASSANLRPNPRVLMQHCPARNPQHGDE 850
851 EGLGGEEEEEEEEEEDDSAEEGGAARLNGRGSWAQDGDESWMQREVWMSV 900
901 FRYLSRKELCECMRVCKTWYKWCCDKRLWTKIDLSRCKAIVPQALSGIIK 950
951 RQPVSLDLSWTNISKKQLTWLVNRLPGLKDLLLAGCSWSAVSALSTSSCP 1000
1001 LLRTLDLRWAVGIKDPQIRDLLTPPTDKPGQDNRSKLRNMTDFRLAGLDI 1050
1051 TDATLRLIIRHMPLLSRLDLSHCSHLTDQSSNLLTAVGSSTRYSLTELNM 1100
1101 AGCNKLTDQTLFFLRRIANVTLIDLRGCKQITRKACEHFISDLSINSLYC 1150
1151 LSDEKLIQKIS 1161
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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