 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q7Z6E9 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MSCVHYKFSSKLNYDTVTFDGLHISLCDLKKQIMGREKLKAADCDLQITN 50
51 AQTKEEYTDDNALIPKNSSVIVRRIPIGGVKSTSKTYVISRTEPAMATTK 100
101 AIDDSSASISLAQLTKTANLAEANASEEDKIKAMMSQSGHEYDPINYMKK 150
151 PLGPPPPSYTCFRCGKPGHYIKNCPTNGDKNFESGPRIKKSTGIPRSFMM 200
201 EVKDPNMKGAMLTNTGKYAIPTIDAEAYAIGKKEKPPFLPEEPSSSSEED 250
251 DPIPDELLCLICKDIMTDAVVIPCCGNSYCDECIRTALLESDEHTCPTCH 300
301 QNDVSPDALIANKFLRQAVNNFKNETGYTKRLRKQLPPPPPPIPPPRPLI 350
351 QRNLQPLMRSPISRQQDPLMIPVTSSSTHPAPSISSLTSNQSSLAPPVSG 400
401 NPSSAPAPVPDITATVSISVHSEKSDGPFRDSDNKILPAAALASEHSKGT 450
451 SSIAITALMEEKGYQVPVLGTPSLLGQSLLHGQLIPTTGPVRINTARPGG 500
501 GRPGWEHSNKLGYLVSPPQQIRRGERSCYRSINRGRHHSERSQRTQGPSL 550
551 PATPVFVPVPPPPLYPPPPHTLPLPPGVPPPQFSPQFPPGQPPPAGYSVP 600
601 PPGFPPAPANLSTPWVSSGVQTAHSNTIPTTQAPPLSREEFYREQRRLKE 650
651 EEKKKSKLDEFTNDFAKELMEYKKIQKERRRSFSRSKSPYSGSSYSRSSY 700
701 TYSKSRSGSTRSRSYSRSFSRSHSRSYSRSPPYPRRGRGKSRNYRSRSRS 750
751 HGYHRSRSRSPPYRRYHSRSRSPQAFRGQSPNKRNVPQGETEREYFNRYR 800
801 EVPPPYDMKAYYGRSVDFRDPFEKERYREWERKYREWYEKYYKGYAAGAQ 850
851 PRPSANRENFSPERFLPLNIRNSPFTRGRREDYVGGQSHRSRNIGSNYPE 900
901 KLSARDGHNQKDNTKSKEKESENAPGDGKGNKHKKHRKRRKGEESEGFLN 950
951 PELLETSRKSREPTGVEENKTDSLFVLPSRDDATPVRDEPMDAESITFKS 1000
1001 VSEKDKRERDKPKAKGDKTKRKNDGSAVSKKENIVKPAKGPQEKVDGERE 1050
1051 RSPRSEPPIKKAKEETPKTDNTKSSSSSQKDEKITGTPRKAHSKSAKEHQ 1100
1101 ETKPVKEEKVKKDYSKDVKSEKLTTKEEKAKKPNEKNKPLDNKGEKRKRK 1150
1151 TEEKGVDKDFESSSMKISKLEVTEIVKPSPKRKMEPDTEKMDRTPEKDKI 1200
1201 SLSAPAKKIKLNRETGKKIGSTENISNTKEPSEKLESTSSKVKQEKVKGK 1250
1251 VRRKVTGTEGSSSTLVDYTSTSSTGGSPVRKSEEKTDTKRTVIKTMEEYN 1300
1301 NDNTAPAEDVIIMIQVPQSKWDKDDFESEEEDVKSTQPISSVGKPASVIK 1350
1351 NVSTKPSNIVKYPEKESEPSEKIQKFTKDVSHEIIQHEVKSSKNSASSEK 1400
1401 GKTKDRDYSVLEKENPEKRKNSTQPEKESNLDRLNEQGNFKSLSQSSKEA 1450
1451 RTSDKHDSTRASSNKDFTPNRDKKTDYDTREYSSSKRRDEKNELTRRKDS 1500
1501 PSRNKDSASGQKNKPREERDLPKKGTGDSKKSNSSPSRDRKPHDHKATYD 1550
1551 TKRPNEETKSVDKNPCKDREKHVLEARNNKESSGNKLLYILNPPETQVEK 1600
1601 EQITGQIDKSTVKPKPQLSHSSRLSSDLTRETDEAAFEPDYNESDSESNV 1650
1651 SVKEEESSGNISKDLKDKIVEKAKESLDTAAVVQVGISRNQSHSSPSVSP 1700
1701 SRSHSPSGSQTRSHSSSASSAESQDSKKKKKKKEKKKHKKHKKHKKHKKH 1750
1751 AGTEVELEKSQKHKHKKKKSKKNKDKEKEKEKDDQKVKSVTV 1792
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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