 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P97868 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MSCVHYKFSSKLNYDTVTFDGLHISLCDLKKQIMGREKLKAADSDLQITN 50
51 AQTKEEYTDDNALIPKNSSVIVRRIPIGGVKSTSKTYVISRTEPVMGTTK 100
101 AIDDASASISLAQLTKTANLAEANASEEDKIKAMMSQSGHEYDPINYMKK 150
151 TLVGPPPPSYTCFRCGKPGHYIKNCPTNGDKNFESGPRIKKSTGIPRSFM 200
201 MEVKDPNMKGAMLTNTGKYAIPTIDAEAYAIGKKEKPPFLPEEPSSSSEE 250
251 DDPIPDELLCLICKDIMTDAVVIPCCGNSYCDECIRTALLESDEHTCPTC 300
301 HQNDVSPDALIANKFLRQAVNNFKNETGYTKRLRKQLPPPPPPVPPPRPL 350
351 MQRNLQPLMRSPISRQQDPLMIPVTSSSAHSAPSISSLTSNPSALAPSVS 400
401 GNPSSAPAPVPDITATVSISVHSEKSDGPFRDSDNKLLPAAALTSEHSKG 450
451 ASSIAITALMEEKGYQVPVLGTPSLLGQSLLHGQLIPTTGPVRINAARPG 500
501 GGRPGWEHSNKLGYLVSPPQQIRRGERSCYRSINRGRHHSERSQRTQGPS 550
551 LPATPVFVPVPPPPLYPPPPHTLPLPPGVPPPQFSPQFPPGQPPPAGYSV 600
601 PPPGFPPAPANISTPWVSSGVQTAHSNTIPTTQAPPLSREEFYREQRRLK 650
651 EEEKKKSKLDEFTNDFAKELMEYKKIQKERRRSFSRSKSPYSGSSYSRSS 700
701 YTYSKSRSGSTRSRSYSRSFSRSHSRSYSRSPPYPRRGRGKSRNYRSRSR 750
751 SHGYHRSRSRSPPYRRYHSRSRSPQAFRGQSPTKRNVPQGETEREYFNRY 800
801 REVPPPYDIKAYYGRSVDFRDPFEKERYREWERKYREWYEKYYKGYAVGA 850
851 QPRPSANREDFSPERLLPLNIRNSPFTRGRREDYAAGQSHRNRNLGGNYP 900
901 EKLSTRDSHNAKDNPKSKEKESENVPGDGKGNKHKKHRKRRKGEESESFL 950
951 NPELLETSRKCRESSGIDETKTDTLFVLPSRDDATPVRDEPMDAESITFK 1000
1001 SVSDKDKREKDKPKVKSDKTKRKSDGSATAKKDNVLKPSKGPQEKVDGDR 1050
1051 EKSPRSEPPLKKAKEEATKIDSVKPSSSSQKDEKVTGTPRKAHSKSAKEH 1100
1101 QEAKPAKDEKVKKDCSKDIKSEKPASKDEKAKKPEKNKLLDSKGEKRKRK 1150
1151 TEEKSVDKDFESSSMKISKVEGTEIVKPSPKRKMEGDVEKLERTPEKDKI 1200
1201 ASSTTPAKKIKLNRETGKKIGNAENASTTKEPSEKLESTSSKIKQEKVKG 1250
1251 KAKRKVAGSEGSSSTLVDYTSTSSTGGSPVRKSEEKTDTKRTVIKTMEEY 1300
1301 NNDNTAPAEDVIIMIQVPQSKWDKDDFESEEEDVKTTQPIQSVGKPSSII 1350
1351 KNVTTKPSATAKYTEKESEQPEKLQKLPKEASHELMQHELRSSKGSASSE 1400
1401 KGRAKDREHSGSEKDNPDKRKSGAQPDKESTVDRLSEQGHFKTLSQSSKE 1450
1451 TRTSEKHESVRGSSNKDFTPGRDKKVDYDSRDYSSSKRRDERGELARRKD 1500
1501 SPPRGKESLSGQKSKLREERDLPKKGAESKKSNSSPPRDKKPHDHKAPYE 1550
1551 TKRPCEETKPVDKNSGKEREKHAAEARNGKESSGGKLPCIPNPPDPPMEK 1600
1601 ELAAGQVEKSAVKPKPQLSHSSRLSSDLTRETDEAAFEPDYNESDSESNV 1650
1651 SVKEEEAVASISKDLKEKTTEKAKESLTVATASQPGADRSQSQSSPSVSP 1700
1701 SRSHSPSGSQTRSHSSSASSAGSQDSKKKKKKKEKKKHKKHKKHKKHKKH 1750
1751 AGADGDVEKSQKHKHKKKKAKKNKDKEKEKDDQKVRSVTV 1790
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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