 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P78524 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MTMTANKNSSITHGAGGTKAPRGTLSRSQSVSPPPVLSPPRSPIYPLSDS 50
51 ETSACRYPSHSSSRVLLKDRHPPAPSPQNPQDPSPDTSPPTCPFKTASFG 100
101 YLDRSPSACKRDAQKESVQGAAQDVAGVAACLPLAQSTPFPGPAAGPRGV 150
151 LLTRTGTRAHSLGIREKISAWEGRREASPRMSMCGEKREGSGSEWAASEG 200
201 CPSLGCPSVVPSPCSSEKTFDFKGLRRMSRTFSECSYPETEEEGEALPVR 250
251 DSFYRLEKRLGRSEPSAFLRGHGSRKESSAVLSRIQKIEQVLKEQPGRGL 300
301 PQLPSSCYSVDRGKRKTGTLGSLEEPAGGASVSAGSRAVGVAGVAGEAGP 350
351 PPEREGSGSTKPGTPGNSPSSQRLPSKSSLDPAVNPVPKPKRTFEYEADK 400
401 NPKSKPSNGLPPSPTPAAPPPLPSTPAPPVTRRPKKDMRGHRKSQSRKSF 450
451 EFEDASSLQSLYPSSPTENGTENQPKFGSKSTLEENAYEDIVGDLPKENP 500
501 YEDVDLKSRRAGRKSQQLSENSLDSLHRMWSPQDRKYNSPPTQLSLKPNS 550
551 QSLRSGNWSERKSHRLPRLPKRHSHDDMLLLAQLSLPSSPSSLNEDSLST 600
601 TSELLSSRRARRIPKLVQRINSIYNAKRGKKRLKKLSMSSIETASLRDEN 650
651 SESESDSDDRFKAHTQRLVHIQSMLKRAPSYRTLELELLEWQERELFEYF 700
701 VVVSLKKKPSRNTYLPEVSYQFPKLDRPTKQMREAEERLKAIPQFCFPDA 750
751 KDWLPVSEYSSETFSFMLTGEDGSRRFGYCRRLLPSGKGPRLPEVYCVIS 800
801 RLGCFGLFSKVLDEVERRRGISAALVYPFMRSLMESPFPAPGKTIKVKTF 850
851 LPGAGNEVLELRRPMDSRLEHVDFECLFTCLSVRQLIRIFASLLLERRVI 900
901 FVADKLSTLSSCSHAVVALLYPFSWQHTFIPVLPASMIDIVCCPTPFLVG 950
951 LLSSSLPKLKELPVEEALMVNLGSDRFIRQMDDEDTLLPRKLQAALEQAL 1000
1001 ERKNELISQDSDSDSDDECNTLNGLVSEVFIRFFVETVGHYSLFLTQSEK 1050
1051 GERAFQREAFRKSVASKSIRRFLEVFMESQMFAGFIQDRELRKCRAKGLF 1100
1101 EQRVEQYLEELPDTEQSGMNKFLRGLGNKMKFLHKKN 1137
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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