 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O75132 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MENNLKTCPKEDGDFVSDKIKFKIEEEDDDGIPPDSLERMDFKSEQEDMK 50
51 QTDSGGERAGLGGTGCSCKPPGKYLSAESEDDYGALFSQYSSTLYDVAME 100
101 AVTQSLLSSRNMSSRKKSPAWKHFFISPRDSTKAICMYCVKEFSRGKNEK 150
151 DLSTSCLMRHVRRAHPTVLIQENGSVSAVSSFPSPSLLLPPQPADAGDLS 200
201 TILSPIKLVQKVASKIPSPDRITEESVSVVSSEEISSDMSVSEKCGREEA 250
251 LVGSSPHLPALHYDEPAENLAEKSLPLPKSTSGSRRRSAVWKHFYLSPLD 300
301 NSKAVCIHCMNEFSRGKNGKDLGTSCLIRHMWRAHRAIVLQENGGTGIPP 350
351 LYSTPPTLLPSLLPPEGELSSVSSSPVKPVRESPSASSSPDRLTEDLQSH 400
401 LNPGDGLMEDVAAFSSSDDIGEASASSPEKQQADGLSPRLFESGAIFQQN 450
451 KKVMKRLKSEVWHHFSLAPMDSLKAECRYCGCAISRGKKGDVGTSCLMRH 500
501 LYRRHPEVVGSQKGFLGASLANSPYATLASAESSSSKLTDLPTVVTKNNQ 550
551 VMFPVNSKKTSKLWNHFSICSADSTKVVCLHCGRTISRGKKPTNLGTSCL 600
601 LRHLQRFHSNVLKTEVSETARPSSPDTRVPRGTELSGASSFDDTNEKFYD 650
651 SHPVAKKITSLIAEMIALDLQPYSFVDNVGFNRLLEYLKPQYSLPAPSYF 700
701 SRTAIPGMYDNVKQIIMSHLKEAESGVIHFTSGIWMSNQTREYLTLTAHW 750
751 VSFESPARPRCDDHHCSALLDVSQVDCDYSGNSIQKQLECWWEAWVTSTG 800
801 LQVGITVTDNASIGKTLNEGEHSSVQCFSHTVNLIVSEAIKSQRMVQNLL 850
851 SLARKICERVHRSPKAKEKLAELQREYALPQHHLIQDVPSKWSTSFHMLE 900
901 RLIEQKRAINEMSVECNFRELISCDQWEVMQSVCRALKPFEAASREMSTQ 950
951 MSTLSQVIPMVHILNRKVEMLFEETMGIDTMLRSLKEAMVSRLSATLHDP 1000
1001 RYVFATLLDPRYKASLFTEEEAEQYKQDLIRELELMNSTSEDVAASHRCD 1050
1051 AGSPSKDSAAEENLWSLVAKVKKKDPREKLPEAMVLAYLEEEVLEHSCDP 1100
1101 LTYWNLKKASWPGLSALAVRFLGCPPSIVPSEKLFNTPTENGSLGQSRLM 1150
1151 MEHFEKLIFLKVNLPLIYFQY 1171
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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