 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9WU70 from www.uniprot.org...
The NucPred score for your sequence is 0.73 (see score help below)
1 MRKFNIRKVLDGLTAGSSSASQQQQQQQHPPGNREPEIQETLQSEHFQLC 50
51 KTVRHGFPYQPSALAFDPVQKILAVGTQTGALRLFGRPGVECYCQHDSGA 100
101 AVIQLQFLINEGALVSALADDTLHLWNLRQKRPAVLHSLKFCRERVTFCH 150
151 LPFQSKWLYVGTERGNIHIVNVESFTLSGYVIMWNKAIELSSKSHPGPVV 200
201 HISDNPMDEGKLLIGFESGTVVLWDLKSKKADYRYTYDEAIHSVAWHHEG 250
251 KQFICSHSDGTLTIWNVRSPTKPVQTITPHGKQLKDGKKPEPCKPILKVE 300
301 FKTTRSGEPFIILSGGLSYDTVGRRPCLTVMHGKSTAVLEMDYSIVDFLT 350
351 LCETPYPNDFQEPYAVVVLLEKDLVLIDLAQNGYPIFENPYPLSIHESPV 400
401 TCCEYFADCPVDLIPALYSVGARQKRQGYSKKEWPINGGNWGLGAQSYPE 450
451 IIITGHADGSIKFWDASAITLQVLYKLKTSKVFEKSRNKDDRQNTDIVDE 500
501 DPYAIQIISWCPESRMLCIAGVSAHVIIYRFSKQEVVTEVIPMLEVRLLY 550
551 EINDVETPEGEQPPPLSTPVGSSTSQPIPPQSHPSTSSSSSDGLRDNVPC 600
601 LKVKNSPLKQSPGYQTELVIQLVWVGGEPPQQITSLALNSSYGLVVFGNS 650
651 NGIAMVDYLQKAVLLNLSTIELYGSNDPYRREPRSPRKSRQPSGAGLCDI 700
701 TEGTVVPEDRCKSPTSGSSSPHNSDDEQKVNNFIEKVKTQSRKFSKMVAS 750
751 DLAKMSRKLSLPTDLKPDLDVKDNSFSRSRSSSVTSIDKESREAISALHF 800
801 CETFTRKADSSPSPCLWVGTTVGTAFVITLNLPLGPEQRLLQPVIVSPSG 850
851 TILRLKGAILRMAFLDAAGCLMPPAYEPWTEHNVPEEKDEKEKLKKRRPV 900
901 SVSPSSSQEISENQYAVICSEKQAKVISLPTQNCAYKQNITETSFVLRGD 950
951 IVALSNSVCLACFCANGHIMTFSLPSLRPLLDVYYLPLTNMRIARTFCFA 1000
1001 NSGQALYLVSPTEIQRLTYSQETCENLQEMLGELFTPVETPEAPNRGFFK 1050
1051 GLFGGGAQSLDREELFGESSSGKASRSLAQHIPGPGGIEGVKGAASGVVG 1100
1101 ELARARLALDERGQKLSDLEERTAAMMSSADSFSKHAHEMMLKYKDKKWY 1150
1151 QF 1152
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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