 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q924N4 from www.uniprot.org...
The NucPred score for your sequence is 0.11 (see score help below)
1 MHPPEATTKMSSVRFMVTPTKIDDIPGLSDTSPDLSSRSSSRVRFSSRES 50
51 VPETSRSEPMSELSGATTSLATVALDPSSDRTSNPQDVTEDPSQNSITGE 100
101 HSQLLDDGHKKARNAYLNNSNYEEGDEYFDKNLALFEEEMDTRPKVSSLL 150
151 NRMANYTNLTQGAKEHEEAENITEGKKKPTKSPQMGTFMGVYLPCLQNIF 200
201 GVILFLRLTWVVGTAGILQAFAIVLICCCCTMLTAISMSAIATNGVVPAG 250
251 GSYFMISRALGPEFGGAVGLCFYLGTTFAAAMYILGAIEIFLVYIVPRAA 300
301 IFRSDDALKESAAMLNNMRVYGTAFLVLMVLVVFIGVRYVNKFASLFLAC 350
351 VIVSILAIYAGAIKSSFAPPHFPVCMLGNRTLSSRHLDICSKTKEVDNMT 400
401 VPSKLWGFFCNSSQFFNATCDEYFVHNNVISIQGIPGLASGIITENLWSN 450
451 YLPKGEIIEKPSAKSSDVLGNLNHEYVLADITTSFTLLVGIFFPSVTGIM 500
501 AGSNRSGDLKDAQKSIPIGTILAILTTSFVYLSNVVLFGACIEGVVLRDK 550
551 FGDAVKGNLVVGTLSWPSPWVIVIGSFFSTCGAGLQSLTGAPRLLQAIAK 600
601 DNIIPFLRVFGHSKANGEPTWALLLTAAIAELGILIASLDLVAPILSMFF 650
651 LMCYLFVNLACALQTLLRTPNWRPRFRYYHWALSFMGMSICLALMFISSW 700
701 YYAIVAMVIAGMIYKYIEYQGAEKEWGDGIRGLSLSAARFALLRLEEGPP 750
751 HTKNWRPQLLVLLKLDEDLHVKHPRLLTFASQLKAGKGLTIVGSVIVGNF 800
801 LENYGDALAAEQTIKHLMEAEKVKGFCQLVVAAKLKEGISHLIQSCGLGG 850
851 MKHNTVVMGWPNGWRQSEDARAWKTFIGTVRVTTAAHLALLVAKNVSFFP 900
901 SNVEQFSEGNIDVWWIVHDGGMLMLLPFLLKQHKVWRKCSIRIFTVAQLE 950
951 DNSIQMKKDLATFLYHLRIEAEVEVVEMHDSDISAYTYERTLMMEQRSQM 1000
1001 LRHMRLSKTERDREAQLVKDRNSMLRLTSIGSDEDEETETYQEKVHMTWT 1050
1051 KDKYMASRGQKVKSMEGFQDLLNMRPDQSNVRRMHTAVKLNEVIVNKSHE 1100
1101 AKLVLLNMPGPPRNPEGDENYMEFLEVLTEGLERVLLVRGGGSEVITIYS 1150
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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