 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P93194 from www.uniprot.org...
The NucPred score for your sequence is 0.49 (see score help below)
1 MKVAVNTFLLFLCSTSSIYAAFALNSDGAALLSLTRHWTSIPSDITQSWN 50
51 ASDSTPCSWLGVECDRRQFVDTLNLSSYGISGEFGPEISHLKHLKKVVLS 100
101 GNGFFGSIPSQLGNCSLLEHIDLSSNSFTGNIPDTLGALQNLRNLSLFFN 150
151 SLIGPFPESLLSIPHLETVYFTGNGLNGSIPSNIGNMSELTTLWLDDNQF 200
201 SGPVPSSLGNITTLQELYLNDNNLVGTLPVTLNNLENLVYLDVRNNSLVG 250
251 AIPLDFVSCKQIDTISLSNNQFTGGLPPGLGNCTSLREFGAFSCALSGPI 300
301 PSCFGQLTKLDTLYLAGNHFSGRIPPELGKCKSMIDLQLQQNQLEGEIPG 350
351 ELGMLSQLQYLHLYTNNLSGEVPLSIWKIQSLQSLQLYQNNLSGELPVDM 400
401 TELKQLVSLALYENHFTGVIPQDLGANSSLEVLDLTRNMFTGHIPPNLCS 450
451 QKKLKRLLLGYNYLEGSVPSDLGGCSTLERLILEENNLRGGLPDFVEKQN 500
501 LLFFDLSGNNFTGPIPPSLGNLKNVTAIYLSSNQLSGSIPPELGSLVKLE 550
551 HLNLSHNILKGILPSELSNCHKLSELDASHNLLNGSIPSTLGSLTELTKL 600
601 SLGENSFSGGIPTSLFQSNKLLNLQLGGNLLAGDIPPVGALQALRSLNLS 650
651 SNKLNGQLPIDLGKLKMLEELDVSHNNLSGTLRVLSTIQSLTFINISHNL 700
701 FSGPVPPSLTKFLNSSPTSFSGNSDLCINCPADGLACPESSILRPCNMQS 750
751 NTGKGGLSTLGIAMIVLGALLFIICLFLFSAFLFLHCKKSVQEIAISAQE 800
801 GDGSLLNKVLEATENLNDKYVIGKGAHGTIYKATLSPDKVYAVKKLVFTG 850
851 IKNGSVSMVREIETIGKVRHRNLIKLEEFWLRKEYGLILYTYMENGSLHD 900
901 ILHETNPPKPLDWSTRHNIAVGTAHGLAYLHFDCDPAIVHRDIKPMNILL 950
951 DSDLEPHISDFGIAKLLDQSATSIPSNTVQGTIGYMAPENAFTTVKSRES 1000
1001 DVYSYGVVLLELITRKKALDPSFNGETDIVGWVRSVWTQTGEIQKIVDPS 1050
1051 LLDELIDSSVMEQVTEALSLALRCAEKEVDKRPTMRDVVKQLTRWSIRSY 1100
1101 SSSVRNKSK 1109
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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