 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P14713 from www.uniprot.org...
The NucPred score for your sequence is 0.41 (see score help below)
1 MVSGVGGSGGGRGGGRGGEEEPSSSHTPNNRRGGEQAQSSGTKSLRPRSN 50
51 TESMSKAIQQYTVDARLHAVFEQSGESGKSFDYSQSLKTTTYGSSVPEQQ 100
101 ITAYLSRIQRGGYIQPFGCMIAVDESSFRIIGYSENAREMLGIMPQSVPT 150
151 LEKPEILAMGTDVRSLFTSSSSILLERAFVAREITLLNPVWIHSKNTGKP 200
201 FYAILHRIDVGVVIDLEPARTEDPALSIAGAVQSQKLAVRAISQLQALPG 250
251 GDIKLLCDTVVESVRDLTGYDRVMVYKFHEDEHGEVVAESKRDDLEPYIG 300
301 LHYPATDIPQASRFLFKQNRVRMIVDCNATPVLVVQDDRLTQSMCLVGST 350
351 LRAPHGCHSQYMANMGSIASLAMAVIINGNEDDGSNVASGRSSMRLWGLV 400
401 VCHHTSSRCIPFPLRYACEFLMQAFGLQLNMELQLALQMSEKRVLRTQTL 450
451 LCDMLLRDSPAGIVTQSPSIMDLVKCDGAAFLYHGKYYPLGVAPSEVQIK 500
501 DVVEWLLANHADSTGLSTDSLGDAGYPGAAALGDAVCGMAVAYITKRDFL 550
551 FWFRSHTAKEIKWGGAKHHPEDKDDGQRMHPRSSFQAFLEVVKSRSQPWE 600
601 TAEMDAIHSLQLILRDSFKESEAAMNSKVVDGVVQPCRDMAGEQGIDELG 650
651 AVAREMVRLIETATVPIFAVDAGGCINGWNAKIAELTGLSVEEAMGKSLV 700
701 SDLIYKENEATVNKLLSRALRGDEEKNVEVKLKTFSPELQGKAVFVVVNA 750
751 CSSKDYLNNIVGVCFVGQDVTSQKIVMDKFINIQGDYKAIVHSPNPLIPP 800
801 IFAADENTCCLEWNMAMEKLTGWSRSEVIGKMIVGEVFGSCCMLKGPDAL 850
851 TKFMIVLHNAIGGQDTDKFPFPFFDRNGKFVQALLTANKRVSLEGKVIGA 900
901 FCFLQIPSPELQQALAVQRRQDTECFTKAKELAYICQVIKNPLSGMRFAN 950
951 SLLEATDLNEDQKQLLETSVSCEKQISRIVGDMDLESIEDGSFVLKREEF 1000
1001 FLGSVINAIVSQAMFLLRDRGLQLIRDIPEEIKSIEVFGDQIRIQQLLAE 1050
1051 FLLSIIRYAPSQEWVEIHLSQLSKQMADGFAAIRTEFRMACPGEGLPPEL 1100
1101 VRDMFHSSRWTSPEGLGLSVCRKILKLMNGEVQYIRESERSYFLIILELP 1150
1151 VPRKRPLSTASGSGDMMLMMPY 1172
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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