 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P93527 from www.uniprot.org...
The NucPred score for your sequence is 0.36 (see score help below)
1 MASGSRATPTRSPSSARPEAPRHAHHHHHHHSQSSGGSTSRAGGGGGGGG 50
51 GGGGTAATATATATESVSKAVAQYTLDARLHAVFEQSGASGRSFDYSQSL 100
101 RAPPTPSSEQQIAAYLSRIQRGGHIQPFGCTLAVADDSSFRLLAFSENAA 150
151 DLLDLSPHHSVPSLDSAAPPPVSLGADARLLFSPSSAVLLERAFAAREIS 200
201 LLNPLWIHSRVSSKPFYAILHRIDVGVVIDLEPARTEDPALSIAGAVQSQ 250
251 KLAVRAISRLQALPGGDIKLLCDTVVEHVRELTGYDRVMVYRFHEDEHGE 300
301 VVAESRRDNLEPYLGLHYPATDIPQASRFLFRQNRVRMIADCHATPVRVI 350
351 QDPGMSQPLCLVGSTLRAPHGCHAQYMANMGSIASLVMAVIISSGGDDEQ 400
401 TGRGGISSAMKLWGLVVCHHTSPRCIPFPLRYACEFLMQAFGLQLNMELQ 450
451 LAHQLSEKHILRTQTLLCDMLLRDSPTGIVTQSPSIMDLVKCDGAALYYH 500
501 GKYYPLGVTPTESQIKDIIEWLTVCHGDSTGLSTDSLADAGYLGAAALGD 550
551 AVCGMAVAYITPSDYLFWFRSHTAKEIKWGGAKHHPEDKDDGQRMHPRSS 600
601 FKAFLEVVKSRSLPWENAEMDAIHSLQLILRDSFRDAAEGTSNSKAIVNG 650
651 QVQLGELELRGINELSSVAREMVRLIETATVPIFAVDTDGCINGWNAKIA 700
701 ELTGLSVEEAMGKSLVNDLIFKESEEIVEKLLSRALRGEEDKNVEIKLKT 750
751 FGSEQSNGAIFVIVNACSSRDYTQNIVGVCFVGQDVTGQKVVMDKFINIQ 800
801 GDYKAIVHNPNPLIPPIFASDENTSCSEWNTAMEKLTGWSRGEVVGKFLI 850
851 GEVFGSFCRLKGPDALTKFMVVIHNAIGGQDYEKFPFSFFDKNGKYVQAL 900
901 LTANTRSKMDGKSIGAFCFLQIASAEIQQAFEIQRQQEKKCYARMKELAY 950
951 ICQEIKNPLSGIRFTNSLLQMTDLNDDQRQFLETCSACEKQMSKIVKDAT 1000
1001 LQSIEDGSLVLEKSEFSFGDVMNAVVSQAMLLLRERDLQLIRDIPDEIKD 1050
1051 ASAYGDQFRIQQVLADFLLSMVRSAPSENGWVEIQVRPNVKQNSDGTDTE 1100
1101 LFIFRFACPGEGLPADIVQDMFSNSQWSTQEGVGLSTCRKILKLMGGEVQ 1150
1151 YIRESERSFFLIVLELPQPRPAADREIS 1178
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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