 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P42497 from www.uniprot.org...
The NucPred score for your sequence is 0.47 (see score help below)
1 MVSGGGSKTSGGEAASSGHRRSRHTSAAEQAQSSANKALRSQNQQPQNHG 50
51 GGTESTNKAIQQYTVDARLHAVFEQSGESGKSFDYSQSLKTAPYDSSVPE 100
101 QQITAYLSRIQRGGYTQPFGCLIAVEESTFTIIGYSENAREMLGLMSQSV 150
151 PSIEDKSEVLTIGTDLRSLFKSSSYLLLERAFVAREITLLNPIWIHSNNT 200
201 GKPFYAILHRVDVGILIDLEPARTEDPALSIAGAVQSQKLAVRAISHLQS 250
251 LPSGDIKLLCDTVVESVRDLTGYDRVMVYKFHEDEHGEVVAESKRNDLEP 300
301 YIGLHYPATDIPQASRFLFKQNRVRMIVDCYASPVRVVQDDRLTQFICLV 350
351 GSTLRAPHGCHAQYMTNMGSIASLAMAVIINGNEEDGNGVNTGGRNSMRL 400
401 WGLVVCHHTSARCIPFPLRYACEFLMQAFGLQLNMELQLALQVSEKRVLR 450
451 MQTLLCDMLLRDSPAGIVTQRPSIMDLVKCNGAAFLYQGKYYPLGVTPTD 500
501 SQINDIVEWLVANHSDSTGLSTDSLGDAGYPRAAALGDAVCGMAVACITK 550
551 RDFLFWFRSHTEKEIKWGGAKHHPEDKDDGQRMNPRSSFQTFLEVVKSRC 600
601 QPWETAEMDAIHSLQLILRDSFKESEAMDSKAAAAGAVQPHGDDMVQQGM 650
651 QEIGAVAREMVRLIETATVPIFAVDIDGCINGWNAKIAELTGLSVEDAMG 700
701 KSLVRELIYKEYKETVDRLLSCALKGDEGKNVEVKLKTFGSELQGKAMFV 750
751 VVNACSSKDYLNNIVGVCFVGQDVTGHKIVMDKFINIQGDYKAIIHSPNP 800
801 LIPPIFAADENTCCLEWNTAMEKLTGWPRSEVIGKLLVREVFGSYCRLKG 850
851 PDALTKFMIVLHNAIGGQDTDKFPFPFFDRKGEFIQALLTLNKRVSIDGK 900
901 IIGAFCFLQIPSPELQQALEVQRRQESEYFSRRKELAYIFQVIKNPLSGL 950
951 RFTNSLLEDMDLNEDQKQLLETSVSCEKQISKIVGDMDVKSIDDGSFLLE 1000
1001 RTEFFIGNVTNAVVSQVMLVVRERNLQLIRNIPTEVKSMAVYGDQIRLQQ 1050
1051 VLAEFLLSIVRYAPMEGSVELHLCPTLNQMADGFSAVRLEFRMACAGEGV 1100
1101 PPEKVQDMFHSSRWTSPEGLGLSVCRKILKLMNGGVQYIREFERSYFLIV 1150
1151 IELPVPLMMMMPSS 1164
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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