 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O42649 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MYITKIVIQGFKSYKDYTVIEPLSPHHNVIVGRNGSGKSNFFAAIRFVLS 50
51 DAYTHLSREERQALLHEGPGATVMSAYVEVTFANADNRFPTGKSEVVLRR 100
101 TIGLKKDEYSLDKKTVSKTEVINLLESAGFSRSNPYYIVPQGRVTSLTNA 150
151 KDSERLELLKEVAGTQIYENRRAESNKIMDETIQKSEKIDELLQYIEERL 200
201 RELEEEKNDLAVYHKKDNERRCLEYAIYSREHDEINSVLDALEQDRIAAL 250
251 ERNDDDSGAFIQREERIERIKAEITELNHSLELLRVEKQQNDEDYTNIMK 300
301 SKVALELQSSQLSRQIEFSKKDESSKLNILSELESKISEKENELSEILPK 350
351 YNAIVSEADDLNKRIMLLKNQKQSLLDKQSRTSQFTTKKERDEWIRNQLL 400
401 QINRNINSTKENSDYLKTEYDEMENELKAKLSRKKEIEISLESQGDRMSQ 450
451 LLANITSINERKENLTDKRKSLWREEAKLKSSIENVKDDLSRSEKALGTT 500
501 MDRNTSNGIRAVKDIAERLKLEGYYGPLCELFKVDNRFKVAVEATAGNSL 550
551 FHIVVDNDETATQILDVIYKENAGRVTFMPLNKLRPKAVTYPDASDALPL 600
601 IQYLEFDPKFDAAIKQVFSKTIVCPSIETASQYARSHQLNGITLSGDRSD 650
651 KKGALTAGYRDYRNSRLDAIKNVKTYQIKFSDLQESLEKCRSEIESFDQK 700
701 ITACLDDLQKAQLSLKQFERDHIPLKDELVTITGETTDLQESMHHKSRML 750
751 ELVVLELHTLEQQANDLKSELSSEMDELDPKDVEALKSLSGQIENLSHEF 800
801 DAIIKERAHIEARKTALEYELNTNLYLRRNPLKAEIGSDNRIDESELNSV 850
851 KRSLLKYENKLQIIKSSSSGLEEQMQRINSEISDKRNELESLEELQHEVA 900
901 TRIEQDAKINERNAAKRSLLLARKKECNEKIKSLGVLPEEAFIKYVSTSS 950
951 NAIVKKLHKINEALKDYGSVNKKAYEQFNNFTKQRDSLLARREELRRSQE 1000
1001 SISELTTVLDQRKDEAIERTFKQVAKSFSEIFVKLVPAGRGELVMNRRSE 1050
1051 LSQSIEQDISMDIDTPSQKSSIDNYTGISIRVSFNSKDDEQLNINQLSGG 1100
1101 QKSLCALTLIFAIQRCDPAPFNILDECDANLDAQYRSAIAAMVKEMSKTS 1150
1151 QFICTTFRPEMVKVADNFYGVMFNHKVSTVESISKEEAMAFVEG 1194
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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