 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P24063 from www.uniprot.org...
The NucPred score for your sequence is 0.27 (see score help below)
1 MSFRIAGPRLLLLGLQLFAKAWSYNLDTRPTQSFLAQAGRHFGYQVLQIE 50
51 DGVVVGAPGEGDNTGGLYHCRTSSEFCQPVSLHGSNHTSKYLGMTLATDA 100
101 AKGSLLACDPGLSRTCDQNTYLSGLCYLFPQSLEGPMLQNRPAYQECMKG 150
151 KVDLVFLFDGSQSLDRKDFEKILEFMKDVMRKLSNTSYQFAAVQFSTDCR 200
201 TEFTFLDYVKQNKNPDVLLGSVQPMFLLTNTFRAINYVVAHVFKEESGAR 250
251 PDATKVLVIITDGEASDKGNISAAHDITRYIIGIGKHFVSVQKQKTLHIF 300
301 ASEPVEEFVKILDTFEKLKDLFTDLQRRIYAIEGTNRQDLTSFNMELSSS 350
351 GISADLSKGHAVVGAVGAKDWAGGFLDLREDLQGATFVGQEPLTSDVRGG 400
401 YLGYTVAWMTSRSSRPLLAAGAPRYQHVGQVLLFQAPEAGGRWNQTQKIE 450
451 GTQIGSYFGGELCSVDLDQDGEAELLLIGAPLFFGEQRGGRVFTYQRRQS 500
501 LFEMVSELQGDPGYPLGRFGAAITALTDINGDRLTDVAVGAPLEEQGAVY 550
551 IFNGKPGGLSPQPSQRIQGAQVFPGIRWFGRSIHGVKDLGGDRLADVVVG 600
601 AEGRVVVLSSRPVVDVVTELSFSPEEIPVHEVECSYSAREEQKHGVKLKA 650
651 CFRIKPLTPQFQGRLLANLSYTLQLDGHRMRSRGLFPDGSHELSGNTSIT 700
701 PDKSCLDFHFHFPICIQDLISPINVSLNFSLLEEEGTPRDQKVGRAMQPI 750
751 LRPSIHTVTKEIPFEKNCGEDKKCEANLTLSSPARSGPLRLMSSASLAVE 800
801 WTLSNSGEDAYWVRLDLDFPRGLSFRKVEMLQPHSRMPVSCEELTEGSSL 850
851 LTKTLKCNVSSPIFKAGQEVSLQVMFNTLLNSSWEDFVELNGTVHCENEN 900
901 SSLQEDNSAATHIPVLYPVNILTKEQENSTLYISFTPKGPKTQQVQHVYQ 950
951 VRIQPSAYDHNMPTLEALVGVPWPHSEDPITYTWSVQTDPLVTCHSEDLK 1000
1001 RPSSEAEQPCLPGVQFRCPIVFRREILIQVTGTVELSKEIKASSTLSLCS 1050
1051 SLSVSFNSSKHFHLYGSKASEAQVLVKVDLIHEKEMLHVYVLSGIGGLVL 1100
1101 LFLIFLALYKVGFFKRNLKEKMEADGGVPNGSPPEDTDPLAVPGEETKDM 1150
1151 GCLEPLRESDKD 1162
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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