 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P0C090 from www.uniprot.org...
The NucPred score for your sequence is 0.89 (see score help below)
1 MPVQAAQWTEFLSCPICYNEFDENVHKPISLGCSHTVCKTCLNKLHRKAC 50
51 PFDQTAINTDIDVLPVNFALLQLVGAQVPDHQSIKLSNLGENKHYEVAKK 100
101 CVEDLALYLKPLSGGKGVASLNQSALSRPMQRKLVTLVNCQLVEEEGRVR 150
151 AMRAARSLGERTVTELILQHQNPQQLSANLWAAVRARGCQFLGPAMQEEA 200
201 LKLVLLALEDGSALSRKVLVLFVVQRLEPRFPQASKTSIGHVVQLLYRAS 250
251 CFKVTKRDEDSSLMQLKEEFRSYEALRREHDAQIVHIAMEAGLRISPEQW 300
301 SSLLYGDLAHKSHMQSIIDKLQSPESFAKSVQELTIVLQRTGDPANLNRL 350
351 RPHLELLANIDPNPDAVSPTWEQLENAMVAVKTVVHGLVDFIQNYSRKGH 400
401 ETPQPQPNSKYKTSMCRDLRQQGGCPRGTNCTFAHSQEELEKYRLRNKKM 450
451 SATVRTFPLLNKVGVNSTVTTTAGNVISVIGSTETTGKIVASTNGISNTE 500
501 SSVSQLIPRGTDSAVRTLETVKKVGKVGTNAQNAGPSAESVSENKIGSPP 550
551 KTPVSNAAATSAGPSNFGTELNSLPPKSSPFLTRVPVYPQHSESIQYFQD 600
601 PRTQIPFEVPQYPQTGYYPPPPTVPAGVTPCVPRFVRSSNVPESSLPPAS 650
651 MPYADHYSTFSPRDRMNSSPYQPPPPQQYGPVPPVPSGMYAPVYDSRRIW 700
701 RPAMYQRDDIIRSNSLPPMDVMHSSVYQTSLRERYNSLDGYYSVACQPPN 750
751 DPRTTVPLPREPCGHLKTSCEEQLRRKPDQWTQYHTQKTPVSSTLPVATQ 800
801 SPTPPSPLFSVDFRSDFSESVSGAKFEEDHLSHYSPWSCGTIGSCINAID 850
851 SEPKDVIANSNAVLMDLDSGDVKRRVHLFEAQRRTKEEDPIIPFSDGPII 900
901 SKWGAISRSSRTGYHTTDPVQATASQGSATKPISVSDYVPYVNAVDSRWS 950
951 SYGNDATSSAHYIERDRFIVTDLSGHRKHSSTGDLLSIELQQAKSNSLLL 1000
1001 QREANALAMQQKWNSLDEGRHLTLNLLSKEIELRNGENDYTEDTVDTKPD 1050
1051 RDIELELSALDTDEPDGQSEQIEEILDIQLGISSQNDQLLNGTAVENGHP 1100
1101 AQQHQKDPGKPKRQSLGEDHVILEEQKPILPVTSCFSQPRPMSISSASCL 1150
1151 PITTSVSVGNLILKTHVMSEDKNDFLKPIANGKMVNS 1187
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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