 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q7Z2W7 from www.uniprot.org...
The NucPred score for your sequence is 0.17 (see score help below)
1 MSFRAARLSMRNRRNDTLDSTRTLYSSASRSTDLSYSESDLVNFIQANFK 50
51 KRECVFFTKDSKATENVCKCGYAQSQHMEGTQINQSEKWNYKKHTKEFPT 100
101 DAFGDIQFETLGKKGKYIRLSCDTDAEILYELLTQHWHLKTPNLVISVTG 150
151 GAKNFALKPRMRKIFSRLIYIAQSKGAWILTGGTHYGLMKYIGEVVRDNT 200
201 ISRSSEENIVAIGIAAWGMVSNRDTLIRNCDAEGYFLAQYLMDDFTRDPL 250
251 YILDNNHTHLLLVDNGCHGHPTVEAKLRNQLEKYISERTIQDSNYGGKIP 300
301 IVCFAQGGGKETLKAINTSIKNKIPCVVVEGSGQIADVIASLVEVEDALT 350
351 SSAVKEKLVRFLPRTVSRLPEEETESWIKWLKEILECSHLLTVIKMEEAG 400
401 DEIVSNAISYALYKAFSTSEQDKDNWNGQLKLLLEWNQLDLANDEIFTND 450
451 RRWESADLQEVMFTALIKDRPKFVRLFLENGLNLRKFLTHDVLTELFSNH 500
501 FSTLVYRNLQIAKNSYNDALLTFVWKLVANFRRGFRKEDRNGRDEMDIEL 550
551 HDVSPITRHPLQALFIWAILQNKKELSKVIWEQTRGCTLAALGASKLLKT 600
601 LAKVKNDINAAGESEELANEYETRAVELFTECYSSDEDLAEQLLVYSCEA 650
651 WGGSNCLELAVEATDQHFIAQPGVQNFLSKQWYGEISRDTKNWKIILCLF 700
701 IIPLVGCGFVSFRKKPVDKHKKLLWYYVAFFTSPFVVFSWNVVFYIAFLL 750
751 LFAYVLLMDFHSVPHPPELVLYSLVFVLFCDEVRQWYVNGVNYFTDLWNV 800
801 MDTLGLFYFIAGIVFRLHSSNKSSLYSGRVIFCLDYIIFTLRLIHIFTVS 850
851 RNLGPKIIMLQRMLIDVFFFLFLFAVWMVAFGVARQGILRQNEQRWRWIF 900
901 RSVIYEPYLAMFGQVPSDVDGTTYDFAHCTFTGNESKPLCVELDEHNLPR 950
951 FPEWITIPLVCIYMLSTNILLVNLLVAMFGYTVGTVQENNDQVWKFQRYF 1000
1001 LVQEYCSRLNIPFPFIVFAYFYMVVKKCFKCCCKEKNMESSVCCFKNEDN 1050
1051 ETLAWEGVMKENYLVKINTKANDTSEEMRHRFRQLDTKLNDLKGLLKEIA 1100
1101 NKIK 1104
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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