 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8R4D5 from www.uniprot.org...
The NucPred score for your sequence is 0.18 (see score help below)
1 MSFEGARLSMRSRRNGTMGSTRTLYSSVSRSTDVSYSDSDLVNFIQANFK 50
51 KRECVFFTRDSKAMENICKCGYAQSQHIEGTQINQNEKWNYKKHTKEFPT 100
101 DAFGDIQFETLGKKGKYLRLSCDTDSETLYELLTQHWHLKTPNLVISVTG 150
151 GAKNFALKPRMRKIFSRLIYIAQSKGAWILTGGTHYGLMKYIGEVVRDNT 200
201 ISRNSEENIVAIGIAAWGMVSNRDTLIRSCDDEGHFSAQYIMDDFTRDPL 250
251 YILDNNHTHLLLVDNGCHGHPTVEAKLRNQLEKYISERTSQDSNYGGKIP 300
301 IVCFAQGGGRETLKAINTSVKSKIPCVVVEGSGQIADVIASLVEVEDVLT 350
351 SSMVKEKLVRFLPRTVSRLPEEEIESWIKWLKEILESSHLLTVIKMEEAG 400
401 DEIVSNAISYALYKAFSTNEQDKDNWNGQLKLLLEWNQLDLASDEIFTND 450
451 RRWESADLQEVMFTALIKDRPKFVRLFLENGLNLQKFLTNEVLTELFSTH 500
501 FSTLVYRNLQIAKNSYNDALLTFVWKLVANFRRSFWKEDRSSREDLDVEL 550
551 HDASLTTRHPLQALFIWAILQNKKELSKVIWEQTKGCTLAALGASKLLKT 600
601 LAKVKNDINAAGESEELANEYETRAVELFTECYSNDEDLAEQLLVYSCEA 650
651 WGGSNCLELAVEATDQHFIAQPGVQNFLSKQWYGEISRDTKNWKIILCLF 700
701 IIPLVGCGLVSFRKKPIDKHKKLLWYYVAFFTSPFVVFSWNVVFYIAFLL 750
751 LFAYVLLMDFHSVPHTPELILYALVFVLFCDEVRQWYMNGVNYFTDLWNV 800
801 MDTLGLFYFIAGIVFRLHSSNKSSLYSGRVIFCLDYIIFTLRLIHIFTVS 850
851 RNLGPKIIMLQRMLIDVFFFLFLFAVWMVAFGVARQGILRQNEQRWRWIF 900
901 RSVIYEPYLAMFGQVPSDVDSTTYDFSHCTFSGNESKPLCVELDEHNLPR 950
951 FPEWITIPLVCIYMLSTNILLVNLLVAMFGYTVGIVQENNDQVWKFQRYF 1000
1001 LVQEYCNRLNIPFPFVVFAYFYMVVKKCFKCCCKEKNMESNACCFRNEDN 1050
1051 ETLAWEGVMKENYLVKINTKANDNSEEMRHRFRQLDSKLNDLKSLLKEIA 1100
1101 NNIK 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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