 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q16821 from www.uniprot.org...
The NucPred score for your sequence is 0.62 (see score help below)
1 MEPSEVPSQISKDNFLEVPNLSDSLCEDEEVTFQPGFSPQPSRRGSDSSE 50
51 DIYLDTPSSGTRRVSFADSFGFNLVSVKEFDCWELPSASTTFDLGTDIFH 100
101 TEEYVLAPLFDLPSSKEDLMQQLQIQKAILESTESLLGSTSIKGIIRVLN 150
151 VSFEKLVYVRMSLDDWQTHYDILAEYVPNSCDGETDQFSFKIVLVPPYQK 200
201 DGSKVEFCIRYETSVGTFWSNNNGTNYTFICQKKEQEPEPVKPWKEVPNR 250
251 QIKGCLKVKSSKEESSVTSEENNFENPKNTDTYIPTIICSHEDKEDLEAS 300
301 NRNVKDVNREHDEHNEKELELMINQHLIRTRSTASRDERNTFSTDPVNFP 350
351 NKAEGLEKKQIHGEICTDLFQRSLSPSSSAESSVKGDFYCNEKYSSGDDC 400
401 THQPSEETTSNMGEIKPSLGDTSSDELVQLHTGSKEVLDDNANPAHGNGT 450
451 VQIPCPSSDQLMAGNLNKKHEGGAKNIEVKDLGCLRRDFHSDTSACLKES 500
501 TEEGSSKEDYYGNGKDDEEQRIYLGVNEKQRKNFQTILHDQERKMGNPKI 550
551 SVAGIGASNRDLATLLSEHTAIPTRAITADVSHSPRTNLSWEEAVLTPEH 600
601 HHLTSEGSALGGITGQVCSSRTGNVLRNDYLFQVEEKSGGINSEDQDNSP 650
651 QHKQSWNVLESQGKSRENKTNITEHIKGQTDCEDVWGKRDNTRSLKATTE 700
701 ELFTCQETVCCELSSLADHGITEKAEAGTAYIIKTTSESTPESMSAREKA 750
751 IIAKLPQETARSDRPIEVKETAFDPHEGRNDDSHYTLCQRDTVGVIYDND 800
801 FEKESRLGICNVRVDEMEKEETMSMYNPRKTHDREKCGTGNITSVEESSW 850
851 VITEYQKATSKLDLQLGMLPTDKTVFSENRDLRQVQELSKKTDSDAIVHS 900
901 AFNSDTNRAPQNSSPFSKHHTEISVSTNEQAIAVENAVTTMASQPISTKS 950
951 ENICNSTREIQGIEKHPYPESKPEEVSRSSGIVTSGSRKERCIGQIFQTE 1000
1001 EYSVEKSLGPMILINKPLENMEEARHENEGLVSSGQSLYTSGEKESDSSA 1050
1051 STSLPVEESQAQGNESLFSKYTNSKIPYFLLFLIFLITVYHYDLMIGLTF 1100
1101 YVLSLSWLSWEEGRQKESVKKK 1122
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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