 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9H7T0 from www.uniprot.org...
The NucPred score for your sequence is 0.17 (see score help below)
1 MESPLIYVSVLLLNIFEFSSGIVYNKDDTEKRFACSNKGFPQENEIIKLY 50
51 LFLENLKIQCFFQTENEIASKAMLSVFTSGGLAPSLGIMNSTYNGIFHFN 100
101 LTLFSDRILWLVDIPRENITQSTDIAAVEEWLVRITLHHGLNIYATEGTL 150
151 LDVIREPILQWTPGDVIPESEISKLYPHVVDLKVTKCPCANDVALLGFIV 200
201 DTIVDGVYIGITFGGFWHDYDTTWFNMTQTIYSQLQEEYEDLSLVDMVLT 250
251 NHFLVILTSLGLFVSEDLRYPSRHSLSFSRADFCGFERVDYVKGKLWYNE 300
301 RCFANREHFEVDYVTVTFERNRTLSESSSCFYSQEPFLEWVPCLPHIFKG 350
351 IKIFPTVLTFLVDQERGTGVYLFYNKVRKTAIASVSTLRNNEPNSQSKFP 400
401 IFRFPSSFSSPVGMVFHPRSHFLYAYGNQIWLSVDGGNTFQLIANFHDDI 450
451 IKKTFHSFYTSAITFVSQRGKVYSTKAGMGRYSAVGSVTERIFTLYYDHL 500
501 GFLHKLTLGRFEASGPPTAFGNSRNLFGQPPDMGFETALAPQHTSLDEII 550
551 FFAYVPENEPQETIYSKKFGNIHYGKVIHSGKTGRAYIRKVLQHTTPKGF 600
601 LSSVIAEMKEPFGLEEVNESSCLSSSLLINKAGNVYKLTLDSQVVQALFE 650
651 DTDIEKTVVLPGYSSFLITSILDNKNALAIATMPESAPNNMTFLKSTWFL 700
701 YNFGQRNGRTWKIYSKPCNYWFQHDDSPSLNIVKYIDLGNSYVLKAKVIR 750
751 NAKGFRMLEIPLLTVFVGNPNLLEVTAEVTFDDTDSYVITISAASKVLHQ 800
801 GSTSLAFIMWSASTECFVTTMVPTLKSSCSYLRSMHHIPSKFIPFEDWIS 850
851 GVHKDSQGFNLIKTLPINYRPPSNMGIAIPLTDNFYHADPSKPIPRNMFH 900
901 MSKKTGKFKQCANVSTREECNCTKDQKFSHAVAFSDCREKVPRFKFPITQ 950
951 YPVSLEIINEDGRVPLQSPYLVTVTEVNMRHNWKLKHTVPENIKRMKQLV 1000
1001 EPILGAAVYNPSGLNLSIKGSELFHFRVTVISGVTFCNLIEEFQIYVDEA 1050
1051 PLPFPGHTLIAVATAVVLGGLIFIAFMFQLQGIHPWRTFQRWIRRNQEKF 1100
1101 SSISLSELIHRSKSEE 1116
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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