 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9LRE6 from www.uniprot.org...
The NucPred score for your sequence is 0.65 (see score help below)
1 MSSGGRGGKRRGAPPPGPSGAAAKRAHPGGTPQPPPPAATAAAPVAEEED 50
51 MMDEDVFLDETILAEDEEALLLLDRDEALASRLSRWRRPALPADLASGCS 100
101 RNVAFQQLEIDYVIGESHKVLLPNSSGPAAILRIFGVTREGHSVCCQVHG 150
151 FEPYFYISCPMGMGPDDISRFHQTLEGRMKDSNRNSNVPRFVKRIELVQK 200
201 QTIMHYQPQQSQPFLKIVVALPTMVASCRGILERGITIEGLGSKSFLTYE 250
251 SNILFALRFMIDCNIVGGNWIEVPAGKYMKAARIMSYCQLELDCLYSDLV 300
301 SHAAEGEHSKMAPFRILSFDIECAGRKGHFPEPTHDPVIQIANLVTLQGE 350
351 GQPFVRNVMTLKSCSPIVGVDVMSFDTERDVLLAWRDFIREVDPDIIIGY 400
401 NICKFDLPYLIERAEVLKIVEFPILGRIRNSRVRVRDTTFSSRQYGMRES 450
451 KDVAVEGRVQFDLLQAMQRDYKLSSYSLNSVSAHFLGEQKEDVHHSIISD 500
501 LQNGNSETRRRLAVYCLKDAYLPQRLLDKLMYIYNYVEMARVTGVPISFL 550
551 LSRGQSIKVLSQLLRKAKQKNLVIPNIKGQASGQDTFEGATVLEARAGFY 600
601 EKPIATLDFASLYPSIMMAYNLCYCTLVPPEDARKLNLPPESVNKTPSGE 650
651 TFVKPDVQKGILPEILEELLAARKRAKADLKEAKDPFERAVLDGRQLALK 700
701 ISANSVYGFTGATVGQLPCLEISSSVTSYGRQMIEHTKKLVEDKFTTLGG 750
751 YEHNAEVIYGDTDSVMVQFGVSTVEDAMKLGREAADYISGTFIKPIKLEF 800
801 EKIYFPYLLISKKRYAGLYWTNPEKFDKMDTKGIETVRRDNCLLVKNLVT 850
851 ECLHKILVDRDVPGAVQYVKNTISDLLMNRVDLSLLVITKGLTKTGEDYA 900
901 VKAAHVELAERMRKRDAATAPTVGDRVPYVIIKAAKGAKAYERSEDPIYV 950
951 LDNNIPIDPQYYLENQISKPLLRIFEPILKNASRELLHGSHTRAVSISTP 1000
1001 SNSGIMKFAKKQLTCLGCKAVISGSNQTLCFHCKGREAELYCKTVGNVSE 1050
1051 LEMLFGRLWTQCQECQGSLHQDVLCTSRDCPIFYRRRKAQKDMAEARVQL 1100
1101 QRWDF 1105
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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