 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9HE88 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MADRYGQQPQSYRGNSGFGNLGRRNDDYDPYGDGYPSDRYGTSTNPASRP 50
51 STASRNAPPPRSAQRGRTGAGDMQIQSNAERQIGNVLDLIKREWPAMVET 100
101 DCIPVQLALQLLDTSSVGRAHEYRNFQQTHQFLQESLKNIVHDHHQGFNS 150
151 SIGTFHKIQGSIQSSQKKVRALKESLAASKTALCTTNPELKQLHATSRMY 200
201 DGVLQTLNELDDLRTVPDQLEARISEKRFLTAVEVLQNALRKLRKPELDN 250
251 IGALSDLRSYLANQETALMDILVEELHEHLYLKSPYCQERWQNLAKVQGI 300
301 SHETYGDAPGVAPFHGILDTIDWEKSVAEDPQKNPEADTFYYVTLLVEAL 350
351 NRLGRLETAVDMLKQRLPVELFAVVNETINDVDQKHPSSLRGGASGSHGL 400
401 NIYGHRETRMRADVIHDLLSTLYGKFEAIAEGHRVLHEAIKALIRREGAG 450
451 NNSVLLGGFKELWNLYQNEIRALLHNYVTTDADVYQFSRTPRPGMGMNGR 500
501 ADSARDNLFKFSEVDAKSAEMASEYEALDSIIRAAVPGLTDSTRRDNKKG 550
551 SLIIPRSEPITSRKSAGYGSGSSQQNSGTYKSLVEPSVFNMSLLLPPTLI 600
601 FLQRLKSIVPPGSDLATSTLTSFLDNFLVNVFQPQLDETLGKLSDTVFGE 650
651 ADAFQQDSDWAQVAKRPVYKGTTAFFTVITAFCRMLGTIPHDQALSTLII 700
701 TQMVRYYDRCFSWYKALVTKTQEGGDKQIREKEKLRASAILATEPSEVRE 750
751 TIQRLWKSENLNDLELLYREVNQLIAWANGRDLDASDIIQDRDMIQSMCL 800
801 LYTSMKWLSVKIHGLRHITRNETDSSKSSFPTKAEKKRWTLLNDPSKATG 850
851 GEAPVYLPMTEETVENFDSILVSYDELASTALLTLHLEIRTRILHSLQTA 900
901 LSPLTTAPYLLDQEVNEPDPEILSLNSEMVAYDEILVRCLRLREVQFVRN 950
951 GLGKLINGFLIKNAPMTAPMNAKGCGRMQLNILVLQQNLKNIEEGVDLVR 1000
1001 ASNYFEMFERGVDAILEKAREGVASSGSQETGDAGRKSEDAGEEGAETPN 1050
1051 SRKSAEIFGDDKDRFSYDELKALVELCYSEQLADPERGVAAAAKRQMADK 1100
1101 LLNLSEYMWQS 1111
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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