 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q86TI0 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MEPITFTARKHLLSNEVSVDFGLQLVGSLPVHSLTTMPMLPWVVAEVRRL 50
51 SRQSTRKEPVTKQVRLCVSPSGLRCEPEPGRSQQWDPLIYSSIFECKPQR 100
101 VHKLIHNSHDPSYFACLIKEDAVHRQSICYVFKADDQTKVPEIISSIRQA 150
151 GKIARQEELHCPSEFDDTFSKKFEVLFCGRVTVAHKKAPPALIDECIEKF 200
201 NHVSGSRGSESPRPNPPHAAPTGSQEPVRRPMRKSFSQPGLRSLAFRKEL 250
251 QDGGLRSSGFFSSFEESDIENHLISGHNIVQPTDIEENRTMLFTIGQSEV 300
301 YLISPDTKKIALEKNFKEISFCSQGIRHVDHFGFICRESSGGGGFHFVCY 350
351 VFQCTNEALVDEIMMTLKQAFTVAAVQQTAKAPAQLCEGCPLQSLHKLCE 400
401 RIEGMNSSKTKLELQKHLTTLTNQEQATIFEEVQKLRPRNEQRENELIIS 450
451 FLRCLYEEKQKEHIHIGEMKQTSQMAAENIGSELPPSATRFRLDMLKNKA 500
501 KRSLTESLESILSRGNKARGLQEHSISVDLDSSLSSTLSNTSKEPSVCEK 550
551 EALPISESSFKLLGSSEDLSSDSESHLPEEPAPLSPQQAFRRRANTLSHF 600
601 PIECQEPPQPARGSPGVSQRKLMRYHSVSTETPHERKDFESKANHLGDSG 650
651 GTPVKTRRHSWRQQIFLRVATPQKACDSSSRYEDYSELGELPPRSPLEPV 700
701 CEDGPFGPPPEEKKRTSRELRELWQKAILQQILLLRMEKENQKLQASEND 750
751 LLNKRLKLDYEEITPCLKEVTTVWEKMLSTPGRSKIKFDMEKMHSAVGQG 800
801 VPRHHRGEIWKFLAEQFHLKHQFPSKQQPKDVPYKELLKQLTSQQHAILI 850
851 DLGRTFPTHPYFSAQLGAGQLSLYNILKAYSLLDQEVGYCQGLSFVAGIL 900
901 LLHMSEEEAFKMLKFLMFDMGLRKQYRPDMIILQIQMYQLSRLLHDYHRD 950
951 LYNHLEEHEIGPSLYAAPWFLTMFASQFPLGFVARVFDMIFLQGTEVIFK 1000
1001 VALSLLGSHKPLILQHENLETIVDFIKSTLPNLGLVQMEKTINQVFEMDI 1050
1051 AKQLQAYEVEYHVLQEELIDSSPLSDNQRMDKLEKTNSSLRKQNLDLLEQ 1100
1101 LQVANGRIQSLEATIEKLLSSESKLKQAMLTLELERSALLQTVEELRRRS 1150
1151 AEPSDREPECTQPEPTGD 1168
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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