 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9NQT8 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MGDSKVKVAVRIRPMNRRETDLHTKCVVDVDANKVILNPVNTNLSKGDAR 50
51 GQPKVFAYDHCFWSMDESVKEKYAGQDIVFKCLGENILQNAFDGYNACIF 100
101 AYGQTGSGKSYTMMGTADQPGLIPRLCSGLFERTQKEENEEQSFKVEVSY 150
151 MEIYNEKVRDLLDPKGSRQTLKVREHSVLGPYVDGLSKLAVTSYKDIESL 200
201 MSEGNKSRTVAATNMNEESSRSHAVFKITLTHTLYDVKSGTSGEKVGKLS 250
251 LVDLAGSERATKTGAAGDRLKEGSNINKSLTTLGLVISALADQSAGKNKN 300
301 KFVPYRDSVLTWLLKDSLGGNSKTAMVATVSPAADNYDETLSTLRYADRA 350
351 KHIVNHAVVNEDPNARIIRDLREEVEKLREQLTKAEAMKSPELKDRLEES 400
401 EKLIQEMTVTWEEKLRKTEEIAQERQKQLESLGISLQSSGIKVGDDKCFL 450
451 VNLNADPALNELLVYYLKEHTLIGSANSQDIQLCGMGILPEHCIIDITSE 500
501 GQVMLTPQKNTRTFVNGSSVSSPIQLHHGDRILWGNNHFFRLNLPKKKKK 550
551 AEREDEDQDPSMKNENSSEQLDVDGDSSSEVSSEVNFNYEYAQMEVTMKA 600
601 LGSNDPMQSILNSLEQQHEEEKRSALERQRLMYEHELEQLRRRLSPEKQN 650
651 CRSMDRFSFHSPSAQQRLRQWAEEREATLNNSLMRLREQIVKANLLVREA 700
701 NYIAEELDKRTEYKVTLQIPASSLDANRKRGSLLSEPAIQVRRKGKGKQI 750
751 WSLEKLDNRLLDMRDLYQEWKECEEDNPVIRSYFKRADPFYDEQENHSLI 800
801 GVANVFLESLFYDVKLQYAVPIINQKGEVAGRLHVEVMRLSGDVGERIAG 850
851 GDEVAEVSFEKETQENKLVCMVKILQATGLPQHLSHFVFCKYSFWDQQEP 900
901 VIVAPEVDTSSSSVSKEPHCMVVFDHCNEFSVNITEDFIEHLSEGALAIE 950
951 VYGHKINDPRKNPALWDLGIIQAKTRSLRDRWSEVTRKLEFWVQILEQNE 1000
1001 NGEYCPVEVISAKDVPTGGIFQLRQGQSRRVQVEVKSVQESGTLPLMEEC 1050
1051 ILSVGIGCVKVRPLRAPRTHETFHEEEEDMDSYQDRDLERLRRKWLNALT 1100
1101 KRQEYLDQQLQKLVSKRDKTEDDADREAQLLEMRLTLTEERNAVMVPSAG 1150
1151 SGIPGAPAEWTPVPGMETHIPVIFLDLNADDFSSQDNLDDPEAGGWDATL 1200
1201 TGEEEEEFFELQIVKQHDGEVKAEASWDSAVHGCPQLSRGTPVDERLFLI 1250
1251 VRVTVQLSHPADMQLVLRKRICVNVHGRQGFAQSLLKKMSHRSSIPGCGV 1300
1301 TFEIVSNIPEDAQGVEEREALARMAANVENPASADSEAYIEKYLRSVLAV 1350
1351 ENLLTLDRLRQEVAVKEQLTGKGKLSRRSISSPNVNRLSGSRQDLIPSYS 1400
1401 LGSNKGRWESQQDVSQTTVSRGIAPAPALSVSPQNNHSPDPGLSNLAASY 1450
1451 LNPVKSFVPQMPKLLKSLFPVRDEKRGKRPSPLAHQPVPRIMVQSASPDI 1500
1501 RVTRMEEAQPEMGPDVLVQTMGAPALKICDKPAKVPSPPPVIAVTAVTPA 1550
1551 PEAQDGPPSPLSEASSGYFSHSVSTATLSDALGPGLDAAAPPGSMPTAPE 1600
1601 AEPEAPISHPPPPTAVPAEEPPGPQQLVSPGRERPDLEAPAPGSPFRVRR 1650
1651 VRASELRSFSRMLAGDPGCSPGAEGNAPAPGAGGQALASDSEEADEVPEW 1700
1701 LREGEFVTVGAHKTGVVRYVGPADFQEGTWVGVELDLPSGKNDGSIGGKQ 1750
1751 YFRCNPGYGLLVRPSRVRRATGPVRRRSTGLRLGAPEARRSATLSGSATN 1800
1801 LASLTAALAKADRSHKNPENRKSWAS 1826
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.) |
Go back to the NucPred Home Page.