 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P10586 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MAPEPAPGRTMVPLVPALVMLGLVAGAHGDSKPVFIKVPEDQTGLSGGVA 50
51 SFVCQATGEPKPRITWMKKGKKVSSQRFEVIEFDDGAGSVLRIQPLRVQR 100
101 DEAIYECTATNSLGEINTSAKLSVLEEEQLPPGFPSIDMGPQLKVVEKAR 150
151 TATMLCAAGGNPDPEISWFKDFLPVDPATSNGRIKQLRSGALQIESSEES 200
201 DQGKYECVATNSAGTRYSAPANLYVRVRRVAPRFSIPPSSQEVMPGGSVN 250
251 LTCVAVGAPMPYVKWMMGAEELTKEDEMPVGRNVLELSNVVRSANYTCVA 300
301 ISSLGMIEATAQVTVKALPKPPIDLVVTETTATSVTLTWDSGNSEPVTYY 350
351 GIQYRAAGTEGPFQEVDGVATTRYSIGGLSPFSEYAFRVLAVNSIGRGPP 400
401 SEAVRARTGEQAPSSPPRRVQARMLSASTMLVQWEPPEEPNGLVRGYRVY 450
451 YTPDSRRPPNAWHKHNTDAGLLTTVGSLLPGITYSLRVLAFTAVGDGPPS 500
501 PTIQVKTQQGVPAQPADFQAEVESDTRIQLSWLLPPQERIIMYELVYWAA 550
551 EDEDQQHKVTFDPTSSYTLEDLKPDTLYRFQLAARSDMGVGVFTPTIEAR 600
601 TAQSTPSAPPQKVMCVSMGSTTVRVSWVPPPADSRNGVITQYSVAYEAVD 650
651 GEDRGRHVVDGISREHSSWDLVGLEKWTEYRVWVRAHTDVGPGPESSPVL 700
701 VRTDEDVPSGPPRKVEVEPLNSTAVHVYWKLPVPSKQHGQIRGYQVTYVR 750
751 LENGEPRGLPIIQDVMLAEAQWRPEESEDYETTISGLTPETTYSVTVAAY 800
801 TTKGDGARSKPKIVTTTGAVPGRPTMMISTTAMNTALLQWHPPKELPGEL 850
851 LGYRLQYCRADEARPNTIDFGKDDQHFTVTGLHKGTTYIFRLAAKNRAGL 900
901 GEEFEKEIRTPEDLPSGFPQNLHVTGLTTSTTELAWDPPVLAERNGRIIS 950
951 YTVVFRDINSQQELQNITTDTRFTLTGLKPDTTYDIKVRAWTSKGSGPLS 1000
1001 PSIQSRTMPVEQVFAKNFRVAAAMKTSVLLSWEVPDSYKSAVPFKILYNG 1050
1051 QSVEVDGHSMRKLIADLQPNTEYSFVLMNRGSSAGGLQHLVSIRTAPDLL 1100
1101 PHKPLPASAYIEDGRFDLSMPHVQDPSLVRWFYIVVVPIDRVGGSMLTPR 1150
1151 WSTPEELELDELLEAIEQGGEEQRRRRRQAERLKPYVAAQLDVLPETFTL 1200
1201 GDKKNYRGFYNRPLSPDLSYQCFVLASLKEPMDQKRYASSPYSDEIVVQV 1250
1251 TPAQQQEEPEMLWVTGPVLAVILIILIVIAILLFKRKRTHSPSSKDEQSI 1300
1301 GLKDSLLAHSSDPVEMRRLNYQTPGMRDHPPIPITDLADNIERLKANDGL 1350
1351 KFSQEYESIDPGQQFTWENSNLEVNKPKNRYANVIAYDHSRVILTSIDGV 1400
1401 PGSDYINANYIDGYRKQNAYIATQGPLPETMGDFWRMVWEQRTATVVMMT 1450
1451 RLEEKSRVKCDQYWPARGTETCGLIQVTLLDTVELATYTVRTFALHKSGS 1500
1501 SEKRELRQFQFMAWPDHGVPEYPTPILAFLRRVKACNPLDAGPMVVHCSA 1550
1551 GVGRTGCFIVIDAMLERMKHEKTVDIYGHVTCMRSQRNYMVQTEDQYVFI 1600
1601 HEALLEAATCGHTEVPARNLYAHIQKLGQVPPGESVTAMELEFKLLASSK 1650
1651 AHTSRFISANLPCNKFKNRLVNIMPYELTRVCLQPIRGVEGSDYINASFL 1700
1701 DGYRQQKAYIATQGPLAESTEDFWRMLWEHNSTIIVMLTKLREMGREKCH 1750
1751 QYWPAERSARYQYFVVDPMAEYNMPQYILREFKVTDARDGQSRTIRQFQF 1800
1801 TDWPEQGVPKTGEGFIDFIGQVHKTKEQFGQDGPITVHCSAGVGRTGVFI 1850
1851 TLSIVLERMRYEGVVDMFQTVKTLRTQRPAMVQTEDQYQLCYRAALEYLG 1900
1901 SFDHYAT 1907
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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