 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O88466 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MVRERKCILCHIVYGSKKEMDEHMRSMLHHRELENLKGRDISHECRVCRV 50
51 TEVGLSAYAKHISGQLHKDNVDAQEREDDGKEEEEEEYFDKELVQLIQER 100
101 KEQSRQDEPPSNSQEVNSDDRQPQWRREDRIPYQDRESYSQPPRHHRGPP 150
151 QRDWKWEKDGFNSTRKNSFPHSLRNSGGPRGSSVWHKGATRGSSTWFLNH 200
201 SNSGGGWHSNNGMVDWNYNGTGRNSSWHSEGTGGFPSWHMNNSNGNWKSS 250
251 VRGTNSWNYNGLGDKFQQGRNRNPNYQMEDMTKMWNKKSNKPSKYSQERC 300
301 KWQRQDRDKAAKYRSPPEGYASDTFPSEGLLEFNFEQRESQTTKQTDTAA 350
351 SKINGKNGTKARDKFRRWTPYPSQKTLDLQSALKEVIGSKSDTLEKPLFN 400
401 FSLITAGLRKPVDKTSNPPVIKTQKAGPPGSPSHKAISDGTAFCEVARAC 450
451 SITEQSEPHQKSNKIPLLKSPLLPLPTPKSGPHKQNLKNRSKNKETKSFP 500
501 SGDHSHLLNTSTLEGSHGSSYTSKSLGLCPRVLKENKTVSGTQKEPDEKL 550
551 NNTSQKAQDTVLQCPKTLQNPLPTTPKRMENDAKESSVEESAKDSLSIES 600
601 QPHSAGNSAMTSDAENHGIKSEGVASLTTEVVSCSTHTVDKEQGSQIPGT 650
651 PENLSTSPRNSTVLQKEAEVQVSAATSPHSGLLLDLKTSLEDAQDNNLVK 700
701 SDGPFETESFEDTSLDTELQKPDLNNQPPGTLLPELSKLGFPASLQRDLS 750
751 RHISLKSKTGTHLPEPNLNSARRIRNVSGHRKNETEKESGLKPTLRQILN 800
801 ASRRNVNWEQVIQQVTKKKQELGKGLPRFGIEMVPLVQNEQEVLDLDEEP 850
851 DLSSLEGFQWEGVSIPSSSGLARKRSLSESSVVMDRAPVYSFFTGEGTGK 900
901 ENEAQQSPSPNTALSAAQSQKTAMYLEQEVAPLTPSVGTGERVGNIPTQR 950
951 RHSAQLPSGHIMPVMHSARDLHSQERSTPLSERHAQESTGEGNSLSSNAS 1000
1001 SGHAVSSLADAATDSSCTSGAEQTDGHSIRKKRRATGDGSSPELPSLERK 1050
1051 NKRRKIKGKKERSQVDQLLTISLREEELSKSLQCMDNKLLQARAALQTAY 1100
1101 VEVQRLLVLKQQITVEMSALRTHRIQILQGLQETYEPPEHPDQAPCSLIS 1150
1151 REQRNSRSQTSFETALLPAPFFPGFLDPPPSHASLPSPGNPLQITMSTFQ 1200
1201 AHGTAPDSSVQIKQEPMSPEQEGNMNALPQGCASNVSKELLQTNRVVDDG 1250
1251 SSVYPAIPAVIASESTENCQEVSKDLNFSVEQGNSRSKGNSPSCQSPDLP 1300
1301 GINRGEETAKGSSGSEACSSSFLRLSFTPETPAEKETQSPADQPEQQAES 1350
1351 TLASAETRGSKKKKKLRKKKTLRATHVPENSDTEQDVFTAKPARKVKTAK 1400
1401 AAKGAKVTTSQTGQEQGTARDEPDSDSSLEVLEVTNPQLEVVAIDTSESG 1450
1451 DEKPDSPSKKDAWIAAEQNPIETSRSGCDEVSSTSELGTRYKDGVPVSVA 1500
1501 ETQTVISIKASKHSSEISSEPGDDEEPTEGSFEGHQAAVNAIQIFGNFLY 1550
1551 TCSADTTVRVYNLVSRKCVGVFEGHTSKVNCLLVTHTSGKSSVLYTGSSD 1600
1601 HTIRCYNIKTRECMEQLQLEDRVLCLHNRWRTLYAGLANGTVVTFDIKNN 1650
1651 KRQEIFECHGPRAVSCLATAQEGARKLLVVGSYDCTISVRDARNGLLLRT 1700
1701 LEGHSKTVLCMKVVNDLVFSGSSDQSVHAHNIHTGELVRIYKGHNHAVTV 1750
1751 VNILGKVMVTACLDKFVRVYELQSHDRLQVYGGHKDMIMCMTIHKSVIYT 1800
1801 GCYDGSIQAVRLNLMQNYRCWWYGCTLIFGVVDHLKQHLLTDHTNPNFQT 1850
1851 LKCRWRNCDAFFTARKGSKQDVAGHIERHAEDDSKIDS 1888
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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