 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q4PGL2 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MSGEDRGAPYRYAGERPPAYPADGYASYQRGLKSSIPSRSYDTAYPEQGY 50
51 DRHLRERVHPAYTTGPSYGVPPPPPTDYRRPPPQAAYRYEEDYHHRRAPA 100
101 QPHLEYAPEHRRQPVHSDAYREADDRRSFEQARSDPYASPSRHAYQDSYR 150
151 SELPHERRHAPAAYERISVRDDARSAPENRHIDEPAVYRRDEYDAHSREI 200
201 QPSYRRGAHRSPRLSPQPVVSAHERSSEYATRDYSASAARYAEPVHQPES 250
251 PPRPSMSIFNMLNDRSANGAVESVHSSPTKSSIAAEHESYPTTAQEPSHA 300
301 SRAAFDPAREQSYSTARDSQAYSRGSEARAGAARVNYTVHPEDEAAISQR 350
351 RNSSAHQQSSIKSVRHPANAVDEPLIRVKHEEATSASLDAQMSAEPPVSH 400
401 TLNNGERLSAQDAKVATEDSSLAANGNGKVDSAGGTARPAPAKTQLKLRN 450
451 NPLPASKSNSSFVAPPPPAKKNRDPDGWESDLSNEENQPFWQTELDDYIF 500
501 DVRERQRLIEDAFVASMREKHVEVERRLARAYEGRYFAVIRQIRLREQQE 550
551 ASQRDMERRQDHVRQQRDHEIDLELLGTLSDGQQNMGTRKKKGGRGTDDD 600
601 GLLQADDDDDDDSDDVALADLAARNGSKSNIIKLKRSKGKPAAADPRNKK 650
651 RRLENGAALSPAPGSEVDSTLADFGGNFDGDDSAFASHQASPTPDDVSFA 700
701 LDVDASGKVPIDARRAQQLEDAHRRIWTTIAKRDVPKVYRTVLQSASSKT 750
751 MYWRRISSVVQREAKRGAARNNKTVKDVQLRARKVMREVLVFWKRNEKEE 800
801 RELRKKAEREALEKAKKEEEMREAKRQARKLNFLISQTELYSHFVGSKLK 850
851 TAEAEESEETAGSSKIIDPNAQPSDATVLPINPHSELADAEARLAELDDI 900
901 DFDDEDESNLRAHAARNAQEAVRLAKEKAQAFDVAAAEERRRNEAAARER 950
951 EGLDAGPVKQIEEKDLGKAFDSDDMNFLNPTSMGQTEIKQPKMLTCQLKE 1000
1001 YQLKGLNWLANLYEQGINGILADEMGLGKTVQSISLMAYLAEVHDIWGPF 1050
1051 LVIAPASTLHNWQQEISKFVPTLKALPYWGNVKDRAVLRKFWNRKQISYN 1100
1101 RDAPFHVLVTSYQLVVSDEKYFQRVKWQYMILDEAQAIKSSSSIRWKTLL 1150
1151 GFNCRNRLLLTGTPVQNSMQELWALLHFIMPSLFDSHDEFSEWFSKDIES 1200
1201 HAEQKGTLNEHQLRRLHMILKPFMLRRIKKNVQNELGDKIEIDVFCDLSA 1250
1251 RQKMLYRGLRANISVAELMDRATSNDEAGLKSLMNLVMQFRKVCNHPELF 1300
1301 ERADVRAPFALADFARSGSLAREGDLLNLPDSTTSLIELQVPKLLVREGG 1350
1351 IFDIPGHNSRKGFDTGYLQNLFNIWRAPHIHESLQEERSTFASLPLIGVS 1400
1401 PSEAQKTFHSTGIKRILAAAAEERHWRSLEAFASDDTFAAASVRPLAKML 1450
1451 RPMPTTSGRSPSVLMPLEEVAADYRRHSYLAKDSARAVVAPAVAPPIKLY 1500
1501 SNDGPFMQAQERFSRDAQVSVTLFGLSPEGRESVKRVEELQSELPEVPPQ 1550
1551 GVMRDSSIDQLPYNGMQVPQMNKLIVDSSKLAKLDVLLRELKANGHRVLI 1600
1601 YFQMTRMIDLMEEYLIYRQYKYLRLDGASKISDRRDMVTDWQTKPELFIF 1650
1651 LLSTRAGGLGINLTAADTVIFYDHDWNPSNDSQAMDRAHRLGQTKQVTVY 1700
1701 RLITKGTIDERIVRLARNKKEVQDIVVGTKAYSETGMAKPQEIVSLLLDD 1750
1751 DELAESMLRKKQAEEAQTAQEKADLARASHAKRRLNKDRAAAAVESPAPV 1800
1801 GSTWSLEDDEDDFFGARPPSKADTDTAETTPQLQSKKRSVGGGGGGSGGA 1850
1851 KRGRISEVASPRMTPLSLDDGALMASGEQLASPSKGAAAKRKSKSHRKKT 1900
1901 VDELAGVDLD 1910
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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