 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Y7B3 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MSLDPSSFPRSNSPASSDSSLTRSRLRGKEGSLKKDKNYRRYASSVERAL 50
51 SLFDNTLQEWADYISFLSRLLKALQTHPPDQPVVPHKVLVAKRLSQSMNP 100
101 SLPSGVHQKALEVYTYIFNLIKPEGLSHDLPLYYPGIAPTLTFASLTVRP 150
151 LFLSLVETYVCDLEPWAIRPALKAIILSLLPGLEEETSDDFDSTLRLLNT 200
201 LREIASRMDTQRPGAETNSSGQYFWQCFFLASITSPSRRLGILAYLNRYL 250
251 PKLGVTDRRPSRHEEVDATAMPLEMQVAVDSVILPEPGLLIRCFATGLTD 300
301 EQVLVQRNFLDLLVTHLPLSSPILQTRITKDDLQRLIVAAAGVVSRRDMS 350
351 LNRRLWAWLLGPDPVGDRASFEARPSISSTASKTAAESEELSQSQYFSRF 400
401 GLQPLASGLLQLLKRDTELPSEKAKPFRITLSLMDRWEVGGHVVPAVFLP 450
451 FMRSVKAYKLAAPKAHFDEVFRSATSFFDGVESGMIFSELLNLIDWDAKS 500
501 LANDAPRILDNLNLAHFILDHFNVREEDMVLNHAPLLTLAALIKMSELSS 550
551 EATTSVAHDQLQAVSNGLSKVVTLLTGLLIDRAFLRKSDSKNSVNEPSML 600
601 LDRPGSAILRQIHQFYDRSRNSLDLQPPPFPPTDLADLIIKNVYEQAISA 650
651 VNACNDAKSIPERLNLLIVVLKKLPRSRILRDKRLYVAIGKHIQASTVEL 700
701 STASFSHLSSMSTTITSLYCIHKLGYYITYEEVSDLIPPLVRQLWQFLSP 750
751 LSPKFHVEAVRCLWNLHSVSWSDHLVESTITSLMFTPPAPGSYQLSSEEE 800
801 AGRYFILWNHSHHGTYELPPKNLQDSAQSQASYHSSMLERPLFTVLDLLS 850
851 QGSTQPAQVVQLWLQDLPSVAKVFRIIISKLEGVLHRGNQLGTFEGNTVV 900
901 SPDDYAECNYLFETIHNVLGALNHNGWVSLLTQTMAHNSRRHDASASEDN 950
951 AEAPSLHSVVFQASLKIVSGYKSGTATANAEEVKLQQISLLVMRQLLLGP 1000
1001 GVEELVESGIDSLLVDRLYSMLDEGGDIAIQAALIDTLLAVLKARFSQAY 1050
1051 LPPPPTKPKHQRGGSREKLTSPSLLSFTSDKPEKSSVLLPSPEPPQRLLD 1100
1101 CLLKGLSSPRSRAIIDKWIMLLCEILPIYSTCIFQILLTLVDCLGREIRQ 1150
1151 SYTNLQSAFEKTEGWPKDRPEQTTISLLTGFETCIAAAHERLLMEEVNAP 1200
1201 AAKSPDQTHGFFGNMVSGVFASDSNHPRSAAMNNRLTVLLSFQDAVRLCF 1250
1251 SIWSWGAAERSSLPQDTESIASFQYTSLRMRNRSRRILEHLFTAEALECL 1300
1301 ETMVDMWIKSDRDTSPLIFNLLHTLDGSRPKIAIPAIFNAIYTRTNPAAL 1350
1351 DPSRKSTMTTALTETELAGFLVTYARSLDDDVLDEIWIDCTTFLRDVLSN 1400
1401 PFPHRQILPRLVEFAAILGVKLENTNFGEDRRMRKELGDVILRLLTAIFT 1450
1451 SKPMGFTQEQGLLGRASLDYDSSSIQRIGPDDMLSILVASMPAFSMTLGD 1500
1501 MDRITTAVSNISTNIIGPFIRARLFPNNLNTSFMALMQHISKIPQVAKVW 1550
1551 KKDVADAFNDPRFFGSQLDLVKGGWMTLLRQWALVDKDRLSEIMTRLTPP 1600
1601 ATAGIMFGVGASAARLDADRKAQLNLRRISLLVLSTAEDYFIAEMPALLQ 1650
1651 KLEDLLGATASSSPSSATRAEIFMVLRALILKSTTTTLSPFWPLINSELQ 1700
1701 EAISAISSGNQQELYNPYSLLQACKLLDTLLVLAPDDFQLLEWLYVTDTV 1750
1751 DAIYPPEQFEPTALADEVSHNLGVRWSTSSDPTRESTNLHHGVRYPGLAA 1800
1801 DWIRETAKDEIVDRVLRPFFDQLSIHAFESTYSISNPNLEACRDDLLADL 1850
1851 FNESTMAN 1858
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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