 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P18296 from www.uniprot.org...
The NucPred score for your sequence is 0.74 (see score help below)
1 MSTRSIVTSKVSWTPEKFISALSYPEHCSITLVKRLKASVKLKDLKQNIS 50
51 RDAPSWTFEHLFVAFKCAVSNLAKQWAELSTTDKEKTRRMFCTPSRLNTA 100
101 HRPEVFYLLECCTYILEQMQVVTKNTSHLYDCIRSGVSICNRLLDMEIFE 150
151 PAISLLMKTHKNLIILLTYRDHDAIPTATLLNPTLDVSEIQLESCLFVPM 200
201 VPASYFLNIGTIVVTFQLNVLRCLSLSQINGLSLNTINNLQSEDGPFQWI 250
251 ERSFPSQVQLANSRREILARLLTRFSMIQNNALQSFKLLILSIALWLNIL 300
301 SSQRADDKEFDVNQLETRILQLFSKVVQLCKSEDIEGSILNKDMTQLHHL 350
351 LENLSKESRLHILLQLSQLYYKYNDFQLSAAYVIRGYSLSFEDISFKLKF 400
401 LLFSFRLSIHDNSICFPFNLIQELSSLQQLFVENALPYSEALHLLDSIER 450
451 SFRLFNDSTVFDDTVFALNISEILSWILSSVVRDILVEDELLNLQLKIRK 500
501 FLMFTFHIIRSFSELTKFQSSLEGCLNLAAYYEDAEFPQKLSNHLYNLCV 550
551 KSSNVNYARECISLSIKIAVSHKLTNDETYLLKILKNFQLRYHDSLQLQE 600
601 KCDVLHTTFNQLDLYVGTTSVGKSSVLDNILKRIFNSLTSINDSNIEKLL 650
651 ESISYSLLKLFFKCANEGSRYNASAALSFKLSLMLHEKEEVLLLKTNVSC 700
701 VLANHGYNDIKFEEMVLCVIKGDQNLLEHNSNNNAKLALNESLLCSWENL 750
751 LCYRRAEDDSRILTIIESWTIFISRFSSVISRCSFTDFEINSILNFFFCF 800
801 LHTVEPSGKLTFELAFLEIFYELFNCLLHLQFSKYLVIIGTLLSDKYMTL 850
851 GFSGKAHLFYTKCYSYLRQCKSSPFINFWNVSYGKYLILTGNTDKGILQL 900
901 KKYSLSSEEDFNSNGLSRTVSLNLLLYERIQLSDALFQLGYTTVSLGFIM 950
951 QNLKVIKGLFSKSSKEHFNGGKYITWRLFAVSAHSNVCAARIYEHMGQAR 1000
1001 EAEFFYRQACSISEKMPFSCFSATFQLRLCSLLTRAGKLEKGEKILFDLT 1050
1051 EAMKSTDTYHKLLWNYGAAEVCATKSELDGAICHYSECVKLLEIIKSEYY 1100
1101 LFFNRNREKSLTKGIKRLSLSSQPTFVTESNTTEFDDWSILQNTAANLLR 1150
1151 LISMFELKRGNLEIAKALMTDSTKCSIASFFNIVSANILKSKLIVCEADS 1200
1201 TLFGDPVLRTLPDSVISLPGISHKFQKNQSKTKALGENTGFRKGSKRLDY 1250
1251 LRERLKINLQNVRLSCEIIFSNAYERSSVCVCREVNELISYSTIMQSALT 1300
1301 TIGETTDVDSSSASFFLEIPKALGFHRRREAQKFRNQHKELHFSSLEQIL 1350
1351 NSRLSIPDVRTFQDNFIDSLPSIWNVVSITINNSGEDLFISKIRKGHSPL 1400
1401 IFRLPLQRHNSRDADEEILVFTKAQTELFRIISKSNQMAQNGKHYTRRED 1450
1451 KETWWKERRHLDQCLQQLLENIEISWLGGFKGIFNPHKIDTSLFAKFSSQ 1500
1501 FQNIIAKNFNMDKKTPVPTLSPEILELFITLGKPGYEGYEQLLEDLIYFI 1550
1551 LDIFQFRGLHFAYDEIDTDQLSMDLQDALNAYFNNYVSEENRSHTVLVLD 1600
1601 KSVHQFPWESLPCLNRQSVSRVPSLSILRDILSQSFVVNGEYVEVRKEAG 1650
1651 SYILNPSLDLKHTQEMFEHKLVEGGWKGLIASQPSNRDFIKMLSGNDFFL 1700
1701 YFGHGGGEQYTTSYDLATLKRCAVTILMGCSSGALYECGSFEPWGTPLDY 1750
1751 LSAGCPTLVANLWDVTDKDIDRFSLKMLESWGLFENKAPFVNSTSICTAV 1800
1801 SESRSCCHLRYLNGAAPVIYGIPAYIIP 1828
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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