 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9H1H9 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MSDTKVKVAVRVRPMNRRELELNTKCVVEMEGNQTVLHPPPSNTKQGERK 50
51 PPKVFAFDYCFWSMDESNTTKYAGQEVVFKCLGEGILEKAFQGYNACIFA 100
101 YGQTGSGKSFSMMGHAEQLGLIPRLCCALFKRISLEQNESQTFKVEVSYM 150
151 EIYNEKVRDLLDPKGSRQSLKVREHKVLGPYVDGLSQLAVTSFEDIESLM 200
201 SEGNKSRTVAATNMNEESSRSHAVFNIIITQTLYDLQSGNSGEKVSKVSL 250
251 VDLAGSERVSKTGAAGERLKEGSNINKSLTTLGLVISSLADQAAGKGKSK 300
301 FVPYRDSVLTWLLKDNLGGNSQTSMIATISPAADNYEETLSTLRYADRAK 350
351 RIVNHAVVNEDPNAKVIRELREEVEKLREQLSQAEAMKAPELKEKLEESE 400
401 KLIKELTVTWEEKLRKTEEIAQERQRQLESMGISLEMSGIKVGDDKCYLV 450
451 NLNADPALNELLVYYLKDHTRVGADTSQDIQLFGIGIQPQHCEIDIASDG 500
501 DVTLTPKENARSCVNGTLVCSTTQLWHGDRILWGNNHFFRINLPKRKRRD 550
551 WLKDFEKETGPPEHDLDAASEASSEPDYNYEFAQMEVIMKTLNSNDPVQN 600
601 VVQVLEKQYLEEKRSALEEQRLMYERELEQLRQQLSPDRQPQSSGPDRLA 650
651 YSSQTAQQKVTQWAEERDELFRQSLAKLREQLVKANTLVREANFLAEEMS 700
701 KLTDYQVTLQIPAANLSANRKRGAIVSEPAIQVRRKGKSTQVWTIEKLEN 750
751 KLIDMRDLYQEWKEKVPEAKRLYGKRGDPFYEAQENHNLIGVANVFLECL 800
801 FCDVKLQYAVPIISQQGEVAGRLHVEVMRVTGAVPERVVEDDSSENSSES 850
851 GSLEVVDSSGEIIHRVKKLTCRVKIKEATGLPLNLSNFVFCQYTFWDQCE 900
901 STVAAPVVDPEVPSPQSKDAQYTVTFSHCKDYVVNVTEEFLEFISDGALA 950
951 IEVWGHRCAGNGSSIWEVDSLHAKTRTLHDRWNEVTRRIEMWISILELNE 1000
1001 LGEYAAVELHQAKDVNTGGIFQLRQGHSRRVQVTVKPVQHSGTLPLMVEA 1050
1051 ILSVSIGCVTARSTKLQRGLDSYQRDDEDGDDMDSYQEEDLNCVRERWSD 1100
1101 ALIKRREYLDEQIKKVSNKTEKTEDDVEREAQLVEQWVGLTEERNAVLVP 1150
1151 APGSGIPGAPADWIPPPGMETHIPVLFLDLNADDLSANEQLVGPHASGVN 1200
1201 SILPKEHGSQFFYLPIIKHSDDEVSATASWDSSVHDSVHLNRVTPQNERI 1250
1251 YLIVKTTVQLSHPAAMELVLRKRIAANIYNKQSFTQSLKRRISLKNIFYS 1300
1301 CGVTYEIVSNIPKATEEIEDRETLALLAARSENEGTSDGETYIEKYTRGV 1350
1351 LQVENILSLERLRQAVTVKEALSTKARHIRRSLSTPNVHNVSSSRPDLSG 1400
1401 FDEDDKGWPENQLDMSDYSSSYQDVACYGTLPRDSPRRNKEGCTSETPHA 1450
1451 LTVSPFKAFSPQPPKFFKPLMPVKEEHKKRIALEARPLLSQESMPPPQAH 1500
1501 NPGCIVPSGSNGSSMPVEHNSKREKKIDSEEEENELEAINRKLISSQPYV 1550
1551 PVEFADFSVYNASLENREWFSSKVDLSNSRVLEKEVSRSPTTSSITSGYF 1600
1601 SHSASNATLSDMVVPSSDSSDQLAIQTKDADSTEHSTPSLVHDFRPSSNK 1650
1651 ELTEVEKGLVKDKIIVVPLKENSALAKGSPSSQSIPEKNSKSLCRTGSCS 1700
1701 ELDACPSKISQPARGFCPREVTVEHTTNILEDHSFTEFMGVSEGKDFDGL 1750
1751 TDSSAGELSSRRSLPNKTGGKTVSDGLHHPSQLHSKLENDQVIIPEAAFW 1800
1801 VLCCQ 1805
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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