 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P81282 from www.uniprot.org...
The NucPred score for your sequence is 0.63 (see score help below)
1 MLINIKSILWMCSTLIAAHALQKVNMEKSPPVKGSLSGKVNLPCHFSTMP 50
51 TLPPSYNTTSEFLRIKWSKIELDKTGKDLKETTVLVAQNGNIKIGQDYKG 100
101 RVSVPTHPEDVGDASLTMVKLLASDAGRYRCDVMYGIEDTQDTVSLTVEG 150
151 VVFHYRAATSRYTLNFEMAQKACVDIGAVIATPEQLHAAYEDGFEQCDAG 200
201 WLSDQTVRYPIRVPREGCYGDMMGKEGVRTYGFRAPHETYDVYCYVDHLD 250
251 GDVFHITAPNKFTFEEAGEECKTQDARLATVGELQAAWRNGFDRCDYGWL 300
301 LDASVRHPVTVARAQCGGGLLGVRTLYRFENQTGFPTPDSRFDAYCFKPK 350
351 QNISEATTIELNMLAETVSPTLLEELQVGLDRMTPIVPLITELPVITTKV 400
401 PPIGNIVNFEQKSTVQPLTSTHRSATESLPPDGSMKKPWDMDYYSPSASG 450
451 PLGEPDVSEIKEEVPQSTTVVSHHAPDSWDGVKEDLQIKDSVTQIEQIEV 500
501 GPLVTSMEISKHIPSKEFTVTVTPFVSTTMTLESKTEKKAISTVSESVTT 550
551 SHYGFTLREGDGEDRISTVRSGQSTSIFSQIPEVITVSKTSEDTTRGQLE 600
601 DVESVSASTIVSPDSDGSPMDHRQEKQTHGRITEGFLGQYVSTTPFPSQH 650
651 HTEVELFPYSGDKRLVEGTSTVISPTPRTGRERTETLRPAMRTVTYTNDE 700
701 IQEKITKDSSIEKIEEEGFSGMKFPTASPEQIHHTKYSVGMTKSFESPAL 750
751 MTTTKPGVTPTEATDVEEDFTTPSGLETDGYQDTTEYEEGITTVHLIQST 800
801 LNVEVVTVSKWSLDEDNTTSKPLGSTEHVGSPKLPPALITTTGVSGKDKE 850
851 MPSLTEDGRDEFTRIPGSTQRPLEEFTEEDTTDHEKFTVRFQPTTSIATT 900
901 EKSTLRDSITEERVPPFTSTEVRVTHATIEGSALDEGEDVDVSKPLSTVP 950
951 QFAHPSDVEGSTFVNYSSTQEPTTYVDTSHTIPLPVIPKTEWGVLVPSIP 1000
1001 SEGEVLGEPSQDIRVINQTHFEASMYPETVRTTTEITQEATREDFLWKEQ 1050
1051 TPEKPVSPPSSTTDTAKETTPPLDEQESDGSAYTVFEDRSVMGSDRVSVL 1100
1101 VTTPIGKFEQHTSFPPGAVTKAKTDEVVTLTPTTGSKVTFSPWPKQKYET 1150
1151 EGTSPRGFVSPFSIGVTQLIEETTTEKREKTSLDYIDLGSGLFEKPKATE 1200
1201 LPEFSTVKATVPSDTAAFSSADRLHTPSASTEKPPLIDREPDEETTSDMV 1250
1251 IIGESTSRVPPTTLEDIVAKKTETDIDREYFTTSSTSTTQPTRPPTVEGK 1300
1301 EAFGPQAFSTPEPPAGTKFHPDINVYIIEVRENKTGRMSDFSVSGHPIDS 1350
1351 ESKEDEPCSEETDPEHDLIAEILPELLGMLHSEEDEEDEECANATDVTTT 1400
1401 PSVQYINGKHVVTTVPKDPEAAEARRGQFESVAPSQNFSDSSESDSHQFI 1450
1451 ITHAGLPTAMQPNESKETTESLEITWRPEIYPETAEPFSSGEPDIFPTAS 1500
1501 IHEGEATEGPDSITEKNPELDHLVHEHAESVPLFPEESSGDAAIDQESQK 1550
1551 IIFSGATEGTFGEEAEKSSTTHTPSMVASSVSAPVSEDASFILTGTPQSD 1600
1601 EPLSTVESWVEITPRHTVEFSGSPSIPIPEGSGEAEEDKDKIFAMITDLS 1650
1651 QRNTTDPRVTSDTSKIMITESLVDVPTTTIYSISEQVSAVVPTKFVRETD 1700
1701 TYEWVFSPPLEETTRKEEEKGTTGTASTVEVHSPTQRLDQFVSPSELESS 1750
1751 SETPPDDSAAATRKSFTSQMTPTQSERETTSSTVVFKETEVLDNLAAQTT 1800
1801 DPSLSSQPGVLEVSPTVPGSPVSLFMEQGSGEAAVDPETTTVSSLSLNIE 1850
1851 PEILAKEEAAGAWSPNVETVFPFEPTEQVLSTAVDREVAETISQTSKENL 1900
1901 VSEISGEPTHRAEIKGFSTDFPLEEDFSGDFREYSTVSYPITKEEIVMME 1950
1951 GSGDAAFKDTQMLPSVTPTSDLSNHTADSEEPGSTLVSTSAFPWEEFTAS 2000
2001 AEGSGEPLLSVSSSVDQVFPSAAGKASGTDSPFIDQRLGEEGAINETDQR 2050
2051 STILPTAEAESTKASTEEGEVKENHTVSMDFPPTVEPDELWPRQEVNPVR 2100
2101 QGNGSEIVSEEKTQEQESFEPLQSSVAPEQTTFDSQTFPEPGLQTTGYFT 2150
2151 LTTKKTYSTDERMEEEVISLADVSTPTLDSKGLVLYTTLPEVTEKSHFFL 2200
2201 ATASVTESVPAESVIAGSTIKEEESIKPFPKVTSPIIKESDTDLIFSGLG 2250
2251 SGEEVLPTLGSVNFTEIEQVLSTLYPLTSQVQSLEASILNDTSGDYEGME 2300
2301 NVANEMRPLISKTDSIFEDGETASSTTLPEILSDARTEGPFTAPLTFSTG 2350
2351 PGQPQNQTHRRAEEIQTSRPQPLTDQVSSENSVTAETKETATPATDFLAR 2400
2401 TYDLEMAKGFVTPTPKPSDLFYEHSGEGSGELDAVGAEVHASGMTQATRQ 2450
2451 GSTTFVSDRSLEKHPKVPSVEAVTVNGFPTVSMVLPLHPEQREGSPEATG 2500
2501 TPASTASYEKATEGAADSFQDHFWGFKDSTLKPDKRKATESIIIDLDKED 2550
2551 KDLILTMTESTILEIIPELTSDKNTVIDIDHTKPIYEDILGMQTDLDPEV 2600
2601 PSGPPDSSEESTQVQEKYEAAVNLSSTEENFEASGDILLANYTQATPESK 2650
2651 APEDRNPLDHTDFIFTTGIPILSSETELDVLLPTATSLPIPSKSATVNPE 2700
2701 SKTEAKTLEDIFESSTLSDGQAIADQSEVISTLGYLERTQNEDEAKKYVS 2750
2751 PSFQPEFSSGAEEALTDPTPYVSIGTTYLTAQSLTEAPDVMEGARLPDSI 2800
2801 DTSTVSAFSELLSQTPSFPPLSIHLGSGDSEHSEDLQPSALPSTDASTPP 2850
2851 VSSGELANIEATFKPSSEEDFYITEPPSLPPDTEPSEDESKPKLLEPTEA 2900
2901 SATELIAQEEIEIFQNSDNTTSVQVSGEAVKVFPSIETPEAEAIVTAASE 2950
2951 TKLEGATLRPHSTSASVIHGVEAGVVPQPSPQTSERPTILSPLEISPETQ 3000
3001 AALIRGEDSTVAAPKQQVPTRMLDSNKQATLSTTELNTELATPSFPLLET 3050
3051 SNETSFLIGINEESVEGTAVYLPGPDRCKMNPCLNGGTCYPTETSYVCTC 3100
3101 VPGYSGDRCELDFDECHSNPCRNGATCIDGFNTFRCLCLPSYVGALCEQD 3150
3151 TETCDYGWHKFQGQCYKYFAHRRTWDAAERECRLQGAHLTSILSHEEQMF 3200
3201 VNRVGHDYQWIGLNDKMFEHDFRWTDGSTLQYENWRPNQPDSFFSTGEDC 3250
3251 VVIIWHENGQWNDVPCNYHLTYTCKKGTVACGQPPVVENAKTFGKMKPRY 3300
3301 EINSLIRYHCKDGFIQRHLPTIRCLGNGRWAMPKITCLNPSAYQRTYSKK 3350
3351 YFKNSSSAKDNSINTSKHDHRWSRRWQESRR 3381
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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