 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q96DT5 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MAAQVAAREARDFREAPTLRLTSGAGLEAVGAVELEEEEENEEEAAARRA 50
51 RSFAQDARVRFLGGRLAMMLGFTEEKWSQYLESEDNRQVLGEFLESTSPA 100
101 CLVFSFAASGRLAASQEIPRDANHKLVFISKKITESIGVNDFSQVVLFGE 150
151 LPALSLGHVSAFLDEILVPVLSNKNNHKSWSCFTSQDMEYHIEVMKKKMY 200
201 IFRGKMSRRTLLPIPTVAGKMDLDQNCSENKPPSNERIILHAIESVVIEW 250
251 SHQIQEIIERDSVQRLLNGLHLSPQAELDFWMMRRENLSCIYDQLQAPVV 300
301 LKMVKILTTKQSSYFPTLKDIFLAVENALLEAQDVELYLRPLRRHIQCLQ 350
351 ETEFPQTRILIAPLFHTICLIWSHSKFYNTPARVIVLLQEFCNLFINQAT 400
401 AYLSPEDLLRGEIEESLEKVQVAVNILKTFKNSFFNYRKKLASYFMGRKL 450
451 RPWDFQSHLVFCRFDKFLDRLIKIEDIFATTLEFEKLERLEFGGTKGAIL 500
501 NGQVHEMSEELMELCKLFKQSTYDPSDCTNMEFESDYVAFKSKTLEFDRR 550
551 LGTIICEAFFNCNGLEAAFKLLTIFGNFLEKPVVMEIFSLHYSTLVHMFN 600
601 TELDVCKQLYNEHMKQIECGHVVLNKNMPFTSGNMKWAQQVLQRLQMFWS 650
651 NFASLRYLFLGNPDHALVYQKYVEMTTLLDQFESRIYNEWKSNVDEICEF 700
701 NLNQPLVKFSAINGLLCVNFDPKLVAVLREVKYLLMLKKQDIPDSALAIF 750
751 KKRNTILKYIGNLDLLVQGYNKLKQTLLEVEYPLIEDELRAIDEQLTAAT 800
801 TWLTWQDDCWGYIERVRAATSELEHRVERTQKNVKVIQQTMRGWARCVLP 850
851 PRREHRREAAFTLEDKGDLFTKKYKLIQGDGCKIHNLVEENRKLFKANPS 900
901 LDTWKIYVEFIDDIVVEGFFQAIMHDLDFFLKNTEKQLKPAPFFQAQMIL 950
951 LPPEIVFKPSLDREAGDGFYDLVEEMLCNSFRMSAQMNRIATHLEIKNYQ 1000
1001 NDMDNMLGLAEVRQEIMNRVVNVINKVLDFRNTLETHTYLWVDDRAEFMK 1050
1051 HFLLYGHAVSSDEMDAHANEEIPEQPPTLEQFKEQIDIYEALYVQMSKFE 1100
1101 DFRVFDSWFKVDMKPFKVSLLTIIKKWSWMFQEHLLRFVIDSLNELQEFI 1150
1151 KETDSGLQRELNEGDHDGLVDIMVHLLAVRSRQRATDELFEPLKETITLL 1200
1201 ESYGQKMPEQVYIQLEELPERWETTKKIAATVRHEVSPLHNAEVTLIRKK 1250
1251 CILFDAKQAEFRERFRHYAPLGFNAENPYTALDKANEELEALEEEMLQMQ 1300
1301 ESTRLFEVALPEYKQMKQCRKEIKLLKGLWDVIIYVRRSIDNWTKTQWRQ 1350
1351 IHVEQMDVELRRFAKEIWSLNKEVRVWDAYTGLEGTVKDMTASLRAITEL 1400
1401 QSPALRDRHWHQLMKAIGVKFLINEATTLADLLALRLHRVEDDVRRIVDK 1450
1451 AVKELGTEKVITEISQTWATMKFSYEVHYRTGIPLLKSDEQLFETLEHNQ 1500
1501 VQLQTLLQSKYVEYFIEQVLSWQNKLNIADLVIFTWMEVQRTWSHLESIF 1550
1551 VCSEDIRIQLVKDARRFDGVDAEFKELMFKTAKVENVLEATCRPNLYEKL 1600
1601 KDLQSRLSLCEKALAEYLETKRIAFPRFYFVSSADLLDILSKGAQPKQVT 1650
1651 CHLAKLFDSIADLQFEDNQDVSAHRAVGMYSKEKEYVPFQAECECVGHVE 1700
1701 TWLLQLEQTMQETVRHSITEAIVAYEEKPRELWIFDFPAQVALTSSQIWW 1750
1751 TTDVGIAFSRLEEGYETALKDFHKKQISQLNTLITLLLGELPPGDRQKIM 1800
1801 TICTIDVHARDVVAKLISQKVVSPQAFTWLSQLRHRWEDTQKHCFVNICD 1850
1851 AQFQYFYEYLGNSPRLVITPLTDRCYITLTQSLHLTMSGAPAGPAGTGKT 1900
1901 ETTKDLGRALGMMVYVFNCSEQMDYKSIGNIYKGLVQTGAWGCFDEFNRI 1950
1951 SVEVLSVVAVQVKMIHDAIRNRKKRFVFLGEAITLKPSVGIFITMNPGYA 2000
2001 GRTELPENLKALFRPCAMVAPDIELICEILLVAEGFVDARALARKFITLY 2050
2051 TLCKELLSKQDHYDWGLRAIKSVLVVAGSLKRGDKNRPEDQVLMRALRDF 2100
2101 NMPKIVTDDIPVFLGLVGDLFPALDVPRRRKLHFEQMVRQSTLELRLQPE 2150
2151 ESFILKVVQLEELLAVRHSVFVVGNAGTGKSKILRTLNRTYVNMKQKPVW 2200
2201 NDLNPKAVTTDELFGFIHHATREWKDGKIVYSYFIGLFSSILREQANLKH 2250
2251 DGPKWIVLDGDIDPMWIESLNTVMDDNKVLTLASNERIALTPFMRLLFEI 2300
2301 HHLRSATPATVSRAGILYVNPQDLGWNPYVASWIDRRRHQSEKANLTILF 2350
2351 DKYVPACLDKLRTSFKTITSIPESSLVQTLCVLLECLLTPENVPSDSPKE 2400
2401 VYEVYFVFACIWAFGGTLLQDQISDYQADFSRWWQKEMKAVKFPSQGTIF 2450
2451 DYYVDHKTKKLLPWADKIAQFTMDPDVPLQTVLVHTTETARLRYFMELLL 2500
2501 EKGKPLMLVGNAGVGKTVFVGDTLASLSEDYIVSRVPFNYYTTSTALQKI 2550
2551 LEKPLEKKAGHNYGPGGNKKLIYFIDDMNMPEVDLYGTVQPHTLIRQHID 2600
2601 YGHWYDRQKVMLKEIHNCQYVACMNPMVGSFTINPRLQRHFTVFAFNFPS 2650
2651 LDALNTIYGQIFSFHFQQQAFAPSILRSGPTLIQATIAFHQTMMCNFLPT 2700
2701 AIKFHYIFNLRDLSNVFQGILFASPECLKGPLDLIHLWLHESARVYGDKL 2750
2751 IDKKDCDLFQRRMLETAYKYFEGIDSHMLLQQPLIYCHFADRGKDPHYMP 2800
2801 VKDWEVLKTILTETLDNYNELNAAMHLVLFEDAMQHVCRISRILRTPQGC 2850
2851 ALLVGVGGSGKQSLSRLAAYLRGLEVFQITLTEGYGIQELRVDLANLYIR 2900
2901 TGAKNMPTVFLLTDAQVLDESFLVLINDLLASGEIPDLFSDEDVDKIISG 2950
2951 IHNEVHALGMVDSRENCWKFFMARVRLQLKIILCFSPVGRTLRVRARKFP 3000
3001 AIVNCTAIDWFHAWPQEALVSVSRRFIEETKGIEPVHKDSISLFMAHVHT 3050
3051 TVNEMSTRYYQNERRHNYTTPKSFLEQISLFKNLLKKKQNEVSEKKERLV 3100
3101 NGIQKLKTTASQVGDLKARLASQEAELQLRNHDAEALITKIGLQTEKVSR 3150
3151 EKTIADAEERKVTAIQTEVFQKQRECEADLLKAEPALVAATAALNTLNRV 3200
3201 NLSELKAFPNPPIAVTNVTAAVMVLLAPRGRVPKDRSWKAAKVFMGKVDD 3250
3251 FLQALINYDKEHIPENCLKVVNEHYLKDPEFNPNLIRTKSFAAAGLCAWV 3300
3301 INIIKFYEVYCDVEPKRQALAQANLELAAATEKLEAIRKKLVDLDRNLSR 3350
3351 LTASFEKATAEKVRCQEEVNQTNKTIKLANRLVKELEAKKIRWGQSIKSF 3400
3401 EAQEKTLCGDVLLTAAFVSYVGPFTRQYRQELVHCKWVPFLQQKVSIPLT 3450
3451 EGLDLISMLTDDATIAAWNNEGLPSDRMSTENAAILTHCERWPLVIDPQQ 3500
3501 QGIKWIKNKYGMDLKVTHLGQKGFLNAIETALAFGDVILIENLEETIDPV 3550
3551 LDPLLGRNTIKKGKYIRIGDKECEFNKNFRLILHTKLANPHYKPELQAQT 3600
3601 TLLNFTVTEDGLEAQLLAEVVSIERPDLEKLKLVLTKHQNDFKIELKYLE 3650
3651 DDLLLRLSAAEGSFLDDTKLVERLEATKTTVAEIEHKVIEAKENERKINE 3700
3701 ARECYRPVAARASLLYFVINDLQKINPLYQFSLKAFNVLFHRAIEQADKV 3750
3751 EDMQGRISILMESITHAVFLYTSQALFEKDKLTFLSQMAFQILLRKKEID 3800
3801 PLELDFLLRFTVEHTHLSPVDFLTSQSWSAIKAIAVMEEFRGIDRDVEGS 3850
3851 AKQWRKWVESECPEKEKLPQEWKKKSLIQKLILLRAMRPDRMTYALRNFV 3900
3901 EEKLGAKYVERTRLDLVKAFEESSPATPIFFILSPGVDALKDLEILGKRL 3950
3951 GFTIDSGKFHNVSLGQGQETVAEVALEKASKGGHWVILQNVHLVAKWLGT 4000
4001 LEKLLERFSQGSHRDYRVFMSAESAPTPDEHIIPQGLLENSIKITNEPPT 4050
4051 GMLANLHAALYNFDQDTLEICSKEQEFKSILFSLCYFHACVAGRLRFGPQ 4100
4101 GWSRSYPFNPGDLTICASVLYNYLEANSKVPWEDLRYLFGEIMYGGHITD 4150
4151 DWDRKLCRVYLEEFMNPSLTEDELMLAPGFAAPPYLDYAGYHQYIEEMLP 4200
4201 PESPALYGLHPNAEIEFLTVTSNTLFRTLLEMQPRNALSGDELGQSTEEK 4250
4251 VKNVLDDILEKLPEEFNMAEIMQKNSNRSPYVLVCFQECERMNILIREIR 4300
4301 ISLEQLDLSLKGELALSPAVEAQQFALSYDTVPDTWSKLAYPSTYGLAQW 4350
4351 FNDLLLRCRELDTWTQDLTLPAVVWLSGFFNPQSFLTAIMQTMARKNEWP 4400
4401 LDKTRLTADVTKKTKEDYGHPPREGAYLHGLFMEGARWDTQAGTIVEARL 4450
4451 KELACPMPVIFAKATPVDRQETKQTYECPVYRTKLRGPSYIWTFRLKSEE 4500
4501 KTAKWVLAGVALLLEA 4516
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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