SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching Q8NFC6 from www.uniprot.org...

The NucPred score for your sequence is 0.93 (see score help below)

   1  MATNPQPQPPPPAPPPPPPQPQPQPPPPPPGPGAGPGAGGAGGAGAGAGD    50
  51  PQLVAMIVNHLKSQGLFDQFRRDCLADVDTKPAYQNLRQRVDNFVANHLA   100
 101  THTWSPHLNKNQLRNNIRQQVLKSGMLESGIDRIISQVVDPKINHTFRPQ   150
 151  VEKAVHEFLATLNHKEEGSGNTAPDDEKPDTSLITQGVPTPGPSANVAND   200
 201  AMSILETITSLNQEASAARASTETSNAKTSERASKKLPSQPTTDTSTDKE   250
 251  RTSEDMADKEKSTADSGGEGLETAPKSEEFSDLPCPVEEIKNYTKEHNNL   300
 301  ILLNKDVQQESSEQKNKSTDKGEKKPDSNEKGERKKEKKEKTEKKFDHSK   350
 351  KSEDTQKVKDEKQAKEKEVESLKLPSEKNSNKAKTVEGTKEDFSLIDSDV   400
 401  DGLTDITVSSVHTSDLSSFEEDTEEEVVTSDSMEEGEITSDDEEKNKQNK   450
 451  TKTQTSDSSEGKTKSVRHAYVHKPYLYSKYYSDSDDELTVEQRRQSIAKE   500
 501  KEERLLRRQINREKLEEKRKQKAEKTKSSKTKGQGRSSVDLEESSTKSLE   550
 551  PKAARIKEVLKERKVLEKKVALSKKRKKDSRNVEENSKKKQQYEEDSKET   600
 601  LKTSEHCEKEKISSSKELKHVHAKSEPSKPARRLSESLHVVDENKNESKL   650
 651  EREHKRRTSTPVIMEGVQEETDTRDVKRQVERSEICTEEPQKQKSTLKNE   700
 701  KHLKKDDSETPHLKSLLKKEVKSSKEKPEREKTPSEDKLSVKHKYKGDCM   750
 751  HKTGDETELHSSEKGLKVEENIQKQSQQTKLSSDDKTERKSKHRNERKLS   800
 801  VLGKDGKPVSEYIIKTDENVRKENNKKERRLSAEKTKAEHKSRRSSDSKI   850
 851  QKDSLGSKQHGITLQRRSESYSEDKCDMDSTNMDSNLKPEEVVHKEKRRT   900
 901  KSLLEEKLVLKSKSKTQGKQVKVVETELQEGATKQATTPKPDKEKNTEEN   950
 951  DSEKQRKSKVEDKPFEETGVEPVLETASSSAHSTQKDSSHRAKLPLAKEK  1000
1001  YKSDKDSTSTRLERKLSDGHKSRSLKHSSKDIKKKDENKSDDKDGKEVDS  1050
1051  SHEKARGNSSLMEKKLSRRLCENRRGSLSQEMAKGEEKLAANTLSTPSGS  1100
1101  SLQRPKKSGDMTLIPEQEPMEIDSEPGVENVFEVSKTQDNRNNNSQQDID  1150
1151  SENMKQKTSATVQKDELRTCTADSKATAPAYKPGRGTGVNSNSEKHADHR  1200
1201  STLTKKMHIQSAVSKMNPGEKEPIHRGTTEVNIDSETVHRMLLSAPSEND  1250
1251  RVQKNLKNTAAEEHVAQGDATLEHSTNLDSSPSLSSVTVVPLRESYDPDV  1300
1301  IPLFDKRTVLEGSTASTSPADHSALPNQSLTVRESEVLKTSDSKEGGEGF  1350
1351  TVDTPAKASITSKRHIPEAHQATLLDGKQGKVIMPLGSKLTGVIVENENI  1400
1401  TKEGGLVDMAKKENDLNAEPNLKQTIKATVENGKKDGIAVDHVVGLNTEK  1450
1451  YAETVKLKHKRSPGKVKDISIDVERRNENSEVDTSAGSGSAPSVLHQRNG  1500
1501  QTEDVATGPRRAEKTSVATSTEGKDKDVTLSPVKAGPATTTSSETRQSEV  1550
1551  ALPCTSIEADEGLIIGTHSRNNPLHVGAEASECTVFAAAEEGGAVVTEGF  1600
1601  AESETFLTSTKEGESGECAVAESEDRAADLLAVHAVKIEANVNSVVTEEK  1650
1651  DDAVTSAGSEEKCDGSLSRDSEIVEGTITFISEVESDGAVTSAGTEIRAG  1700
1701  SISSEEVDGSQGNMMRMGPKKETEGTVTCTGAEGRSDNFVICSVTGAGPR  1750
1751  EERMVTGAGVVLGDNDAPPGTSASQEGDGSVNDGTEGESAVTSTGITEDG  1800
1801  EGPASCTGSEDSSEGFAISSESEENGESAMDSTVAKEGTNVPLVAAGPCD  1850
1851  DEGIVTSTGAKEEDEEGEDVVTSTGRGNEIGHASTCTGLGEESEGVLICE  1900
1901  SAEGDSQIGTVVEHVEAEAGAAIMNANENNVDSMSGTEKGSKDTDICSSA  1950
1951  KGIVESSVTSAVSGKDEVTPVPGGCEGPMTSAASDQSDSQLEKVEDTTIS  2000
2001  TGLVGGSYDVLVSGEVPECEVAHTSPSEKEDEDIITSVENEECDGLMATT  2050
2051  ASGDITNQNSLAGGKNQGKVLIISTSTTNDYTPQVSAITDVEGGLSDALR  2100
2101  TEENMEGTRVTTEEFEAPMPSAVSGDDSQLTASRSEEKDECAMISTSIGE  2150
2151  EFELPISSATTIKCAESLQPVAAAVEERATGPVLISTADFEGPMPSAPPE  2200
2201  AESPLASTSKEEKDECALISTSIAEECEASVSGVVVESENERAGTVMEEK  2250
2251  DGSGIISTSSVEDCEGPVSSAVPQEEGDPSVTPAEEMGDTAMISTSTSEG  2300
2301  CEAVMIGAVLQDEDRLTITRVEDLSDAAIISTSTAECMPISASIDRHEEN  2350
2351  QLTADNPEGNGDLSATEVSKHKVPMPSLIAENNCRCPGPVRGGKEPGPVL  2400
2401  AVSTEEGHNGPSVHKPSAGQGHPSAVCAEKEEKHGKECPEIGPFAGRGQK  2450
2451  ESTLHLINAEEKNVLLNSLQKEDKSPETGTAGGSSTASYSAGRGLEGNAN  2500
2501  SPAHLRGPEQTSGQTAKDPSVSIRYLAAVNTGAIKADDMPPVQGTVAEHS  2550
2551  FLPAEQQGSEDNLKTSTTKCITGQESKIAPSHTMIPPATYSVALLAPKCE  2600
2601  QDLTIKNDYSGKWTDQASAEKTGDDNSTRKSFPEEGDIMVTVSSEENVCD  2650
2651  IGNEESPLNVLGGLKLKANLKMEAYVPSEEEKNGEILAPPESLCGGKPSG  2700
2701  IAELQREPLLVNESLNVENSGFRTNEEIHSESYNKGEISSGRKDNAEAIS  2750
2751  GHSVEADPKEVEEEERHMPKRKRKQHYLSSEDEPDDNPDVLDSRIETAQR  2800
2801  QCPETEPHDTKEENSRDLEELPKTSSETNSTTSRVMEEKDEYSSSETTGE  2850
2851  KPEQNDDDTIKSQEEDQPIIIKRKRGRPRKYPVETTLKMKDDSKTDTGIV  2900
2901  TVEQSPSSSKLKVMQTDESNKETANLQERSISNDDGEEKIVTSVRRRGRK  2950
2951  PKRSLTVSDDAESSEPERKRQKSVSDPVEDKKEQESDEEEEEEEEDEPSG  3000
3001  ATTRSTTRSEAQRSKTQLSPSIKRKREVSPPGARTRGQQRVEEAPVKKAK  3050
3051  R                                                   3051

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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