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

NucPred

Fetching O95359 from www.uniprot.org...

The NucPred score for your sequence is 0.90 (see score help below)

   1  MGNENSTSDNQRTLSAQTPRSAQPPGNSQNIKRKQQDTPGSPDHRDASSI    50
  51  GSVGLGGFCTASESSASLDPCLVSPEVTEPRKDPQGARGPEGSLLPSPPP   100
 101  SQEREHPSSSMPFAECPPEGCLASPAAAPEDGPQTQSPRREPAPNAPGDI   150
 151  AAAFPAERDSSTPYQEIAAVPSAGRERQPKEEGQKSSFSFSSGIDQSPGM   200
 201  SPVPLREPMKAPLCGEGDQPGGFESQEKEAAGGFPPAESRQGVASVQVTP   250
 251  EAPAAAQQGTESSAVLEKSPLKPMAPIPQDPAPRASDRERGQGEAPPQYL   300
 301  TDDLEFLRACHLPRSNSGAAPEAEVNAASQESCQQPVGAYLPHAELPWGL   350
 351  PSPALVPEAGGSGKEALDTIDVQGHPQTGMRGTKPNQVVCVAAGGQPEGG   400
 401  LPVSPEPSLLTPTEEAHPASSLASFPAAQIPIAVEEPGSSSRESVSKAGM   450
 451  PVSADAAKEVVDAGLVGLERQVSDLGSKGEHPEGDPGEVPAPSPQERGEH   500
 501  LNTEQSHEVQPGVPPPPLPKEQSHEVQPGAPPPPLPKAPSESARGPPGPT   550
 551  DGAKVHEDSTSPAVAKEGSRSPGDSPGGKEEAPEPPDGGDPGNLQGEDSQ   600
 601  AFSSKRDPEVGKDELSKPSSDAESRDHPSSHSAQPPRKGGAGHTDGPHSQ   650
 651  TAEADASGLPHKLGEEDPVLPPVPDGAGEPTVPEGAIWEGSGLQPKCPDT   700
 701  LQSREGLGRMESFLTLESEKSDFPPTPVAEVAPKAQEGESTLEIRKMGSC   750
 751  DGEGLLTSPDQPRGPACDASRQEFHAGVPHPPQGENLAADLGLTALILDQ   800
 801  DQQGIPSCPGEGWIRGAASEWPLLSSEKHLQPSQAQPETSIFDVLKEQAQ   850
 851  PPENGKETSPSHPGFKDQGADSSQIHVPVEPQEDNNLPTHGGQEQALGSE   900
 901  LQSQLPKGTLSDTPTSSPTDMVWESSLTEESELSAPTRQKLPALGEKRPE   950
 951  GACGDGQSSRVSPPAADVLKDFSLAGNFSRKETCCTGQGPNKSQQALADA  1000
1001  LEEGSQHEEACQRHPGASEAADGCSPLWGLSKREMASGNTGEAPPCQPDS  1050
1051  VALLDAVPCLPALAPASPGVTPTQDAPETEACDETQEGRQQPVPAPQQKM  1100
1101  ECWATSDAESPKLLASFPSAGEQGGEAGAAETGGSAGAGDPGKQQAPEKP  1150
1151  GEATLSCGLLQTEHCLTSGEEASTSALRESCQAEHPMASCQDALLPAREL  1200
1201  GGIPRSTMDFSTHQAVPDPKELLLSGPPEVAAPDTPYLHVDSAAQRGAED  1250
1251  SGVKAVSSADPRAPGESPCPVGEPPLALENAASLKLFAGSLAPLLQPGAA  1300
1301  GGEIPAVQASSGSPKARTTEGPVDSMPCLDRMPLLAKGKQATGEEKAATA  1350
1351  PGAGAKASGEGMAGDAAGETEGSMERMGEPSQDPKQGTSGGVDTSSEQIA  1400
1401  TLTGFPDFREHIAKIFEKPVLGALATPGEKAGAGRSAVGKDLTRPLGPEK  1450
1451  LLDGPPGVDVTLLPAPPARLQVEKKQQLAGEAEISHLALQDPASDKLLGP  1500
1501  AGLTWERNLPGAGVGKEMAGVPPTLREDERPEGPGAAWPGLEGQAYSQLE  1550
1551  RSRQELASGLPSPAATQELPVERAAAFQVAPHSHGEEAVAQDRIPSGKQH  1600
1601  QETSACDSPHGEDGPGDFAHTGVPGHVPRSTCAPSPQREVLTVPEANSEP  1650
1651  WTLDTLGGERRPGVTAGILEMRNALGNQSTPAPPTGEVADTPLEPGKVAG  1700
1701  AAGEAEGDITLSTAETQACASGDLPEAGTTRTFSVVAGDLVLPGSCQDPA  1750
1751  CSDKAPGMEGTAALHGDSPARPQQAKEQPGPERPIPAGDGKVCVSSPPEP  1800
1801  DETHDPKLQHLAPEELHTDRESPRPGPSMLPSVPKKDAPRVMDKVTSDET  1850
1851  RGAEGTESSPVADDIIQPAAPADLESPTLAASSYHGDVVGQVSTDLIAQS  1900
1901  ISPAAAHAGLPPSAAEHIVSPSAPAGDRVEASTPSCPDPAKDLSRSSDSE  1950
1951  EAFETPESTTPVKAPPAPPPPPPEVIPEPEVSTQPPPEEPGCGSETVPVP  2000
2001  DGPRSDSVEGSPFRPPSHSFSAVFDEDKPIASSGTYNLDFDNIELVDTFQ  2050
2051  TLEPRASDAKNQEGKVNTRRKSTDSVPISKSTLSRSLSLQASDFDGASSS  2100
2101  GNPEAVALAPDAYSTGSSSASSTLKRTKKPRPPSLKKKQTTKKPTETPPV  2150
2151  KETQQEPDEESLVPSGENLASETKTESAKTEGPSPALLEETPLEPAVGPK  2200
2201  AACPLDSESAEGVVPPASGGGRVQNSPPVGRKTLPLTTAPEAGEVTPSDS  2250
2251  GGQEDSPAKGLSVRLEFDYSEDKSSWDNQQENPPPTKKIGKKPVAKMPLR  2300
2301  RPKMKKTPEKLDNTPASPPRSPAEPNDIPIAKGTYTFDIDKWDDPNFNPF  2350
2351  SSTSKMQESPKLPQQSYNFDPDTCDESVDPFKTSSKTPSSPSKSPASFEI  2400
2401  PASAMEANGVDGDGLNKPAKKKKTPLKTDTFRVKKSPKRSPLSDPPSQDP  2450
2451  TPAATPETPPVISAVVHATDEEKLAVTNQKWTCMTVDLEADKQDYPQPSD  2500
2501  LSTFVNETKFSSPTEELDYRNSYEIEYMEKIGSSLPQDDDAPKKQALYLM  2550
2551  FDTSQESPVKSSPVRMSESPTPCSGSSFEETEALVNTAAKNQHPVPRGLA  2600
2601  PNQESHLQVPEKSSQKELEAMGLGTPSEAIEITAPEGSFASADALLSRLA  2650
2651  HPVSLCGALDYLEPDLAEKNPPLFAQKLQEELEFAIMRIEALKLARQIAL  2700
2701  ASRSHQDAKREAAHPTDVSISKTALYSRIGTAEVEKPAGLLFQQPDLDSA  2750
2751  LQIARAEIITKEREVSEWKDKYEESRREVMEMRKIVAEYEKTIAQMIEDE  2800
2801  QREKSVSHQTVQQLVLEKEQALADLNSVEKSLADLFRRYEKMKEVLEGFR  2850
2851  KNEEVLKRCAQEYLSRVKKEEQRYQALKVHAEEKLDRANAEIAQVRGKAQ  2900
2901  QEQAAHQASLRKEQLRVDALERTLEQKNKEIEELTKICDELIAKMGKS    2948

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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