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

NucPred

Fetching Q60675 from www.uniprot.org...

The NucPred score for your sequence is 0.64 (see score help below)

   1  MPAATAGILLLLLLGTLEGSQTQRRQSQAHQQRGLFPAVLNLASNALITT    50
  51  NATCGEKGPEMYCKLVEHVPGQPVRNPQCRICNQNSSNPYQRHPITNAID   100
 101  GKNTWWQSPSIKNGVEYHYVTITLDLQQVFQIAYVIVKAANSPRPGNWIL   150
 151  ERSLDDVEYKPWQYHAVTDTECLTLYNIYPRTGPPSYAKDDEVICTSFYS   200
 201  KIHPLENGEIHISLINGRPSADDPSPELLEFTSARYIRLRFQRIRTLNAD   250
 251  LMMFAHKDPREIDPIVTRRYYYSVKDISVGGMCICYGHARACPLDPATNK   300
 301  SRCECEHNTCGESCDRCCPGFHQKPWRAGTFLTKSECEACNCHGKAEECY   350
 351  YDETVASRNLSLNIHGKYIGGGVCINCTHNTAGINCETCVDGFFRPKGVS   400
 401  PNYPRPCQPCHCDPTGSLSEVCVKDEKYAQRGLKPGSCHCKTGFGGVNCD   450
 451  RCVRGYHGYPDCQPCNCSGLGSTNEDPCVGPCSCKENVEGEDCSRCKSGF   500
 501  FNLQEDNQKGCEECFCSGVSNRCQSSYWTYGNIQDMRGWYLTDLSGRIRM   550
 551  APQLDNPDSPQQISISNSEARKSLLDGYYWSAPPPYLGNRLPAVGGQLSF   600
 601  TISYDLEEEEDDTEKILQLMIIFEGNDLRISTAYKEVYLEPSEEHIEEVS   650
 651  LKEEAFTIHGTNLPVTRKDFMIVLTNLERVLMQITYNLGMDAIFRLSSVN   700
 701  LESAVPYPTDRRIATDVEVCQCPPGYSGSSCETCWPRHRRVNGTIFGGIC   750
 751  EPCQCFAHAEACDDITGECLNCKDHTGGPYCNECLPGFYGDPTRGSPEDC   800
 801  QPCACPLNIPSNNFSPTCHLDRSLGLICDECPIGYTGPRCERCAEGYFGQ   850
 851  PSIPGGSCQPCQCNDNLDYSIPGSCDSLSGSCLICKPGTTGRYCELCADG   900
 901  YFGDAVNAKNCQPCRCNINGSFSEICHTRTGQCECRPNVQGRHCDECKPE   950
 951  TFGLQLGRGCLPCNCNSFGSKSFDCEASGQCWCQPGVAGKKCDRCAHGYF  1000
1001  NFQEGGCIACDCSHLGNNCDPKTGQCICPPNTTGEKCSECLPNTWGHSIV  1050
1051  TGCKVCNCSTVGSLASQCNVNTGQCSCHPKFSGMKCSECSRGHWNYPLCT  1100
1101  LCDCFLPGTDATTCDLETRKCSCSDQTGQCSCKVNVEGVHCDRCRPGKFG  1150
1151  LDAKNPLGCSSCYCFGVTSQCSEAKGLIRTWVTLSDEQTILPLVDEALQH  1200
1201  TTTKGIAFQKPEIVAKMDEVRQELHLEPFYWKLPQQFEGKKLMAYGGKLK  1250
1251  YAIYFEARDETGFATYKPQVIIRGGTPTHARIITRHMAAPLIGQLTRHEI  1300
1301  EMTEKEWKYYGDDPRISRTVTREDFLDILYDIHYILIKATYGNVVRQSRI  1350
1351  SEISMEVAEPGHVLAGSPPAHLIERCDCPPGYSGLSCETCAPGFYRLRSE  1400
1401  PGGRTPGPTLGTCVPCQCNGHSSQCDPETSVCQNCQHHTAGDFCERCALG  1450
1451  YYGIVRGLPNDCQPCACPLISPSNNFSPSCVLEGLEDYRCTACPRGYEGQ  1500
1501  YCERCAPGYTGSPSSPGGSCQECECDPYGSLPVPCDRVTGLCTCRPGATG  1550
1551  RKCDGCEHWHAREGAECVFCGDECTGLLLGDLARLEQMTMNINLTGPLPA  1600
1601  PYKILYGLENTTQELKHLLSPQRAPERLIQLAEGNVNTLVMETNELLTRA  1650
1651  TKVTADGEQTGQDAERTNSRAESLEEFIKGLVQDAEAINEKAVQLNETLG  1700
1701  NQDKTAERNLEELQKEIDRMLKELRSKDLQTQKEVAEDELVAAEGLLKRV  1750
1751  NKLFGEPRAQNEDMEKDLQQKLAEYKNKLDDAWDLLREATDKTRDANRLS  1800
1801  AANQKNMTILETKKEAIEGSKRQIENTLKEGNDILDEANRLLGEINSVID  1850
1851  YVDDIKTKLPPMSEELSDKIDDLAQEIKDRRLAEKVFQAESHAAQLNDSS  1900
1901  AVLDGILDEAKNISFNATAAFRAYSNIKDYIDEAEKVAREAKELAQGATK  1950
1951  LATSPQGLLKEDAKGSLQKSFRILNEAKKLANDVKGNHNDLNDLKTRLET  2000
2001  ADLRNSGLLGALNDTMDKLSAITNDTAAKLQAVKEKAREANDTAKAVLAQ  2050
2051  VKDLHQNLDGLKQNYNKLADSVAKTNAVVKDPSKNKIIADAGTSVRNLEQ  2100
2101  EADRLIDKLKPIKELEDNLKKNISEIKELINQARKQANSIKVSVSSGGDC  2150
2151  VRTYRPEIKKGSYNNIVVHVKTAVADNLLFYLGSAKFIDFLAIEMRKGKV  2200
2201  SFLWDVGSGVGRVEYPDLTIDDSYWYRIEASRTGRNGSISVRALDGPKAS  2250
2251  MVPSTYHSVSPPGYTILDVDANAMLFVGGLTGKIKKADAVRVITFTGCMG  2300
2301  ETYFDNKPIGLWNFREKEGDCKGCTVSPQVEDSEGTIQFDGEGYALVSRP  2350
2351  IRWYPNISTVMFKFRTFSSSALLMYLATRDLKDFMSVELSDGHVKVSYDL  2400
2401  GSGMTSVVSNQNHNDGKWKAFTLSRIQKQANISIVDIDSNQEENVATSSS  2450
2451  GNNFGLDLKADDKIYFGGLPTLRNLSMKARPEVNVKKYSGCLKDIEISRT  2500
2501  PYNILSSPDYVGVTKGCSLENVYTVSFPKPGFVELAAVSIDVGTEINLSF  2550
2551  STRNESGIILLGSGGTLTPPRRKRRQTTQAYYAIFLNKGRLEVHLSSGTR  2600
2601  TMRKIVIKPEPNLFHDGREHSVHVERTRGIFTVQIDEDRRHMQNLTEEQP  2650
2651  IEVKKLFVGGAPPEFQPSPLRNIPAFQGCVWNLVINSIPMDFAQPIAFKN  2700
2701  ADIGRCTYQKPREDESEAVPAEVIVQPQPVPTPAFPFPAPTMVHGPCVAE  2750
2751  SEPALLTGSKQFGLSRNSHIAIAFDDTKVKNRLTIELEVRTEAESGLLFY  2800
2801  MARINHADFATVQLRNGFPYFSYDLGSGDTSTMIPTKINDGQWHKIKIVR  2850
2851  VKQEGILYVDDASSQTISPKKADILDVVGILYVGGLPINYTTRRIGPVTY  2900
2901  SLDGCVRNLHMEQAPVDLDQPTSSFHVGTCFANAESGTYFDGTGFAKAVG  2950
2951  GFKVGLDLLVEFEFRTTRPTGVLLGVSSQKMDGMGIEMIDEKLMFHVDNG  3000
3001  AGRFTAIYDAGIPGHMCNGQWHKVTAKKIKNRLELVVDGNQVDAQSPNSA  3050
3051  STSADTNDPVFVGGFPGGLNQFGLTTNIRFRGCIRSLKLTKGTGKPLEVN  3100
3101  FAKALELRGVQPVSCPTT                                  3118

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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