 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P30957 from www.uniprot.org...
The NucPred score for your sequence is 0.76 (see score help below)
1 MADGGEGEDEIQFLRTDDEVVLQCTATIHKEQQKLCLAAEGFGNRLCFLE 50
51 STSNSKNVPPDLSICTFVLEQSLLVRALQEMLANTVEKSEGQVDVEKWKF 100
101 MMKTAQGGGHRTLLYGHAILLRHSYSGMYLCCLSTSRSSTDKLAFDVGLQ 150
151 EDTTGEACWWTIHPASKQRSEGEKVRVGDDLILVSVSSERYLHLSYGNGS 200
201 LHVDAAFQQTLWSVAPISSGSEAAQGYLIGGDVLRLLHGHMDECLTVPSG 250
251 EHGEEQRRTVHYEGGAVSVHARSLWRLETLRVAWSGSHIRWGQPFRLRHV 300
301 TTGKYLSLMEDKNLLLMDKEKADVKSTAFTFRSSKEKLDGGVRKEVDGMG 350
351 TSEIKYGDSICYIQHVDTGLWLTYQSVDVKSVRMGSIQRKAIMHHEGHMD 400
401 DGLNLSRSQHEESRTARVIRSTVFLFNRFIRGLDALSKKAKASSVDLPIE 450
451 SVSLSLQDLIGYFHPPDEHLEHEDKQNRLRALKNRQNLFQEEGMINLVLE 500
501 CIDRLHVYSSAAHFADVAGREAGESWKSILNSLYELLAALIRGNRKNCAQ 550
551 FSGSLDWLISRLERLEASSGILEVLHCVLVESPEALNIIKEGHIKSIISL 600
601 LDKHGRNHKVLDVLCSLCVCHGVAVRSNQHLICDNLLPGRDLLLQTRLVN 650
651 HVSSMRPNIFLGVSEGSAQYKKWYYELMVDHTEPFVTAEATHLRVGWAST 700
701 EGYSPYPGGGEEWGGNGVGDDLFSYGFDGLHLWSGCIARTVSSPNQHLLR 750
751 TDDVISCCLDLSAPSISFRINGQPVQGMFENFNIDGLFFPVVSFSAGIKV 800
801 RFLLGGRHGEFKFLPPPGYAPCYEAVLPKEKLKVEHSREYKQERTYTRDL 850
851 LGPTVSLTQAAFTPIPVDTSQIVLPPHLERIREKLAENIHELWVMNKIEL 900
901 GWQYGPVRDDNKRQHPCLVEFSKLPEQERNYNLQMSLETLKTLLALGCHV 950
951 GISDEHAEEKVKKMKLPKNYQLTSGYKPAPMDLSFIKLTPSQEAMVDKLA 1000
1001 ENAHNVWARDRIRQGWTYGIQQDVKNRRNPRLVPYTLLDDRTKKSNKDSL 1050
1051 REAVRTLLGYGYNLEAPDQDHAARAEVCSGTGERFRIFRAEKTYAVKAGR 1100
1101 WYFEFEAVTSGDMRVGWSRPGCQPDQELGSDERAFAFDGFKAQRWHQGNE 1150
1151 HYGRSWQAGDVVGCMVDMNEHTMMFTLNGEILLDDSGSELAFKDFDVGDG 1200
1201 FIPVCSLGVAQVGRMNFGKDVSTLKYFTICGLQEGYEPFAVNTNRDITMW 1250
1251 LSKRLPQFLQVPSNHEHIEVTRIDGTIDSSPCLKVTQKSFGSQNSNTDIM 1300
1301 FYRLSMPIECAEVFSKTVPGGLPGAGLFGPKNDLEDYDADSDFEVLMKTA 1350
1351 HGHLVPDRVDKDKETTKAEFNNHKDYAQEKPSRLKQRFLLRRTKPDYSTS 1400
1401 HSARLTEDVLADDRDDYDFLMQTSTYYYSVRIFPGQEPANVWVGWITSDF 1450
1451 HQYDTGFDLDRVRTVTVTLGDEKGKVHESIKRSNCYMVCAGESMSPGQGR 1500
1501 NNNGLEIGCVVDAASGLLTFIANGKELSTYYQVEPSTKLFPAVFAQATSP 1550
1551 NVFQFELGRIKNVMPLSAGLFKSEHKNPVPQCPPRLHVQFLSHVLWSRMP 1600
1601 NQFLKVDVSRISERQGWLVQCLDPLQFMSLHIPEENRSVDILELTEQEEL 1650
1651 LKFHYHTLRLYSAVCALGNHRVAHALCSHVDEPQLLYAIENKYMPGLLRT 1700
1701 GYYDLLIDIHLSSYATARLMMNNEFIVPMTEETKSITLFPDENKKHGLPG 1750
1751 IGLSTSLRPRMQFSSPSFVSINNECYQYSPEFPLDILKAKTIQMLTEAVK 1800
1801 EGSLHGRDPVGGTTEFLFVPLIKLFYTLLIMGIFHNEDLRHILQLIEPSV 1850
1851 FKDAATPEEEGDTLEEEPSVEDTKLEGAGEEEAKVGKRPKEGLLQMKLPE 1900
1901 PVKLQMCLLLQYLCDCQVRHRIEAIVAFSDDFVAKLQDNQRFRYNEVMQA 1950
1951 LNMSAALTARKTKEFRSPPQEQINMLLNFKDDKSECPCPEEIRDQLLDFH 2000
2001 EDLMTHCGIELDEDGSLDGNSDLTIRGRLLSLVEKVTYLKKKQTEKPVES 2050
2051 DSRKSSTLQQLISETMVRWAQESVIEDPELVRAMFVLLHRQYDGIGGLVR 2100
2101 ALPKTYTINGVSVEDTINLLASLGQIRSLLSVRMGKEEEKLMIRGLGDIM 2150
2151 NNKVFYQHPNLMRALGMHETVMEVMVNVLGGGESKEITFPKMVANCCRFL 2200
2201 CYFCRISRQNQKAMFDHLSYLLENSSVGLASPAMRGSTPLDVAAASVMDN 2250
2251 NELALALREPDLEKVVVRYLAGCGLQSCQMLVSKGYPDIGWNPVEGERYL 2300
2301 DFLRFAVFCNGESVEENANVVVRLLIRRPECFGPALRGEGGNGLLAAMEE 2350
2351 AIKIAEDPSRDGPSPTSGSSKTLDTEEEEDDTIHMGNAIMTFYAALIDLL 2400
2401 GRCAPEMHLIHAGKGEAIRIRSILRSLIPLGDLVGVISIAFQMPTIAKDG 2450
2451 NVVEPDMSAGFCPDHKAAMVLFLDRVYGIEVQDFLLHLLEVGFLPDLRAA 2500
2501 ASLDTAALSATDMALALNRYLCTAVLPLLTRCAPLFAGTEHHASLIDSLL 2550
2551 HTVYRLSKGCSLTKAQRDSIEVCLLSICGQLRPSMMQHLLRRLVFDVPLL 2600
2601 NEHAKMPLKLLTNHYERCWKYYCLPGGWGNFGAASEEELHLSRKLFWGIF 2650
2651 DALSQKKYEQELFKLALPCLSAVAGALPPDYMESNYVSMMEKQSSMDSEG 2700
2701 NFNPQPVDTSNIIIPEKLEYFINKYAEHSHDKWSMDKLANGWIYGEIYSD 2750
2751 SSKIQPLMKPYKLLSEKEKEIYRWPIKESLKTMLAWGWRIERTREGDSMA 2800
2801 LYNRTRRISQTSQVSVDAAHGYSPRAIDMSNVTLSRDLHAMAEMMAENYH 2850
2851 NIWAKKKKLELESKGGGNHPLLVPYDTLTAKEKAKDREKAQDILKFLQIN 2900
2901 GYAVSRGFKDLELDTPSIEKRFAYSFLQQLIRYVDEAHQYILEFDGGSRS 2950
2951 KGEHFPYEQEIKFFAKVVLPLIDQYFKNHRLYFLSAASRPLCSGGHASNK 3000
3001 EKEMVTSLFCKLGVLVRHRISLFGNDATSIVNCLHILGQTLDARTVMKTG 3050
3051 LESVKSALRAFLDNAAEDLEKTMENLKQGQFTHTRNQPRGVTQIINYTTV 3100
3101 ALLPMLSSLFEHIGQHQFGEDLILEDVQVSCYRILTSLYALGTSKSIYVE 3150
3151 RQRSALGECLAAFAGAFPVAFLETHLNKHNIYSIYNTKSSRERAALSLPA 3200
3201 NVEDVCPNIPSLEKLMEEIVELAESGIRYTQMPHVMEVILPMLCSYMSRW 3250
3251 WEHGPESNPGRAEMCCTALNSEHMNTLLGNILKIIYNNLGIDEGAWMKRL 3300
3301 AVFSQPIINKVKPQLLKTHFLPLMEKLKKKAAMVVSEEDHLKAEARGDMS 3350
3351 EAELLILDEFTTLARDLYAFYPLLIRFVDYNRAKWLKEPTPEAEELFRMV 3400
3401 AEVFIYWSKSHNFKREEQNFVVQNEINNMSFLITDTKSKMSKAAVSDQER 3450
3451 KKMKRKGDRYSMQTSLIVAALKRLLPIGLNICAPGDQELIALAKNRFSLK 3500
3501 ATEDEVRDIIRNNIHLQGKLEDPAIRWQMALYKDLPNRTEETSDPEKTVE 3550
3551 RVLDIANVLFHLEQKSKFIGRRYYNLVEHPQRSKKAVWHKLLSKQRKRAV 3600
3601 VACFRMAPLYNLPRHRAVNLFLQGYEKSWIETEEHYFEDKLIEDLAKPGA 3650
3651 EPPEEDEVTKRVDPLHQLILLFSRTALTEKCKLEEDFLYMAYADIMAKSC 3700
3701 HDEEDDDGEEEVKSFEEKEMEKQKLLYQQARLHDRGAAEMVLQTISASKG 3750
3751 ETGPMVAATLKLGIAILNGGNSTVQQKMLDYLKEKKDVGFFQSLAGLMQS 3800
3801 CSVLDLNAFERQNKAEGLGMVTEEGSGEKVLQDDEFTCDLFRFLQLLCEG 3850
3851 HNSDFQNYLRTQTGNNTTVNIIISTVDYLLRVQESISDFYWYYSGKDVID 3900
3901 EQGQRNFSKAIQVAKQVFNTLTEYIQGPCTGNQQSLAHSRLWDAVVGFLH 3950
3951 VFAHMQMKLSQDSSQIELLKELMDLQKDMVVMLLSMLEGNVVNGTIGKQM 4000
4001 VDMLVESSNNVEMILKFFDMFLKLKDLTSSDTFKEYDPDGKGIISKRDFH 4050
4051 KAMESHKHYTQSETEFLLSCAETDENETLDYEEFVKRFHEPAKDIGFNVA 4100
4101 VLLTNLSEHMPNDTRLQTFLELAESVLNYFQPFLGRIEIMGSAKRIERVY 4150
4151 FEISESSRTQWEKPQVKESKRQFIFDVVNEGGEKEKMELFVNFCEDTIFE 4200
4201 MQLAAQISESDLNERSANKEESEKERPEEQGPKMGFFSVLTVRSALFALR 4250
4251 YNILTLMRMLSLKSLKKQMKKMKKMTVKDMVTAFFSSYWSIFMTLLHFVA 4300
4301 SVFRGFFRIVCSLLLGGSLVEGAKKIKVAELLANMPDPTQDEVRGDGEEG 4350
4351 ERKPMETTLPSEDLTDLKELTEESDLLSDIFGLDLKREGGQYKLIPHNPN 4400
4401 AGLSDLMSNPVLIPEEQEKFQEQKTKEEEKEEKEETKSEPEKAEGEDGEK 4450
4451 EEKVKEDKGKQKLRQLHTHRYGEPEVPESAFWKKIIAYQQKLLNYLARNF 4500
4501 YNMRMLALFVAFAINFILLFYKVSTSSVVEGKELPSRSTSENAKVTTSLD 4550
4551 SSSHRIIAVHYVLEESSGYMEPTLRILAILHTVISFFCIIGYYCLKVPLV 4600
4601 IFKREKEVARKLEFDGLYITEQPSEDDIKGQWDRLVINTQSFPNNYWDKF 4650
4651 VKRKVMDKYGEFYGRDRISELLGMDKAALDFSDAREKKKPKKDSSLSAVL 4700
4701 NSIDVKYQMWKLGVVFTDNSFLYLAWYMTMSILGHYNNFFFAAHLLDIAM 4750
4751 GFKTLRTILSSVTHNGKQLVLTVGLLAVVVYLYTVVAFNFFRKFYNKSED 4800
4801 GDTPDMKCDDMLTCYMFHMYVGVRAGGGIGDEIEDPAGDEYEIYRIIFDI 4850
4851 TFFFFVIVILLAIIQGLIIDAFGELRDQQEQVKEDMETKCFICGIGNDYF 4900
4901 DTVPHGFETHTLQEHNLANYLFFLMYLINKDETEHTGQESYVWKMYQERC 4950
4951 WEFFPAGDCFRKQYEDQLN 4969
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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