 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q92736 from www.uniprot.org...
The NucPred score for your sequence is 0.77 (see score help below)
1 MADGGEGEDEIQFLRTDDEVVLQCTATIHKEQQKLCLAAEGFGNRLCFLE 50
51 STSNSKNVPPDLSICTFVLEQSLSVRALQEMLANTVEKSEGQVDVEKWKF 100
101 MMKTAQGGGHRTLLYGHAILLRHSYSGMYLCCLSTSRSSTDKLAFDVGLQ 150
151 EDTTGEACWWTIHPASKQRSEGEKVRVGDDLILVSVSSERYLHLSYGNGS 200
201 LHVDAAFQQTLWSVAPISSGSEAAQGYLIGGDVLRLLHGHMDECLTVPSG 250
251 EHGEEQRRTVHYEGGAVSVHARSLWRLETLRVAWSGSHIRWGQPFRLRHV 300
301 TTGKYLSLMEDKNLLLMDKEKADVKSTAFTFRSSKEKLDVGVRKEVDGMG 350
351 TSEIKYGDSVCYIQHVDTGLWLTYQSVDVKSVRMGSIQRKAIMHHEGHMD 400
401 DGISLSRSQHEESRTARVIRSTVFLFNRFIRGLDALSKKAKASTVDLPIE 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 GISDEHAEDKVKKMKLPKNYQLTSGYKPAPMDLSFIKLTPSQEAMVDKLA 1000
1001 ENAHNVWARDRIRQGWTYGIQQDVKNRRNPRLVPYTLLDDRTKKSNKDSL 1050
1051 REAVRTLLGYGYNLEAPDQDHAARAEVCSGTGERFRIFRAEKTYAVKAGR 1100
1101 WYFEFETVTAGDMRVGWSRPGCQPDQELGSDERAFAFDGFKAQRWHQGNE 1150
1151 HYGRSWQAGDVVGCMVDMNEHTMMFTLNGEILLDDSGSELAFKDFDVGDG 1200
1201 FIPVCSLGVAQVGRMNFGKDVSTLKYFTICGLQEGYEPFAVNTNRDITMW 1250
1251 LSKRLPQFLQVPSNHEHIEVTRIDGTIDSSPCLKVTQKSFGSQNSNTDIM 1300
1301 FYRLSMPIECAEVFSKTVAGGLPGAGLFGPKNDLEDYDADSDFEVLMKTA 1350
1351 HGHLVPDRVDKDKEATKPEFNNHKDYAQEKPSRLKQRFLLRRTKPDYSTS 1400
1401 HSARLTEDVLADDRDDYDFLMQTSTYYYSVRIFPGQEPANVWVGWITSDF 1450
1451 HQYDTGFDLDRVRTVTVTLGDEKGKVHESIKRSNCYMVCAGESMSPGQGR 1500
1501 NNNGLEIGCVVDAASGLLTFIANGKELSTYYQVEPSTKLFPAVFAQATSP 1550
1551 NVFQFELGRIKNVMPLSAGLFKSEHKNPVPQCPPRLHVQFLSHVLWSRMP 1600
1601 NQFLKVDVSRISERQGWLVQCLDPLQFMSLHIPEENRSVDILELTEQEEL 1650
1651 LKFHYHTLRLYSAVCALGNHRVAHALCSHVDEPQLLYAIENKYMPGLLRA 1700
1701 GYYDLLIDIHLSSYATARLMMNNEYIVPMTEETKSITLFPDENKKHGLPG 1750
1751 IGLSTSLRPRMQFSSPSFVSISNECYQYSPEFPLDILKSKTIQMLTEAVK 1800
1801 EGSLHARDPVGGTTEFLFVPLIKLFYTLLIMGIFHNEDLKHILQLIEPSV 1850
1851 FKEAATPEEESDTLEKELSVDDAKLQGAGEEEAKGGKRPKEGLLQMKLPE 1900
1901 PVKLQMCLLLQYLCDCQVRHRIEAIVAFSDDFVAKLQDNQRFRYNEVMQA 1950
1951 LNMSAALTARKTKEFRSPPQEQINMLLNFKDDKSECPCPEEIRDQLLDFH 2000
2001 EDLMTHCGIELDEDGSLDGNSDLTIRGRLLSLVEKVTYLKKKQAEKPVES 2050
2051 DSKKSSTLQQLISETMVRWAQESVIEDPELVRAMFVLLHRQYDGIGGLVR 2100
2101 ALPKTYTINGVSVEDTINLLASLGQIRSLLSVRMGKEEEKLMIRGLGDIM 2150
2151 NNKVFYQHPNLMRALGMHETVMEVMVNVLGGGESKEITFPKMVANCCRFL 2200
2201 CYFCRISRQNQKAMFDHLSYLLENSSVGLASPAMRGSTPLDVAAASVMDN 2250
2251 NELALALREPDLEKVVRYLAGCGLQSCQMLVSKGYPDIGWNPVEGERYLD 2300
2301 FLRFAVFCNGESVEENANVVVRLLIRRPECFGPALRGEGGNGLLAAMEEA 2350
2351 IKIAEDPSRDGPSPNSGSSKTLDTEEEEDDTIHMGNAIMTFYSALIDLLG 2400
2401 RCAPEMHLIHAGKGEAIRIRSILRSLIPLGDLVGVISIAFQMPTIAKDGN 2450
2451 VVEPDMSAGFCPDHKAAMVLFLDRVYGIEVQDFLLHLLEVGFLPDLRAAA 2500
2501 SLDTAALSATDMALALNRYLCTAVLPLLTRCAPLFAGTEHHASLIDSLLH 2550
2551 TVYRLSKGCSLTKAQRDSIEVCLLSICGQLRPSMMQHLLRRLVFDVPLLN 2600
2601 EHAKMPLKLLTNHYERCWKYYCLPGGWGNFGAASEEELHLSRKLFWGIFD 2650
2651 ALSQKKYEQELFKLALPCLSAVAGALPPDYMESNYVSMMEKQSSMDSEGN 2700
2701 FNPQPVDTSNITIPEKLEYFINKYAEHSHDKWSMDKLANGWIYGEIYSDS 2750
2751 SKVQPLMKPYKLLSEKEKEIYRWPIKESLKTMLAWGWRIERTREGDSMAL 2800
2801 YNRTRRISQTSQVSVDAAHGYSPRAIDMSNVTLSRDLHAMAEMMAENYHN 2850
2851 IWAKKKKMELESKGGGNHPLLVPYDTLTAKEKAKDREKAQDILKFLQING 2900
2901 YAVSRGFKDLELDTPSIEKRFAYSFLQQLIRYVDEAHQYILEFDGGSRGK 2950
2951 GEHFPYEQEIKFFAKVVLPLIDQYFKNHRLYFLSAASRPLCSGGHASNKE 3000
3001 KEMVTSLFCKLGVLVRHRISLFGNDATSIVNCLHILGQTLDARTVMKTGL 3050
3051 ESVKSALRAFLDNAAEDLEKTMENLKQGQFTHTRNQPKGVTQIINYTTVA 3100
3101 LLPMLSSLFEHIGQHQFGEDLILEDVQVSCYRILTSLYALGTSKSIYVER 3150
3151 QRSALGECLAAFAGAFPVAFLETHLDKHNIYSIYNTKSSRERAALSLPTN 3200
3201 VEDVCPNIPSLEKLMEEIVELAESGIRYTQMPHVMEVILPMLCSYMSRWW 3250
3251 EHGPENNPERAEMCCTALNSEHMNTLLGNILKIIYNNLGIDEGAWMKRLA 3300
3301 VFSQPIINKVKPQLLKTHFLPLMEKLKKKAATVVSEEDHLKAEARGDMSE 3350
3351 AELLILDEFTTLARDLYAFYPLLIRFVDYNRAKWLKEPNPEAEELFRMVA 3400
3401 EVFIYWSKSHNFKREEQNFVVQNEINNMSFLITDTKSKMSKAAVSDQERK 3450
3451 KMKRKGDRYSMQTSLIVAALKRLLPIGLNICAPGDQELIALAKNRFSLKD 3500
3501 TEDEVRDIIRSNIHLQGKLEDPAIRWQMALYKDLPNRTDDTSDPEKTVER 3550
3551 VLDIANVLFHLEQKSKRVGRRHYCLVEHPQRSKKAVWHKLLSKQRKRAVV 3600
3601 ACFRMAPLYNLPRHRAVNLFLQGYEKSWIETEEHYFEDKLIEDLAKPGAE 3650
3651 PPEEDEGTKRVDPLHQLILLFSRTALTEKCKLEEDFLYMAYADIMAKSCH 3700
3701 DEEDDDGEEEVKSFEEKEMEKQKLLYQQARLHDRGAAEMVLQTISASKGE 3750
3751 TGPMVAATLKLGIAILNGGNSTVQQKMLDYLKEKKDVGFFQSLAGLMQSC 3800
3801 SVLDLNAFERQNKAEGLGMVTEEGSGEKVLQDDEFTCDLFRFLQLLCEGH 3850
3851 NSDFQNYLRTQTGNNTTVNIIISTVDYLLRVQESISDFYWYYSGKDVIDE 3900
3901 QGQRNFSKAIQVAKQVFNTLTEYIQGPCTGNQQSLAHSRLWDAVVGFLHV 3950
3951 FAHMQMKLSQDSSQIELLKELMDLQKDMVVMLLSMLEGNVVNGTIGKQMV 4000
4001 DMLVESSNNVEMILKFFDMFLKLKDLTSSDTFKEYDPDGKGVISKRDFHK 4050
4051 AMESHKHYTQSETEFLLSCAETDENETLDYEEFVKRFHEPAKDIGFNVAV 4100
4101 LLTNLSEHMPNDTRLQTFLELAESVLNYFQPFLGRIEIMGSAKRIERVYF 4150
4151 EISESSRTQWEKPQVKESKRQFIFDVVNEGGEKEKMELFVNFCEDTIFEM 4200
4201 QLAAQISESDLNERSANKEESEKERPEEQGPRMAFFSILTVRSALFALRY 4250
4251 NILTLMRMLSLKSLKKQMKKVKKMTVKDMVTAFFSSYWSIFMTLLHFVAS 4300
4301 VFRGFFRIICSLLLGGSLVEGAKKIKVAELLANMPDPTQDEVRGDGEEGE 4350
4351 RKPLEAALPSEDLTDLKELTEESDLLSDIFGLDLKREGGQYKLIPHNPNA 4400
4401 GLSDLMSNPVPMPEVQEKFQEQKAKEEEKEEKEETKSEPEKAEGEDGEKE 4450
4451 EKAKEDKGKQKLRQLHTHRYGEPEVPESAFWKKIIAYQQKLLNYFARNFY 4500
4501 NMRMLALFVAFAINFILLFYKVSTSSVVEGKELPTRSSSENAKVTSLDSS 4550
4551 SHRIIAVHYVLEESSGYMEPTLRILAILHTVISFFCIIGYYCLKVPLVIF 4600
4601 KREKEVARKLEFDGLYITEQPSEDDIKGQWDRLVINTQSFPNNYWDKFVK 4650
4651 RKVMDKYGEFYGRDRISELLGMDKAALDFSDAREKKKPKKDSSLSAVLNS 4700
4701 IDVKYQMWKLGVVFTDNSFLYLAWYMTMSVLGHYNNFFFAAHLLDIAMGF 4750
4751 KTLRTILSSVTHNGKQLVLTVGLLAVVVYLYTVVAFNFFRKFYNKSEDGD 4800
4801 TPDMKCDDMLTCYMFHMYVGVRAGGGIGDEIEDPAGDEYEIYRIIFDITF 4850
4851 FFFVIVILLAIIQGLIIDAFGELRDQQEQVKEDMETKCFICGIGNDYFDT 4900
4901 VPHGFETHTLQEHNLANYLFFLMYLINKDETEHTGQESYVWKMYQERCWE 4950
4951 FFPAGDCFRKQYEDQLN 4967
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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