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

NucPred

Fetching P39057 from www.uniprot.org...

The NucPred score for your sequence is 0.58 (see score help below)

   1  MGDVVDARLDFISEYILKSYKLKPDKWTKCINVEENKILMLEFLEKADNP    50
  51  QLVFTVNPAGLITPSYEFPSALKNTKAIYFIKKGREPVGKDNIKTNLVYG   100
 101  DLSYTPLEQLSALVDEVLVPLLANPRNHEQWPVVVSQDVLRHVHNLKSSV   150
 151  YVVAGQVKGKTLLPLPVGSEKVETAAGSEEKDDSYDRSLVHAIESVIIDW   200
 201  THQIRDVLKRDSAQPLLEGLNPGPMVEINFWKAKCENLDCIFQQLRDPKV   250
 251  RKMKELLERTQSSYLPSFNNIERDVEAALTEAQDINIHLKPLVYQIESMD   300
 301  ELEFSDLTPRLAPILHTVCLIWSNSDYYNTAPRVIVLLQEICNLLIDLCR   350
 351  TFLDPSEIFKLEPEESLEKVRGALTVLKNWRELYDEHRAKLKDYFKDGKE   400
 401  VKEWEFASPLVFTRMDNFIRRIETIQSLFETNVEFSKLEKTEMGSMKGRM   450
 451  LSQQVEKIHEEFQECAKVFTERPYDGLDPTCQEFLEDYEEFEKKVFDLDR   500
 501  RLGSILCQGFDDCCGLEAAFKMLDCYGPLLDRPVIRNDFECKYPIVLMLY   550
 551  DQELDQSKEIYDEHMRVEEANGNAPLNKNMPDVAGQLKWSAQLRDRISKP   600
 601  MGSLKHMEHPTGVRRILESEDAKVIFQKYEEMLNLLNKYEQKVFENWTKG   650
 651  VDEVCKTNLDQSLITRDDASKLIMVNFDPKLVSVLREVKYLQIRGEETIP   700
 701  ESAASIYEKHETLRKYVANLDLTVAWYNKVRKTVLEVEFPLIEGQLADLD   750
 751  TRLRQAEADLNWTSDSVWEYIQETRDQVRDLEKRVQQTKDNVDRIKKIMA   800
 801  EWTKQPLFERKELKKESLLALDDRQDRLKKRYAEITTAGEKIHSLMKENL   850
 851  DLFKAEASSDIWKAYVDYVDDMVIDGFFNCIHCTLTYLLENTDPRHCAAP   900
 901  LFEARLELQVPDMIFNPSLDYGIADGFYDLVEMLISDTYKMASLVNRLAE   950
 951  HNGQEHYQADLEGMDDLSDVRNDLMDRVQTIMTKAQEYRNSFDNYAYLYV  1000
1001  DDRKEFMRQFLLYNHVLTTEEIEAHAEDGVPECPPTLDQFKEQVDTYEKI  1050
1051  YSEADEIEPEQVFDAWFRVDSKPFKAALLNIIKKWSFMFKQHLIDHVTNS  1100
1101  LSELQEFIKVGNSGLTKTVEDGDYNGLVDCMGHLMAVKERQAATDEMFEP  1150
1151  IKQTIELLKTYDQEMSEEVHTQLQELPEQWNNTKKIAITIKQQVAPLQAN  1200
1201  EVAIIRRKCTSFDVRQHEFRERFRKEAPFIFLFDGPYQCLDKCHSEIYEM  1250
1251  EEHMAKLQESAGLFEVNMPDYKQLKACRREVRLLKGLWDLIMVVRTSIED  1300
1301  WKTTPWLEINVEQMEMDCKKFAKDIRSLDKEMRAWDAYNGLDATVKNMLT  1350
1351  SLRAVSELQNPAIRERHWQQLMAATKVKFTMDKETTLSDLLALNLHNFED  1400
1401  EVRNIVDKAVKEMGMEKVLKELNTTWSSMDFDYEPHSRTGISLLKSNEEL  1450
1451  IETLEDNQVQLQNLMTSKHIAHFLEEVSGWQKKLSTTDSVITIWFEVQRT  1500
1501  WSHLESIFIGSEDIRNQLPEDSKRFDGIDTDFKELAAEMEKTPNVVEATN  1550
1551  KARLFDRLEAIQGSLVVCEKALAEYLETKRLAFPRFYFVSSADLLDILSQ  1600
1601  GNNPTQVQRHLSKLFDNMAKLKFKQDDEGNDTKLALGMYSKEGEYVDFDK  1650
1651  ECECTGQVEVWLNRVMDTMRSTVRSQFADAVVSYEEKPREQWLYDYPAQV  1700
1701  ALATTQVWWTTEVNISFARLEEGHENSMKDYNKKQILQLNTLIGLLIGKL  1750
1751  TKGDRQKIMTICTIDVHARDVVAMMVLKKVDSAQAFQWLSQLRHRWADDD  1800
1801  KHCYANICDAQFKYSYEYLGNTPRLVITPLTDRCYITLTQSLHLVMSGAP  1850
1851  AGPAGTGKTETTKDLGRALGIMVYVFNCSEQMDYKSCGNIYKGLAQTGAW  1900
1901  GCFDEFNRISVEVLSVVAVQVKCVQDAIRDKKERFNFMGEEISLIPSVGI  1950
1951  FITMNPGYAGRTELPENLKALFRPCAMVVPDFELICEIMLVAEGFLEARL  2000
2001  LARKFITLYTLCKELLSKQDHYDWGLRAIKSVLVVAGSLKRGDPQRPEDQ  2050
2051  VLMRALRDFNVPKIVSDDTPVFMGLIGDLFPALDVPRRRDLDFEKVVKQS  2100
2101  TLDLKLQAEDSFVLKVVQLEELLAVRHSVFVIGNAGTGKSQVLKVLNKTY  2150
2151  SNMKRKPVFIDLNPKAVTNDELFGIINPATREWKDGLFSVIMRDMSNITH  2200
2201  DGPKWIVLDGDIDPMWIESLNTVMDDNKVLTLASNERIPLTPSMRLLFEI  2250
2251  SHLKTATPATVSRAGILYINPSDLGWNPIVTSWIDTREVQSERANLTILF  2300
2301  DKYLPTLLDTLRIRFKKIIPIPEQSMVQMLCYLLECLLTPENTPADCPKE  2350
2351  LYELYFVFASIWAFGGSMFQDQLVDYRVEFSKWWITEFKTIKFPNQGTVF  2400
2401  DYYIDQESKKFLPWSEKVPTFELDPEIPMQAVLVHTNETTRVRFFMDLLM  2450
2451  ERGRPVMLVGNAGLGKSVLVGDKLSNLGEDSMVANVPFNYYTTSEMLQRV  2500
2501  LEKPLEKKAGRNYGPPGTKKLVYFIDDMNMPEVDTYGTVQPHTLIRQHMD  2550
2551  YKHWYDRAKLTLKEIHKCQYVSCMNPTSGSFTINSRLQRHFCVFALSFPG  2600
2601  QDALSTIYNSILSQHLANIAVSNALQKLSPTVVSATLDLHKKVAQSFLPT  2650
2651  AIKFHYVFNLRDLSNVFQGLLYSGSDLLKSPIDFARLWMHECQRVYGDKM  2700
2701  INDQDIEAFEKLVFEYAKKFFEDVDEEALKAKPNIHCHFATGIGDPKYMP  2750
2751  CATWPELNKILVEALDTYNEINAVMNLVLFEDAMQHVCRINRILESPRGN  2800
2801  ALLVGVGGSGKQSLARLASYISSLEVFQITLRKGYGIPDLKLDLATVCMK  2850
2851  AGLKNIGTVFLMTDAQVSDEKFLVLINDLLASGEIPDLFADDEVENIIGG  2900
2901  VRNEVKGMGLQDTRENCWKFFIDRLRRQLKTVLCFSPVGTTLRVRSRKFP  2950
2951  AVVNCTSIDWFHEWPQEALVSVSKRFLDEVELLKGDIKNSIAEFMAYVHV  3000
3001  SVNESSKQYLTNERRYNYTTPKSFLEQIKLYESLLAMKSKELTAKMERLE  3050
3051  NGLTKLQSTAQQVDDLKAKLASQEVELAQKNEDADKLIQVVGVETEKVSK  3100
3101  EKATVDDEEKKVAIINEEVSKKAKDCSEDLAKAEPALLAAQEALNTLNKN  3150
3151  NLTELKSFGSPPSAVLKVAAAVMVLLAPNGKIPKDRSWKAAKVVMNKVDA  3200
3201  FLDSLINYDEENIHENCQKAIKEYLNDPEFEPEYIKGKSLAAGGLCSWVV  3250
3251  NIVKFYNVYCDVEPKRIALQKANDELKAAQDKLALIKAKIAELDANLAEL  3300
3301  TAQFEKATSDKLKCQQEAEATSRTITLANRLVGGLASENVRWGEAVANFK  3350
3351  IQEKTLPGDVLLITAFVSYIGCFTKNYRVDLQDRMWLPFLKSQKDPIPIT  3400
3401  EGLDVLSMLTDDADIAVWNNEGLPSDRMSTENATILSNCQRWPLMIDPQL  3450
3451  QGIKWIKQKYGDELRVIRIGQRGYLDTIENAISSGDTVLIENMEESIDPV  3500
3501  LDPVLGRNTIKKGRYIKIGDKEVEYNPEFRLILQTKLANPHYKPEMQAQT  3550
3551  TLINFTVTRDGLEDQLLANVVAQERPDLEKLKSDLTKQQNDFKIILKELE  3600
3601  DNLLSRLSSAEGNFLGDTALVENLETTKRTAAEISVKVEEAKVTEVKINE  3650
3651  ARELYRPAAARASLLYFILNDLNKINPIYQFSLKAFNTVFSLSIARAEPC  3700
3701  EDVKERVVNLIDCITYSVFIYTTRGLFEADKLIFTTQVAFQVLLMKKEIA  3750
3751  QNELDFLLRFPIQVGLTSPVDFLTNSAWGAIKSLSAMEDFRNLDRDIEGS  3800
3801  AKRWKKFVESECPEKEKFPQEWKNKSALQKLCMMRALRADRMSYAVRNFI  3850
3851  EEKLGSKYVEGRQVEFAKSYEETDPATPVFFILSPGVDPLKDVEALGKKL  3900
3901  GFTFDNNNFHNVSLGQGQEIVAEQCMDLAAKEGHWVILQNIHLVAKWLST  3950
3951  LEKKLEQYSIGSHESYRVYMSAEPAGSPESHIIPQGILESSIKITNEPPT  4000
4001  GMFANLHKALYNFNQDTLEMCAREAEFKVILFALCYFHAVVCERQKFGPQ  4050
4051  GWNRSYPFNTGDLTISVNVLYNYLEANSKVPWQDLRYLFGEIMYGGHITD  4100
4101  DWDRRLCRTYLEEYMAPEMLDGDLYLAPGFPVPPNSDYKGYHQYIDEILP  4150
4151  PESPYLYGLHPNAEIGFLTTESDNLFKVVLELQPRDAGGGGGGGSSREEK  4200
4201  IKSLLDEIVEKLPEEFNMMEIMGKVEDRTPYVVVAFQECERMNTLTSEIR  4250
4251  RSLKELDLGLKGELTITPDMEDLSNALFLDQIPASWVKRAYPSLFGLSAW  4300
4301  YADLLQRIKELEQWTADFALPNVVWLGGFFNPQSFLTAIMQSMARKNEWP  4350
4351  LDKMCLQCDVTKKNKEDFSSAPREGSYVHGLFMEGARWDTQTNMIADARL  4400
4401  KELAPNMPVIFIKAIPVDKQDTRNIYECPVYKTKQRGPTFVWTFNLKSKE  4450
4451  KAAKWTLAGVALLLQV                                    4466

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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