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

NucPred

Fetching Q9BGZ0 from www.uniprot.org...

The NucPred score for your sequence is 0.56 (see score help below)

   1  MVFESVVVDVLNRFLGDYVVDLDTSQLSLGIWKGAVALKNLQIKENALSQ    50
  51  LDVPFKVKVGHIGNLKLIIPWKNLYSQPVEAVLEEIYLLIVPSSRIKYDP   100
 101  IKEEKQLMEAKQQELKRIEEAKQKVVDQEQHLLEKQDTFAEKLVTQIIKN   150
 151  LQVKISSIHIRYEDDITNRDKPLSFGISLQNLSMQTTDQYWVPCLHDETE   200
 201  KLVRKLIRLDNLFAYWNVKSQMFYLNDYDDSLDDLRNGIVNENIVPEGYD   250
 251  FVFRPISANAKLVMNRRSDFDFSAPKINLDVELHNIAIEFNKPQYFSIME   300
 301  LLESVDMMTQNMPYRKFRPDVPLHHHAREWWAYAIHGVLEVNVCPRLRMW   350
 351  SWKHIRKHRGKMKQYKELYKKKLTSKKPPGELLVSLEELEKTLDVLNITI   400
 401  ARQQAEVEVKKAGYKIYKEGVKDPEDNKGWFSWLWSWSEQNTNEQQPDVK   450
 451  PGILEEMLTPEEKALLYEAIGYSETAVDPTLPKTFEALKFFVHLKSMSVV   500
 501  LRENHQKPELIDIVIEEFSTLIVQRPGAQAVKFETKIDSFHITGLPDNSE   550
 551  KPRLLSSLDDAMSLFQITFEINPLDETVTQRCIIEAEPLEIIYDARTVNS   600
 601  IVEFFRPPKEVHLAQLTSATLTKLEEFRNKTATGLLYIIETQKVLDLRIN   650
 651  LKASYIIVPQDGIFSPTSNLLLLDLGHLKVTSKSRSELPDVKQGEANLKE   700
 701  IMDIAYDSFDIQLTSIQLLYSRVGDNWREARKLNVSTQHILVPMHFNLEL   750
 751  SKAMVFMDVRMPKFKIFGKLPLISLRISDKKLQGIMELVESIPKPEPVTE   800
 801  VSAPVKSFQIQTSTSLGTSQISQKIIPLLELPSVSEDDSEEEFFDAPCSP   850
 851  LDEPLQFPTGVKSIRTRKLQKQDCSVNMTTFKIRFEVPKVLIEFYHLVGD   900
 901  CELSVVEIHVLGLGTEIEIRTYDLKANAFLKEFCLKCPEYLDENRKPVYL   950
 951  VTTLDNTMEDLLTLEYVKAEKNVPNLKSTYNNVLQLIKVNFSSLDIHLHT  1000
1001  EALLNTINYLHNILPQSEEKSAPVSTTETEDKGDVIKKLALKLSTNEDII  1050
1051  TLQILAELSCLQIFIQDQKRNISEIKIEGLDSEMIMRPSETEINAKLRNI  1100
1101  IVLDSDITAIYKKAVYITGKEVFSFKMVSYMDATAGSAYTDMNVVDIQVN  1150
1151  LVVGCIEVVFVTKFLCSILAFIDNFQAAKQALAEATVQAAGMAATGVKEL  1200
1201  ARRSSRMALDINIKAPVVVIPQSPVSENVFVADFGLITMTNTFHMITESQ  1250
1251  SSPPPVIDLITIKLSEMRLYRSQFINDAYQEVLDLLLPLNLEVVVERNLC  1300
1301  WEWYQEVPCFNVNAQLKPMEFILSQEDITTIFKTLHGNIWYEKDGSASPA  1350
1351  VTKDQYSATSGVTTNASHHSGGATVVTAAVVEVHSRASLVKTTLNVSFKT  1400
1401  DYLTMVLYSPGPKQASFTDVRDPSLKLAEFKLENIISTLKMYTDDSTFSS  1450
1451  FSLKNCILDDKRPHVKKATPRMIGLTVGFDKKDMMDIKYRKVRDGCVTDA  1500
1501  VFQEMYICASVEFLQTVANVFLEAYTTGTAVETSVQTWTAKEEVPTQELE  1550
1551  KWEINVIIKNPEIVFVADMTKNDAPALVITTQCEICYKGNLENSTMTAAI  1600
1601  KDLQVRACPFLPIKRKGKVTTVLQPCDLFYQTTQAGTDPQVIDMSVKSLT  1650
1651  LKVSPVIINTMITITSALYTTKETIPEETASSTAQLWEKKDTKTLKMWFL  1700
1701  EESNETEKIAPTTELIPKGEMIKMNIDSIFIVLEAGIGHRTVPMLLAKSR  1750
1751  FSGEGKNWSSLINLHCQLELEVHYYNEMFGVWEPLLEPLEIDQTEDFRPW  1800
1801  NLGIKMKKKAKKAIVESDPEEENYKVPEYKTVISFHSKDQLNITLSKCGL  1850
1851  VMLNNLAKAFTEAATGSSADFVKDLAPFIILNSLGLTISVSPSDSFSVLN  1900
1901  IPMAKSYVLKNEESLSMDYVRTKDNDHFNAMTSLSSKLFFILLTPVNHST  1950
1951  ADKIPLTKVGRRLYTVRHRESGVERSIVCQIDTVEGSKKVTIRSPVQIRN  2000
2001  HFSVPLSVYEGDTLLGTASPENEFNIPLGSYRSFLFLKPEDEDYQRCEGI  2050
2051  DFEEIVKNDGALLKKKCRSQNPSKKSFLINIVPEKDNLTSLSVYSEDGWD  2100
2101  LPYIMHLWPPILLRNLLPYKIAYYIEGIENSVFTLSEGHSAQICTVQLDK  2150
2151  ARLRLKLLDYLNHDWKSEYHIKPNQQDISFVNFTCITEMEKTDLDIAVHM  2200
2201  TYNTGQTVVAFHSPYWMVNKTGRMLQYKADGIHRKHPPNYKKPVLFSFQP  2250
2251  NHFFNNNKVQLMVTDSELSDQFSIDTVGSHGAVKCKGLKMDYQVGVTIDL  2300
2301  SSFNITRIVTFTPFYMIKNKSKYRISVAEEGTDKWLSLDLEQCIPFWPED  2350
2351  ASSKLLIQVEGSEDPPKRIYFNKQENCILLRLDNELGGIIAEVNLAEHST  2400
2401  VITFLDYHDGAATFLLINHTKNELVQYNQSSLSEIEDSLPPGKAVFYTWA  2450
2451  DPVGSRRLKWRCRKSHGEVTQKDDMMMPIDLGKKTIYLVSFFEGLQRIIL  2500
2501  FTEDPKVFKVTYESEKAELAEQEIAVALQDVGISLVNNYTKQEVAYIGIT  2550
2551  SSDVVWETKPKKKARWKPMSVKHTEKLEREFKEYTESSPSEDKVIELDTN  2600
2601  IPVRLTPTGHNMKILQPRVIALRRNYLPALKVEYNTSAHQSSFRIQIYRI  2650
2651  QIQNQIHGAVFPFVFYPVKPPKSVTMDSAPKPFTDVSIVMRSAGHSQISR  2700
2701  IKYFKVLIQEMDLRLDLGFIYALTDLMTEAEVTENTEVELFHKDIEAFKE  2750
2751  EYKTASLVDQSQVSLYEYFHISPLKLHLSVSLSSGGEEAKDSKQNGGLIP  2800
2801  VHSLNLLLKSIGATLTDVQDVVFKLAFFELNYQFHTTSDLQSEVIRHYSK  2850
2851  QAIKQMYVLILGLDVLGNPFGLIREFSEGVEAFFYEPYQGAIQGPEEFVE  2900
2901  GMALGLKALVGGAVGGLAGAASKITGAMAKGVAAMTMDEDYQQKRREAMN  2950
2951  KQPAGFREGITRGGKGLVSGFVSGITGIVTKPIKGAQKEGAAGFFKGVGK  3000
3001  GLVGAVARPTGGIIDMASSTFQGIKRATETSEVESLRPPRFFNEDGVIRP  3050
3051  YRLRDGTGNQMLQKIQFCREWIMTHSSSSDDDDGDDDESDLNR         3093

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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