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

NucPred

Fetching Q96RL7 from www.uniprot.org...

The NucPred score for your sequence is 0.54 (see score help below)

   1  MVFESVVVDVLNRFLGDYVVDLDTSQLSLGIWKGAVALKNLQIKENALSQ    50
  51  LDVPFKVKVGHIGNLKLIIPWKNLYTQPVEAVLEEIYLLIVPSSRIKYDP   100
 101  LKEEKQLMEAKQQELKRIEEAKQKVVDQEQHLPEKQDTFAEKLVTQIIKN   150
 151  LQVKISSIHIRYEDDITNRDKPLSFGISLQNLSMQTTDQYWVPCLHDETE   200
 201  KLVRKLIRLDNLFAYWNVKSQMFYLSDYDNSLDDLKNGIVNENIVPEGYD   250
 251  FVFRPISANAKLVMNRRSDFDFSAPKINLEIELHNIAIEFNKPQYFSIME   300
 301  LLESVDMMAQNLPYRKFKPDVPLHHHAREWWAYAIHGVLEVNVCPRLWMW   350
 351  SWKHIRKHRQKVKQYKELYKKKLTSKKPPGELLVSLEELEKTLDVFNITI   400
 401  ARQTAEVEVKKAGYKIYKEGVKDPEDNKGWFSWLWSWSEQNTNEQQPDVQ   450
 451  PETLEEMLTPEEKALLYEAIGYSETAVDPTLLKTFEALKFFVHLKSMSIV   500
 501  LRENHQKPELVDIVIEEFSTLIVQRPGAQAIKFETKIDSFHITGLPDNSE   550
 551  KPRLLSSLDDAMSLFQITFEINPLDETVSQRCIIEAEPLEIIYDARTVNS   600
 601  IVEFFRPPKEVHLAQLTAATLTKLEEFRSKTATGLLYIIETQKVLDLKIN   650
 651  LKASYIIVPQDGIFSPTSNLLLLDLGHLKVTSKSRSELPDVKQGEANLKE   700
 701  IMDRAYDSFDIQLTSVQLLYSRVGDNWREARKLSVSTQHILVPMHFNLEL   750
 751  SKAMVFMDVRMPKFKIYGKLPLISLRISDKKLQGIMELIESIPKPEPVTE   800
 801  VSAPVKSFQIQTSTSLGTSQISQKIIPLLELPSVSEDDSEEEFFDAPCSP   850
 851  LEEPLQFPTGVKSIRTRKLQKQDCSVNMTTFKIRFEVPKVLIEFYHLVGD   900
 901  CELSVVEILVLGLGAEIEIRTYDLKANAFLKEFCLKCPEYLDENKKPVYL   950
 951  VTTLDNTMEDLLTLEYVKAEKNVPDLKSTYNNVLQLIKVNFSSLDIHLHT  1000
1001  EALLNTINYLHNILPQSEEKSAPVSTTETEDKGDVIKKLALKLSTNEDII  1050
1051  TLQILAELSCLQIFIQDQKCNISEIKIEGLDSEMIMRPSETEINAKLRNI  1100
1101  IVLDSDITAIYKKAVYITGKEVFSFKMVSYMDATAGSAYTDMNVVDIQVN  1150
1151  LIVGCIEVVFVTKFLYSILAFIDNFQAAKQALAEATVQAAGMAATGVKEL  1200
1201  AQRSSRMALDINIKAPVVVIPQSPVSENVFVADFGLITMTNTFHMITESQ  1250
1251  SSPPPVIDLITIKLSEMRLYRSRFINDAYQEVLDLLLPLNLEVVVERNLC  1300
1301  WEWYQEVPCFNVNAQLKPMEFILSQEDITTIFKTLHGNIWYEKDGSASPA  1350
1351  VTKDQYSATSGVTTNASHHSGGATVVTAAVVEVHSRALLVKTTLNISFKT  1400
1401  DDLTMVLYSPGPKQASFTDVRDPSLKLAEFKLENIISTLKMYTDGSTFSS  1450
1451  FSLKNCILDDKRPHVKKATPRMIGLTVGFDKKDMMDIKYRKVRDGCVTDA  1500
1501  VFQEMYICASVEFLQTVANVFLEAYTTGTAVETSVQTWTAKEEVPTQESV  1550
1551  KWEINVIIKNPEIVFVADMTKNDAPALVITTQCEICYKGNLENSTMTAAI  1600
1601  KDLQVRACPFLPVKRKGKITTVLQPCDLFYQTTQKGTDPQVIDMSVKSLT  1650
1651  LKVSPVIINTMITITSALYTTKETIPEETASSTAHLWEKKDTKTLKMWFL  1700
1701  EESNETEKIAPTTELVPKGEMIKMNIDSIFIVLEAGIGHRTVPMLLAKSR  1750
1751  FSGEGKNWSSLINLHCQLELEVHYYNEMFGVWEPLLEPLEIDQTEDFRPW  1800
1801  NLGIKMKKKAKMAIVESDPEEENYKVPEYKTVISFHSKDQLNITLSKCGL  1850
1851  VMLNNLVKAFTEAATGSSADFVKDLAPFMILNSLGLTISVSPSDSFSVLN  1900
1901  IPMAKSYVLKNGESLSMDYIRTKDNDHFNAMTSLSSKLFFILLTPVNHST  1950
1951  ADKIPLTKVGRRLYTVRHRESGVERSIVCQIDTVEGSKKVTIRSPVQIRN  2000
2001  HFSVPLSVYEGDTLLGTASPENEFNIPLGSYRSFIFLKPEDENYQMCEGI  2050
2051  DFEEIIKNDGALLKKKCRSKNPSKESFLINIVPEKDNLTSLSVYSEDGWD  2100
2101  LPYIMHLWPPILLRNLLPYKIAYYIEGIENSVFTLSEGHSAQICTAQLGK  2150
2151  ARLHLKLLDYLNHDWKSEYHIKPNQQDISFVSFTCVTEMEKTDLDIAVHM  2200
2201  TYNTGQTVVAFHSPYWMVNKTGRMLQYKADGIHRKHPPNYKKPVLFSFQP  2250
2251  NHFFNNNKVQLMVTDSELSNQFSIDTVGSHGAVKCKGLKMDYQVGVTIDL  2300
2301  SSFNITRIVTFTPFYMIKNKSKYHISVAEEGNDKWLSLDLEQCIPFWPEY  2350
2351  ASSKLLIQVERSEDPPKRIYFNKQENCILLRLDNELGGIIAEVNLAEHST  2400
2401  VITFLDYHDGAATFLLINHTKNELVQYNQSSLSEIEDSLPPGKAVFYTWA  2450
2451  DPVGSRRLKWRCRKSHGEVTQKDDMMMPIDLGEKTIYLVSFFEGLQRIIL  2500
2501  FTEDPRVFKVTYESEKAELAEQEIAVALQDVGISLVNNYTKQEVAYIGIT  2550
2551  SSDVVWETKPKKKARWKPMSVKHTEKLEREFKEYTESSPSEDKVIQLDTN  2600
2601  VPVRLTPTGHNMKILQPHVIALRRNYLPALKVEYNTSAHQSSFRIQIYRI  2650
2651  QIQNQIHGAVFPFVFYPVKPPKSVTMDSAPKPFTDVSIVMRSAGHSQISR  2700
2701  IKYFKVLIQEMDLRLDLGFIYALTDLMTEAEVTENTEVELFHKDIEAFKE  2750
2751  EYKTASLVDQSQVSLYEYFHISPIKLHLSVSLSSGREEAKDSKQNGGLIP  2800
2801  VHSLNLLLKSIGATLTDVQDVVFKLAFFELNYQFHTTSDLQSEVIRHYSK  2850
2851  QAIKQMYVLILGLDVLGNPFGLIREFSEGVEAFFYEPYQGAIQGPEEFVE  2900
2901  GMALGLKALVGGAVGGLAGAASKITGAMAKGVAAMTMDEDYQQKRREAMN  2950
2951  KQPAGFREGITRGGKGLVSGFVSGITGIVTKPIKGAQKGGAAGFFKGVGK  3000
3001  GLVGAVARPTGGIIDMASSTFQGIKRATETSEVESLRPPRFFNEDGVIRP  3050
3051  YRLRDGTGNQMLQVMENGRFAKYKYFTHVMINKTDMLMITRRGVLFVTKG  3100
3101  TFGQLTCEWQYSFDEFTKEPFIVHGRRLRIEAKERVKSVFHAREFGKIIN  3150
3151  FKTPEDARWILTKLQEAREPSPSL                            3174

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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