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

NucPred

Fetching Q7Z7G8 from www.uniprot.org...

The NucPred score for your sequence is 0.82 (see score help below)

   1  MLESYVTPILMSYVNRYIKNLKPSDLQLSLWGGDVVLSKLELKLDVLEQE    50
  51  LKLPFTFLSGHIHELRIHVPWTKLGSEPVVITINTMECILKLKDGIQDDH   100
 101  ESCGSNSTNRSTAESTKSSIKPRRMQQAAPTDPDLPPGYVQSLIRRVVNN   150
 151  VNIVINNLILKYVEDDIVLSVNITSAECYTVGELWDRAFMDISATDLVLR   200
 201  KVINFSDCTVCLDKRNASGKIEFYQDPLLYKCSFRTRLHFTYENLNSKMP   250
 251  SVIKIHTLVESLKLSITDQQLPMFIRIMQLGIALYYGEIGNFKEGEIEDL   300
 301  TCHNKDMLGNITGSEDETRIDMQYPAQHKGQELYSQQDEEQPQGWVSWAW   350
 351  SFVPAIVSYDDGEEDFVGNDPASTMHQQKAQTLKDPIVSIGFYCTKATVT   400
 401  FKLTEMQVESSYYSPQKVKSKEVLCWEQEGTTVEALMMGEPFFDCQIGFV   450
 451  GCRAMCLKGIMGVKDFEENMNRSETEACFFICGDNLSTKGFTYLTNSLFD   500
 501  YRSPENNGTRAEFILDSTHHKETYTEIAGMQRFGAFYMDYLYTMENTSGK   550
 551  GSTNQQDFSSGKSEDLGTVQEKSTKSLVIGPLDFRLDSSAVHRILKMIVC   600
 601  ALEHEYEPYSRLKSDIKDENETILNPEEVALLEEYIPTRHTSVTLLKCTC   650
 651  TISMAEFNLLDHLLPVIMGEKNSSNFMNTTNFQSLRPLPSIRILVDKINL   700
 701  EHSVPMYAEQLVHVVSSLTQPSDNLLHYCYVHCYLKIFGFQAGLTSLDCS   750
 751  GSYCLPVPVIPSFSTALYGKLLKLPTCWTKRSQIAITEGIFELPNLTIQA   800
 801  TRAQTLLLQAIYQSWSHLGNVSSSAVIEALINEIFLSIGVKSKNPLPTLE   850
 851  GSIQNVELKYCSTSLVKCASGTMGSIKICAKAPVDSGKEKLIPLLQGPSD   900
 901  TKDLHSTKWLNESRKPESLLAPDLMAFTIQVPQYIDYCHNSGAVLLCSIQ   950
 951  GLAVNIDPILYTWLIYQPQKRTSRHMQQQPVVAVPLVMPVCRRKEDEVSI  1000
1001  GSAPLAKQQSYQASEYASSPVKTKTVTESRPLSVPVKAMLNISESCRSPE  1050
1051  ERMKEFIGIVWNAVKHLTLQLEVQSCCVFIPNDSLPSPSTIVSGDIPGTV  1100
1101  RSWYHGQTSMPGTLVLCLPQIKIISAGHKYMEPLQEIPFVIPRPILEEGD  1150
1151  AFPWTISLHNFSIYTLLGKQVTLCLVEPMGCTSTLAVTSQKLLATGPDTR  1200
1201  HSFVVCLHVDLESLEIKCSNPQVQLFYELTDIMNKVWNKIQKRGNLNLSP  1250
1251  TSPETMAGPVPTSPVRSSIGTAPPDTSTCSPSADIGTTTEGDSIQAGEES  1300
1301  PFSDSVTLEQTTSNIGGTSGRVSLWMQWVLPKITIKLFAPDPENKGTEVC  1350
1351  MVSELEDLSASIDVQDVYTKVKCKIESFNIDHYRSSLGEECWSLGQCGGV  1400
1401  FLSCTDKLNRRTLLVRPISKQDPFSNCSGFFPSTTTKLLDGTHQQHGFLS  1450
1451  LTYTKAVTKNVRHKLTSRNERRSFHKLSEGLMDGSPHFLHEILLSAQAFD  1500
1501  IVLYFPLLNAIASIFQAKLPKTQKEKRKSPGQPMRTHTLTSRNLPLIYVN  1550
1551  TSVIRIFIPKTEEMQPTVEANQAAKEDTVVLKIGSVAMAPQADNPLGRSV  1600
1601  LRKDIYQRALNLGILRDPGSEIEDRQYQIDLQSINIGTAQWHQLKPEKES  1650
1651  VSGGVVTETERNSQNPALEWNMASSIRRHQERRAILTPVLTDFSVRITGA  1700
1701  PAVIFTKVVSPENLHTEEILVCGHSLEVNITTNLDFFLSVAQVQLLHQLI  1750
1751  VANMTGLEPSNKAAEISKQEQKKVDIFDGGMAETSSRYSGAQDSGIGSDS  1800
1801  VKIRIVQIEQHSGASQHRIARPSRQSSIVKNLNFIPFDIFITASRISLMT  1850
1851  YSCMALSKSKSQEQKNNEKTDKSSLNLPEVDSDVAKPNQACISTVTAEDL  1900
1901  LRSSISFPSGKKIGVLSLESLHASTRSSARQALGITIVRQPGRRGTGDLQ  1950
1951  LEPFLYFIVSQPSLLLSCHHRKQRVEVSIFDAVLKGVASDYKCIDPGKTL  2000
2001  PEALDYCTVWLQTVPGEIDSKSGIPPSFITLQIKDFLNGPADVNLDISKP  2050
2051  LKANLSFTKLDQINLFLKKIKNAHSLAHSEETSAMSNTMVNKDDLPVSKY  2100
2101  YRGKLSKPKIHGDGVQKISAQENMWRAVSCFQKISVQTTQIVISMETVPH  2150
2151  TSKPCLLASLSNLNGSLSVKATQKVPGIILGSSFLLSINDFLLKTSLKER  2200
2201  SRILIGPCCATANLEAKWCKHSGNPGPEQSIPKISIDLRGGLLQVFWGQE  2250
2251  HLNCLVLLHELLNGYLNEEGNFEVQVSEPVPQMSSPVEKNQTFKSEQSSD  2300
2301  DLRTGLFQYVQDAESLKLPGVYEVLFYNETEDCPGMMLWRYPEPRVLTLV  2350
2351  RITPVPFNTTEDPDISTADLGDVLQVPCSLEYWDELQKVFVAFREFNLSE  2400
2401  SKVCELQLPDINLVNDQKKLVSSDLWRIVLNSSQNGADDQSSASESGSQS  2450
2451  TCDPLVTPTALAACTRVDSCFTPWFVPSLCVSFQFAHLEFHLCHHLDQLG  2500
2501  TAAPQYLQPFVSDRNMPSELEYMIVSFREPHMYLRQWNNGSVCQEIQFLA  2550
2551  QADCKLLECRNVTMQSVVKPFSIFGQMAVSSDVVEKLLDCTVIVDSVFVN  2600
2601  LGQHVVHSLNTAIQAWQQNKCPEVEELVFSHFVICNDTQETLRFGQVDTD  2650
2651  ENILLASLHSHQYSWRSHKSPQLLHICIEGWGNWRWSEPFSVDHAGTFIR  2700
2701  TIQYRGRTASLIIKVQQLNGVQKQIIICGRQIICSYLSQSIELKVVQHYI  2750
2751  GQDGQAVVREHFDCLTAKQKLPSYILENNELTELCVKAKGDEDWSRDVCL  2800
2801  ESKAPEYSIVIQVPSSNSSIIYVWCTVLTLEPNSQVQQRMIVFSPLFIMR  2850
2851  SHLPDPIIIHLEKRSLGLSETQIIPGKGQEKPLQNIEPDLVHHLTFQARE  2900
2901  EYDPSDCAVPISTSLIKQIATKVHPGGTVNQILDEFYGPEKSLQPIWPYN  2950
2951  KKDSDRNEQLSQWDSPMRVKLSIWKPYVRTLLIELLPWALLINESKWDLW  3000
3001  LFEGEKIVLQVPAGKIIIPPNFQEAFQIGIYWANTNTVHKSVAIKLVHNL  3050
3051  TSPKWKDGGNGEVVTLDEEAFVDTEIRLGAFPGHQKLCQFCISSMVQQGI  3100
3101  QIIQIEDKTTIINNTPYQIFYKPQLSVCNPHSGKEYFRVPDSATFSICPG  3150
3151  GEQPAMKSSSLPCWDLMPDISQSVLDASLLQKQIMLGFSPAPGADSSQCW  3200
3201  SLPAIVRPEFPRQSVAVPLGNFRENGFCTRAIVLTYQEHLGVTYLTLSED  3250
3251  PSPRVIIHNRCPVKMLIKENIKDIPKFEVYCKKIPSECSIHHELYHQISS  3300
3301  YPDCKTKDLLPSLLLRVEPLDEVTTEWSDAIDINSQGTQVVFLTGFGYVY  3350
3351  VDVVHQCGTVFITVAPEGKAGPILTNTNRAPEKIVTFKMFITQLSLAVFD  3400
3401  DLTHHKASAELLRLTLDNIFLCVAPGAGPLPGEEPVAALFELYCVEICCG  3450
3451  DLQLDNQLYNKSNFHFAVLVCQGEKAEPIQCSKMQSLLISNKELEEYKEK  3500
3501  CFIKLCITLNEGKSILCDINEFSFELKPARLYVEDTFVYYIKTLFDTYLP  3550
3551  NSRLAGHSTHLSGGKQVLPMQVTQHARALVNPVKLRKLVIQPVNLLVSIH  3600
3601  ASLKLYIASDHTPLSFSVFERGPIFTTARQLVHALAMHYAAGALFRAGWV  3650
3651  VGSLDILGSPASLVRSIGNGVADFFRLPYEGLTRGPGAFVSGVSRGTTSF  3700
3701  VKHISKGTLTSITNLATSLARNMDRLSLDEEHYNRQEEWRRQLPESLGEG  3750
3751  LRQGLSRLGISLLGAIAGIVDQPMQNFQKTSEAQASAGHKAKGVISGVGK  3800
3801  GIMGVFTKPIGGAAELVSQTGYGILHGAGLSQLPKQRHQPSDLHADQAPN  3850
3851  SHVKYVWKMLQSLGRPEVHMALDVVLVRGSGQEHEGCLLLTSEVLFVVSV  3900
3901  SEDTQQQAFPVTEIDCAQDSKQNNLLTVQLKQPRVACDVEVDGVRERLSE  3950
3951  QQYNRLVDYITKTSCHLAPSCSSMQIPCPVVAAEPPPSTVKTYHYLVDPH  4000
4001  FAQVFLSKFTMVKNKALRKGFP                              4022

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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