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

NucPred

Fetching Q9P4Z1 from www.uniprot.org...

The NucPred score for your sequence is 0.97 (see score help below)

   1  MGKITKTMQQKHRDTLSPWLKEFVDTASSAPLPLILQRLDEFPRRWMFPR    50
  51  GDLYHWIPLLNRFDNILESICATYELSKGPQTRDFGRDVLLNNGGPSLEY   100
 101  RDEPWTVERLAEAGYKEDGDCQLLVAILKFTRMLLEHCGNRSIYASSHHI   150
 151  DRLLNSPYLEVQAAALEVGLELAQRYNASVKRMPSPPRSVNPTLLANHYN   200
 201  IDLEKVQLLARPFVRTPIVKSLEASSAAPAVSAGSTAKAKDKEKEKEKAT   250
 251  GPKNVASMYANDLKALASSRPDEEDLWKSWGDLKMSYYPDTTNGEPSARD   300
 301  SKDAQPVEPRTTSSPAAPTPLRRSSTMNVSQSSRTQRVGSSEEGSPLKPS   350
 351  GAATDDRDGPKVVEIPRSVIESKRIYDLYDMCPSDMPATMKFEFTSRLRT   400
 401  CKALLGTHRERQLALAVRLLAITNLAYILPEAVFVDKVLKYDKDEPRTFQ   450
 451  LVYQLAELIAPSPDGNPSEVPKWLQSITLALLEAISHQQEKHSDVLAALN   500
 501  VNVNHGILLYVIRIAVAEMKEDAPVDDQDELDADNWRGNLFGLTLQIAMA   550
 551  TRIGQEMMTAGLMDCLVEILNLRTAVASRHYSMVLAFLDSLTYAYQTAFS   600
 601  QLNSAGGLDAITNLIVHTVGESKTLTEAGLGTKPELHASLVDYSIPFYHQ   650
 651  QTLKWLLKFIHHVMANTYSFDGTNTERLLRNLVDNSSLLGSLCTIARQNR   700
 701  FFGTVVWSNATTLLSDFINNDPTSFAAISESGWIQAFLESVTNRPVSIPA   750
 751  EQPSLEPSQSRANDGSEGNDADDDSEDDGSAHDAAPDSDGQAATQPHPPT   800
 801  QEMLEAPRDFPPAHGILPSSESMNIIPTVLNSISLNNRGMKMVLSSGAIQ   850
 851  SYMEIFESATHVQCMAHDPELASTIGSSLDELSRHHPALRPAIANAIIDM   900
 901  IARVTHLVKTMDATKACGARLEAPESSASPVAEPAQATEVKGKGKEKATD   950
 951  DTDVEMAEASSSSSGNNKPAQAPSIPYIQVLSTFLQPIISNSHLKGALIS  1000
1001  AGVIEILLDLAESPSLPHDFGETPACRMLVAAISQLIESAQIVGLPSLLF  1050
1051  RMENTVKVLQPRINPTTTEPFFAPFLTLNSSVSPVQDEEPASEKRTPDVS  1100
1101  SGTETVKALLNIQTMIKILYHCFPFSNRSQMVSMPAVNVYDYYIRLIQSL  1150
1151  GPLLRGVLKEEAAVNGAVPHHWTLKNKPYQTNSLGSRTDVQDLLTDSAAQ  1200
1201  DSSANGKKLTPEEQSTAEYKNFQTLRVLLHSFMPSIFPFFQTIGKALLPR  1250
1251  RNNNDPYIRSRHLAIAEASAETLIQQLQQSKAELTVRDIHNWIIMIHTFG  1300
1301  EMVVDTNHRTASGGAFLILPVITAFKELGGFEALNVMLKRLADMVSTGAT  1350
1351  EGQEATKAKLSMIGMKKVLDIYCFIISGKNLSDSMAQIALAPKPTERTTR  1400
1401  EFSHQLVVELRMSILPVVREIWASNIIEKSTGTVVSKIIDIIKTIAAGDL  1450
1451  ESNAQSRSDKESLPHLFKNRESVPFKWISRKDAETLATEQGVDVDLALEA  1500
1501  LYRANGKESDTKEYIKYQKAHLVRNRNPVPAEDAFKEVPSPNLSSSAGMS  1550
1551  LSNLLNTPTFPVSDLLGAEPMALDPVPNQPLGEASGETALGHATESSEDG  1600
1601  SDEGQPGTSRETNVGASTTAPQQLPVLPSQQPATESQSNTPRITREDLVE  1650
1651  ERAKLYDTLIDRSLEVISSHPEAVFEVADLIQNTILKTDNEDRRVEVGEI  1700
1701  LANALMSFASDDADELKENGSSIAAYAHLLALLLQQTAFMRTTVDMLRDY  1750
1751  VGDYLGFLKLPPASSNDSLPPWIPYILLIFEIMLFKDAQPPDIKWKQPVK  1800
1801  EDDPIEESVIEVKEPNFLAEHRSSLLTTLLDFLPRIGKEESLAVSVLRIL  1850
1851  VVLTRDHAVAKIVGEKKNLQRLFVMAKQLCGAGSTRLKQTHISEHIRQIL  1900
1901  RHIIEDEETLRQIFETDIRHLMTSRQRPSAAPGLEPQAYLRTQAHLALRS  1950
1951  PKTFVEVSTELLKLNRAVSHLGDGTLRSTPFLVLKERPADASVSPKESSV  2000
2001  EPAVQATEDLTISDVKPSTEVTDKDMHDAPKNPAQDLKRPILEHPDGVVH  2050
2051  FLLTELLNYKDVDERENVPAPPAAASKPESDATAANEATPSPSGDEQNSE  2100
2101  SKEKEKKLAKSPFKAEDHPIFIYRCFLLDCLAELLQSYNRAKVEFINFKR  2150
2151  SAPVQANAPVKPRSSVLNYILNDLLCFSPASGVPESIAMKKKAATAVPAR  2200
2201  AVLVALVSKTGEKPQNRTHEPFEYDDEPDLLFIRRFVLDTILRAYKDASV  2250
2251  SGEPADIRYGKMNALAELMAQMIGEGDKDSRPNNPRGTDPSIGRSQAQLK  2300
2301  RLMYEKGYLTSLTASIADIDLTFPHNRAPLKPILAVIKTLSKTAVSMSQL  2350
2351  GLIPASGTAGTDQAEDEFLSDGSSVSEDLTDDREETPDLYRNSTLGMLEP  2400
2401  GRDDEFSDEDEDDDEDMYDDEQYDDELDYGDDMSQDNEDNPSDEEDDLGE  2450
2451  MGEMGGMPGQPGVVEVLMGENDDEEDNDDMDDMDEDDEMDEDDEQELSDE  2500
2501  DEDEEVGSEDMDDLEDDIHIVDEEGNAIDDDGASWDDGTDEDEEDEEELD  2550
2551  YEAEAQNMQEAQLHNRRTFPEIMRAAMENAGDDLDAEPIRDFDGHYIDDD  2600
2601  EDGEEDDDEDEGEDDMDDDMYFDGDGLHDDLLAPNMPSGLGWDIAIEPNH  2650
2651  RHRPSRSPWPNSPFVVGRHRDAIDFQNFFRRPAHLSRHLPPPADVMSGTN  2700
2701  PLLLPQSRREVPRHAHSQLVRLGITPNMIGGLGMGMGVEPLAFISDLVQH  2750
2751  LPDVRLSAGGGPLALHFTADGPGGIIRELNAIPIPPPHSRESRPTEARRD  2800
2801  TYQEPHQAVQFSPESTHERWQQEVKMIFGFGYQDKAQKLAPLILSKLTPA  2850
2851  AIQAEKEEKARKAEADRKAEEERKKRQEEERKKREAKEAEEKAAREKKEA  2900
2901  EERERLERERAEAAAQAAAQAAADQEANAVSQEAHPMEGVETQGPGENAE  2950
2951  QQAEDERPRVYYTLRNQQIDITELGIDAEYLEALPEEFRDEVIAQAISTR  3000
3001  RSQAREQVSQEGENTEVFQEFLEALPEELRNEILHQEQHEQRRRERQNAA  3050
3051  GGQDLGPADMDPASILLTFPPGLRQQVLLDQGEDIMEHLGPELAAEARTL  3100
3101  VARHRQLHAQQGGQAASRSRDAQRPTEAGAGTVQKIQKRTVVQMLDKQGI  3150
3151  ATLLRLMFVSQQGSIRSSLFSIFANLCENRQNRLDVISSLLQILQDGCAN  3200
3201  MDAVERSFAQISHKAKQLKEKDAKTPHPLKRSLTGGTNNNGQFPASSEVS  3250
3251  PLLIVQQCLDLLVELSKLNPHIPSVFLTEHETVASTLKRSLSRKGKGKDV  3300
3301  NGKAQKFAINSLLTLLDRSLVMESSAVMQVLADLLNKVTIPLQAIERRRK  3350
3351  EAEEQAKKKKEAEEKAATEREAANAPEEQASTSTEQTPAQQEATQQPSES  3400
3401  TPAAASGQQPAQQDQENKELEAPKEKADEKDVQSDEKKIRQLTPPTIPEH  3450
3451  NLKLVINIFVARECSSKTFQNTISTIKNLSNIPGAKKVFGDELVRQARVL  3500
3501  SENILSDLDNLLPHILKAESGTQIQGVALAKFSPGASEQNKLLRVLTALD  3550
3551  HLFDSKSKKQDKPAEGENTKEDLLGSLYWNPTFGKMWDKLSACLSAIRQR  3600
3601  DNMLNVATILLPLIESLMVVCKNTTLSDASAVSNANSQKEMLLTSPPPED  3650
3651  RIAGLFFTFTEEHRRILNELVRHNPKLMSGTFSLLVKNPKVLEFDNKRNY  3700
3701  FNRSVHSKYQQTRHSFPPLQLQVRREHVFHDSFRSLYYKKADELKFGKLN  3750
3751  IRFQGEEGVDAGGVTREWFQVLSRQMFDPNYVLFVPVSSDRTTFHPNKLS  3800
3801  PINDEHLPFFKFIGRIIGKALYEGRLLECYFSRAVYKRILGKPVSVKDME  3850
3851  SFDPDYYKSLVWMLENDITDIITETFSVEDDVFGEVKVVDLIENGRNIPV  3900
3901  TEENKHEYVRLIVEHKLITSVKDQMKAFLTGFHEIIPEELIAIFNEQELE  3950
3951  LLISGLPDIDIDDWKANTEYHNYSAGAPQIQWFWRAVRSFDKEELAKLLQ  4000
4001  FVTGTSKVPLNGFKELEGMNGVSRFNIHRDYGSKDRLPSSHTCFNQLDLP  4050
4051  EYENYETLRSQLLKAITAGSDYFGFA                          4076

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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