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

NucPred

Fetching O43103 from www.uniprot.org...

The NucPred score for your sequence is 0.62 (see score help below)

   1  MSVSKNAHAPFPDVTGLRTSGPDFSRVGPQCKLLRVQQSLHLDARAASSF    50
  51  SGQDLHLALLSAWCVILFHYGSHEATNIEVLHTSQHGHETLVELDFAEQR   100
 101  ELVPFTPSQLAAFVHQARQQQLPAKDLDHGYAAFYQPSHTNEPSPHFQQL   150
 151  QLSSRPDISLALSYHLQGSAQLVLEMKAAPSVHSHQSAQLQLRQVAALLE   200
 201  SYHSDTQQHALSVERFDWKLRASDNPNYQHLPDPNHIQDRHADRLETEFE   250
 251  YFAATTPDALALDFRFDLQDLKSTKWSYAEMNQRAEKVKHLLWSHGVGSA   300
 301  SSDPQADHIVALYLEKSPETYLSFIGVLKAGAAWCPIDTDWPASRRQALL   350
 351  AKSNAKIVLTHDDKISEQLRHDLESQLVKDKGEITAIRLDQLDAELSQVQ   400
 401  VVPPANANRSIQQLAYMIWTSGTTGLPKGVGIQHLAIIQAMRALRIYIPY   450
 451  GKDKIGTDQIRYLQYSAYNFDLSIMDCFYTWGLGGTICSCPRGVLLQDLV   500
 501  EVGNSLQPTHTLLTPAVMAMTERHRVPSLKVVISGGEKLSQVVADEWSKD   550
 551  CCLLNLYGPAEATLIAMNRRVPFGDRVKAPNIGVALPTVSCHALDKYDQI   600
 601  VIKGAVGELVLGGPQLARGYVGDPVKTADKFFPHPQLGRVYRTGDLVRQL   650
 651  DNQEFEYLGRIDDQVKINGIRIELLEINAAIKNSHDKIKDSETMAFSKKD   700
 701  NESEQQIINFSALPGGEPGQLLRTDQDAIAVARELQANAKDSLPSYMVPN   750
 751  LFVILSHFPRTSSAKIDRVALKNVLASFDQLDWENKLANEGDDDQVDPAT   800
 801  AQAEACLRKWLAKLCNVDASKIGRKTPFTSVGLDSIRAMMFSKRVSEEGF   850
 851  AVSVLDVARFPTLKSLGEHLQSSGASSEERAKRAASFLADFDAAFRPVVS   900
 901  SWVKQRKADASAIQSILPCTPLQEGMLAESQRDSSSYRIQRQYRLASDCD   950
 951  QARLSKALIETVAHFDSLRTSFADVGSLDVGLHQREWPFQPHFLQIVWKS  1000
1001  FTPLIEQLDVDDGSDAEQAILSAAKTKLDLDPFGTSPPVAFLFVKQALSR  1050
1051  SLVVVAHHSTYDARSLGIFEDHVEAVYNRQKPPTSLQFSTALAQILPIDQ  1100
1101  TEAQRHADVWQKALSNYPRGEYASFPTLSLTRPSEADADQASLHQSRYLE  1150
1151  ANINWAKIESACRELGVSARPLVQTAWALVLSAFTESQHLILGDSVSGRT  1200
1201  LSAELDLAYGPVLSTVPVPFMLRPEQKLGNLIKQMDDFQTSIMEAQHTDL  1250
1251  GAIRRMLQVPPRESLFHSVFVLEPAPEQPEDIDSSQFRLSKMADLGVATE  1300
1301  HVLGVEVLPASDGSVKLGLSWQKNIISEGFGGLILEQFDRSLTALCSSLD  1350
1351  ADVGSLLYCHPRSDQQASSFYSVTKPVSKQKCSSATFTGVASSLNKQAIS  1400
1401  DNSNAVEIYQDMADSPSSRKPAATMSYPELEQASNGVANLFRHLPRNSVV  1450
1451  GVCLERGLESYIVPLAILKAGHAYLPLDATLPLDRKKELVKDSGAALIVA  1500
1501  SSKFTDFDSLTGVEMLGTDSRQFKDAVKDGKATVSVESRSDDVAFIIYTS  1550
1551  GSTGKPKGCLLTQANLAAAVEGFYYNYEKEAPGSFESRARFLARSAEAFD  1600
1601  VHLLEIFLSLRVGATIVTGPRALIHDDIAKTMSTLEVTHACVVPSLFFSK  1650
1651  GKRIEPSVVPSLRVLIIGGEALTQDLCQIWGSEGSERPVVLNAYGPSEAT  1700
1701  IGNSVARVSKKSRPSNIGAPFPGTQYLVLKDVNGQLVPTLRGEPGELYIG  1750
1751  GEQVAKGYLNRPDSSSFITYQGQQIYRTGDMVRLHPSDEAEYLGRIDGSQ  1800
1801  VKVRGARLELAEVDAALSASLNENLGTVGTAVTIHADHPKIEGAARLVSF  1850
1851  FAQDCVRTKAQDSVDPGALLVQAPEAVKQSAELRRSVRARLPQYMVPSLV  1900
1901  LALTYLPISPLSGKADRRLLKELYHSIDPSKLSTLSDKNESRQRELTDSE  1950
1951  QTVAELVRSSVRLSSDVHLMHDLDLIMAGLDSLTVVTLANKLRKHGYDAT  2000
2001  VSSIMNEPTIEAIAGRRIDKLSSNETSDVEWKQTVSQLTDKVRSLPQYRG  2050
2051  TQIETALPCVPIQVALVSQAVSDDRSTPRYITTISIDLSSNEFSADRIRN  2100
2101  AWMTALSRHEIYRTVFAEVDRTLVQVVLSAESLTSNWSATSEPIPSPDSL  2150
2151  ADYHASTAKDIVANISSVPALRLKLWQGENGAPTLTLTCSHAIYDGDSIR  2200
2201  MLLKEASDCLVTKSKVVPALPFQEAARCIVGDAEDEEAKQFWTTTLADFL  2250
2251  PTTVPNLTGVRPEHNVSRGEELTIASHLSFTQLEKAARAAKVTIQSILVA  2300
2301  AFAHLLGLYAGESDVTLGLVLSGRSIPVDGVESIHGPCVTTVPLRLTDAR  2350
2351  SNASSDLCKRAHQAVNAILPHQHVSLPQLMRWLDLSKAPFEALFSYLGQS  2400
2401  ERSSEKPYFSERASQMERDYSLALEVSAVGDAVNLHLAFDTRSMPAEQAK  2450
2451  RMLCQYDGFLTVFTGTKRVDDDGKHLSILNKSCYVPTSANETIVARFTEH  2500
2501  VKANPDAPAIVFASSMQEPPKVTSYAELDSLSTKIAFHLVHAAGPFVGVH  2550
2551  LNKEGPELYATILAIWKAGKAYLPLDPSLPVERLSYMIESVGDCPVVASH  2600
2601  STKENLASFRCKVLDLKELVKPRSGAHELPSQNLDALCYLLFTSGSTGKP  2650
2651  KAVQINQRALAGALYSWERILPFTRTSRFLQLASIGFDVCLIEMCMPLSL  2700
2701  GFSIGTAPKQELLEDLTHSIKHLGITIADLPAALAGAVHPEDVRLEWLMS  2750
2751  GGDVIDSRVVDEWNHAKRLLINAWGPTEATIGNTLGQVKRGATRNLIGGV  2800
2801  YPSSSMFVLDENSTRILPSGAIGELAVGGPQLADCYYGREELTAEKFILL  2850
2851  EDGTRVYRTGDLGRFLVDDTVECLGRIGSDRQVKVNGQRMELDEVCSVIS  2900
2901  AQAGVYDADVQYLKHPSMGSKQLVAFVAAAETQAKQGDMDVRDDDKAIDL  2950
2951  CIRLEQEAAKRLATYMVPTHWIVMKHGLPLTHNNKTDHKALAAFYGRMDA  3000
3001  TLLRSLGAKREGAISSHAWTQSELKLRALVSDFCNVPQDQLARNTSFHRL  3050
3051  GIDSISAIRLVKQLRTSGFTFSVADVLSTPNIAALADKQMQSSACSSDHA  3100
3101  QPNEGLNEWIGQISSVAENEAWKWSSKDSLVSVLPCTPLQSGMIAQSLAS  3150
3151  AGGLYFHHHAFELQSTEKQHVVAAWRKLVERLDILRTTFHPVDGLHPWTQ  3200
3201  AVHSEVQPRIVQHSGSFQSCGLDAIDGQPSFQDEQAFRTPPFALHLWSQE  3250
3251  GKLVVLISIHHALYDGSSLPQLLEDFEALITGNQAKLTSRLPFYKLVPSL  3300
3301  LSQDEDVQHWVNALHAFQPTLLCKRSNKPSGAAVLLEKRLALTSQELESR  3350
3351  CRAIGVSPQVLCNLTFGKLLAIESQTRDVCFGQLFGLLDLMPEADTCVGP  3400
3401  AFNTTATRIRFQELDAPVSKLATTLQQANDAGRPHRRAALRDVQAKLGRG  3450
3451  QLFDALFDYQRSYDQEDSKLRQIELQSDGTERAQYTLNVAFVQGPSQMSI  3500
3501  VAKADGNRYDQKALEGVVYRLEHLLEHLSIRVEEPISVLPDVFGETAFPL  3550
3551  HLAQVSVANGTSTSKASAQNSANQALSQDGSKLASIISQIAGIDEMELHG  3600
3601  ETRLSQLGLDSISAIRIASQARKAGLNLRMGEIVAGETINAILSARSQTN  3650
3651  ATKSSDHVNRNGRGNGHARVSLETAKRVAARLAIDFEQVERVLPVLPGQK  3700
3701  LWLATWAQSQGGGGFSFAYRLAGAEADKVKETWQKLRQLQPILRTAFLVH  3750
3751  HDGGASQVVLKADSVSAGSGFAEVQVERDAELTAKDVVGRRAAQGWPDLT  3800
3801  SPPVELTLVGEIVVFSLHHVLYDAFSIEFLARDFGSLYNAGELVSSNQWP  3850
3851  EVVEHIVEEQQRTRGDAQEYWCRALAPGSSGLLADRPGSTGEAWHVQHNA  3900
3901  ISLSSAVDVRIRKAGLTLAGVLLAAWSTLLSERMHDASPVFGLYQLGRSS  3950
3951  SFESIDKVHGPLLNCLPIQLRGGSLLDKARAAVSELRLRAKFEQTDLQDA  4000
4001  HRWAGLSQHQACYNTFVNILFGDQLDQHLEMHQLDLGHPLDYSHHSQHST  4050
4051  HDRTPPSTPHVALPWQPDVNLDVVLKNHAVDIAIKANTSVVAQADLHTLV  4100
4101  NRLVQLVHATLELL                                      4114

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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