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

NucPred

Fetching Q14789 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MLSRLSGLANVVLHELSGDDDTDQNMRAPLDPELHQESDMEFNNTTQEDV    50
51 QERLAYAEQLVVELKDIIRQKDVQLQQKDEALQEERKAADNKIKKLKLHA 100
101 KAKLTSLNKYIEEMKAQGGTVLPTEPQSEEQLSKHDKSSTEEEMEIEKIK 150
151 HKLQEKEELISTLQAQLTQAQAEQPAQSSTEMEEFVMMKQQLQEKEEFIS 200
201 TLQAQLSQTQAEQAAQQVVREKDARFETQVRLHEDELLQLVTQADVETEM 250
251 QQKLRVLQRKLEEHEESLVGRAQVVDLLQQELTAAEQRNQILSQQLQQME 300
301 AEHNTLRNTVETEREESKILLEKMELEVAERKLSFHNLQEEMHHLLEQFE 350
351 QAGQAQAELESRYSALEQKHKAEMEEKTSHILSLQKTGQELQSACDALKD 400
401 QNSKLLQDKNEQAVQSAQTIQQLEDQLQQKSKEISQFLNRLPLQQHETAS 450
451 QTSFPDVYNEGTQAVTEENIASLQKRVVELENEKGALLLSSIELEELKAE 500
501 NEKLSSQITLLEAQNRTGEADREVSEISIVDIANKRSSSAEESGQDVLEN 550
551 TFSQKHKELSVLLLEMKEAQEEIAFLKLQLQGKRAEEADHEVLDQKEMKQ 600
601 MEGEGIAPIKMKVFLEDTGQDFPLMPNEESSLPAVEKEQASTEHQSRTSE 650
651 EISLNDAGVELKSTKQDGDKSLSAVPDIGQCHQDELERLKSQILELELNF 700
701 HKAQEIYEKNLDEKAKEISNLNQLIEEFKKNADNNSSAFTALSEERDQLL 750
751 SQVKELSMVTELRAQVKQLEMNLAEAERQRRLDYESQTAHDNLLTEQIHS 800
801 LSIEAKSKDVKIEVLQNELDDVQLQFSEQSTLIRSLQSQLQNKESEVLEG 850
851 AERVRHISSKVEELSQALSQKELEITKMDQLLLEKKRDVETLQQTIEEKD 900
901 QQVTEISFSMTEKMVQLNEEKFSLGVEIKTLKEQLNLLSRAEEAKKEQVE 950
951 EDNEVSSGLKQNYDEMSPAGQISKEELQHEFDLLKKENEQRKRKLQAALI 1000
1001 NRKELLQRVSRLEEELANLKDESKKEIPLSETERGEVEEDKENKEYSEKC 1050
1051 VTSKCQEIEIYLKQTISEKEVELQHIRKDLEEKLAAEEQFQALVKQMNQT 1100
1101 LQDKTNQIDLLQAEISENQAIIQKLITSNTDASDGDSVALVKETVVISPP 1150
1151 CTGSSEHWKPELEEKILALEKEKEQLQKKLQEALTSRKAILKKAQEKERH 1200
1201 LREELKQQKDDYNRLQEQFDEQSKENENIGDQLRQLQIQVRESIDGKLPS 1250
1251 TDQQESCSSTPGLEEPLFKATEQHHTQPVLESNLCPDWPSHSEDASALQG 1300
1301 GTSVAQIKAQLKEIEAEKVELELKVSSTTSELTKKSEEVFQLQEQINKQG 1350
1351 LEIESLKTVSHEAEVHAESLQQKLESSQLQIAGLEHLRELQPKLDELQKL 1400
1401 ISKKEEDVSYLSGQLSEKEAALTKIQTEIIEQEDLIKALHTQLEMQAKEH 1450
1451 DERIKQLQVELCEMKQKPEEIGEESRAKQQIQRKLQAALISRKEALKENK 1500
1501 SLQEELSLARGTIERLTKSLADVESQVSAQNKEKDTVLGRLALLQEERDK 1550
1551 LITEMDRSLLENQSLSSSCESLKLALEGLTEDKEKLVKEIESLKSSKIAE 1600
1601 STEWQEKHKELQKEYEILLQSYENVSNEAERIQHVVEAVRQEKQELYGKL 1650
1651 RSTEANKKETEKQLQEAEQEMEEMKEKMRKFAKSKQQKILELEEENDRLR 1700
1701 AEVHPAGDTAKECMETLLSSNASMKEELERVKMEYETLSKKFQSLMSEKD 1750
1751 SLSEEVQDLKHQIEGNVSKQANLEATEKHDNQTNVTEEGTQSIPGETEEQ 1800
1801 DSLSMSTRPTCSESVPSAKSANPAVSKDFSSHDEINNYLQQIDQLKERIA 1850
1851 GLEEEKQKNKEFSQTLENEKNTLLSQISTKDGELKMLQEEVTKMNLLNQQ 1900
1901 IQEELSRVTKLKETAEEEKDDLEERLMNQLAELNGSIGNYCQDVTDAQIK 1950
1951 NELLESEMKNLKKCVSELEEEKQQLVKEKTKVESEIRKEYLEKIQGAQKE 2000
2001 PGNKSHAKELQELLKEKQQEVKQLQKDCIRYQEKISALERTVKALEFVQT 2050
2051 ESQKDLEITKENLAQAVEHRKKAQAELASFKVLLDDTQSEAARVLADNLK 2100
2101 LKKELQSNKESVKSQMKQKDEDLERRLEQAEEKHLKEKKNMQEKLDALRR 2150
2151 EKVHLEETIGEIQVTLNKKDKEVQQLQENLDSTVTQLAAFTKSMSSLQDD 2200
2201 RDRVIDEAKKWERKFSDAIQSKEEEIRLKEDNCSVLKDQLRQMSIHMEEL 2250
2251 KINISRLEHDKQIWESKAQTEVQLQQKVCDTLQGENKELLSQLEETRHLY 2300
2301 HSSQNELAKLESELKSLKDQLTDLSNSLEKCKEQKGNLEGIIRQQEADIQ 2350
2351 NSKFSYEQLETDLQASRELTSRLHEEINMKEQKIISLLSGKEEAIQVAIA 2400
2401 ELRQQHDKEIKELENLLSQEEEENIVLEEENKKAVDKTNQLMETLKTIKK 2450
2451 ENIQQKAQLDSFVKSMSSLQNDRDRIVGDYQQLEERHLSIILEKDQLIQE 2500
2501 AAAENNKLKEEIRGLRSHMDDLNSENAKLDAELIQYREDLNQVITIKDSQ 2550
2551 QKQLLEVQLQQNKELENKYAKLEEKLKESEEANEDLRRSFNALQEEKQDL 2600
2601 SKEIESLKVSISQLTRQVTALQEEGTLGLYHAQLKVKEEEVHRLSALFSS 2650
2651 SQKRIAELEEELVCVQKEAAKKVGEIEDKLKKELKHLHHDAGIMRNETET 2700
2701 AEERVAELARDLVEMEQKLLMVTKENKGLTAQIQSFGRSMSSLQNSRDHA 2750
2751 NEELDELKRKYDASLKELAQLKEQGLLNRERDALLSETAFSMNSTEENSL 2800
2801 SHLEKLNQQLLSKDEQLLHLSSQLEDSYNQVQSFSKAMASLQNERDHLWN 2850
2851 ELEKFRKSEEGKQRSAAQPSTSPAEVQSLKKAMSSLQNDRDRLLKELKNL 2900
2901 QQQYLQINQEITELHPLKAQLQEYQDKTKAFQIMQEELRQENLSWQHELH 2950
2951 QLRMEKSSWEIHERRMKEQYLMAISDKDQQLSHLQNLIRELRSSSSQTQP 3000
3001 LKVQYQRQASPETSASPDGSQNLVYETELLRTQLNDSLKEIHQKELRIQQ 3050
3051 LNSNFSQLLEEKNTLSIQLCDTSQSLRENQQHYGDLLNHCAVLEKQVQEL 3100
3101 QAGPLNIDVAPGAPQEKNGVHRKSDPEELREPQQSFSEAQQQLCNTRQEV 3150
3151 NELRKLLEEERDQRVAAENALSVAEEQIRRLEHSEWDSSRTPIIGSCGTQ 3200
3201 EQALLIDLTSNSCRRTRSGVGWKRVLRSLCHSRTRVPLLAAIYFLMIHVL 3250
3251 LILCFTGHL 3259

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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