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

NucPred

Fetching P68875 from www.uniprot.org...

The NucPred score for your sequence is 0.60 (see score help below)

   1  MKKIITLKNLFLIILVYIFSEKKDLRCNVIKGNNIKDDEDKRFHLFYYSH    50
  51  NLFKTPETKEKKNKKECFYKNGGIYNLSKEIRMRKDTSVKIKQRTCPFHK   100
 101  EGSSFEMGSKNITCFYPIVGKKERKTLDTIIIKKNVTNDHVVSSDMHSNV   150
 151  QEKNMILIRNIDKENKNDIQNVEEKIQRDTYENKDYESDDTLIEWFDDNT   200
 201  NEENFLLTFLKRCLMKIFSSPKRKKTVVQKKHKSNFFINSSLKYIYMYLT   250
 251  PSDSFNLVRRNRNLDEEDMSPRDNFVIDDEEEEEEEEEEEEEEEEEEEEE   300
 301  EEEEYDDYVYEESGDETEEQLQEEHQEEVGAESSEESFNDEDEDSVEARD   350
 351  GDMIRVDEYYEDQDGDTYDSTIKNEDVDEEVGEEVGEEVGEEVGEEVGEE   400
 401  VGEEVGEEVGEEVGEEEGEEVGEGVGEEVGEEEGEEVGEEEGEYVDEKER   450
 451  QGEIYPFGDEEEKDEGGESFTYEKSEVDKTDLFKFIEGGEGDDVYKVDGS   500
 501  KVLLDDDTISRVSKKHTARDGEYGEYGEAVEDGENVIKIIRSVLQSGALP   550
 551  SVGVDELDKIDLSYETTESGDTAVSEDSYDKYASNNTNKEYVCDFTDQLK   600
 601  PTESGPKVKKCEVKVNEPLIKVKIICPLKGSVEKLYDNIEYVPKKSPYVV   650
 651  LTKEETKLKEKLLSKLIYGLLISPTVNEKENNFKEGVIEFTLPPVVHKAT   700
 701  VFYFICDNSKTEDDNKKGNRGIVEVYVEPYGNKINGCAFLDEDEEEEKYG   750
 751  NQIEEDEHNEKIKMKTFFTQNIYKKNNIYPCYMKLYSGDIGGILFPKNIK   800
 801  STTCFEEMIPYNKEIKWNKENKSLGNLVNNSVVYNKEMNAKYFNVQYVHI   850
 851  PTSYKDTLNLFCSIILKEEESNLISTSYLVYVSINEELNFSLFDFYESFV   900
 901  PIKKTIQVAQKNVNNKEHDYTCDFTDKLDKTVPSTANGKKLFICRKHLKE   950
 951  FDTFTLKCNVNKTQYPNIEIFPKTLKDKKEVLKLDLDIQYQMFSKFFKFN  1000
1001  TQNAKYLNLYPYYLIFPFNHIGKKELKNNPTYKNHKDVKYFEQSSVLSPL  1050
1051  SSADSLGKLLNFLDTQETVCLTEKIRYLNLSINELGSDNNTFSVTFQVPP  1100
1101  YIDIKEPFYFMFGCNNNKGEGNIGIVELLISKQEEKIKGCNFHESKLDYF  1150
1151  NENISSDTHECTLHAYENDIIGFNCLETTHPNEVEVEVEDAEIYLQPENC  1200
1201  FNNVYKGLNSVDITTILKNAQTYNINNKKTPTFLKIPPYNLLEDVEISCQ  1250
1251  CTIKQVVKKIKVIITKNDTVLLKREVQSESTLDDKIYKCEHENFINPRVN  1300
1301  KTFDENVEYTCNIKIENFFNYIQIFCPAKDLGIYKNIQMYYDIVKPTRVP  1350
1351  QFKKFNNEELHKLIPNSEMLHKTKEMLILYNEEKVDLLHFYVFLPIYIKD  1400
1401  IYEFNIVCDNSKTMWKNQLGGKVIYHITVSKREQKVKGCSFDNEHAHMFS  1450
1451  YNKTNVKNCIIDAKPKDLIGFVCPSGTLKLTNCFKDAIVHTNLTNINGIL  1500
1501  YLKNNLANFTYKHQFNYMEIPALMDNDISFKCICVDLKKKKYNVKSPLGP  1550
1551  KVLRALYKKLNIKFDNYVTGTDQNKYLMTYMDLHLSHKRNYLKELFHDLG  1600
1601  KKKPADTDANPESIIESLSINESNESGPFPTGDVDAEHLILEGYDTWESL  1650
1651  YDEQLEEVIYNDIESLELKDIEQYVLQVNLKAPKLMMSAQIHNNRHVCDF  1700
1701  SKNNLIVPESLKKKEELGGNPVNIHCYALLKPLDTLYVKCPTSKDNYEAA  1750
1751  KVNISENDNEYELQVISLIEKRFHNFETLESKKPGNGDVVVHNGVVDTGP  1800
1801  VLDNSTFEKYFKNIKIKPDKFFEKVINEYDDTEEEKDLESILPGAIVSPM  1850
1851  KVLKKKDPFTSYAAFVVPPIVPKDLHFKVECNNTEYKDENQYISGYNGII  1900
1901  HIDISNSNRKINGCDFSTNNSSILTSSVKLVNGETKNCEININNNEVFGI  1950
1951  ICDNETNLDPEKCFHEIYSKDNKTVKKFREVIPNIDIFSLHNSNKKKVAY  2000
2001  AKVPLDYINKLLFSCSCKTSHTNTIGTMKVTLNKDEKEEEDFKTAQGIKH  2050
2051  NNVHLCNFFDNPELTFDNNKIVLCKIDAELFSEVIIQLPIFGTKNVEEGV  2100
2101  QNEEYKKFSLKPSLVFDDNNNDIKVIGKEKNEVSISLALKGVYGNRIFTF  2150
2151  DKNGKKGEGISFFIPPIKQDTDLKFIINETIDNSNIKQRGLIYIFVRKNV  2200
2201  SENSFKLCDFTTGSTSLMELNSQVKEKKCTVKIKKGDIFGLKCPKGFAIF  2250
2251  PQACFSNVLLEYYKSDYEDSEHINYYIHKDKKYNLKPKDVIELMDENFRE  2300
2301  LQNIQQYTGISNITDVLHFKNFNLGNLPLNFKNHYSTAYAKVPDTFNSII  2350
2351  NFSCNCYNPEKHVYGTMQVESDNRNFDNIKKNENVIKNFLLPNIEKYALL  2400
2401  LDDEERQKKIKQQQEEEQQEQILKDQDDRLSRHDDYNKNHTYILYDSNEH  2450
2451  ICDYEKNESLISTLPNDTKKIQKSICKINAKALDVVTIKCPHTKNFTPKD  2500
2501  YFPNSSLITNDKKIVITFDKKNFVTYIDPTKKTFSLKDIYIQSFYGVSLD  2550
2551  HLNQIKKIHEEWDDVHLFYPPHNVLHNVVLNNHIVNLSSALEGVLFMKSK  2600
2601  VTGDETATKKNTTLPTDGVSSILIPPYVKEDITFHLFCGKSTTKKPNKKN  2650
2651  TSLALIHIHISSNRNIIHGCDFLYLENQTNDAISNNNNNSYSIFTHNKNT  2700
2701  ENNLICDISLIPKTVIGIKCPNKKLNPQTCFDEVYYVKQEDVPSKTITAD  2750
2751  KYNTFSKDKIGNILKNAISINNPDEKDNTYTYLILPEKFEEELIDTKKVL  2800
2801  ACTCDNKYIIHMKIEKSTMDKIKIDEKKTIGKDICKYDVTTKVATCEIID  2850
2851  TIDSSVLKEHHTVHYSITLSRWDKLIIKYPTNEKTHFENFFVNPFNLKDK  2900
2901  VLYNYNKPINIEHILPGAITTDIYDTRTKIKQYILRIPPYVHKDIHFSLE  2950
2951  FNNSLSLTKQNQNIIYGNVAKIFIHINQGYKEIHGCDFTGKYSHLFTYSK  3000
3001  KPLPNDDDICNVTIGNNTFSGFACLSHFELKPNNCFSSVYDYNEANKVKK  3050
3051  LFDLSTKVELDHIKQNTSGYTLSYIIFNKESTKLKFSCTCSSNYSNYTIR  3100
3101  ITFDPNYIIPEPQSRAIIKYVDLQDKNFAKYLRKL                 3135

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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