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

NucPred

Fetching O13834 from www.uniprot.org...

The NucPred score for your sequence is 0.90 (see score help below)

   1  MRITKSPPKNQYSQPPPRVAEFIRQAQNEEVTSDLGLVSLCSEFRKNDWP    50
  51  YPRGDLYSWVPVLNRFDAILERIVEHYSLKDKVQTKPFDSDTLSILLEIL   100
 101  SFSAHLLSHCANRSIYNSTVYLEYLLNSSVLEVIDSTLALLLHIVQKATI   150
 151  SKRGKQLFSLSQDRLFRFLMFLPQDAMKTGFSQNYETLLFSNEIPQEWCS   200
 201  LELSYYKSSPSKDFSSASQPNSEGFSILKLPYNKVLGKPIEELLVKTLHD   250
 251  NQIPEQYSFDLLVSLMLRQNLYDINRRRLMIRIGLLALSNLVYAHSQAVQ   300
 301  TRFLIADPEITTHLANLVSPDVDLPQNFKAVCFECFKAFFFKKSMIPSVL   350
 351  ASLNVSVSYGLMMNLVRDFSKNLENPNFYYEREYVDSFYDFLQFMTSSPL   400
 401  GGNMACSAGLTSLLGYHLSVKTPQATYVVARSIVMLDHLIDGYSMAFPDF   450
 451  SESKGLDMLVDRVQYELEAGLQDIKSGKGNPEIVLNMDYAISYDRYFLLK   500
 501  NLLKFVLHLIQSGGSVVELRNLIDSSLISSLAFLLEHHEVYGSNLFASTT   550
 551  NIMSTFIHNEPTCYGIIHEKKLSHAFLDAVNRKILNSSDAITSIPLAFGA   600
 601  ICLNSQGFDLFLEKNPIPQLFSIFTSLNHCKSLISSDNAAILGTYIDELM   650
 651  RHQPSLKDPIVKMIFKACDQVSALLDNFNPFQYINAKEYPYLLYLETFSS   700
 701  FLENIITNEGHARYLISKGIVSHVLNLIQHPVLAFGFIDSSAFNSFFVLL   750
 751  HHAVDFDAPEVFRPLLDCIITRCEEGITEFTIVSLKQATISLIKDSNMGH   800
 801  EDANNFLHFSIVGNLLTIFAELFSSHAALKKAGNLPLVQLFISPSRYAGI   850
 851  FDILCNIKSIATSLDIHICLGVSDDFVLCSDSLTTIVTDKDEKEKFETKK   900
 901  KELTQDSSFCKFQNIRSNFSQIAYGVSKFFTSLTRALGNTSVQDFNEYKM   950
 951  IHKLGSNIALVVDELINLSSKQITSHPQSLSIASLEASLIFVLGASSIIR  1000
1001  EDDSKVTLVLLISRLLGGCRTMDVLISLNETVSGFFRLSDRDPLSKSNRV  1050
1051  LLALSSTLLNLILVFTSADFMSETSKTLNMALKSEFDMTDFNNSGSKLMH  1100
1101  VLHARIFISVLHLWRSADDLHLPYITRALLTNVLSNCYQFEDGIKNVVDS  1150
1151  INNLRTSIANGDIKEPLDVVTDDNTNSNFSLEETNASVTDMPESEKHENG  1200
1201  IFQAYLLKEMPNDIVSQFEMLKSKQIELTVQMASYEGDLNQNLCDFLYTR  1250
1251  DDVQMNADVQFSVTSGLIVEIKKLAQSTDCKAKNQLGPAVGLLSLFISHD  1300
1301  FTQNKAKNCVLSELNFFLELLHSLNNGLPSDSHKTSIVCILYLLEVLLAD  1350
1351  SKKPDEFEFNSEDCSLKLTDGAITVDLASQKHIMSSVITLLSLNSANLGV  1400
1401  VVSAFRVVVLLTSASEMIHTFVKLSGLPSLFKAMRACSGFCNESLHIPFI  1450
1451  SILRRLLEFDEVVELMMFDDLVNIFKLQGRARKTELHGFIRANAEMVLRS  1500
1501  PECFIKILKDCCVLGHFTPESEHYYLELKESLPGVLQNGQTDLDPSKEQM  1550
1551  SSVIVSFLLDELMDLTETRQFSDRSPNSEFTPENDSLYMYNVFLLQCLTE  1600
1601  LLSGYNACKRCFLNFQPRRKAPFFNLSRKYNSYLVGFFLEKLLPFGCIRL  1650
1651  SENNEVRKAFSVSNWAISILVFLCAYSNEQQTQAVDEIRREVLTSVLKFY  1700
1701  KSSSSFSENLEAYYCKLLVLAELCYRLCDAQTVSQKAPNHLLRRSQDQNV  1750
1751  KTMIDLGYIPTLTNAISEIDMNYPVSRKVVRHILKPLQLLTKEAIFLSQT  1800
1801  NPEALSGAAQDSMGDQSLSSSSEESSDSDREEPPDLYRNSVLGIFQGDIV  1850
1851  NENDENYEDSEDDGVYEEMEFEDDQSGSADSVVSEDDADDVMYSDNDDMN  1900
1901  IEFMVDEQDASSQNDDSSFDEASSHGDVISIDEEDLDNQGEEFEWEDEDN  1950
1951  ASSGYEDELDYNEDEVGENDSTTFEAMENAFTETSDNDDHLEEADHVSPV  2000
2001  EIDFLENDENSSSEQDDEFQWEWNTETPSGADILSRHGALLRDLFPLPGL  2050
2051  SRRVMIINSNDPSRSRPFLNNNASEGLLKHPLLLRNNLIHTPKATELWEN  2100
2101  LAEIDNHTASGAAFQRLLYYLALEIPNEDSSVLGWTSLKVSKHTDPLRAT  2150
2151  SDFIPLFSMQRWNSITSMFFAHASGSIALRITGSVLFALVPPALEKYNLE  2200
2201  NQKKEILENESKEEETRQPEVNIQPEEPINTSDMEGVTTEANEIGSYQEP  2250
2251  SLINIRGREVDVSSLGIDPTFLLALPEEMREEVVFQHIQERHMESISDSS  2300
2301  RRIDPSFLEVLPSDLRDELLFQEAVQMRLFDHATRNNNSVDHEVEMEEID  2350
2351  QGGTVSEHREKSVKPVKKIPVPNLLDRQGLYSLIRLIFISQHNGKNPYYD  2400
2401  LIVNISENKQHRADIVGLLLYILQEASINDRASEKCYRDLTVKSLNNSQQ  2450
2451  KEVKKSTGLLESLCKVPVVNGISAPALILQQGIDLLSHLATWADHFASFF  2500
2501  LSMHDFSGIASKKSAGRKNRESNVYKIAPINVLLGLLAREELFGNTLVMN  2550
2551  TFSELLSTLTKPLLSFYKSEKLQKDSATTGYTNDQDSRGSTVPKQDPGTT  2600
2601  ASRKDKKILSPPNILDENLRLAASLITTDSCSSRTFQNALSVMFHLSSIP  2650
2651  KAKILIGKELLRHGQEYGNSITNDLSRLCADVKSGKNESELQVALAPFCP  2700
2701  ASSNQAKLLRCLKALDYIFERRPKGQEQSPGNIIQLLEFYDNLKFSSLWE  2750
2751  VLSECLSALRDHTSITHVSTVLLPLIESLMVICRLVFIELPEDVGKHISP  2800
2801  ILERFKTLFISFTEEHRKIINMMVFTTPSLMSGSFSLLVKNPKVLEFENK  2850
2851  RNYFNRQLHEEAAKEQYPPLNITVRRDHVFLDSYRALHFKDADEVKFSKL  2900
2901  NIHFRDEEGVDAGGVTREWLQVLARQMFNPDYALFLPVTGDATTFHPNRD  2950
2951  SSVNPDHLSFFKFTGRIIGKALYDGRLLDCHFSRAVYKHMLHRSVSVKDI  3000
3001  ESLDPDYYKSLVWMLNNDITDIITEEFAVEKDVFGEKTVVDLIPNGRNIP  3050
3051  VTELNKQNYVNRMVDYKLRESVKDQLKSLLDGFSDIIPSHLIQIFNEQEL  3100
3101  ELLISGLPEIDIDDWKNNTEYHGYNVSSPQVQWFWRAVRSFDEEERAKLL  3150
3151  QFATGTSKVPLNGFKELEGMSGFQRFNIHKSYGSLNRLPQSHTCFNQLDL  3200
3201  PEYDTYEQLRSMLLTAINEGSEGFGFA                         3227

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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