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

NucPred

Fetching P42286 from www.uniprot.org...

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

   1  MSTDIATQLKGSRSDVEKVRKTVEAKFRELSGDGLPLRYEVNVLRHICLA    50
  51  LKDNLHQNSDLYCDIMGIMLPRVVPCEEKPSLWEAHLSSLRYIHHGLFHQ   100
 101  RSIEACQKLYNLIRQQPCRLQEESDYKIYLDIHLTHFNGFHVLLQKQKLP   150
 151  LEATSQLCYALESLGDLFAAMTQRQISLCATLLVQLNESLFGKRSRSFFK   200
 201  SLSFLPSESLAKMFNALLMLLASSTSSNLANLFPECLSLTLALVQIDMFS   250
 251  PQSNQQMSLQLLRMSKELFRQESNLCYALQLMYYYIKLIFVREPTGDFKR   300
 301  TYIDLSSKFQHFFEHKVASHAKEQWLADFLVAIQLLQVLIHQSNSKLQSP   350
 351  FQIFWQQFDGESSPEIYTAHFQLLQTCASLAVNITRSPLGCSCSHEACKS   400
 401  VRRHCILAYGLCALDAYINWKPAAEQRANVSPHKPLLGVVKYSMDVAKTM   450
 451  KCLGPTSVEIIKLVRQLTYVADQVTCPEQMSVLLPLLEPLQKLRPLVADQ   500
 501  DMSSLLRRLFKASSHCGDSNIACRIQASYLASITNPARLRSQVCLYYHNL   550
 551  GKKGTEIKRCVYEWHESTPLPFPLTPDQKKQLYDTDFFALLHYLRSPSTA   600
 601  HMESLIRCRTSDYHLVLLARQMRKDDSISKKCIEVHDKLRQQRSLSRMDN   650
 651  LCLGHASVGLLLDALEAQKTKVSTKEITENMFEELLLSKNLWQMNIQREQ   700
 701  RLVNYASEAISAFSNFFDRADQEPLSANETSIDWEALIDDAIATANALSS   750
 751  MGYQSEEDDAWLLLLRMGRLLEDRFTYLRALNHFLSQNEVSSRLNLKLGE   800
 801  EVEVAEELLDDLWPQLKNGKFFKRQQTTVMLCFCHLASYYARMECYSHAQ   850
 851  LLLLHVEQLREEFPERQGKSDIVLLTLQTVRFRIGYQQRKPTNCRLPTPL   900
 901  RQLDILLDNVRSFCNLSSLDGGSLQLLLSTLVRESTECSANRLSERLSFS   950
 951  NIALHLVLQSGLALRAIEVFLAWLWTNLQMESFDKAQSKLRLIEHCLGIK  1000
1001  QLNPTSRPEKEAIKDVAISDLASNMHLLQLVEPIRKQQLLNMASPNLLKM  1050
1051  RPHSPNPQLDLDRYITLDVAPANLRENSQLQCLYFVTGCLHARLRFLQRN  1100
1101  SEQLEEFYGRAHNWMQEKPPMSSALYPMLHAQQLYHLNYLRFARKHVEAI  1150
1151  STAQLGLKMRSRAVDINFEYNFLAQLKTAQLELKPVGQDKPQVKILRRAL  1200
1201  VFNHSPEDKKRTATGSVSAVKNTASKVKQSAKKAPRFRIYEELELRPPSA  1250
1251  TSCSSSGGSGTENTPPSDHVDLNACQAIEISDDDDSPLVSTKKTQPKSRE  1300
1301  KAKPKATSKACKVLTLDNSLEIVETPTITTSTRSTRARLRQPVETPKTAT  1350
1351  LSSKRTRRQVLEAQAPETESISTRTRHRH                       1379

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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