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

NucPred

Fetching Q9Y485 from www.uniprot.org...

The NucPred score for your sequence is 0.85 (see score help below)

   1  MNLHQVLTGAVNPGDHCFSVGSIGDQRFTAYASGCDIVILGSDFERLQII    50
  51  PGAKHGNIQVGCVDCSMQQGKIAASYGNVISIFEPVNLPKQKKNLELYSQ   100
 101  WQKSGQFFLESIAHNITWDPTGSRLLTGSSYLQLWSNTNLEKPTEDENLN   150
 151  KTDLNFGDWKCIWHCKTASQVHLMKFSPDGEFFATAGKDDCLLKVWYNVE   200
 201  NWRTAVTSPDGSSEKQSQGEIDFSFVYLAHPRAVNGFSWRKTSKYMPRAS   250
 251  VCNVLLTCCKDNVCRLWVETFLPNDCLLYGGDCSHWTESINLTNNFKRNA   300
 301  SSKERVQNALEVNLRHFRRGRRRSLALVAHTGYLPHQQDPHHVHRNTPLH   350
 351  ANALCHFHIAASINPATDIPLLPSITSLSLNENEEKTGPFVVHWLNNKEL   400
 401  HFTLSMEVFLQQLRKSFEQPSSEASVEDSNQADVKSDEETDDGVDDLKIN   450
 451  PEKKELGCDKMVPNSSFTSLSSAAIDHQIEVLLSEWSKNADMLFSIHPMD   500
 501  GSLLVWHVDWLDEYQPGMFRQVQVSFVSRIPVAFPTGDANSLCKSIMMYA   550
 551  CTKNVDLAIQQGKQKPSGLTRSTSMLISSGHNKSSNSLKLSIFTPNVMMI   600
 601  SKHADGSLNQWLVSFAEESAFSTVLSISHKSRYCGHRFHLNDLACHSVLP   650
 651  LLLTTSHHNALRTPDVDNPEQPFDALNIEECSLTQQNKSTVDVAFQDPSA   700
 701  VYSELILWRVDPVGPLSFSGGVSELARINSLHVSAFSNVAWLPTLIPSYC   750
 751  LGAYCNSPSACFVASDGQYLRLYEAVIDAKKLLSELSNPEISKYVGEVFN   800
 801  IVSQQSTARPGCIIALDPITKLHGRKTQLLHVFEEDFILNNLEKKSLGKD   850
 851  SILSNAGSSPNGFSEKFYLIVIECTQDNRSLLHMWNLHLKSIPVSLDEKV   900
 901  DTKLSEAVWQPEEHYSSSPEKILSPFSQKYQACRANLQSTSRLTLFSEMV   950
 951  YSQELHLPEGVEIISIKPSAGHLSSSSIYPACSAPYLLATSCSDEKVRFW  1000
1001  RCRVTDGESATSKNGKIDLAYIWEEWPLLIEDGLQSNSSITVPGRPVEVS  1050
1051  CAHTNRLAVAYKQPASNSRSSQDFVMHVSIFECESTGGSCWVLEQTIHLD  1100
1101  ELSTVLDSGISVDSNLVAYNKQDMYLSSKENITSNTKHLVHLDWMSREDG  1150
1151  SHILTVGIGSKLFMYGPLAGKVQDQTGKETLAFPLWESTKVVPLSKFVLL  1200
1201  RSVDLVSSVDGSPPFPVSLSWVRDGILVVGMDCEMHVYCQWQPSSKQEPV  1250
1251  ITDSYSGSTPSITSLIKQSNSSSGLHPPKKTLTRSMTSLAQKICGKKTAF  1300
1301  DPSVDMEDSGLFEAAHVLSPTLPQYHPLQLLELMDLGKVRRAKAILSHLV  1350
1351  KCIAGEVVALNEAESNHERRLRSLTISASGSTTRDPQAFNKAENTDYTEI  1400
1401  DSVPPLPLYALLAADDDSCYSSLEKSSNESTLSKSNQLSKESYDELFQTQ  1450
1451  LLMTDTHMLETDEENTKPRVIDLSQYSPTYFGPEHAQVLSGHLLHSSLPG  1500
1501  LSRMEQMSLMALADTIATTSTDIGESRDRSQGGETLDECGLKFLLAVRLH  1550
1551  TFLTTSLPAYRAQLLHQGLSTSHFAWAFHSVAEEELLNMLPAMQKDDPTW  1600
1601  SELRAMGVGWWVRNTRILRKCIEKVAKAAFYRKNDPLDAAIFYLAMKKKA  1650
1651  VIWGLYRAEKNTRMTQFFGHNFEDERWRKAALKNAFSLLGKQRFEHSAAF  1700
1701  FLLAGCLRDAIEVCLEKLNDIQLALVIARLYESEFDTSAAYKSILRKKVL  1750
1751  GIDSPVSELCSLNINMHHDPFLRSMAYWILEDYSGALETLIKQPIRENDD  1800
1801  QVLSASNPTVFNFYNYLRTHPLLLRRHFGSSDTFSTHMSLTGKSGLAGTI  1850
1851  NLSERRLFFTTASAHLKAGCPMLALEVLSKMPKVIKKTRPFYRASSFLDT  1900
1901  SKDCSPSSPLKLDAREDKSSAVDWSQSLINGFGSSSEGSSEKQSNSTLSF  1950
1951  DWSQPSVVFQDDSLELKWDSDNDEENEDVPISMKELKPLQRKTDKKLDDI  2000
2001  SSNYTESFSTLDENDLLNPSEDIIAVQLKFRACLKILTVELRTLSTGYEI  2050
2051  DGGKLRYQLYHWLEKEVIALQRTCDFCSDAEELQSAFGRNEDEFGLNEDA  2100
2101  EDLPHQTKVKQLRENFQEKRQWLLKYQSLLRMFLSYCILHGSHGGGLASV  2150
2151  RMELILLLQESQQETSEPLFSSPLSEQTSVPLLFACTANAKTVVANPLLH  2200
2201  LSNLTHDILHAIINFDSPPHPDIQSNKVYVMHTLAASLSACIYQCLCGSH  2250
2251  NYSSFQTNQFTGMVYQTVLLPHRPSLKTGSLDEALTPNTSPAQWPGITCL  2300
2301  IRLLNSSGEEAQSGLTVLLCEILTAVYLSLFIHGLATHSSNELFRIVAHP  2350
2351  LNEKMWSAVFGGGAHVPSKEQTHSKTLPVSSLVEEGEKQNKRFRPSKMSC  2400
2401  RESAPLTPSSAPVSQESLAVKEKFIPPELSIWDYFIAKPFLPSSQSRAEY  2450
2451  DSEESLGSDDDDNDDDDDVLASDFHLQEHSNSNSYSWSLMRLAMVQLVLN  2500
2501  NLKTFYPFAGHDLAELPVSSPLCHAVLKTLQCWEQVLLRRLEIHGGPPQN  2550
2551  YIASHTAEESLSAGPAILRHKALLEPTNTPFKSKHHLALSVKRLWQYLVK  2600
2601  QEEIQETFIKNIFTKKRCLNEIEADLGYPGGKARIIHKESDIITAFAVNK  2650
2651  ANRNCIAIASSHDVQELDVSGILATQVYTWVDDDIEVETKGSEDFLVIHA  2700
2701  RDDLTAVQGTTPYTHSNPGTPINMPWLGSTQTGRGASVMIKKAINNVRRM  2750
2751  TSHPTLPYYLTGAQDGSVRMFEWGHSQQITCFRSGGNSRVTRMRFNYQGN  2800
2801  KFGIVDADGYLSLYQTNWKCCPVTGSMPKPYLTWQCHNKTANDFVFVSSS  2850
2851  SLIATAGLSTDNRNVCLWDTLVAPANSLVHAFTCHDSGATVLAYAPKHQL  2900
2901  LISGGRKGFTYVFDLCQRQQRQLFQSHDSPVKAVAVDPTEEYFVTGSAEG  2950
2951  NIKIWSLSTFGLLHTFVSEHARQSIFRNIGTGVMQIETGPANHIFSCGAD  3000
3001  GTMKMRILPDQFSPLNEVLKNDVKFML                         3027

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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