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

NucPred

Fetching Q8CHI8 from www.uniprot.org...

The NucPred score for your sequence is 0.95 (see score help below)

   1  MHHGSGPQNVQHQLQRSRSFTGSEEEQPAHPNLPPSPAAPFAPSASPSAP    50
  51  QSPGYQIQQLMSRSPVAGQNVNITLQNVGPVVGGNQQITLAPLPLPNPTS   100
 101  PGFQFGAQQRRFEHGSPSYIQVTSPMSQQVQTQSPTQPSPGPGQTLQNVR   150
 151  AGAPGPGLGICSNSPTGGFVDASVLVRQISLSPSSGGHFVFQEAPGLTQM   200
 201  AQGAQVQLQHSGAPITVRERRLSQPHAQSGGTIHHLGPQSPAAAGGTGLQ   250
 251  PLASPNHITTASLPPQISSIIQGQLIQQQQQVLQGQPMNRSLGFERTPGV   300
 301  LLPGVGGPSAFGMTSPPPPTSPSRTTMPPGLSSVPLTSMGSSGMKKVPKK   350
 351  LEEIPPASQEMAQMRKQCLDYHYKEMEALKEVFKEYLIELFFLQHLQGNM   400
 401  MDFLAFKKKHYAPLQAYLRQNDLDIEEEEEEEEEEEGKSEVINDEVKVVT   450
 451  GKDGQTGTPVAIATQLPPNVSAAFSSQQQPFQHQSLTGSLVVGPGSATEA   500
 501  DPFKRQQVMPPTEQSKRPRLEVGHPGVVFQHPGVNAGVPLQQLMPTVQGG   550
 551  MPPTPQATQLTGQKQSQQQYDPSTGPPVQNAASLHTPPPQLPARLPPASV   600
 601  PATALPSTLQFSQQSQMVEASTQLQIPVKTQQLNAPIPAPLPSQLPAPSS   650
 651  QPAQPALHVPMPGKAQMQTSQLSSQTQTVASTRPPLDSAQPCQRSLPTSS   700
 701  SSSSLVPVSGSGPGPSPARSSPVNRPSSATNKALSPITSRSPGVAVSAPP   750
 751  KPQSPAQNAASSQDGSQDKLAEQITLENQIHQRIADLRKEGLWSLRRLPK   800
 801  LQEAPRPKSHWDYLLEEMQWMATDFAQERRWKLAAAKKLVRTVARHHEEK   850
 851  KLREERGKKEEQSRLRRIAATTAREIEYFWSNIEQVVEIKLQVELEEKRK   900
 901  KALNLQKVSRRGKESRLKGFDTSPEHSLDLGISGRKRKASTSLTDDEVED   950
 951  EEETIEEEEAHEGLVDHHTELTNLAKEAELPLIDLMKLYEGAFLPNFQWP  1000
1001  QPEPDHEESSGEEDVEDCPSDRESRRDSVLIDSLFIMDQFKAAERMSIGK  1050
1051  SNTKDITEVTAVAEAILPKGSARVTTAVKFSAPSLLYGALRDYQKIGLDW  1100
1101  LAKLYRKNLNGILADEAGLGKTVQIIAFFAHLACNEGNWGPHLVVMRSCN  1150
1151  ILKWELELKRWCPGLKTLSYVGSHRELKAKRQEWTEPNNFHICITSYKQF  1200
1201  FRGYTAFSRVHWKCLVVDEMQRVKGMTERHWEAIFKLQSQQRLLLIDVPL  1250
1251  HNTFLELWTMVHFLIPGISRPYLSFPLKAPNEENQDYYHKMVIRLHRVTQ  1300
1301  PFILRRTKRDVEKQLTRKYEHVLKCRLSSRQKALYEDVILQPRTQEALKS  1350
1351  GHFVSVLSVLTRLQRICNHPGLVEPRVPGSSFAAGSLQYKSASLILRVLE  1400
1401  REFWKETDLSIFDLIGLENKITRHEAELLCKKKVTRKLMEEVFASPPPSA  1450
1451  RPAAVKLKASRLFQPVQYGQKPEGRTVAFPSTHPPRMANTNTSTATPQGQ  1500
1501  VRGRPPIATFSANPDTKGGEVVKIAQLASIAGPQSRVAQPETPVTLQFQG  1550
1551  NKFTLSHSQLRQLTAGQPLQLQGSVLQIVSAPGQPYLRAPGPVVMQTVSQ  1600
1601  AGAVHSTLGSKPPTSGPSPAPLTPQVGVPGRVAVSAMAVGEPGLASKPAS  1650
1651  PAAGPTQEEKSRLLKERLDQIHFINERRCSQAPVYGRDLLRICSLPGRRK  1700
1701  RPLCWSLDSNFGKGPKGVNYDMSLSKSEGDLILTLSQESLQDVLGRVACV  1750
1751  IPPVVATPPSLWVARPPSLYSSRLRALRQCLREHTGPYHRQLQQLTALRS  1800
1801  LQFPELRLVQFDSGKLEALAILLQKLKSEGRRVLILSQMVLMLDILEMFL  1850
1851  NFHYLTYVRIDENANSEQRQELMRSFNRDRRIFCALLSTHSRATGINLVE  1900
1901  ADTVVFYDNDLNPVMDAKAQEWCDRIGRCKDIHIYRLVSGNSIEEKLLKN  1950
1951  GTKDLIREVAAQGNDYSMAFLTQRTIQELFEVYSPMDDTGFPVKAEEFVV  2000
2001  LSQEPSVSETIAPKIARPFIEALKSIECLEEDAQRSTEEAVPGSSSVAVS  2050
2051  SDSDGSRYDEEPSQLEELADFMEQLTPIEKYALNYLELFHTTTEQEKERI  2100
2101  SEDLVMASMKDWETRNARALQEREARLQLEQEEAELLTYTREDAYTMEYV  2150
2151  YEDADGQTEVMPLWTPPTPPQDDNDIYIDSVMCLMYETTPIPEAKLPPVY  2200
2201  VRKERKRHKTDPSAAGRKKKQRHGEAVVPPRSLFDRATPGMLKIRREGKE  2250
2251  QKKNLLLKQQTPFAKPLPTYVKSSGEPAQDSPDWLIGEDWALLQAVKQLL  2300
2301  ELPLNLTIVSPAHTPNWDLVSDVVNSCSRIYRSSKQCRNRYENVIIPREE  2350
2351  GKSKNNRPLRTSQIYAQDENATHTQLYTSHFELMKMTAGKRSPPIKPLLG  2400
2401  MNPFQKNPKHASVLAESGINYDKPLPPIQVASLRAERIAKEKKALADQQK  2450
2451  AQQPPVTQPPPQQQQQQQQQQQQQQQQQQPPPPPQQPPPPVPQPQAASSQ  2500
2501  TPAGQPAVQPQPQPQVQTQPQPVQPQSKGQPTMTTVGSAAVLAGTIKTSV  2550
2551  TGTSIPTGTVSGNVIVNTIAGVPAATFQSINKRLASPVAPGTLTTSGGSA  2600
2601  PAQVVHTQQRAVGSPATATTDLVSMTTTQGVRAVTSVTASAVVTTNLTPV  2650
2651  QTPTRSLVTQVSQATGVQLPGKTITPAAHFQLLRQQQQQQQQQQQQQQTS  2700
2701  QVQVPQLQSQAQSPAQIKAVSKLGPEHIIKMQKQKMQLPPQPPPPQAQPG  2750
2751  PPQQPAQVQVQTPQPPQQQQSPQLTTVTAPRPGALLTGTTVTNLQVARLT  2800
2801  RVPTSQLQAQGQMQTQTPQPAQVALAKPPVVSVPAAVVSSPGVTTLPMNV  2850
2851  AGISVAIGQPQKTAGQTVVAQPVNVQQLLKYKQQTAVQQQKAIQPQVAQG  2900
2901  QAAVQQKLTTQQITTQGPQQKVAYAAQPALKTQFLTTPISQAQKLAGTQQ  2950
2951  VQTQIQVAKLPQVVQQQTPVASIQQVASASQQASPQTVTLTQATAAGQQV  3000
3001  QMIPTVTATAQLVQQKLIQQQVVTTASASLQTPGGPSPAQLPASSDSPSQ  3050
3051  QPKLQMRVPAVRLKTPTKPPCQ                              3072

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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