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

NucPred

Fetching Q9Y8A5 from www.uniprot.org...

The NucPred score for your sequence is 0.30 (see score help below)

   1  MAQSMYPNEPIVVVGSGCRFPGDANTPSKLWELLQHPRDVQSRIPKERFD    50
  51  VDTFYHPDGKHHGRTNAPYAYVLQDDLGAFDAAFFNIQAGEAESMDPQHR   100
 101  LLLETVYEAVTNAGMRIQDLQGTSTAVYVGVMTHDYETVSTRDLESIPTY   150
 151  SATGVAVSVASNRISYFFDWHGPSMTIDTACSSSLVAVHLAVQQLRTGQS   200
 201  SMAIAAGANLILGPMTFVLESKLSMLSPSGRSRMWDAGADGYARGEAVCS   250
 251  VVLKTLSQALRDGDTIECVIRETGVNQDGRTTGITMPNHSAQEALIKATY   300
 301  AQAGLDITKAEDRCQFFEAHGTGTPAGDPQEAEAIATAFFGHEQVARSDG   350
 351  NERAPLFVGSAKTVVGHTEGTAGLAGLMKASFAVRHGVIPPNLLFDKISP   400
 401  RVAPFYKNLRIPTEATQWPALPPGQPRRASVNSFGFGGTNAHAIIEEYME   450
 451  PEQNQLRVSNNEDCPPMTGVLSLPLVLSAKSQRSLKIMMEEMLQFLQSHP   500
 501  EIHLHDLTWSLLRKRSVLPFRRAIVGHSHETIRRALEDAIEDGIVSSDFT   550
 551  TEVRGQPSVLGIFTGQGAQWPGMLKNLIEASPYVRNIVRELDDSLQSLPE   600
 601  KYRPSWTLLDQFMLEGEASNVQYATFSQPLCCAVQIVLVRLLEAARIRFT   650
 651  AVVGHSSGEIACAFAAGLISASLAIRIAYLRGVVSAGGARGTPGAMLAAG   700
 701  MSFEEAQEICELDAFEGRICVAASNSPDSVTFSGDANAIDHLKGMLEDES   750
 751  TFARLLKVDTAYHSHHMLPCADPYMQALEECGCAVADAGSPAGSVPWYSS   800
 801  VDAENRQMAARDVTAKYWKDNLVSPVLFSHAVQRAVVTHKALDIGIEVGC   850
 851  HPALKSPCVATIKDVLSGVDLAYTGCLERGKNDLDSFSRALAYLWERFGA   900
 901  SSFDADEFMRAVAPDRPCMSVSKLLPAYPWDRSRRYWVESRATRHHLRGP   950
 951  KPHLLLGKLSEYSTPLSFQWLNFVRPRDIEWLDGHALQGQTVFPAAGYIV  1000
1001  MAMEAALMIAGTHAKQVKLLEILDMSIDKAVIFDDEDSLVELNLTADVSR  1050
1051  NAGEAGSMTISFKIDSCLSKEGNLSLSAKGQLALTIEDVNPRTTSASDQH  1100
1101  HLPPPEEEHPHMNRVNINAFYHELGLMGYNYSKDFRRLHNMQRADLRASG  1150
1151  TLDFIPLMDEGNGCPLLLHPASLDVAFQTVIGAYSSPGDRRLRCLYVPTH  1200
1201  VDRITLVPSLCLATAESGCEKVAFNTINTYDKGDYLSGDIVVFDAEQTTL  1250
1251  FQVENITFKPFSPPDASTDHAMFARWSWGPLTPDSLLDNPEYWATAQDKE  1300
1301  AIPIIERIVYFYIRSFLSQLTLEERQQAAFHLQKQIEWLEQVLASAKEGR  1350
1351  HLWYDPGWENDTEAQIEHLCTANSYHPHVRLVQRVGQHLLPTVRSNGNPF  1400
1401  DLLDHDGLLTEFYTNTLSFGPALHYARELVAQIAHRYQSMDILEIGAGTG  1450
1451  GATKYVLATPQLGFNSYTYTDISTGFFEQAREQFAPFEDRMVFEPLDIRR  1500
1501  SPAEQGFEPHAYDLIIASNVLHATPDLEKTMAHARSLLKPGGQMVILEIT  1550
1551  HKEHTRLGFIFGLFADWWAGVDDGRCTEPFVSFDRWDAILKRVGFSGVDS  1600
1601  RTTDRDANLFPTSVFSTHAIDATVEYLDAPLASSGTVKDSYPPLVVVGGQ  1650
1651  TPQSQRLLNDIKAIMPPRPLQTYKRLVDLLDAEELPMKSTFVMLTELDEE  1700
1701  LFAGLTEETFEATKLLLTYASNTVWLTENAWVQHPHQASTIGMLRSIRRE  1750
1751  HPDLGVHVLDVDAVETFDATFLVEQVLRLEEHTDELASSTTWTQEPEVSW  1800
1801  CKGRPWIPRLKRDLARNNRMNSSRRPIYEMIDSSRAPVALQTARDSSSYF  1850
1851  LESAETWFVPESVQQMETKTIYVHFSCPHALRVGQLGFFYLVQGHVQEGN  1900
1901  REVPVVALAERNASIVHVRPDYIYTEADNNLSEGGGSLMVTVLAAAVLAE  1950
1951  TVISTAKCLGVTDSILVLNPPSICGQMLLHAGEEIGLQVHLATTSGNRSS  2000
2001  VSAGDAKSWLTLHARDTDWHLRRVLPRGVQALVDLSADQSCEGLTQRMMK  2050
2051  VLMPGCAHYRAADLFTDTVSTELHSGSRHQASLPAAYWEHVVSLARQGLP  2100
2101  SVSEGWEVMPCTQFAAHADKTRPDLSTVISWPRESDEATLPTRVRSIDAE  2150
2151  TLFAADKTYLLVGLTGDLGRSLGRWMVQHGACHIVLTSRNPQVNPKWLAH  2200
2201  VEELGGRVTVLSMDVTSQNSVEAGLAKLKDLHLPPVGGIAFGPLVLQDVM  2250
2251  LNNMELPMMEMVLNPKVEGVRILHEKFSDPTSSNPLDFFVMFSSIVAVMG  2300
2301  NPGQANYSAANCYLQALAQQRVASGLAASTIDIGAVYGVGFVTRAELEED  2350
2351  FNAIRFMFDSVEEHELHTLFAEAVVAGRRAVHQQEQQRKFATVLDMADLE  2400
2401  LTTGIPPLDPALKDRITFFDDPRIGNLKIPEYRGAKAGEGAAGSKGSVKE  2450
2451  QLLQATNLDQVRQIVIDGLSAKLQVTLQIPDGESVHPTIPLIDQGVDSLG  2500
2501  AVTVGTWFSKQLYLDLPLLKVLGGASITDLANEAAARLPPSSIPLVAATD  2550
2551  GGAESTDNTSENEVSGREDTDLSAAATITEPSSADEDDTEPGDEDVPRSH  2600
2601  HPLSLGQEYSWRIQQGAEDPTVFNNTIGMFMKGSIDLKRLYKALRAVLRR  2650
2651  HEIFRTGFANVDENGMAQLVFGQTKNKVQTIQVSDRAGAEEGYRQLVQTR  2700
2701  YNPAAGDTLRLVDFFWGQDDHLLVVAYHRLVGDGSTTENIFVEAGQLYDG  2750
2751  TSLSPHVPQFADLAARQRAMLEDGRMEEDLAYWKKMHYRPSSIPVLPLMR  2800
2801  PLVGNSSRSDTPNFQHCGPWQQHEAVARLDPMVAFRIKERSRKHKATPMQ  2850
2851  FYLAAYQVLLARLTDSTDLTVGLADTNRATVDEMAAMGFFANLLPLRFRD  2900
2901  FRPHITFGEHLIATRDLVREALQHARVPYGVLLDQLGLEVPVPTSNQPAP  2950
2951  LFQAVFDYKQGQAESGTIGGAKITEVIATRERTPYDVVLEMSDDPTKDPL  3000
3001  LTAKLQSSRYEAHHPQAFLESYMSLLSMFSMNPALKLA              3038

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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