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

NucPred

Fetching Q9NYQ8 from www.uniprot.org...

The NucPred score for your sequence is 0.40 (see score help below)

   1  MTIALLGFAIFLLHCATCEKPLEGILSSSAWHFTHSHYNATIYENSSPKT    50
  51  YVESFEKMGIYLAEPQWAVRYRIISGDVANVFKTEEYVVGNFCFLRIRTK   100
 101  SSNTALLNREVRDSYTLIIQATEKTLELEALTRVVVHILDQNDLKPLFSP   150
 151  PSYRVTISEDMPLKSPICKVTATDADLGQNAEFYYAFNTRSEMFAIHPTS   200
 201  GVVTVAGKLNVTWRGKHELQVLAVDRMRKISEGNGFGSLAALVVHVEPAL   250
 251  RKPPAIASVVVTPPDSNDGTTYATVLVDANSSGAEVESVEVVGGDPGKHF   300
 301  KAIKSYARSNEFSLVSVKDINWMEYLHGFNLSLQARSGSGPYFYSQIRGF   350
 351  HLPPSKLSSLKFEKAVYRVQLSEFSPPGSRVVMVRVTPAFPNLQYVLKPS   400
 401  SENVGFKLNARTGLITTTKLMDFHDRAHYQLHIRTSPGQASTVVVIDIVD   450
 451  CNNHAPLFNRSSYDGTLDENIPPGTSVLAVTATDRDHGENGYVTYSIAGP   500
 501  KALPFSIDPYLGIISTSKPMDYELMKRIYTFRVRASDWGSPFRREKEVSI   550
 551  FLQLRNLNDNQPMFEEVNCTGSIRQDWPVGKSIMTMSAIDVDELQNLKYE   600
 601  IVSGNELEYFDLNHFSGVISLKRPFINLTAGQPTSYSLKITASDGKNYAS   650
 651  PTTLNITVVKDPHFEVPVTCDKTGVLTQFTKTILHFIGLQNQESSDEEFT   700
 701  SLSTYQINHYTPQFEDHFPQSIDVLESVPINTPLARLAATDPDAGFNGKL   750
 751  VYVIADGNEEGCFDIELETGLLTVAAPLDYEATNFYILNVTVYDLGTPQK   800
 801  SSWKLLTVNVKDWNDNAPRFPPGGYQLTISEDTEVGTTIAELTTKDADSE   850
 851  DNGRVRYTLLSPTEKFSLHPLTGELVVTGHLDRESEPRYILKVEARDQPS   900
 901  KGHQLFSVTDLIITLEDVNDNSPQCITEHNRLKVPEDLPPGTVLTFLDAS   950
 951  DPDLGPAGEVRYVLMDGAHGTFRVDLMTGALILERELDFERRAGYNLSLW  1000
1001  ASDGGRPLARRTLCHVEVIVLDVNENLHPPHFASFVHQGQVQENSPSGTQ  1050
1051  VIVVAAQDDDSGLDGELQYFLRAGTGLAAFSINQDTGMIQTLAPLDREFA  1100
1101  SYYWLTVLAVDRGSVPLSSVTEVYIEVTDANDNPPQMSQAVFYPSIQEDA  1150
1151  PVGTSVLQLDAWDPDSSSKGKLTFNITSGNYMGFFMIHPVTGLLSTAQQL  1200
1201  DRENKDEHILEVTVLDNGEPSLKSTSRVVVGILDVNDNPPIFSHKLFNVR  1250
1251  LPERLSPVSPGPVYRLVASDLDEGLNGRVTYSIEDSDEEAFSIDLVTGVV  1300
1301  SSSSTFTAGEYNILTIKATDSGQPPLSASVRLHIEWIPWPRPSSIPLAFD  1350
1351  ETYYSFTVMETDPVNHMVGVISVEGRPGLFWFNISGGDKDMDFDIEKTTG  1400
1401  SIVIARPLDTRRRSNYNLTVEVTDGSRTIATQVHIFMIANINHHRPQFLE  1450
1451  TRYEVRVPQDTVPGVELLRVQAIDQDKGKSLIYTIHGSQDPGSASLFQLD  1500
1501  PSSGVLVTVGKLDLGSGPSQHTLTVMVRDQEIPIKRNFVWVTIHVEDGNL  1550
1551  HPPRFTQLHYEASVPDTIAPGTELLQVRAMDADRGVNAEVHYSLLKGNSE  1600
1601  GFFNINALLGIITLAQKLDQANHAPHTLTVKAEDQGSPQWHDLATVIIHV  1650
1651  YPSDRSAPIFSKSEYFVEIPESIPVGSPILLVSAMSPSEVTYELREGNKD  1700
1701  GVFSMNSYSGLISTQKKLDHEKISSYQLKIRGSNMAGAFTDVMVVVDIID  1750
1751  ENDNAPMFLKSTFVGQISEAAPLYSMIMDKNNNPFVIHASDSDKEANSLL  1800
1801  VYKILEPEALKFFKIDPSMGTLTIVSEMDYESMPSFQFCVYVHDQGSPVL  1850
1851  FAPRPAQVIIHVRDVNDSPPRFSEQIYEVAIVGPIHPGMELLMVRASDED  1900
1901  SEVNYSIKTGNADEAVTIHPVTGSISVLNPAFLGLSRKLTIRASDGLYQD  1950
1951  TALVKISLTQVLDKSLQFDQDVYWAAVKENLQDRKALVILGAQGNHLNDT  2000
2001  LSYFLLNGTDMFHMVQSAGVLQTRGVAFDREQQDTHELAVEVRDNRTPQR  2050
2051  VAQGLVRVSIEDVNDNPPKFKHLPYYTIIQDGTEPGDVLFQVSATDEDLG  2100
2101  TNGAVTYEFAEDYTYFRIDPYLGDISLKKPFDYQALNKYHLKVIARDGGT  2150
2151  PSLQSEEEVLVTVRNKSNPLFQSPYYKVRVPENITLYTPILHTQARSPEG  2200
2201  LRLIYNIVEEEPLMLFTTDFKTGVLTVTGPLDYESKTKHVFTVRATDTAL  2250
2251  GSFSEATVEVLVEDVNDNPPTFSQLVYTTSISEGLPAQTPVIQLLASDQD  2300
2301  SGRNRDVSYQIVEDGSDVSKFFQINGSTGEMSTVQELDYEAQQHFHVKVR  2350
2351  AMDKGDPPLTGETLVVVNVSDINDNPPEFRQPQYEANVSELATCGHLVLK  2400
2401  VQAIDPDSRDTSRLEYLILSGNQDRHFFINSSSGIISMFNLCKKHLDSSY  2450
2451  NLRVGASDGVFRATVPVYINTTNANKYSPEFQQHLYEAELAENAMVGTKV  2500
2501  IDLLAIDKDSGPYGTIDYTIINKLASEKFSINPNGQIATLQKLDRENSTE  2550
2551  RVIAIKVMARDGGGRVAFCTVKIILTDENDNPPQFKASEYTVSIQSNVSK  2600
2601  DSPVIQVLAYDADEGQNADVTYSVNPEDLVKDVIEINPVTGVVKVKDSLV  2650
2651  GLENQTLDFFIKAQDGGPPHWNSLVPVRLQVVPKKVSLPKFSEPLYTFSA  2700
2701  PEDLPEGSEIGIVKAVAAQDPVIYSLVRGTTPESNKDGVFSLDPDTGVIK  2750
2751  VRKPMDHESTKLYQIDVMAHCLQNTDVVSLVSVNIQVGDVNDNRPVFEAD  2800
2801  PYKAVLTENMPVGTSVIQVTAIDKDTGRDGQVSYRLSADPGSNVHELFAI  2850
2851  DSESGWITTLQELDCETCQTYHFHVVAYDHGQTIQLSSQALVQVSITDEN  2900
2901  DNAPRFASEEYRGSVVENSEPGELVATLKTLDADISEQNRQVTCYITEGD  2950
2951  PLGQFGISQVGDEWRISSRKTLDREHTAKYLLRVTASDGKFQASVTVEIF  3000
3001  VLDVNDNSPQCSQLLYTGKVHEDVFPGHFILKVSATDLDTDTNAQITYSL  3050
3051  HGPGAHEFKLDPHTGELTTLTALDRERKDVFNLVAKATDGGGRSCQADIT  3100
3101  LHVEDVNDNAPRFFPSHCAVAVFDNTTVKTPVAVVFARDPDQGANAQVVY  3150
3151  SLPDSAEGHFSIDATTGVIRLEKPLQVRPQAPLELTVRASDLGTPIPLST  3200
3201  LGTVTVSVVGLEDYLPVFLNTEHSVQVPEDAPPGTEVLQLATLTRPGAEK  3250
3251  TGYRVVSGNEQGRFRLDARTGILYVNASLDFETSPKYFLSIECSRKSSSS  3300
3301  LSDVTTVMVNITDVNEHRPQFPQDPYSTRVLENALVGDVILTVSATDEDG  3350
3351  PLNSDITYSLIGGNQLGHFTIHPKKGELQVAKALDREQASSYSLKLRATD  3400
3401  SGQPPLHEDTDIAIQVADVNDNPPRFFQLNYSTTVQENSPIGSKVLQLIL  3450
3451  SDPDSPENGPPYSFRITKGNNGSAFRVTPDGWLVTAEGLSRRAQEWYQLQ  3500
3501  IQASDSGIPPLSSLTSVRVHVTEQSHYAPSALPLEIFITVGEDEFQGGMV  3550
3551  GKIHATDRDPQDTLTYSLAEEETLGRHFSVGAPDGKIIAAQGLPRGHYSF  3600
3601  NVTVSDGTFTTTAGVHVYVWHVGQEALQQAMWMGFYQLTPEELVSDHWRN  3650
3651  LQRFLSHKLDIKRANIHLASLQPAEAVAGVDVLLVFEGHSGTFYEFQELA  3700
3701  SIITHSAKEMEHSVGVQMRSAMPMVPCQGPTCQGQICHNTVHLDPKVGPT  3750
3751  YSTARLSILTPRHHLQRSCSCNGTATRFSGQSYVRYRAPAARNWHIHFYL  3800
3801  KTLQPQAILLFTNETASVSLKLASGVPQLEYHCLGGFYGNLSSQRHVNDH  3850
3851  EWHSILVEEMDASIRLMVDSMGNTSLVVPENCRGLRPERHLLLGGLILLH  3900
3901  SSSNVSQGFEGCLDAVVVNEEALDLLAPGKTVAGLLETQALTQCCLHSDY  3950
3951  CSQNTCLNGGKCSWTHGAGYVCKCPPQFSGKHCEQGRENCTFAPCLEGGT  4000
4001  CILSPKGASCNCPHPYTGDRCEMEARGCSEGHCLVTPEIQRGDWGQQELL  4050
4051  IITVAVAFIIISTVGLLFYCRRCKSHKPVAMEDPDLLARSVGVDTQAMPA  4100
4101  IELNPLSASSCNNLNQPEPSKASVPNELVTFGPNSKQRPVVCSVPPRLPP  4150
4151  AAVPSHSDNEPVIKRTWSSEEMVYPGGAMVWPPTYSRNERWEYPHSEVTQ  4200
4201  GPLPPSAHRHSTPVVMPEPNGLYGGFPFPLEMENKRAPLPPRYSNQNLED  4250
4251  LMPSRPPSPRERLVAPCLNEYTAISYYHSQFRQGGGGPCLADGGYKGVGM  4300
4301  RLSRAGPSYAVCEVEGAPLAGQGQPRVPPNYEGSDMVESDYGSCEEVMF   4349

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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