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

NucPred

Fetching P78716 from www.uniprot.org...

The NucPred score for your sequence is 0.74 (see score help below)

   1  MEVTSAAAPSTGSSANGVTAAAPFPTIEPERVVEHLAAVCEIALGATRDE    50
  51  LEQLGSLLHKARYGETVSRCTRFASDSQNVLYIQKDIANPSAVEAGADPA   100
 101  APVTYNYTLSTEISSSSTTVSSLVLIKSPQPIDPTRPLTSQIFITNLPGP   150
 151  ASLNAGVGEQGTALSPWEVLHSQVHHALVPYFDANTKSQQLANGSRGRAD   200
 201  VDAKTGIPVTKKRLNDLELSLLHLQQNVDIPEISLTFHSIVQNVLDDAES   250
 251  RHTRPSLDAIPQIFSQDSTFLNRLQANVNTWIKSIQGITKLTKDPSSNAN   300
 301  QEFNTASQEVNFWLSMESALEGIEGQLRSEGVLLTLEILKHAKRFQATVS   350
 351  FTADTGLKEAMDKVQKYNQLMRDFPLDELLSATSLSKAQEAIAQIFGHLN   400
 401  KKLRICPYPIRRALPLVEAISADLDEVLHRLLPGTELVNLDYQQFQTIMQ   450
 451  TCDDIFRTWEENVKEFTNVAREVTRRRNEKFIPIKINKKHSELESRIKYV   500
 501  STFRDNHEQLQRTIINVLGPQATIPGVTETTGSNGIVMEEMGDVDAVEEV   550
 551  KQAWEALHNVDLLDVTDQGKERWVRAENLYNERTTRVENSIIARLRDRLA   600
 601  TAKTANEMFRVFSKFNALFVRPKIRGAIQEYQNQLMDHVKQAINGLHERF   650
 651  KQQYGHSETHAMAQLRDLPPVSGAIIWARQIEFQLDGYMRKVEAVLGPDW   700
 701  TMHTEGHKLQEESELFKQKLDTARIYEAWIADVGRRKISISGQLFEIARV   750
 751  RSAGGILELTVNFDPQVITLFKETRNLTWQSYSVPHAVTTVSKDAKRVYP   800
 801  YAVSLMESVRTLSQTLRQISVMGEESVLLFGYRNDVYKLISEGVPLRWES   850
 851  FINSHELFYSDNRQTRPLLPGGTDFGLAKNTESKHGMFIRGFAAAVSVLQ   900
 901  QKAVSLNFIHATVEQALKELNTCPYEEAAFHSRLDTIQAAVDQLNLEQYV   950
 951  NLDFWVRGLNSKVQSILLTRLQSAVHAWIEAFEDDTPDDEMRRKVNNNNE  1000
1001  EAKPDGPTMKRLVLELAMRNQVIYLDLLLEFARASWFLHLHEWLGIVCNL  1050
1051  RKIKATRYQMSLTTTANDEPRFTDLPSECAGLLQRVYVSVEKKLHEVSAY  1100
1101  VDKWLQFQSLWDLQSEQVYDALGEQLPRWLQLLQEIRKTRSTFDTQEVSR  1150
1151  AFGHLTIDYDQVQTKVNAKYDQWQHEILMKFASRLGNRMREINAEIEKAR  1200
1201  KHLESQSSDASSTAQAVQFITVVQSCKRNVKTWAPEIDMFRQGQSTLVRQ  1250
1251  RYQFPNDWLHIEQIDSQWEALKEILEKKSRIVQDQTDALQAKIVAEDKLI  1300
1301  NERIAEIAAQWNEEKPVSGTIQPDVASATLSSFESRISKLQDDAQMVAKA  1350
1351  KEALDIPASPDTSLEATLEEVRDFQSVWSNLSTIWASLNETRDVLWTAVQ  1400
1401  PRKIRSKVDDLIKSTKEMPSRMRQYAAFEHVQGILRGFLKVNSILSDLKS  1450
1451  DAIRERHWHKIYKQIKPQKRFSPSSMTLGDVWDLNLVATEVIVKDIIAQA  1500
1501  QGEMVLEEFLKQVRETWQNYALEMVNYQNKIGLIRGWDDLFAKCSENLNS  1550
1551  LQAMKHSPYYKEFEEEAVAWEDKLNRVHVLFDVWIDVQRQWVYLEGVFTG  1600
1601  NADIKHLLPIESGRFQNINSEFLAVMKKANKSPYVLEVLNIPNVQKSLER  1650
1651  LAEMLNKIQKALGEYLEKERVSFPRFYFVGDEDLLEMIGNSNDTLRIAKH  1700
1701  FKKMFAGLSGLVMDDETVISGFTSKEGEVVRLKKEISLAKTPKINDWLAL  1750
1751  LEGGMKSTLAELLAEAVDQYTPIFESETIDREALNGFMDAYPSQIVVLAT  1800
1801  QVVWTTAVHKSLTTGGETLKAIFDREVRVLRVLADTVLGELEVILRKKCE  1850
1851  QQITECVHQRDTIEKLINAKANSTNHYLWQLQMRYVYEPQGEYLDRLYIK  1900
1901  MANAKLNYGFEYLGVPERLVRTPLTDRCFLTLTQALCQRLGGSPYGPAGT  1950
1951  GKTESVKALGVQLGRFTLVFCCDDTFDFQAMGRIFLGICQVGAWGCFDEF  2000
2001  NRLEERILSAVSQEIQNIQLGLKQGVEDDQSQIELVGRHLHVNENTGIFI  2050
2051  TMNPGYAGRSNLPDNLKKLFRSVAMSKPDKELIAEVMLYSQGFNQAKQLS  2100
2101  KQTVPFFDQCSGRLSKQAHYDFGLRALKSVLVSSGGLKRARLGEGSLGAE  2150
2151  EVVEPEIIVQSIRETIAPKLIKSDVDIMATIETDCFPGVQYVPANLEALE  2200
2201  NAIRELAAERHLVVNELWMTKVLQLYQIQKIHHGVMMVGNSGSGKSAAWR  2250
2251  LLLDALQKVEGVEGVSHVIDSKVMSKEALYGNLDSTTREWTDGLFTSILR  2300
2301  KIVDNLRGEDSKRHWIVFDGDVDPEWVENLNSVLDDNKLLTLPNGERLNL  2350
2351  PANVRIMFEVETLKYATLATVSRCGMVWFSEDTVSPTMMVQNYLSTLRSV  2400
2401  PFEDLDEDSVATGHTPAKTLAVQSEFASLLHVYLTDENFILPALQRAEGY  2450
2451  NHIMEFTTARVLTTLFSLLNKAVRDAIEYNGQHSDFPLESEQIESFISKK  2500
2501  LLLALVWALTGDCPLTDRKSFGDDICALANFGSPPLDGNSSLIDFDVTLP  2550
2551  KAEWAPWQNQVPSVEVNTHSITQTDVVIPTLDTVRHENVLYSWLAEHKPL  2600
2601  LLCGPPGSGKTMTLFSALRKLPNMEVVGLNFSSATTPDLLIKTFEQYCEY  2650
2651  KKTLNGVMLSPTQIGRWLVIFCDEINLPAPDKYGTQRAISFLRQLVEHNG  2700
2701  FWRTSDKSWVTLDRIQFVGACNPPTDAGRTPMGARFLRHAPLIMVDYPGE  2750
2751  LSLNQIYGTFNSAVLKIIPSLRGYAEPLTHAMVRFYLESQQRFTPKIQPH  2800
2801  YVYSPRELTRWVRGVYEAIRPLEALTIEGLIRIWAHEALRLFQDRLVAEE  2850
2851  ERQWTDESVRRIALEFFPNIDEEKALGGPILFSNWLSKNYVPVDREQLRD  2900
2901  FVKARLKTFCEEEVDVPLILFNDVLEHVLRIDRVFRQPQGHLILIGVSGS  2950
2951  GKTTLSRFVAWMNGLKVFQIKVHGKYSAEDFDDDLRDVLRRCGCKGEKIC  3000
3001  FIMDESNVLDSGFLERMNTLLANAEVPGLFEGDEYAALMTACKEGAQRQN  3050
3051  LRLDSPEEMYKWFTQQIVKNLHVVFTMNPPEDGLSSKAATSPALFNRCVL  3100
3101  NWFGDWSDQALFQVGHELTQSIDLDRSNFECPDTIPVAYRGLQLPPSHRE  3150
3151  RVVNSMVHIHYSLQRYNEKLLKQQGKVTFLRPRHFLDFVTQYIKLYNEKR  3200
3201  EDLEEQQRHLNVGLEKLRDTVDKVRDLRVSLAEKKKQLEQKDAEANEKLQ  3250
3251  RMVADQREAEQRKNTSLEIQANLEKQEAEVASRKKVVLEDLAKAEPAVEE  3300
3301  AKASVSNIKRQHLTEVRSMGNPPQGVRLAMDAVCTLLGHRINDWKAVQGI  3350
3351  LRKDDFIASILMFDNAKQMTKGLRNKMRNDFLSNPEFTFEKVNRASKACG  3400
3401  PLVQWVAAQVNYFDILDRVGPLKIEVEQLEDQALETKAQAKSVQNNIADL  3450
3451  EASINTYKTEYAALISETQAIKAEMSRVQFKVDRSVRLLDSLSSERVRWE  3500
3501  AGSKSFEIQISTLVGDVLVAAAFLAYSGLYDQTFRKSMMDDWFHQLHLSG  3550
3551  IQYKSPNPVTEYLSTADERLGWQENALPVDDLCTENAIILKRFNRYPLII  3600
3601  DPSGRVTEFLQKECKDRRLTVTSFLDDTFTKQLESSLRFGNPILIQDAEH  3650
3651  LDPILNHVLNKECQRTGGRVLIQLGKQEIDFSPAFKLYLSTRDPSATFAP  3700
3701  DICSRTTFVNFTVTQSSLQTQSLNDVLKSERPDVDERRSNLIKLQGEFKI  3750
3751  HLRQLEKRLLQALNESRGNILDDDNVIETLETLKTEAAEISAKMSNTEGV  3800
3801  MAEVEEITQQYSIIARSCSAVFAVLEQLHYLNHFYQFSLQYFLDIFQSVL  3850
3851  HGNKNLANETDHNARRDVIVHDLFVNTFKRTALGLLQKDRITLGMLLAQA  3900
3901  SPYKMDKSVIDMILDNRVEGKDLSSHPDDRENAFAQAKKLSAIKDKIDAI  3950
3951  STEDWDKFFTEELAENAVPHIWDEKTEAIDQALLSLLLVKLFRMDRFVPA  4000
4001  AERFVAQVFGSDIFDIVEDLKQTVTQVSATLPISLVSSPGFDASYKVDNL  4050
4051  VERMRVKCTNIAMGSNEGLASADKAISNAAQTGSWVLVKNVHLAPTWLQS  4100
4101  LEKRMESLNPHSDFRLFLSMESSPKIPVNLLRASRVLMYEQPAGVRANMK  4150
4151  DSMSSLSTRATKSPVERTRLYLLLSFLHAVVQERLRYAPNLGWKGFWEFN  4200
4201  DSDYECSAYIVDTWIDGVAGNRTNLAPQNIPWEMLRYLVTETYGGKIDDE  4250
4251  GDFKLLSQLVTSFLTPAAYEVDHKLVDGPEGGLVVPSGTSFQDFNAWIHR  4300
4301  LPEREPPTYLGLPANAEKLLLGGLGRSLIGNLRKVTELLDEGEQLVTEV   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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