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

NucPred

Fetching P19137 from www.uniprot.org...

The NucPred score for your sequence is 0.75 (see score help below)

   1  MRGSGTGAALLVLLASVLWVTVRSQQRGLFPAILNLATNAHISANATCGE    50
  51  KGPEMFCKLVEHVPGRPVRHAQCRVCDGNSTNPRERHPISHAIDGTNNWW   100
 101  QSPSIQNGREYHWVTVTLDLRQVFQVAYIIIKAANAPRPGNWILERSVDG   150
 151  VKFKPWQYYAVSDTECLTRYKITPRRGPPTYRADNEVICTSYYSKLVPLE   200
 201  HGEIHTSLINGRPSADDPSPQLLEFTSARYIRLRLQRIRTLNADLMTLSH   250
 251  RDLRDLDPIVTRRYYYSIKDISVGGMCICYGHASSCPWDEEAKQLQCQCE   300
 301  HNTCGESCDRCCPGYHQQPWRPGTISSGNECEECNCHNKAKDCYYDSSVA   350
 351  KERRSLNTAGQYSGGGVCVNCSQNTTGINCETCIDQYYRPHKVSPYDDHP   400
 401  CRPCNCDPVGSLSSVCIKDDRHADLANGKWPGQCPCRKGYAGDKCDRCQF   450
 451  GYRGFPNCIPCDCRTVGSLNEDPCIEPCLCKKNVEGKNCDRCKPGFYNLK   500
 501  ERNPEGCSECFCFGVSGVCDSLTWSISQVTNMSGWLVTDLMSTNKIRSQQ   550
 551  DVLGGHRQISINNTAVMQRLTSTYYWAAPEAYLGNKLTAFGGFLKYTVSY   600
 601  DIPVETVDSDLMSHADIIIKGNGLTISTRAEGLSLQPYEEYFNVVRLVPE   650
 651  NFRDFDTRREIDRDQLMTVLANVTHLLIRANYNSAKMALYRLDSVSLDIA   700
 701  SPNAIDLAVAADVEHCECPQGYTGTSCEACLPGYYRVDGILFGGICQPCE   750
 751  CHGHASECDIHGICSVCTHNTTGDHCEQCLPGFYGTPSRGTPGDCQPCAC   800
 801  PLSIDSNNFSPTCHLTDGEEVVCDQCAPGYSGSWCERCADGYYGNPTVPG   850
 851  GTCVPCNCSGNVDPLEAGHCDSVTGECLKCLWNTDGAHCERCADGFYGDA   900
 901  VTAKNCRACDCHENGSLSGICHLETGLCDCKPHVTGQQCDQCLSGYYGLD   950
 951  TGLGCVPCNCSVEGSVSDNCTEEGQCHCGPGVSGKQCDRCSHGFYAFQDG  1000
1001  GCTPCDCAHTQNNCDPASGECLCPPHTQGLKCEECEEAYWGLDPEQGCQA  1050
1051  CNCSAVGSTSAQCDVLSGHCPCKKGFGGQSCHQCSLGYRSFPDCVPCGCD  1100
1101  LRGTLPDTCDLEQGLCSCSEDGGTCSCKENAVGPQCSKCQAGTFALRGDN  1150
1151  PQGCSPCFCFGLSQLCSELEGYVRTLITLASDQPLLHVVSQSNLKGTIEG  1200
1201  VHFQPPDTLLDAEAVRQHIYAEPFYWRLPKQFQGDQLLAYGGKLQYSVAF  1250
1251  YSTLGTGTSNYEPQVLIKGGRARKHVIYMDAPAPENGVRQDYEVRMKEEF  1300
1301  WKYFNSVSEKHVTHSDFMSVLSNIDYILIKASYGQGLQQSRIANISMEVG  1350
1351  RKAVELPAEGEAALLLELCVCPPGTAGHSCQDCAPGYYREKLPESGGRGP  1400
1401  RPLLAPCVPCNCNNHSDVCDPETGKCLSCRDHTSGDHCELCASGYYGKVT  1450
1451  GLPGDCTPCTCPHHPPFSFSPTCVVEGDSDFRCNACLPGYEGQYCERCSA  1500
1501  GYHGNPRAAGGSCQTCDCNPQGSVHSDCDRASGQCVCKPGATGLHCEKCL  1550
1551  PRHILMESDCVSCDDDCVGPLLNDLDSVGDAVLSLNLTGVSPAPYGILEN  1600
1601  LENTTKYFQRYLIKENAKKIRAEIQLEGIAEQTENLQKELTRVLARHQKV  1650
1651  NAEMERTSNGTQALATFIEQLHANIKEITEKVATLNQTARKDFQPPVSAL  1700
1701  QSMHQNISSLLGLIKERNFTEMQQNATLELKAAKDLLSRIQKRFQKPQEK  1750
1751  LKALKEANSLLSNHSEKLQAAEELLKEAGSKTQESNLLLLLVKANLKEFQ  1800
1801  EKKLRVQEEQNVTSELIAKGREWVDAAGTHTAAAQDTLTQLEHHRDELLL  1850
1851  WARKIRSHVDDLVMQMSKRRARDLVHRAEQHASELQSRAGALDRDLENVR  1900
1901  NVSLNATSAAHVHSNIQTLTEEAEMLAADAHKTANKTDLISESLASRGKA  1950
1951  VLQRSSRFLKESVSTRRKQQGITMKLDELKNLTSQFQESMDNIMKQANDS  2000
2001  LAMLRESPGGMREKGRKARELAAAANESAVKTLEDVLALSLRVFNTSEDL  2050
2051  SRVNATVQETNDLLHNSTMTTLLAGRKMKDMEMQANLLLDRLKPLKTLEE  2100
2101  NLSRNLSEIKLLISRARKQVASIKVAVSADRDCIRAYQPQTSSTNYNTLI  2150
2151  LNVKTQEPDNLLFYLGSSSSSDFLAVEMRRGKVAFLWDLGSGSTRLEFPE  2200
2201  VSINNNRWHSIYITRFGNMGSLSVKEASAAENPPVRTSKSPGPSKVLDIN  2250
2251  NSTLMFVGGLGGQIKKSPAVKVTHFKGCMGEAFLNGKSIGLWNYIEREGK  2300
2301  CNGCFGSSQNEDSSFHFDGSGYAMVEKTLRPTVTQIVILFSTFSPNGLLF  2350
2351  YLASNGTKDFLSIELVRGRVKVMVDLGSGPLTLMTDRRYNNGTWYKIAFQ  2400
2401  RNRKQGLLAVFDAYDTSDKETKQGETPGAASDLNRLEKDLIYVGGLPHSK  2450
2451  AVRKGVSSRSYVGCIKNLEISRSTFDLLRNSYGVRKGCALEPIQSVSFLR  2500
2501  GGYVEMPPKSLSPESSLLATFATKNSSGILLVALGKDAEEAGGAQAHVPF  2550
2551  FSIMLLEGRIEVHVNSGDGTSLRKALLHAPTGSYSDGQEHSISLVRNRRV  2600
2601  ITIQVDENSPVEMKLGPLTEGKTIDISNLYIGGLPEDKATPMLKMRTSFH  2650
2651  GCIKNVVLDAQLLDFTHATGSEQVELDTCLLAEEPMQSLHREHGELPPEP  2700
2701  PTLPQPELCAVDTAPGYVAGAHQFGLSQNSHLVLPLNQSDVRKRLQVQLS  2750
2751  IRTFASSGLIYYVAHQNQMDYATLQLQEGRLHFMFDLGKGRTKVSHPALL  2800
2801  SDGKWHTVKTEYIKRKAFMTVDGQESPSVTVVGNATTLDVERKLYLGGLP  2850
2851  SHYRARNIGTITHSIPACIGEIMVNGQQLDKDRPLSASAVDRCYVVAQEG  2900
2901  TFFEGSGYAALVKEGYKVRLDLNITLEFRTTSKNGVLLGISSAKVDAIGL  2950
2951  EIVDGKVLFHVNNGAGRITATYQPRAARALCDGKWHTLQAHKSKHRIVLT  3000
3001  VDGNSVRAESPHTHSTSADTNDPIYVGGYPAHIKQNCLSSRASFRGCVRN  3050
3051  LRLSRGSQVQSLDLSRAFDLQGVFPHSCPGPEP                   3083

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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