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

NucPred

Fetching P12111 from www.uniprot.org...

The NucPred score for your sequence is 0.44 (see score help below)

   1  MRKHRHLPLVAVFCLFLSGFPTTHAQQQQADVKNGAAADIIFLVDSSWTI    50
  51  GEEHFQLVREFLYDVVKSLAVGENDFHFALVQFNGNPHTEFLLNTYRTKQ   100
 101  EVLSHISNMSYIGGTNQTGKGLEYIMQSHLTKAAGSRAGDGVPQVIVVLT   150
 151  DGHSKDGLALPSAELKSADVNVFAIGVEDADEGALKEIASEPLNMHMFNL   200
 201  ENFTSLHDIVGNLVSCVHSSVSPERAGDTETLKDITAQDSADIIFLIDGS   250
 251  NNTGSVNFAVILDFLVNLLEKLPIGTQQIRVGVVQFSDEPRTMFSLDTYS   300
 301  TKAQVLGAVKALGFAGGELANIGLALDFVVENHFTRAGGSRVEEGVPQVL   350
 351  VLISAGPSSDEIRYGVVALKQASVFSFGLGAQAASRAELQHIATDDNLVF   400
 401  TVPEFRSFGDLQEKLLPYIVGVAQRHIVLKPPTIVTQVIEVNKRDIVFLV   450
 451  DGSSALGLANFNAIRDFIAKVIQRLEIGQDLIQVAVAQYADTVRPEFYFN   500
 501  THPTKREVITAVRKMKPLDGSALYTGSALDFVRNNLFTSSAGYRAAEGIP   550
 551  KLLVLITGGKSLDEISQPAQELKRSSIMAFAIGNKGADQAELEEIAFDSS   600
 601  LVFIPAEFRAAPLQGMLPGLLAPLRTLSGTPEVHSNKRDIIFLLDGSANV   650
 651  GKTNFPYVRDFVMNLVNSLDIGNDNIRVGLVQFSDTPVTEFSLNTYQTKS   700
 701  DILGHLRQLQLQGGSGLNTGSALSYVYANHFTEAGGSRIREHVPQLLLLL   750
 751  TAGQSEDSYLQAANALTRAGILTFCVGASQANKAELEQIAFNPSLVYLMD   800
 801  DFSSLPALPQQLIQPLTTYVSGGVEEVPLAQPESKRDILFLFDGSANLVG   850
 851  QFPVVRDFLYKIIDELNVKPEGTRIAVAQYSDDVKVESRFDEHQSKPEIL   900
 901  NLVKRMKIKTGKALNLGYALDYAQRYIFVKSAGSRIEDGVLQFLVLLVAG   950
 951  RSSDRVDGPASNLKQSGVVPFIFQAKNADPAELEQIVLSPAFILAAESLP  1000
1001  KIGDLHPQIVNLLKSVHNGAPAPVSGEKDVVFLLDGSEGVRSGFPLLKEF  1050
1051  VQRVVESLDVGQDRVRVAVVQYSDRTRPEFYLNSYMNKQDVVNAVRQLTL  1100
1101  LGGPTPNTGAALEFVLRNILVSSAGSRITEGVPQLLIVLTADRSGDDVRN  1150
1151  PSVVVKRGGAVPIGIGIGNADITEMQTISFIPDFAVAIPTFRQLGTVQQV  1200
1201  ISERVTQLTREELSRLQPVLQPLPSPGVGGKRDVVFLIDGSQSAGPEFQY  1250
1251  VRTLIERLVDYLDVGFDTTRVAVIQFSDDPKVEFLLNAHSSKDEVQNAVQ  1300
1301  RLRPKGGRQINVGNALEYVSRNIFKRPLGSRIEEGVPQFLVLISSGKSDD  1350
1351  EVDDPAVELKQFGVAPFTIARNADQEELVKISLSPEYVFSVSTFRELPSL  1400
1401  EQKLLTPITTLTSEQIQKLLASTRYPPPAVESDAADIVFLIDSSEGVRPD  1450
1451  GFAHIRDFVSRIVRRLNIGPSKVRVGVVQFSNDVFPEFYLKTYRSQAPVL  1500
1501  DAIRRLRLRGGSPLNTGKALEFVARNLFVKSAGSRIEDGVPQHLVLVLGG  1550
1551  KSQDDVSRFAQVIRSSGIVSLGVGDRNIDRTELQTITNDPRLVFTVREFR  1600
1601  ELPNIEERIMNSFGPSAATPAPPGVDTPPPSRPEKKKADIVFLLDGSINF  1650
1651  RRDSFQEVLRFVSEIVDTVYEDGDSIQVGLVQYNSDPTDEFFLKDFSTKR  1700
1701  QIIDAINKVVYKGGRHANTKVGLEHLRVNHFVPEAGSRLDQRVPQIAFVI  1750
1751  TGGKSVEDAQDVSLALTQRGVKVFAVGVRNIDSEEVGKIASNSATAFRVG  1800
1801  NVQELSELSEQVLETLHDAMHETLCPGVTDAAKACNLDVILGFDGSRDQN  1850
1851  VFVAQKGFESKVDAILNRISQMHRVSCSGGRSPTVRVSVVANTPSGPVEA  1900
1901  FDFDEYQPEMLEKFRNMRSQHPYVLTEDTLKVYLNKFRQSSPDSVKVVIH  1950
1951  FTDGADGDLADLHRASENLRQEGVRALILVGLERVVNLERLMHLEFGRGF  2000
2001  MYDRPLRLNLLDLDYELAEQLDNIAEKACCGVPCKCSGQRGDRGPIGSIG  2050
2051  PKGIPGEDGYRGYPGDEGGPGERGPPGVNGTQGFQGCPGQRGVKGSRGFP  2100
2101  GEKGEVGEIGLDGLDGEDGDKGLPGSSGEKGNPGRRGDKGPRGEKGERGD  2150
2151  VGIRGDPGNPGQDSQERGPKGETGDLGPMGVPGRDGVPGGPGETGKNGGF  2200
2201  GRRGPPGAKGNKGGPGQPGFEGEQGTRGAQGPAGPAGPPGLIGEQGISGP  2250
2251  RGSGGAAGAPGERGRTGPLGRKGEPGEPGPKGGIGNRGPRGETGDDGRDG  2300
2301  VGSEGRRGKKGERGFPGYPGPKGNPGEPGLNGTTGPKGIRGRRGNSGPPG  2350
2351  IVGQKGDPGYPGPAGPKGNRGDSIDQCALIQSIKDKCPCCYGPLECPVFP  2400
2401  TELAFALDTSEGVNQDTFGRMRDVVLSIVNDLTIAESNCPRGARVAVVTY  2450
2451  NNEVTTEIRFADSKRKSVLLDKIKNLQVALTSKQQSLETAMSFVARNTFK  2500
2501  RVRNGFLMRKVAVFFSNTPTRASPQLREAVLKLSDAGITPLFLTRQEDRQ  2550
2551  LINALQINNTAVGHALVLPAGRDLTDFLENVLTCHVCLDICNIDPSCGFG  2600
2601  SWRPSFRDRRAAGSDVDIDMAFILDSAETTTLFQFNEMKKYIAYLVRQLD  2650
2651  MSPDPKASQHFARVAVVQHAPSESVDNASMPPVKVEFSLTDYGSKEKLVD  2700
2701  FLSRGMTQLQGTRALGSAIEYTIENVFESAPNPRDLKIVVLMLTGEVPEQ  2750
2751  QLEEAQRVILQAKCKGYFFVVLGIGRKVNIKEVYTFASEPNDVFFKLVDK  2800
2801  STELNEEPLMRFGRLLPSFVSSENAFYLSPDIRKQCDWFQGDQPTKNLVK  2850
2851  FGHKQVNVPNNVTSSPTSNPVTTTKPVTTTKPVTTTTKPVTTTTKPVTII  2900
2901  NQPSVKPAAAKPAPAKPVAAKPVATKMATVRPPVAVKPATAAKPVAAKPA  2950
2951  AVRPPAAAAAKPVATKPEVPRPQAAKPAATKPATTKPMVKMSREVQVFEI  3000
3001  TENSAKLHWERAEPPGPYFYDLTVTSAHDQSLVLKQNLTVTDRVIGGLLA  3050
3051  GQTYHVAVVCYLRSQVRATYHGSFSTKKSQPPPPQPARSASSSTINLMVS  3100
3101  TEPLALTETDICKLPKDEGTCRDFILKWYYDPNTKSCARFWYGGCGGNEN  3150
3151  KFGSQKECEKVCAPVLAKPGVISVMGT                         3177

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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