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

NucPred

Fetching Q99715 from www.uniprot.org...

The NucPred score for your sequence is 0.35 (see score help below)

   1  MRSRLPPALAALGAALLLSSIEAEVDPPSDLNFKIIDENTVHMSWAKPVD    50
  51  PIVGYRITVDPTTDGPTKEFTLSASTTETLLSELVPETEYVVTITSYDEV   100
 101  EESVPVIGQLTIQTGSSTKPVEKKPGKTEIQKCSVSAWTDLVFLVDGSWS   150
 151  VGRNNFKYILDFIAALVSAFDIGEEKTRVGVVQYSSDTRTEFNLNQYYQR   200
 201  DELLAAIKKIPYKGGNTMTGDAIDYLVKNTFTESAGARVGFPKVAIIITD   250
 251  GKSQDEVEIPARELRNVGVEVFSLGIKAADAKELKQIASTPSLNHVFNVA   300
 301  NFDAIVDIQNEIISQVCSGVDEQLGELVSGEEVVEPPSNLIAMEVSSKYV   350
 351  KLNWNPSPSPVTGYKVILTPMTAGSRQHALSVGPQTTTLSVRDLSADTEY   400
 401  QISVSAMKGMTSSEPISIMEKTQPMKVQVECSRGVDIKADIVFLVDGSYS   450
 451  IGIANFVKVRAFLEVLVKSFEISPNRVQISLVQYSRDPHTEFTLKKFTKV   500
 501  EDIIEAINTFPYRGGSTNTGKAMTYVREKIFVPSKGSRSNVPKVMILITD   550
 551  GKSSDAFRDPAIKLRNSDVEIFAVGVKDAVRSELEAIASPPAETHVFTVE   600
 601  DFDAFQRISFELTQSICLRIEQELAAIKKKAYVPPKDLSFSEVTSYGFKT   650
 651  NWSPAGENVFSYHITYKEAAGDDEVTVVEPASSTSVVLSSLKPETLYLVN   700
 701  VTAEYEDGFSIPLAGEETTEEVKGAPRNLKVTDETTDSFKITWTQAPGRV   750
 751  LRYRIIYRPVAGGESREVTTPPNQRRRTLENLIPDTKYEVSVIPEYFSGP   800
 801  GTPLTGNAATEEVRGNPRDLRVSDPTTSTMKLSWSGAPGKVKQYLVTYTP   850
 851  VAGGETQEVTVRGDTTNTVLQGLKEGTQYALSVTALYASGAGDALFGEGT   900
 901  TLEERGSPQDLVTKDITDTSIGAYWTSAPGMVRGYRVSWKSLYDDVDTGE   950
 951  KNLPEDAIHTMIENLQPETKYRISVFATYSSGEGEPLTGDATTELSQDSK  1000
1001  TLKVDEETENTMRVTWKPAPGKVVNYRVVYRPHGRGKQMVAKVPPTVTST  1050
1051  VLKRLQPQTTYDITVLPIYKMGEGKLRQGSGTTASRFKSPRNLKTSDPTM  1100
1101  SSFRVTWEPAPGEVKGYKVTFHPTGDDRRLGELVVGPYDNTVVLEELRAG  1150
1151  TTYKVNVFGMFDGGESSPLVGQEMTTLSDTTVMPILSSGMECLTRAEADI  1200
1201  VLLVDGSWSIGRANFRTVRSFISRIVEVFDIGPKRVQIALAQYSGDPRTE  1250
1251  WQLNAHRDKKSLLQAVANLPYKGGNTLTGMALNFIRQQNFRTQAGMRPRA  1300
1301  RKIGVLITDGKSQDDVEAPSKKLKDEGVELFAIGIKNADEVELKMIATDP  1350
1351  DDTHAYNVADFESLSRIVDDLTINLCNSVKGPGDLEAPSNLVISERTHRS  1400
1401  FRVSWTPPSDSVDRYKVEYYPVSGGKRQEFYVSRMETSTVLKDLKPETEY  1450
1451  VVNVYSVVEDEYSEPLKGTEKTLPVPVVSLNIYDVGPTTMHVQWQPVGGA  1500
1501  TGYILSYKPVKDTEPTRPKEVRLGPTVNDMQLTDLVPNTEYAVTVQAVLH  1550
1551  DLTSEPVTVREVTLPLPRPQDLKLRDVTHSTMNVFWEPVPGKVRKYIVRY  1600
1601  KTPEEDVKEVEVDRSETSTSLKDLFSQTLYTVSVSAVHDEGESPPVTAQE  1650
1651  TTRPVPAPTNLKITEVTSEGFRGTWDHGASDVSLYRITWAPFGSSDKMET  1700
1701  ILNGDENTLVFENLNPNTIYEVSITAIYPDESESDDLIGSERTLPILTTQ  1750
1751  APKSGPRNLQVYNATSNSLTVKWDPASGRVQKYRITYQPSTGEGNEQTTT  1800
1801  IGGRQNSVVLQKLKPDTPYTITVSSLYPDGEGGRMTGRGKTKPLNTVRNL  1850
1851  RVYDPSTSTLNVRWDHAEGNPRQYKLFYAPAAGGPEELVPIPGNTNYAIL  1900
1901  RNLQPDTSYTVTVVPVYTEGDGGRTSDTGRTLMRGLARNVQVYNPTPNSL  1950
1951  DVRWDPAPGPVLQYRVVYSPVDGTRPSESIVVPGNTRMVHLERLIPDTLY  2000
2001  SVNLVALYSDGEGNPSPAQGRTLPRSGPRNLRVFGETTNSLSVAWDHADG  2050
2051  PVQQYRIIYSPTVGDPIDEYTTVPGRRNNVILQPLQPDTPYKITVIAVYE  2100
2101  DGDGGHLTGNGRTVGLLPPQNIHISDEWYTRFRVSWDPSPSPVLGYKIVY  2150
2151  KPVGSNEPMEAFVGEMTSYTLHNLNPSTTYDVNVYAQYDSGLSVPLTDQG  2200
2201  TTLYLNVTDLKTYQIGWDTFCVKWSPHRAATSYRLKLSPADGTRGQEITV  2250
2251  RGSETSHCFTGLSPDTDYGVTVFVQTPNLEGPGVSVKEHTTVKPTEAPTE  2300
2301  PPTPPPPPTIPPARDVCKGAKADIVFLTDASWSIGDDNFNKVVKFIFNTV  2350
2351  GGFDEISPAGIQVSFVQYSDEVKSEFKLNTYNDKALALGALQNIRYRGGN  2400
2401  TRTGKALTFIKEKVLTWESGMRKNVPKVLVVVTDGRSQDEVKKAALVIQQ  2450
2451  SGFSVFVVGVADVDYNELANIASKPSERHVFIVDDFESFEKIEDNLITFV  2500
2501  CETATSSCPLIYLDGYTSPGFKMLEAYNLTEKNFASVQGVSLESGSFPSY  2550
2551  SAYRIQKNAFVNQPTADLHPNGLPPSYTIILLFRLLPETPSDPFAIWQIT  2600
2601  DRDYKPQVGVIADPSSKTLSFFNKDTRGEVQTVTFDTEEVKTLFYGSFHK  2650
2651  VHIVVTSKSVKIYIDCYEIIEKDIKEAGNITTDGYEILGKLLKGERKSAA  2700
2701  FQIQSFDIVCSPVWTSRDRCCDIPSRRDEGKCPAFPNSCTCTQDSVGPPG  2750
2751  PPGPAGGPGAKGPRGERGISGAIGPPGPRGDIGPPGPQGPPGPQGPNGLS  2800
2801  IPGEQGRQGMKGDAGEPGLPGRTGTPGLPGPPGPMGPPGDRGFTGKDGAM  2850
2851  GPRGPPGPPGSPGSPGVTGPSGKPGKPGDHGRPGPSGLKGEKGDRGDIAS  2900
2901  QNMMRAVARQVCEQLISGQMNRFNQMLNQIPNDYQSSRNQPGPPGPPGPP  2950
2951  GSAGARGEPGPGGRPGFPGTPGMQGPPGERGLPGEKGERGTGSSGPRGLP  3000
3001  GPPGPQGESRTGPPGSTGSRGPPGPPGRPGNSGIRGPPGPPGYCDSSQCA  3050
3051  SIPYNGQGYPGSG                                       3063

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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