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

NucPred

Fetching Q9C1M7 from www.uniprot.org...

The NucPred score for your sequence is 0.87 (see score help below)

   1  MTDDQVAQALVGYVFNVAKLFLKLGAEQDAFARTHGAKFEEWVGNGNMRT    50
  51  LFLVKDEEGEVQVLEELEGPEAEGPEQSGRLLLIKNRPFVDGSAPMESQV   100
 101  QVLHLPPRAHFDGFKSFVSFGVATMFDAVVSTNSNLEAKQESINSARRKI   150
 151  KDLSLSLQSLQQFIEVPDISATAHPLIKKIIAEGANPRNYTTYISDEQFA   200
 201  DSQFLNSLQKIANGWIKSAQNLTKLTRNIEDGSATDEIRFWINLEQSLLA   250
 251  LEKQIAIPEVEITLSILTAAKRFHATVTFISDTGLRDRILETQSYNQIMS   300
 301  DFPLADLQTATSFTKLGEAIESISIALKKLKVSTYPLARAVTFVEKISTE   350
 351  LDQKLREMLPNLISSDFISFQEDYDHCIKIINQWEALLKEFTSLIRELMR   400
 401  KRSEKFIFMKIDTQTDSLKEVLNTVAAFRKKHDILIHVLKGIGYDTLSED   450
 451  IQSIYEPVQYQDPLRDNASKWANAEAAYNQRVSLLENKLVDMLKKKLDDC   500
 501  KSSDSMFSIFEKYRPLMKRPRIQGAVREYQHELLHNVKDDLEHIHQQLSL   550
 551  QKWNSELSRLNDIPPVSASIIWSKQLTKKLQNLTSRLGLILGEDWISTSE   600
 601  GSQIFVECSSIMKVLNTDKLFETWVSNVSSQNFLLDEPIFKILITNEEYE   650
 651  LHVNFDSVVGSLFKEVRNLMWMGFNVPSNIIKNSRRVRSLYPHAVMVSEL   700
 701  LQTFVSAVQSFQERPHTWLLLKDETENIWQLLSAMITDTWDSVPLFEDDA   750
 751  SHERAEDIQRDEPSILRLEHSIGELLSKFQQLDGLEKGLSSCLQQLEVFG   800
 801  KLDLQNLEVLINKIQLLVDQATLHGFHNMSGFIDYLNTRIRSYLVSTVSK   850
 851  ILEESQLSPKKHYILQQGKKVTISPSIEETKKAWMRDFQKTLEVATNLPK   900
 901  ITDKKFDITEGQMENFTDIGTDLSESLIKAFLRIEDACHAIDEHFQKWKK   950
 951  LELLWCLDELTLLERLGSDVEVSYRFLLDFMEERKAIDMVDSEITIAGDT  1000
1001  MINNEQVYVRVSAKYDNWQRVLCEKLLENYMDHASEFDTQLVHSRRLLET  1050
1051  SIINLGSLSKTTELIAYVDDIKNNLDLMFTRYSLLLNTQKLLQKLRFRIP  1100
1101  QNFIHAEQIESDLVSLREICLRKEDLINKNRDAISNKLEAELLKIQEVAN  1150
1151  SLSQSWSKKKPLSVSIQPSEALSVLNTFEDSIAKVNTERELINRAAKILL  1200
1201  VPIKLQNVLSPVIEEVNDYKAVWSSVDGLWNSFNATLSVKWADFESTAVK  1250
1251  HRLEALMKKCQDMPPKVLQYKIFQNIAGSIEATLKSMHLLKALKEPAIKP  1300
1301  RHWSILFKQLGASHIVSGNIDDQTFTLEDILQLNILLNEVSVKKIVIKAR  1350
1351  NENVLESSLSQMKARWRATKFDQFVHSSGLVLVKGWDVIFSNCNDDLNMI  1400
1401  TSMKNSPYFKVFEQEALEWETKLSNFYDIVLSWVEVQRQWMYLFGILAKK  1450
1451  TEMKNLLPIEASKFASLTSEYNSLLLKLYGSEIAIDILHVHSTLPTLKRM  1500
1501  AESLTKIRKSLNDFLETQRRLFPRFYFVGNEDLLQIIGAGDNFSEFSRHL  1550
1551  SKLFSSVSDFIYDESLIQGVYSLEGETLLFANPVRVTPSSKLDQWMNEVD  1600
1601  LEIKLTLSTLVKNCLESYRTSGSLKHIIEKYPFQALLLALQCTWTNKIET  1650
1651  SMTKDNFGSICSSIDEEMASLAAVIDSYPTVTEKRKVESLIVELVHLKTI  1700
1701  TETLKNVELEQIDFHWKQTQRFYWDDNSNDPLNSITIEQSCVSFCYGFEY  1750
1751  IGVPERLIYTPLLDSCFNAMVLALSEHMGGCPFGPAGTGKTETIKALGQN  1800
1801  FGRMVLVFNCDDSFDFQAMSRLLFGITQVGAWGCFDEFNRLEEKILSAVS  1850
1851  TQVEAIQLSLVQGKPEIEVLDKKGSLNSNTGIFITMNPGYAGRSELPENL  1900
1901  KKMFREFAMMKPDALVIAEVILTILGLENPRVLAEKIVSLFKLLNDKTTS  1950
1951  QKHYDFGLRALKSVLRNCLTILRSTTDLDSTQVLLRSLNEMVVPKLISVD  2000
2001  EAVYEEAIADFFPGSRIKPSNEQLLSYLASYCESNQLVASDLFIKKCSQF  2050
2051  YDIQKTQQAIILAGDAGTGKTSVWKSVINSMKRSGAKENIVYIIDTKTLK  2100
2101  KEDLYGKLDPVTFDWKDGIFTHLLRKTLLDTMGNFKNSNIWIVFDSDLDP  2150
2151  NYTETLNSVLDDNKVLTLPNGERLKIPPNLHILFEVQDLEHATAATVSRC  2200
2201  GMIWFANNTLAAQDILISCLSREVATLQQDADVHDNIIATIQDIFAQFIQ  2250
2251  GSTLGNVIEATYKADHIMGVDFCRFIETAVTLLSCDIKKNKKQLSRLSQV  2300
2301  ACVRYMSKRLALVLIWAFVGGSDLETREKFSETICELLGISDIPTGSKFL  2350
2351  LDYDVSVATQDWVPVSAEVPKTSLESHEVLIPDLIIPTVDTVRHETLLFD  2400
2401  LLNADRPLILCGPPGSGKTMTLYNTLKRSDRFNIIGINFSKDTSVELFLK  2450
2451  TLEQHTICTPTSRGIIMQPKAHGKQLVVFCDEINLPMLDEYGSQPVILFL  2500
2501  RQLIEKRGFWNVQESKWVFIERIQIVGACNPPGHAGRVSITPRFLRHASI  2550
2551  VMVDYPGQIAMEQIYETFFNAIFKLTPKLKGFASDFTKASLQVYYDCKAT  2600
2601  YTSEAHSHYIYSPRELTRWVRGIHFTISDSGNIDLAYMLELWAHESLRLF  2650
2651  SDRLVSSSEKNIFQSILQNAITTHFPNQPLGSLESSQLLFSNWLSLNYSK  2700
2701  VVKSEMYTFIKERLKTFAEEELDTELTIYDDMIDNILRIDRILKQVQGHG  2750
2751  ILVGPNYSGKTTITRFVAWMNGIKVVRPTIHRHFTIENFDEFLKQMLLRC  2800
2801  GTESEKICLIIDESNILETSFLERMNTLLANSDVPGLFEADEYEALLSKI  2850
2851  GQRISQLGLLLDTEQEMYDWFTSEISKNLHVIFNINDPDNRESTQLITSP  2900
2901  ALFNRSVINWIGTWSSRSCLHVVNEVIKNMPLDRADYTIPHHAAANLIVP  2950
2951  DGNLVTIRDVVANLFVLFHEQYHRLLGNSQGSPSAFLTSLRRFQSLYMSK  3000
3001  LKELEEHQRFTLVGLEKLKDTVIKVKQLNQSLSQKQVELQQKEKEARDTL  3050
3051  DKMLVDQNEAERKQEASVEIQKILALQEKEINERRKIIMADLAVAEPAIL  3100
3101  EAQRGVKNIKKQQFTELRSMLNPPDAVKTTLEAVCVILGYSCKTWKDIQL  3150
3151  AIRKDEFVTDIVYYNTETMMTPAMKQDIETDYLSRPKFNYESVNRASLAC  3200
3201  GPLYQWIVAQISYSEMLVKVTPLKEEMVKVENEMLQNKARLMAAGEMIKE  3250
3251  LQTSIESSKVSYSKLIREVEITKTEMESVQSKVERSIKLMESLTGEKERW  3300
3301  IKNTEHFKDWNKNLIGNCFLSSLYESYCGPHDQSLRLKLFTIWSNTLAKF  3350
3351  GIEYEPTYSFITDMVNPLTKVNWVACGLPDNELFVANFHIAMNSCHYPYV  3400
3401  IDPSSTIVDTFANFYGRKMMITSFLDVGFVKQLENALRFGGCILIQDGEF  3450
3451  FDPIISHLIAKEFKKAGGRLTVQIGDHEVDVSTSFQLIIHSKDPNSYMSS  3500
3501  FVKTRMAVINFTVSKGSIEAQALQITLEKENPELQKQRTDLLKLNGEYKL  3550
3551  HLRSLEDKLLESLNESDGSILENDSLISTLEQLKIESSEIAKKIEETNTV  3600
3601  IVKVEDLVNEYNVLGEQSVLIFNLLESITQWHWFYQIPIEQFMECFSSIF  3650
3651  ATKTRENMTRSEHLLLALYEHVYMWFSHVFKDRDRMAFGILLFASYHHSR  3700
3701  ESKFFSEHFWKIIEGIASDTLGTVEHITDTKLEQLVAAANEKDYLKGLKS  3750
3751  LLEFLPESSWHDSVPKYQNIIVACERGVDGTFKVQQLAQEMGKTVHSVAL  3800
3801  GSAESISMAEQDLIQYSGEGKWLLLQNLQMSHEWANTVLPKKLESIKANP  3850
3851  DFRVFMTCGIQSKPLVVPLLSRSYKIAYEGEPGVLNTVCELWRTQSEELK  3900
3901  NVKPVEKLHSKFILVWFHSIIMARCRLAPIGFTKKYDFHDGDFHAGSKFL  3950
3951  DHIFEQSSNGKEHVDPDLVPWKLVSDTIGKIIYGGKVDDPADLDWCKRSA  4000
4001  RRMFSSDAYLNNFEVVQGLTVPIDRSSYSQYDKWFKSLDAAAERTTAWLE  4050
4051  LSDASALQNFYAHEARMICKKIIQTNGPTSLIH                   4083

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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