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

NucPred

Fetching Q8N2C7 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MVKRKSSEGQEQDGGRGIPLPIQTFLWRQTSAFLRPKLGKQYEASCVSFE    50
  51  RVLVENKLHGLSPALSEAIQSISRWELVQAALPHVLHCTATLLSNRNKLG   100
 101  HQDKLGVAETKLLHTLHWMLLEAPQDCNNERFGGTDRGSSWGGSSSAFIH   150
 151  QVENQGSPGQPCQSSSNDEEENNRRKIFQNSMATVELFVFLFAPLVHRIK   200
 201  ESDLTFRLASGLVIWQPMWEHRQPGVSGFTALVKPIRNIITAKRSSPINS   250
 251  QSRTCESPNQDARHLEGLQVVCETFQSDSISPKATISGCHRGNSFDGSLS   300
 301  SQTSQERGPSHSRASLVIPPCQRSRYATYFDVAVLRCLLQPHWSEEGTQW   350
 351  SLMYYLQRLRHMLEEKPEKPPEPDIPLLPRPRSSSMVAAAPSLVNTHKTQ   400
 401  DLTMKCNEEEKSLSSEAFSKVSLTNLRRSAVPDLSSDLGMNIFKKFKSRK   450
 451  EDRERKGSIPFHHTGKRRPRRMGVPFLLHEDHLDVSPTRSTFSFGSFSGL   500
 501  GEDRRGIEKGGWQTTILGKLTRRGSSDAATEMESLSARHSHSHHTLVSDL   550
 551  PDPSNSHGENTVKEVRSQISTITVATFNTTLASFNVGYADFFNEHMRKLC   600
 601  NQVPIPEMPHEPLACANLPRSLTDSCINYSYLEDTEHIDGTNNFVHKNGM   650
 651  LDLSVVLKAVYLVLNHDISSRICDVALNIVECLLQLGVVPCVEKNRKKSE   700
 701  NKENETLEKRPSEGAFQFKGVSGSSTCGFGGPAVSGAGDGGGEEGGGGDG   750
 751  GGGGGDGGGGGGGGGGPYEKNDKNQEKDESTPVSNHRLALTMLIKIVKSL   800
 801  GCAYGCGEGHRGLSGDRLRHQVFRENAQNCLTKLYKLDKMQFRQTMRDYV   850
 851  NKDSLNNVVDFLHALLGFCMEPVTDNKAGFGNNFTTVDNKSTAQNVEGII   900
 901  VSAMFKSLITRCASTTHELHSPENLGLYCDIRQLVQFIKEAHGNVFRRVA   950
 951  LSALLDSAEKLAPGKKVEENEQESKPAGSKRSEAGSIVDKGQVSSAPEEC  1000
1001  RSFMSGRPSQTPEHDEQMQGANLGRKDFWRKMFKSQSAASDTSSQSEQDT  1050
1051  SECTTAHSGTTSDRRARSRSRRISLRKKLKLPIGKRNWLKRSSLSGLADG  1100
1101  VEDLLDISSVDRLSFIRQSSKVKFTSAVKLSEGGPGSGMENGRDEEENFF  1150
1151  KRLGCHSFDDHLSPNQDGGKSKNVVNLGAIRQGMKRFQFLLNCCEPGTIP  1200
1201  DASILAAALDLEAPVVARAALFLECARFVHRCNRGNWPEWMKGHHVNITK  1250
1251  KGLSRGRSPIVGNKRNQKLQWNAAKLFYQWGDAIGVRLNELCHGESESPA  1300
1301  NLLGLIYDEETKRRLRKEDEEEDFLDDSTVNPSKCGCPFALKMAACQLLL  1350
1351  EITTFLRETFSCLPRPRTEPLVDLESCRLRLDPELDRHRYERKISFAGVL  1400
1401  DENEDSKDSLHSSSHTLKSDAGVEEKKEGSPWSASEPSIEPEGMSNAGAE  1450
1451  ENYHRNMSWLHVMILLCNQQSFICTHVDYCHPHCYLHHSRSCARLVRAIK  1500
1501  LLYGDSVDSLRESSNISSVALRGKKQKECSDKSCLRTPSLKKRVSDANLE  1550
1551  GKKDSGMLKYIRLQVMSLSPAPLSLLIKAAPILTEEMYGDIQPAAWELLL  1600
1601  SMDEHMAGAAAAMFLLCAVKVPEAVSDMLMSEFHHPETVQRLNAVLKFHT  1650
1651  LWRFRYQVWPRMEEGAQQIFKIPPPSINFTLPSPVLGMPSVPMFDPPWVP  1700
1701  QCSGSVQDPINEDQSKSFSARAVSRSHQRAEHILKNLQQEEEKKRLGREA  1750
1751  SLITAIPITQEACYEPTCTPNSEPEEEVEEVTNLASRRLSVSPSCTSSTS  1800
1801  HRNYSFRRGSVWSVRSAVSAEDEEHTTEHTPNHHVPQPPQAVFPACICAA  1850
1851  VLPIVHLMEDGEVREDGVAVSAVAQQVLWNCLIEDPSTVLRHFLEKLTIS  1900
1901  NRQDELMYMLRKLLLNIGDFPAQTSHILFNYLVGLIMYFVRTPCEWGMDA  1950
1951  ISATLTFLWEVVGYVEGLFFKDLKQTMKKEQCEVKLLVTASMPGTKTLVV  2000
2001  HGQNECDIPTQLPVHEDTQFEALLKECLEFFNIPESQSTHYFLMDKRWNL  2050
2051  IHYNKTYVRDIYPFRRSVSPQLNLVHMHPEKGQELIQKQVFTRKLEEVGR  2100
2101  VLFLISLTQKIPTAHKQSHVSMLQEDLLRLPSFPRSAIDAEFSLFSDPQA  2150
2151  GKELFGLDTLQKSLWIQLLEEMFLGMPSEFPWGDEIMLFLNVFNGALILH  2200
2201  PEDSALLRQYAATVINTAVHFNHLFSLSGYQWILPTMLQVYSDYESNPQL  2250
2251  RQAIEFACHQFYILHRKPFVLQLFASVAPLLEFPDAANNGPSKGVSAQCL  2300
2301  FDLLQSLEGETTDILDILELVKAEKPLKSLDFCYGNEDLTFSISEAIKLC  2350
2351  VTVVAYAPESFRSLQMLMVLEALVPCYLQKLKRQTSQVETVPAAREEIAA  2400
2401  TAALATSLQALLYSVEVLTRPMTAPQMSRCDQGHKGTTTANHTMSSGVNT  2450
2451  RYQEQGAKLHFIRENLHLLEEGQGIPREELDERIAREEFRRPRESLLNIC  2500
2501  TEFYKHCGPRLKILQNLAGEPRVIALELLDVKSHMRLAEIAHSLLKLAPY  2550
2551  DTQTMESRGLRRYIMEMLPITDWTAEAVRPALILILKRLDRMFNKIHKMP  2600
2601  TLRRQVEWEPASNLIEGVCLTLQRQPIISFLPHLRSLINVCVNLVMGVVG  2650
2651  PSSVADGLPLLHLSPYLSPPLPFSTAVVRLVALQIQALKEDFPLSHVISP  2700
2701  FTNQERREGMLLNLLIPFVLTVGSGSKDSPWLEQPEVQLLLQTVINVLLP  2750
2751  PRIISTSRSKNFMLESSPAHCSTPGDAGKDLRREGLAESTSQAAYLALKV  2800
2801  ILVCFERQLGSQWYWLSLQVKEMALRKVGGLALWDFLDFIVRTRIPIFVL  2850
2851  LRPFIQCKLLAQPAENHEELSARQHIADQLERRFIPRPLCKSSLIAEFNS  2900
2901  ELKILKEAVHSGSAYQGKTSISTVGTSTSAYRLSLATMSRSNTGTGTVWE  2950
2951  QDSEPSQQASQDTLSRTDEEDEENDSISMPSVVSEQEAYLLSAIGRRRFS  3000
3001  SHVSSMSVPQAEVGMLPSQSEPNVLDDSQGLAAEGSLSRVASIQSEPGQQ  3050
3051  NLLVQQPLGRKRGLRQLRRPLLSRQKTQTEPRNRQGARLSTTRRSIQPKT  3100
3101  KPSADQKRSVTFIEAQPEPAAAPTDALPATGQLQGCSPAPSRKPEAMDEP  3150
3151  VLTSSPAIVVADLHSVSPKQSENFPTEEGEKEEDTEAQGATAHSPLSAQL  3200
3201  SDPDDFTGLETSSLLQHGDTVLHISEENGMENPLLSSQFTFTPTELGKTD  3250
3251  AVLDESHV                                            3258

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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