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

NucPred

Fetching Q14517 from www.uniprot.org...

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

   1  MGRHLALLLLLLLLFQHFGDSDGSQRLEQTPLQFTHLEYNVTVQENSAAK    50
  51  TYVGHPVKMGVYITHPAWEVRYKIVSGDSENLFKAEEYILGDFCFLRIRT   100
 101  KGGNTAILNREVKDHYTLIVKALEKNTNVEARTKVRVQVLDTNDLRPLFS   150
 151  PTSYSVSLPENTAIRTSIARVSATDADIGTNGEFYYSFKDRTDMFAIHPT   200
 201  SGVIVLTGRLDYLETKLYEMEILAADRGMKLYGSSGISSMAKLTVHIEQA   250
 251  NECAPVITAVTLSPSELDRDPAYAIVTVDDCDQGANGDIASLSIVAGDLL   300
 301  QQFRTVRSFPGSKEYKVKAIGGIDWDSHPFGYNLTLQAKDKGTPPQFSSV   350
 351  KVIHVTSPQFKAGPVKFEKDVYRAEISEFAPPNTPVVMVKAIPAYSHLRY   400
 401  VFKSTPGKAKFSLNYNTGLISILEPVKRQQAAHFELEVTTSDRKASTKVL   450
 451  VKVLGANSNPPEFTQTAYKAAFDENVPIGTTVMSLSAVDPDEGENGYVTY   500
 501  SIANLNHVPFAIDHFTGAVSTSENLDYELMPRVYTLRIRASDWGLPYRRE   550
 551  VEVLATITLNNLNDNTPLFEKINCEGTIPRDLGVGEQITTVSAIDADELQ   600
 601  LVQYQIEAGNELDFFSLNPNSGVLSLKRSLMDGLGAKVSFHSLRITATDG   650
 651  ENFATPLYINITVAASHKLVNLQCEETGVAKMLAEKLLQANKLHNQGEVE   700
 701  DIFFDSHSVNAHIPQFRSTLPTGIQVKENQPVGSSVIFMNSTDLDTGFNG   750
 751  KLVYAVSGGNEDSCFMIDMETGMLKILSPLDRETTDKYTLNITVYDLGIP   800
 801  QKAAWRLLHVVVVDANDNPPEFLQESYFVEVSEDKEVHSEIIQVEATDKD   850
 851  LGPNGHVTYSIVTDTDTFSIDSVTGVVNIARPLDRELQHEHSLKIEARDQ   900
 901  AREEPQLFSTVVVKVSLEDVNDNPPTFIPPNYRVKVREDLPEGTVIMWLE   950
 951  AHDPDLGQSGQVRYSLLDHGEGNFDVDKLSGAVRIVQQLDFEKKQVYNLT  1000
1001  VRAKDKGKPVSLSSTCYVEVEVVDVNENLHPPVFSSFVEKGTVKEDAPVG  1050
1051  SLVMTVSAHDEDARRDGEIRYSIRDGSGVGVFKIGEETGVIETSDRLDRE  1100
1101  STSHYWLTVFATDQGVVPLSSFIEIYIEVEDVNDNAPQTSEPVYYPEIME  1150
1151  NSPKDVSVVQIEAFDPDSSSNDKLMYKITSGNPQGFFSIHPKTGLITTTS  1200
1201  RKLDREQQDEHILEVTVTDNGSPPKSTIARVIVKILDENDNKPQFLQKFY  1250
1251  KIRLPEREKPDRERNARREPLYHVIATDKDEGPNAEISYSIEDGNEHGKF  1300
1301  FIEPKTGVVSSKRFSAAGEYDILSIKAVDNGRPQKSSTTRLHIEWISKPK  1350
1351  PSLEPISFEESFFTFTVMESDPVAHMIGVISVEPPGIPLWFDITGGNYDS  1400
1401  HFDVDKGTGTIIVAKPLDAEQKSNYNLTVEATDGTTTILTQVFIKVIDTN  1450
1451  DHRPQFSTSKYEVVIPEDTAPETEILQISAVDQDEKNKLIYTLQSSRDPL  1500
1501  SLKKFRLDPATGSLYTSEKLDHEAVHQHTLTVMVRDQDVPVKRNFARIVV  1550
1551  NVSDTNDHAPWFTASSYKGRVYESAAVGSVVLQVTALDKDKGKNAEVLYS  1600
1601  IESGNIGNSFMIDPVLGSIKTAKELDRSNQAEYDLMVKATDKGSPPMSEI  1650
1651  TSVRIFVTIADNASPKFTSKEYSVELSETVSIGSFVGMVTAHSQSSVVYE  1700
1701  IKDGNTGDAFDINPHSGTIITQKALDFETLPIYTLIIQGTNMAGLSTNTT  1750
1751  VLVHLQDENDNAPVFMQAEYTGLISESASINSVVLTDRNVPLVIRAADAD  1800
1801  KDSNALLVYHIVEPSVHTYFAIDSSTGAIHTVLSLDYEETSIFHFTVQVH  1850
1851  DMGTPRLFAEYAANVTVHVIDINDCPPVFAKPLYEASLLLPTYKGVKVIT  1900
1901  VNATDADSSAFSQLIYSITEGNIGEKFSMDYKTGALTVQNTTQLRSRYEL  1950
1951  TVRASDGRFAGLTSVKINVKESKESHLKFTQDVYSAVVKENSTEAETLAV  2000
2001  ITAIGNPINEPLFYHILNPDRRFKISRTSGVLSTTGTPFDREQQEAFDVV  2050
2051  VEVTEEHKPSAVAHVVVKVIVEDQNDNAPVFVNLPYYAVVKVDTEVGHVI  2100
2101  RYVTAVDRDSGRNGEVHYYLKEHHEHFQIGPLGEISLKKQFELDTLNKEY  2150
2151  LVTVVAKDGGNPAFSAEVIVPITVMNKAMPVFEKPFYSAEIAESIQVHSP  2200
2201  VVHVQANSPEGLKVFYSITDGDPFSQFTINFNTGVINVIAPLDFEAHPAY  2250
2251  KLSIRATDSLTGAHAEVFVDIIVDDINDNPPVFAQQSYAVTLSEASVIGT  2300
2301  SVVQVRATDSDSEPNRGISYQMFGNHSKSHDHFHVDSSTGLISLLRTLDY  2350
2351  EQSRQHTIFVRAVDGGMPTLSSDVIVTVDVTDLNDNPPLFEQQIYEARIS  2400
2401  EHAPHGHFVTCVKAYDADSSDIDKLQYSILSGNDHKHFVIDSATGIITLS  2450
2451  NLHRHALKPFYSLNLSVSDGVFRSSTQVHVTVIGGNLHSPAFLQNEYEVE  2500
2501  LAENAPLHTLVMEVKTTDGDSGIYGHVTYHIVNDFAKDRFYINERGQIFT  2550
2551  LEKLDRETPAEKVISVRLMAKDAGGKVAFCTVNVILTDDNDNAPQFRATK  2600
2601  YEVNIGSSAAKGTSVVKVLASDADEGSNADITYAIEADSESVKENLEINK  2650
2651  LSGVITTKESLIGLENEFFTFFVRAVDNGSPSKESVVLVYVKILPPEMQL  2700
2701  PKFSEPFYTFTVSEDVPIGTEIDLIRAEHSGTVLYSLVKGNTPESNRDES  2750
2751  FVIDRQSGRLKLEKSLDHETTKWYQFSILARCTQDDHEMVASVDVSIQVK  2800
2801  DANDNSPVFESSPYEAFIVENLPGGSRVIQIRASDADSGTNGQVMYSLDQ  2850
2851  SQSVEVIESFAINMETGWITTLKELDHEKRDNYQIKVVASDHGEKIQLSS  2900
2901  TAIVDVTVTDVNDSPPRFTAEIYKGTVSEDDPQGGVIAILSTTDADSEEI  2950
2951  NRQVTYFITGGDPLGQFAVETIQNEWKVYVKKPLDREKRDNYLLTITATD  3000
3001  GTFSSKAIVEVKVLDANDNSPVCEKTLYSDTIPEDVLPGKLIMQISATDA  3050
3051  DIRSNAEITYTLLGSGAEKFKLNPDTGELKTSTPLDREEQAVYHLLVRAT  3100
3101  DGGGRFCQASIVLTLEDVNDNAPEFSADPYAITVFENTEPGTLLTRVQAT  3150
3151  DADAGLNRKILYSLIDSADGQFSINELSGIIQLEKPLDRELQAVYTLSLK  3200
3201  AVDQGLPRRLTATGTVIVSVLDINDNPPVFEYREYGATVSEDILVGTEVL  3250
3251  QVYAASRDIEANAEITYSIISGNEHGKFSIDSKTGAVFIIENLDYESSHE  3300
3301  YYLTVEATDGGTPSLSDVATVNVNVTDINDNTPVFSQDTYTTVISEDAVL  3350
3351  EQSVITVMADDADGPSNSHIHYSIIDGNQGSSFTIDPVRGEVKVTKLLDR  3400
3401  ETISGYTLTVQASDNGSPPRVNTTTVNIDVSDVNDNAPVFSRGNYSVIIQ  3450
3451  ENKPVGFSVLQLVVTDEDSSHNGPPFFFTIVTGNDEKAFEVNPQGVLLTS  3500
3501  SAIKRKEKDHYLLQVKVADNGKPQLSSLTYIDIRVIEESIYPPAILPLEI  3550
3551  FITSSGEEYSGGVIGKIHATDQDVYDTLTYSLDPQMDNLFSVSSTGGKLI  3600
3601  AHKKLDIGQYLLNVSVTDGKFTTVADITVHIRQVTQEMLNHTIAIRFANL  3650
3651  TPEEFVGDYWRNFQRALRNILGVRRNDIQIVSLQSSEPHPHLDVLLFVEK  3700
3701  PGSAQISTKQLLHKINSSVTDIEEIIGVRILNVFQKLCAGLDCPWKFCDE  3750
3751  KVSVDESVMSTHSTARLSFVTPRHHRAAVCLCKEGRCPPVHHGCEDDPCP  3800
3801  EGSECVSDPWEEKHTCVCPSGRFGQCPGSSSMTLTGNSYVKYRLTENENK  3850
3851  LEMKLTMRLRTYSTHAVVMYARGTDYSILEIHHGRLQYKFDCGSGPGIVS  3900
3901  VQSIQVNDGQWHAVALEVNGNYARLVLDQVHTASGTAPGTLKTLNLDNYV  3950
3951  FFGGHIRQQGTRHGRSPQVGNGFRGCMDSIYLNGQELPLNSKPRSYAHIE  4000
4001  ESVDVSPGCFLTATEDCASNPCQNGGVCNPSPAGGYYCKCSALYIGTHCE  4050
4051  ISVNPCSSKPCLYGGTCVVDNGGFVCQCRGLYTGQRCQLSPYCKDEPCKN  4100
4101  GGTCFDSLDGAVCQCDSGFRGERCQSDIDECSGNPCLHGALCENTHGSYH  4150
4151  CNCSHEYRGRHCEDAAPNQYVSTPWNIGLAEGIGIVVFVAGIFLLVVVFV  4200
4201  LCRKMISRKKKHQAEPKDKHLGPATAFLQRPYFDSKLNKNIYSDIPPQVP  4250
4251  VRPISYTPSIPSDSRNNLDRNSFEGSAIPEHPEFSTFNPESVHGHRKAVA  4300
4301  VCSVAPNLPPPPPSNSPSDSDSIQKPSWDFDYDTKVVDLDPCLSKKPLEE  4350
4351  KPSQPYSARESLSEVQSLSSFQSESCDDNGYHWDTSDWMPSVPLPDIQEF  4400
4401  PNYEVIDEQTPLYSADPNAIDTDYYPGGYDIESDFPPPPEDFPAADELPP  4450
4451  LPPEFSNQFESIHPPRDMPAAGSLGSSSRNRQRFNLNQYLPNFYPLDMSE  4500
4501  PQTKGTGENSTCREPHAPYPPGYQRHFEAPAVESMPMSVYASTASCSDVS  4550
4551  ACCEVESEVMMSDYESGDDGHFEEVTIPPLDSQQHTEV              4588

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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