 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q62504 from www.uniprot.org...
The NucPred score for your sequence is 1.00 (see score help below)
1 MVRETRHLWVGNLPENVREEKIIEHFKRYGRVESVKILPKRGSEGGVAAF 50
51 VDFVDIKSAQKAHNSVNKMGDRDLRTDYNEPGTIPSAARGLDETVSIASR 100
101 SREVSGFRGSAGGPAYGPPPSLHAREGRYERRLDGASDNRERAYEHSAYG 150
151 HHERGTGAFDRTRHYDQDYYRDPRERTLQHGLYYTSRSRSPNRFDAHDPR 200
201 YEPRAREQFTLPSVVHRDIYRDDITREVRGRRPERSYQHSRSRSPHSSQS 250
251 RNQSPQRLASQASRPTRSPSGSGSRSRSSSSDSISSSSSSSNTDSSDSSS 300
301 TASDDSPARSVQSAAVPAPTSQLLSSLEKDEPRKSFGIKVQNLPVRSIDT 350
351 SLKDGLFHEFKKFGKVTSVQIHGASEERYGLVFFRQQEDQEKALTASKGK 400
401 LFFGMQIEVTAWVGPETESENEFRPLDERIDEFHPKATRTLFIGNLEKTT 450
451 TYHDLRNIFQRFGEIVDIDIKKVNGVPQYAFLQYCDIASVCKAIKKMDGE 500
501 YLGNNRLKLGFGKSMPTNCVWLDGLSSNVSDQYLTRHFCRYGPVVKVVFD 550
551 RLKGMALVLYSEIEDAQAAVKETKGRKIGGNKIKVDFANRESQLAFYHCM 600
601 EKSGQDMRDFYEMLTERRAGQMAQSKHEDWSADAQSPHKCREERRGSYEY 650
651 SQERTYYENVRTPGTYPEDSRRDYPARGREFYSEWETYQGEYYDSRYYDE 700
701 PREYREYRSDPYEQDIREYSYRQRERERERERFESDRDHERRPIERSQSP 750
751 VHLRRPQSPGVSPAHSERLPSDSERRLYRRSSERSGSCSSVSPPRYDKLE 800
801 KARLERYTKNEKADKERTFDPERVERERRIVRKEKGEKDKAERQKRKGKA 850
851 HSPSSQPSETEQENDREQSPEKPRGSTKLSRDRADKEGPAKNRLELVPCV 900
901 VLTRVKEKEGKVIEHPPPEKLKARLGRDTTKASALDQKPQAAQGEPAKSD 950
951 PARGKALREKVLPSHAEVGEKEGRTKLRKHLKAEQTPELSALDLEKLEAR 1000
1001 KRRFADSGLKIEKQKPEIKKTSPETEDTRILLKKQPDTSRDGVLLREGES 1050
1051 ERKPVRKEILKRESKKTKLERLNSALSPKDCQDPAAVSAGSGSRPSSDVH 1100
1101 AGLGELTHGSVETQETQPKKAIPSKPQPKQLQLLENQGPEKEEVRKNYCR 1150
1151 PREEPAEHRAGQEKPHGGNAEEKLGIDIDHTQSYRKQMEQSRRKQRMEME 1200
1201 IAKAEKFGSPKKDVDDYERRSLVHEVGKPPQDVTDDSPPSKKRRTDHVDF 1250
1251 DICTKRERNYRSSRQISEDSERTSCSPSVRHGSFHDDDDPRGSPRLVSVK 1300
1301 GSPKGDEKGLPYPNAAVRDDPLKCNPYDSGKREQTADTAKIKLSVLNSEG 1350
1351 EPSRWDPPMKQDPSRFDVSFPNSVIKRDSLRKRSVRDLEPGEVPSDSDED 1400
1401 AEHRSQSPRASSFYDSPRLSFLLRDRDQKLRERDERLASSLERNKFYSFA 1450
1451 LDKTITPDTKALLERAKSLSSSREENWSFLDWDSRFANFRNNKDKEKVDS 1500
1501 APRPIPSWYMKKKKIRTDSEGKLDDKKDERREEEQERQELFASRFLHSSI 1550
1551 FEQDSKRLQHLERKSEESDFPPGRLYGRQASEGANSTSDSVQEPVVLFHS 1600
1601 RFMELTRMQQKEKEKDQKPKEAEKQEEPETHPKTPEPAAETKEPEPKAPV 1650
1651 SAGLPAVTITVVTPEPASSAPEKAEEAAEAPSPAGEKPAEPAPVSEETKL 1700
1701 VSEPVSVPVEQPRQSDVPPGEDSRDSQDSAALAPSAPQESAATDAVPCVN 1750
1751 AEPLTPGTTVSQVESSVDPKPSSPQPLSKLTQRSEEAEEGKVEKPDTTPS 1800
1801 TEPDATQNAGVASEAQPPASEDVEANPPVAAKDRKTNKSKRSKTSVQAAA 1850
1851 ASVVEKPVTRKSERIDREKLKRSSSPRGEAQKLLELKMEAEKITRTASKS 1900
1901 SGGDTEHPEPSLPLSRSRRRNVRSVYATMTDHESRSPAKEPVEQPRVTRK 1950
1951 RLERELQEAVVPPTTPRRGRPPKTRRRAEEDGEHERKEPAETPRPAEGWR 2000
2001 SPRSQKSAAAAGPQGKRGRNEQKVEAAAEAGAQASTREGNPKSRGEREAA 2050
2051 SEPKRDRRDPSTDKSGPDTFPVEVLERKPPEKTYKSKRGRARSTRSGMDR 2100
2101 AAHQRSLEMAARAAGQAADKEAGPAAASPQESESPQKGSGSSPQLANNPA 2150
2151 DPDREAEEESASASTAPPEGTQLARQIELEQAVQNIAKLPEPSAAAASKG 2200
2201 TATATATAASEEPAPEHGHKPAHQASETELAAAIGSIISDASGEPENFSA 2250
2251 PPSVPPGSQTHPREGMEPGLHEAESGILETGTATESSAPQVSALDPPEGS 2300
2301 ADTKETRGNSGDSVQEAKGSKAEVTPPRKDKGRQKTTRRRKRNANKKVVA 2350
2351 ITETRASEAEQTQSESPAAEEATAATPEAPQEEKPSEKPPSPPAECTFDP 2400
2401 SKTPPAESLSQENSAAEKTPCKAPVLPALPPLSQPALMDDGPQARFKVHS 2450
2451 IIESDPVTPPSDSGIPPPTIPLVTIAKLPPPVIPGGVPHQSPPPKVTEWI 2500
2501 TRQEEPRAQSTPSPALPPDTKASDMDTSSSTLRKILMDPKYVSATGVTST 2550
2551 SVTTAIAEPVSAPCLQEAPAPPCDPKHPPLEGVSAAAVPNADTQASEVPV 2600
2601 AADKEKVAPVIAPKITSVISRMPVSIDLENSQKITLAKPAPQTLTGLVSA 2650
2651 LTGLVNVSLVPVNALKGPVKGSVATLKGLVSTPAGPVNLLKGPVNVLTGP 2700
2701 VNVLTTPVSATVGTVNAAPGPVTAACGVTATTGTAAVTGAVTAPAAKGKQ 2750
2751 RASSNENSRFHPGSMSVIDDRPADTGSGAGLRVNTSEGVVLLSYSGQKTE 2800
2801 GPQRISAKISQIPPASAMDIEFQQSVSKSQVKADSITPTQSAPKGPQTPS 2850
2851 AFANVAAHSTLVLTAQTYNASPVISSVKTDRPSLEKPEPIHLSVSTPVTQ 2900
2901 GGTVKVLTQGINTPPVLVHNQLVLTPSIVTTNKKLADPVTLKIETKVLQP 2950
2951 ANLGPTLTPHHPPALPSKLPAEVNHVPSGPSTPADRTIAHLATPKPDTHS 3000
3001 PRPTGPTPGLFPRPCHPSSTTSTALSTNATVMLAAGIPVPQFISSIHPEQ 3050
3051 SVIMPPHSITQTVSLGHLSQGEVRMSTPTLPSITYSIRPETLHSPRAPLQ 3100
3101 PQQIEARAPQRVGTPQPATTGVPALATQHPPEEEVHYHLPVARAAAPVQS 3150
3151 EVLVMQSEYRLHPYTVPRDVRIMVHPHVTAVSEQPRATEGVVKVPPANKA 3200
3201 PQQLVKEAVKTSDAKAVPAPAPVPVPVPVPTPAPPPHGEARILTVTPSSQ 3250
3251 LQGLPLTPPVVVTHGVQIVHSSGELFQEYRYGDVRTYHAPAQQLTHTQFP 3300
3301 VASSISLASRTKTSAQVPPEGEPLQSTQSAQPAPSTQATQPIPPAPPCQP 3350
3351 SQLSQPAQPPSGKIPQVSQEAKGTQTGGVEQTRLPAIPTNRPSEPHAQLQ 3400
3401 RAPVETAQPAHPSPVSVSMKPDLPSPLSSQAAPKQPLFVPANSGPSTPPG 3450
3451 LALPHAEVQPAPKQESSPHGTPQRPVDMVQLLKKYPIVWQGLLALKNDTA 3500
3501 AVQLHFVSGNNVLAHRSLPLSEGGPPLRIAQRMRLEASQLEGVARRMTVE 3550
3551 TDYCLLLALPCGRDQEDVVSQTESLKAAFITYLQAKQAAGIINVPNPGSN 3600
3601 QPAYVLQIFPPCEFSESHLSRLAPDLLASISNISPHLMIVIASV 3644
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
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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