 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P33450 from www.uniprot.org...
The NucPred score for your sequence is 0.72 (see score help below)
1 MERLLLLFFLLLAGRESLCQTGDTKLELLAPRGRSYATTYEQYAAFPRRR 50
51 SSSSSPSGEMQSRAVDTSADFEVLEGQPRGTTVGFIPTKPKFSYRFNEPP 100
101 REFTLDPVTGEVKTNVVLDREGMRDHYDLVVLSSQPTYPIEVRIKVLDVN 150
151 DNSPEFPEPSIAISFSESATSGTRLLLDAATDADVGENGVTDQYEIVAGN 200
201 VDNKFRLVTTANPSGDTSYLHLETTGNLDRESRGSYQLNISARDGGSPPR 250
251 FGYLQVNVTILDVNDNPPIFDHSDYNVSLNETALPGTPVVTVMASDNDLG 300
301 DNSKITYYLAETEHQFTVNPETGVISTTERVNCPQQTNVKSSASQKSCVF 350
351 TVFARDHGSPRQDGRTYVTVNLLDTNDHDPIISFRFFPDGGKVATVDENA 400
401 VNGTVVAAVAVKDSDSGLNGRTSVRIVSGNELGHFRLEEAADLHIVRVNG 450
451 VLDREEIGKYNLTVVAMDQGTPARTTTAHLIIDVNDVNDHEPVFEKSEYS 500
501 AVLSELAPTGSFVASITATDEDTGVNAQVHYDILSGNELKWFSMDPLTGL 550
551 IVTTGPLDREIRDTVELSISARDGGPNPKFAYTQLKVIILDENDEAPQFS 600
601 QREQNVTLGEDAPPQTIVALMTATDHDQGTNGSVTFALAPSVERLYPLQF 650
651 ALDALTGQLTTRRPLDREKMSQYEISVIARDQGAPTPQSATATVWLNVAD 700
701 VNDNDPQFYPRHYIYSLADDDDDIKLKKEVEKERILLHVTASDKDDGDNA 750
751 LIEYRLESGGEGLFQLDARSGAISLRGDAPASMHWKPHYKLLVSARDAGQ 800
801 RRSQQDAIVEIVLKSKLEMLECGQAQAGGYEFQMVEDHEQQRNSQPNREV 850
851 GIVQVKSTNGKANSHIEYDIIQGDRAQNFRIDTRSGRITTARPLDREEQA 900
901 NYRLTILASSSSSSSAAASSVSYGQCIVNIAIIDLNDNAPVFALDRESEP 950
951 TISLPENAAVGQEIYLSRVRDRDAGVNSRISYSLTNNPNQQFRIGPVTGV 1000
1001 LYLQRPIRAEPGSLIHVELMATDAGSPPLSSKLSLSVLIADVNDHTPVFD 1050
1051 HTSYETSLPETTKVNTRFFALAATDIDLGDNGRISYEIIEGNTERMFGVF 1100
1101 PDGYLFVRAPLDREERDYYALTVSCRDAGQPSRSSVVPVVIHVIDENDNA 1150
1151 PQFTNSTFTFSIPENAPADTFVGKLTAVDRDIGRNAELSFTLSSQTQDFT 1200
1201 IDTRNGFIKTLRPFDREALVKVSRNAEASGEDGSLRGSMAGNYMLLEATV 1250
1251 SDNGIPRLQDKVKVKVIVTDVNDNAPEFLRAPYHVTISEGASEGTHITHV 1300
1301 FTQDADEGLNGDVYYSLAKGNEAGQFNLDSATGQLSLGRRLDRESQEIHH 1350
1351 LIVVAKDAALKHPLSSNASITIVVLDENDNAPEFTQSSSEVSVLETSPTG 1400
1401 TELMRFRASDADQGVNSQVVFSISAGNRRDTFHIDSITGSLYLHKPLDYE 1450
1451 DITSYTLNITASDCGTPSLSTTVLYNVLVVDDNDNPPIFPSTAIVRQIKE 1500
1501 GIPLKTPIVTVTADDPDSGLNGKVSYAISKQEPQLPQGRHFGINTETGVI 1550
1551 HTLREIDRESIDTFRLTVVATDRAQPSERQLSTEKLVTVIVEDINDNAPV 1600
1601 FVSMNAAILPPKFSTSKGSSTAVMQVHAKDADSSSNGLVTYEIVSGPQEL 1650
1651 FKLQRNTGIITFTPGPQFKQEVRYQLTLKSTDEAVQSERRSSEVYITIIT 1700
1701 PGSGGSESSVPQFEQRSKLSGSVYENEPIGTSILTVTAHLASAEIEYFVT 1750
1751 NVTATGSRGQVDRLFDIDAKLGILSTAAELDREAGPEEYEVEVYAIALGG 1800
1801 QPRTSRTKVRVTVLDKNDSPPQFLDTPFVYNVSEDLQIGHTISTLRAHDP 1850
1851 DTLGSVTFLLMDGHDGKFLLEPSTGKLILNDTLDRETKSKYELRIRVSDG 1900
1901 VQYTEAYATIQVSDTNDNPPLFEDTVYSFDIPENAQRGYQVGQIVARDAD 1950
1951 LGQNAQLSYGVVSDWANDVFSLNPQTGMLTLTARLDYEEVQHYILIVQAQ 2000
2001 DNGQPSLSTTITVYCNVLDLNDNAPIFDPMSYSSEVFENVPIATEVVTVS 2050
2051 AKDIDSGNNGLIEYSITAGDVDSEFGIDSNGTIRTRRNLDREHRSTYTLT 2100
2101 VTARDCADEFASFSELEETQLKLKYRSPRKYQQTRQEFLAHQKQQRLSST 2150
2151 VKVTILIKDVNDEVPVFISANETAIMENVAINTVVIAVKAVDNDEGRNGY 2200
2201 IDYLMKEARDEDMGQSDPLPFSLNPTDGQLRVVDALDRELRSSYLLNITA 2250
2251 RDRGEPPQSTESQLLIRILDENDNSPVFDPKQYSASVAENASIGAMVLQV 2300
2301 SATDVDEGANGRIRYSIVLGDQNHDFSISEDTGVVRVAKNLNYERLSRYS 2350
2351 LTVRAEDCALENPAGDTAELTINILDINDNRPTFLDSPYLARVMENTVPP 2400
2401 NGGYVLTVNAYDADTPPLNSQVRYFLKEGDSDLFRINASSGDIALLKPLD 2450
2451 REQQSEYTLTLVAMDTGSPPLTGTGIVRVEVQDINDNDPVFELQSYHATV 2500
2501 RENLPSGTHVLTPRATDKDEGLNAKLRFNLLGEHMHRFHIDSETGEISTA 2550
2551 TTLDREETSVYHLTLMAQDSSITEPRASSVNLTISVSDVNDNIPKFDSTT 2600
2601 YNVAVPERISKGEFVFGARALDLDDGENAVVHYTISGRDQHYFDINTKTG 2650
2651 VVSTKLELKTKTKSHDDLTYTIVISAMDQGEQSLSSKAELTVILRPPELF 2700
2701 PTFAYMANSHFAMSEDVRPGKMITKVSATSPKKGLVGKIRYAIAGGIMGD 2750
2751 SLRVDPNSGLLSVGQDGLDYELTHLYEIWIEAADGDTPSLRSVTLITLNV 2800
2801 TDANDNAPVMEQLIYNAEVLEEESPPQLIAVVKASDRDSGDNGNVIYRLQ 2850
2851 NDFDGTFEITESGEIYTRMRLDREEIGDYAFVVEAVDQGVPHMTGTASVL 2900
2901 LHLLDKNDNPPKFTRLFSLNVTENAEIGSFVIRVTSSDLDLGANANASYS 2950
2951 FSENPGEKFRIEPQSGNITVAGHLDREQQDEYILKVVASDGAWRAETPIT 3000
3001 ITIQDQNDNAPEFEHSFYSFSFPELQQSIALVGQIIATDRDKQGPNSVIS 3050
3051 YSLQQPSPMFSIDPATGEVFSKKAVRFKHSQYVRSPENMYALTVLATDNG 3100
3101 KPPLYSECLVNINIVDAHNNPPKFEQAEYLAPLPQDAVRGQRIVRVHAND 3150
3151 KQDLGTNEMDYSLMTFNLSSIFSVGRHDGWITLVKPIQVPPNTRYELVVR 3200
3201 ATDRGVPPQSDETRVVIVVTGENMDTPRFSVNSYQVIVPENEPVGSTILT 3250
3251 VGATDDDTGPNGMLRYSISGGNERQDFSVDERTGGIVIQQQLDYDLIQEY 3300
3301 HLNITVQDLGYHPLSSVAMLTIILTDVNDNPPVFNHKEYHCYIPENKPVG 3350
3351 TFVFQAHAADKDSPKNAIIHYAFLPSGPDRHFFIMNQSNGTISSAVSFDY 3400
3401 EERRIYTLQIKAKNPDSSMESYANLYVHVLGVNEFYPQFLQPVFHFDVSE 3450
3451 TSAVGTRVGAVQATDKDSGEDGRVYYLLVGSSNDKGFRIDTNTGLIYVAR 3500
3501 HLDRETQNRVVLTVMAKNYGSIRGNDTDEAQVIISIQDGNDPPEFIKHYY 3550
3551 TSTISEAAPVGTKVTTVKAIDKDVRTQNNQFSYSIINGNLKQSFKIDVQT 3600
3601 GEISTASRLDREETSTYNLVIGAIDTGLPPQTGSATVHIELEDVNDNGPT 3650
3651 FTPEGLNGYISENEPAGTSIMTLIASDPDLPRNGGPFTYQLIGGKHKSWL 3700
3701 SVDRNSGVVRSTTSFDREMTPILEAIIEVEDSGKPKQKSQHLLTITVLDQ 3750
3751 NDNPSTTRSLHIAVSLFNGDLPSNVKLADVRPNDIDIVGDYRCRLQKNPA 3800
3801 QSQLQLAIPRACDLITTSHTTPIASVFSYTGNDGKHGDVSSKVSVAFQSF 3850
3851 NNETLANSVSIMVRNMTAYHFLANHYRPILEMIKSRMSNEDEVILYSLLE 3900
3901 GGSGNSTNLQLLMAVRLAKTSYQQPKYLIERLREKRSAFSELLQKEVIVG 3950
3951 YEPCSEPDVCENGGVCSATMRLLDAHSFVIQDSPALVLSGPRVVHDYSCQ 4000
4001 CTSGFSGEQCSRRQDPCLPNPCHSQVQCRRLGSDFQCMCPANRDGKHCEK 4050
4051 ERSDVCYSKPCRNGGSCQRSPDGSSYFCLCRPGFRGNQCESVSDSCRPNP 4100
4101 CLHGGLCVSLKPGYKCNCTPGRYGRHCERFSYGFQPLSYMTFPALDVTTN 4150
4151 DISIVFATTKPNSLLLYNYGMQSGGRSDFLAIELVHGRAYFSSGGARTAI 4200
4201 STVIAGRNLADGGWHKVTATRNGRVMSLSVAKCADSGDVCTECLPGDSSC 4250
4251 YADEVGPVGTLNFNKQPLMIGGLSSADPILERPGQVHSDDLVGCLHSVHI 4300
4301 GGRALNLSLPLQQKGILAGCNRQACQPALAAERCGGFAGQCIDRWSSSLC 4350
4351 QCGGHLQSPDCSDSLEPITLGEGAFVEFRISEIYRRMQLLDNLYNSKSAW 4400
4401 LDNQQMRERRAVSNFSTASQIYEAPKMLSMLFRTYKDQGQILYAATNQMF 4450
4451 TSLSLREGRLVYYSKQHLTINMTVQETSTLNDGKWHNVSLFSESRSLRLI 4500
4501 VDGRQVGDELDIAGVHDFLDPYLTILNVGGEAFVGCLANVTVNNELQPLN 4550
4551 GSGSIFPEVRYHGKIESGCRGDIGQDAAQVADPLSIGFTLVIVFFVILVV 4600
4601 AILGSYVIYRFRGKQEKIGSLSCGVPGFKIKHPGGPVTQSQVDHVLVRNL 4650
4651 HPSEAPSPPVGAGDHMRPPVGSHHLVGPELLTKKFKEPTAEMPQPQQQQQ 4700
4701 RPQRPDIIERESPLIREDHHLPIPPLHPLPLEHASSVDMGSEYPEHYDLE 4750
4751 NASSIAPSDIDIVYHYKGYREAAGLRKYKASVPPVSAYTHHKHQNSGSQQ 4800
4801 QQQQHRHTAPFVTRNQGGQPPPPPTSASRTHQSTPLARLSPSSELSSQQP 4850
4851 RILTLHDISGKPLQSALLATTSSSGGVGKDVHSNSERSLNSPVMSQLSGQ 4900
4901 SSSASRQKPGVPQQQAQQTSMGLTAEEIERLNGRPRTCSLISTLDAVSSS 4950
4951 SEAPRVSSSALHMSLGGDVDAHSSTSTDESGNDSFTCSEIEYDNNSLSGD 5000
5001 GKYSTSKSLLDGRSPVSRALSGGETSRNPPTTVVKTPPIPPHAYDGFESS 5050
5051 FRGSLSTLVASDDDIANHLSGIYRKANGAASPSATTLGWEYLLNWGPSYE 5100
5101 NLMGVFKDIAELPDTNGPSQQQQQQTQVVSTLRMPSSNGPAAPEEYV 5147
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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