 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9V5N8 from www.uniprot.org...
The NucPred score for your sequence is 0.90 (see score help below)
1 MQTREFPQRPLGLLLVLLVVLLQSSLIKSYLIIVHEDTPPGTVIFNASVY 50
51 KLGSERHYKINAHKSANFVHHLVSVNHKDGQIQLRKALKCDGIYYPNLFT 100
101 FYVDSTSNRLRSIDYYSLPVRIFVSGHSCNEDRRIEQELHHHHYEEEDNT 150
151 GYSKRRRRRSTQEMIQLNGNQLEEVFRQNSTEFRAGDLIFGDSFDNEMRH 200
201 RILSRKRRAVGSPDPLHLQPALHRRISDAKQWISETYASYAIHTTDKWNQ 250
251 ICLRRSQFINSLNAFLPRSVCQHCKVSFLDVNDERFAIEHQSRDLVASRD 300
301 VCIAESMWKVSITFNIRCDRRDIVDSDHRLKIVYHHQEFNDTDIARRVRR 350
351 ELRNQSPYFEQALYVASVLEEQPAGAAVTTVRARDPEDSPVVYSMVSLLD 400
401 SRSQSLFKVDSRTGVVTTSASLDRELMDVHYFRVVATDDSFPPRSGTTTL 450
451 QVNVLDCNDHSPTFEAEQFEASIREGATVGSTVITLRATDQDIGKNAEIE 500
501 YGIEAVTDGAGLAQDQEMPIFRIDSRSGVISTRSSLDRETSDSYHLLVTA 550
551 ADLASAQSERRTATASVQVKVLDDNDNYPQFSERTYTVQVPEDQWGGTED 600
601 NTVAHIRATDADQGNNAAIRYAIIGGNTQSQFSIDSMSGDVSLVKPLDYE 650
651 SVRSYRLVIRAQDGGSPSRSNTTQLLVNVIDANDNAPRFYTSQFQESVLE 700
701 NVPVGYNIIRVQAYDSDEGANAEITYSISERDDNFPLAVDPRTGWVQTIK 750
751 PLDREEQGRFAFQVVAKDGGVPPKSASSSVVITVQDVNDNDPAFNPKYYE 800
801 ANVGEDQPPGTPVTTVTATDPDEDSRLHYEITTGNTRGRFAITSQNGRGL 850
851 ITIAQSLDYKQEKRFLLTVAATDSGGRSDTATVHINITDANNFAPIFENA 900
901 PYSASVFEDAPVGTTVLVVSATDSDVGVNAQITYSLNEESINGLGSPDPF 950
951 SINPQTGAIVTNAPLDRETTSGYLLTVTAKDGGNPSLSDTTDVEIGVTDV 1000
1001 NDNAPAFKSPLYQASILEDALVGTSVIQVAASDPDVGLNGRIKYLLSDRD 1050
1051 IEDGSFVIDPTSGTIRTNKGLDRESVAVFHLTAIAVDKGSPPLSSTVEVQ 1100
1101 IRLEDVNDSPPTFASDKITLYVPENSPVGSVVGEIHAHDPDEGVNAVVHY 1150
1151 SIIGGDDSNAFSLVTRPGSERAQLLTMTELDYESTRKRFELVVRAASPPL 1200
1201 RNDAHIEILVTDVNDNAPVLRDFQVIFNNFRDHFPSGEIGRIPAFDADVS 1250
1251 DKLHYRILSGNNANLLRLNSSSGGLVLSPQLNTNVPKFATMEVSVSDGIN 1300
1301 EAKAIMQLSVRLITEDMLFNSVTVRLNEMTEEAFLSPLLNFFLDGLAAII 1350
1351 PCPKEHIFVFSIQDDTDVSSRILNVSFSARRPDVSHEEFYTPQYLQERVY 1400
1401 LNRAILARLATVEVLPFDDNLCVREPCLNFEECLTVLKFGNASEFIHSDT 1450
1451 VLFRPIYPVNTFACSCPEGFTGSKEHYLCDTEVDLCYSDPCQNGGTCVRR 1500
1501 EGGYTCVCPSTHTGQNCETGVGHLRPCPSETCEGGLSCLSNYPSSQPPPY 1550
1551 TATCELRARAFGRNSFLTFESLKQRHRFNLKLRFATVQENGLLLYNGRYN 1600
1601 ELHDFIALEIHEGHVSFSFSLGDHSERISVIQEAKVSDGKWHQVEVVYLN 1650
1651 RSVTLVLDNCDTAIALSGQLGDRWSCANRTTLKLDKRCSLLTETCHRFLD 1700
1701 LTGPLQVGGLPRIPAHFPVTNRDFVGCISDLRIDDRFVDLNSYVADNGTL 1750
1751 AGCPQKAPLCQSEPCFNGGTCREGWGTYSCECPEGYAGNSCQDNIPAPWR 1800
1801 FSGDGSLSFNPLLRPIQLPWTTSFSLRTRQKEAFLLQIQIGQNSSAAVCL 1850
1851 RQGVLYYIFDGEPMYLAGAFLSDGEWHRVEIRWQQGSEIHFSVDYGQRSG 1900
1901 SVPMSQKVQGLYVGKIVMGSPDGSIGAVPEASPFEGCIQDVRIGAGQSVL 1950
1951 SRPTIRENVEDGCESRAQCPDHCPNHSSCQSSWDLSTCECDSGYVGTDCA 2000
2001 PICTVRPCASGVCRANTSLPRGYDCECNSSSRHGDYCEKELQQPCPGGWW 2050
2051 GERVCGPCRCDLAQGYHPDCNKTTGQCYCKTNHYQPPNETACLSCDCYSI 2100
2101 GSFSGACNPLTGQCECREGVIGRRCDSCSNPYAEVTLSGCEVVYDACPRS 2150
2151 FAGGVWWPRTPLGGVAIEGCPPPARGKGQRSCDVQSGSWNTPDMYNCTSE 2200
2201 PFVELRRQLSQLEKLELELNSFVAIKMAEQLRKACEAVDRRGASKDQKIS 2250
2251 GNGRPNRRYKMESSFLLSNGGNVWSHELEMDYLSDELKFTHDRLYGADLL 2300
2301 VTEGLLQELINYELMQSGLNLSHSQDKYFIKNLVDAASVILDRKYEAEWR 2350
2351 RATELIQRGPDDLVDAFNKYLVVLARSQHDTYTSPFEIVQPNMALGLDIV 2400
2401 TTESLFGYEPEQLSEYHRSKYLKPNAFTTESVVLPDTSGFLQHSARQRPV 2450
2451 ISFPKYNNYILDRRKFDQHTKVLVPLEMLGITPPESDEISQSGRRGSSHD 2500
2501 HRAIVAYAQYKDVGQLLPDLYDETITRRWGVDVELATPILSLQILVPSME 2550
2551 REQETQRLEIPSRKIFSSSSPSSSSSSGSTEQQFVEVFDVPKAPTSSSEQ 2600
2601 QIEDIRITAHEIPPPVSSVEQQEASSDEDGEEREPHIRLNLDDIEFHGNS 2650
2651 GEEVISPDSPEMLNPNYEGVSSTGSDEQPKGENEAVYRDRRLVKRQVEIT 2700
2701 YPSEQMQQTEQVVYRSLGSPHLAQPIKLQMWLDVDSARFGPRSNPQCVRW 2750
2751 NSFTNQWTRLGCQTEIPDFDGDFNPAAQQAILVNCSCTHISSYAVIVDVI 2800
2801 DPEDIPEPSLLVQITSYSAFLVSLPLLLGVLLALALLRGQQTNSNTIHQN 2850
2851 IVLCVFCAELLFFVGMQSRRQLLESEFPCKLTAICLHYFWLAAFAWTTVD 2900
2901 CVHLYRMLTEMRDINHGPMGFYFAMGYGAPAIVVGLSVGVRAHEYGNSLF 2950
2951 CWLSVYEPVVWWLVGPIAGMSVVNLLILFVSVKAAFTLKDHVLGFGNLRT 3000
3001 LLWLSVVSLPLMGVMWVLAVLAASEHSQLLSLLLSGVVLLHALFCLIGYC 3050
3051 IINKRVRENLQRTCLRCMGRKVPLLDSSMVVSNSSHNVNAAARPSNFLAS 3100
3101 GYDTTTRRNIGISASSTTSRSTAKTSSSPYSDGQLRQTSTSTSNYNSASD 3150
3151 APSFLRGFESSTTGRSRGGEEKPSRRQRKDSDSGSETDGRSLELASSHSS 3200
3201 DDDESRTARSSGTHRSTAVSSTPAYLPNITEHVQATTPPELNVVQSPQLF 3250
3251 PSVNKPVYAPRWSSQLPDAYLQSPPNIGRWSQDTGSDNEHVHGQAKMTIS 3300
3301 PNPLPNPDLTDTSYLQQHHNKINMPPSILENIRDAREGYEDSLYGRRGEY 3350
3351 PDKYGSYKPPSHYGSEKDYPGGGSGSQTIGHMRSFHPDAAYLSDNIYDKQ 3400
3401 RTLGSGYLGAKSESPYLSKDRITPDIYGSRDGHYSLKRQPAYATDSLHSV 3450
3451 HSLLKNDYHQQQQQQQQHHLQDRLSEGSDKNGYHFPYTAEEDHLPARKLS 3500
3501 HTQPPSLHGSQLMQPPGVGLVNDVNNPGLMGRHTLNGGSRHSSRASSPPS 3550
3551 TMVAPMQPLGPLTSITDTERNIDDDETTV 3579
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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