 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P98164 from www.uniprot.org...
The NucPred score for your sequence is 0.66 (see score help below)
1 MDRGPAAVACTLLLALVACLAPASGQECDSAHFRCGSGHCIPADWRCDGT 50
51 KDCSDDADEIGCAVVTCQQGYFKCQSEGQCIPNSWVCDQDQDCDDGSDER 100
101 QDCSQSTCSSHQITCSNGQCIPSEYRCDHVRDCPDGADENDCQYPTCEQL 150
151 TCDNGACYNTSQKCDWKVDCRDSSDEINCTEICLHNEFSCGNGECIPRAY 200
201 VCDHDNDCQDGSDEHACNYPTCGGYQFTCPSGRCIYQNWVCDGEDDCKDN 250
251 GDEDGCESGPHDVHKCSPREWSCPESGRCISIYKVCDGILDCPGREDENN 300
301 TSTGKYCSMTLCSALNCQYQCHETPYGGACFCPPGYIINHNDSRTCVEFD 350
351 DCQIWGICDQKCESRPGRHLCHCEEGYILERGQYCKANDSFGEASIIFSN 400
401 GRDLLIGDIHGRSFRILVESQNRGVAVGVAFHYHLQRVFWTDTVQNKVFS 450
451 VDINGLNIQEVLNVSVETPENLAVDWVNNKIYLVETKVNRIDMVNLDGSY 500
501 RVTLITENLGHPRGIAVDPTVGYLFFSDWESLSGEPKLERAFMDGSNRKD 550
551 LVKTKLGWPAGVTLDMISKRVYWVDSRFDYIETVTYDGIQRKTVVHGGSL 600
601 IPHPFGVSLFEGQVFFTDWTKMAVLKANKFTETNPQVYYQASLRPYGVTV 650
651 YHSLRQPYATNPCKDNNGGCEQVCVLSHRTDNDGLGFRCKCTFGFQLDTD 700
701 ERHCIAVQNFLIFSSQVAIRGIPFTLSTQEDVMVPVSGNPSFFVGIDFDA 750
751 QDSTIFFSDMSKHMIFKQKIDGTGREILAANRVENVESLAFDWISKNLYW 800
801 TDSHYKSISVMRLADKTRRTVVQYLNNPRSVVVHPFAGYLFFTDWFRPAK 850
851 IMRAWSDGSHLLPVINTTLGWPNGLAIDWAASRLYWVDAYFDKIEHSTFD 900
901 GLDRRRLGHIEQMTHPFGLAIFGEHLFFTDWRLGAIIRVRKADGGEMTVI 950
951 RSGIAYILHLKSYDVNIQTGSNACNQPTHPNGDCSHFCFPVPNFQRVCGC 1000
1001 PYGMRLASNHLTCEGDPTNEPPTEQCGLFSFPCKNGRCVPNYYLCDGVDD 1050
1051 CHDNSDEQLCGTLNNTCSSSAFTCGHGECIPAHWRCDKRNDCVDGSDEHN 1100
1101 CPTHAPASCLDTQYTCDNHQCISKNWVCDTDNDCGDGSDEKNCNSTETCQ 1150
1151 PSQFNCPNHRCIDLSFVCDGDKDCVDGSDEVGCVLNCTASQFKCASGDKC 1200
1201 IGVTNRCDGVFDCSDNSDEAGCPTRPPGMCHSDEFQCQEDGICIPNFWEC 1250
1251 DGHPDCLYGSDEHNACVPKTCPSSYFHCDNGNCIHRAWLCDRDNDCGDMS 1300
1301 DEKDCPTQPFRCPSWQWQCLGHNICVNLSVVCDGIFDCPNGTDESPLCNG 1350
1351 NSCSDFNGGCTHECVQEPFGAKCLCPLGFLLANDSKTCEDIDECDILGSC 1400
1401 SQHCYNMRGSFRCSCDTGYMLESDGRTCKVTASESLLLLVASQNKIIADS 1450
1451 VTSQVHNIYSLVENGSYIVAVDFDSISGRIFWSDATQGKTWSAFQNGTDR 1500
1501 RVVFDSSIILTETIAIDWVGRNLYWTDYALETIEVSKIDGSHRTVLISKN 1550
1551 LTNPRGLALDPRMNEHLLFWSDWGHHPRIERASMDGSMRTVIVQDKIFWP 1600
1601 CGLTIDYPNRLLYFMDSYLDYMDFCDYNGHHRRQVIASDLIIRHPYALTL 1650
1651 FEDSVYWTDRATRRVMRANKWHGGNQSVVMYNIQWPLGIVAVHPSKQPNS 1700
1701 VNPCAFSRCSHLCLLSSQGPHFYSCVCPSGWSLSPDLLNCLRDDQPFLIT 1750
1751 VRQHIIFGISLNPEVKSNDAMVPIAGIQNGLDVEFDDAEQYIYWVENPGE 1800
1801 IHRVKTDGTNRTVFASISMVGPSMNLALDWISRNLYSTNPRTQSIEVLTL 1850
1851 HGDIRYRKTLIANDGTALGVGFPIGITVDPARGKLYWSDQGTDSGVPAKI 1900
1901 ASANMDGTSVKTLFTGNLEHLECVTLDIEEQKLYWAVTGRGVIERGNVDG 1950
1951 TDRMILVHQLSHPWGIAVHDSFLYYTDEQYEVIERVDKATGANKIVLRDN 2000
2001 VPNLRGLQVYHRRNAAESSNGCSNNMNACQQICLPVPGGLFSCACATGFK 2050
2051 LNPDNRSCSPYNSFIVVSMLSAIRGFSLELSDHSETMVPVAGQGRNALHV 2100
2101 DVDVSSGFIYWCDFSSSVASDNAIRRIKPDGSSLMNIVTHGIGENGVRGI 2150
2151 AVDWVAGNLYFTNAFVSETLIEVLRINTTYRRVLLKVTVDMPRHIVVDPK 2200
2201 NRYLFWADYGQRPKIERSFLDCTNRTVLVSEGIVTPRGLAVDRSDGYVYW 2250
2251 VDDSLDIIARIRINGENSEVIRYGSRYPTPYGITVFENSIIWVDRNLKKI 2300
2301 FQASKEPENTEPPTVIRDNINWLRDVTIFDKQVQPRSPAEVNNNPCLENN 2350
2351 GGCSHLCFALPGLHTPKCDCAFGTLQSDGKNCAISTENFLIFALSNSLRS 2400
2401 LHLDPENHSPPFQTINVERTVMSLDYDSVSDRIYFTQNLASGVGQISYAT 2450
2451 LSSGIHTPTVIASGIGTADGIAFDWITRRIYYSDYLNQMINSMAEDGSNR 2500
2501 TVIARVPKPRAIVLDPCQGYLYWADWDTHAKIERATLGGNFRVPIVNSSL 2550
2551 VMPSGLTLDYEEDLLYWVDASLQRIERSTLTGVDREVIVNAAVHAFGLTL 2600
2601 YGQYIYWTDLYTQRIYRANKYDGSGQIAMTTNLLSQPRGINTVVKNQKQQ 2650
2651 CNNPCEQFNGGCSHICAPGPNGAECQCPHEGNWYLANNRKHCIVDNGERC 2700
2701 GASSFTCSNGRCISEEWKCDNDNDCGDGSDEMESVCALHTCSPTAFTCAN 2750
2751 GRCVQYSYRCDYYNDCGDGSDEAGCLFRDCNATTEFMCNNRRCIPREFIC 2800
2801 NGVDNCHDNNTSDEKNCPDRTCQSGYTKCHNSNICIPRVYLCDGDNDCGD 2850
2851 NSDENPTYCTTHTCSSSEFQCASGRCIPQHWYCDQETDCFDASDEPASCG 2900
2901 HSERTCLADEFKCDGGRCIPSEWICDGDNDCGDMSDEDKRHQCQNQNCSD 2950
2951 SEFLCVNDRPPDRRCIPQSWVCDGDVDCTDGYDENQNCTRRTCSENEFTC 3000
3001 GYGLCIPKIFRCDRHNDCGDYSDERGCLYQTCQQNQFTCQNGRCISKTFV 3050
3051 CDEDNDCGDGSDELMHLCHTPEPTCPPHEFKCDNGRCIEMMKLCNHLDDC 3100
3101 LDNSDEKGCGINECHDPSISGCDHNCTDTLTSFYCSCRPGYKLMSDKRTC 3150
3151 VDIDECTEMPFVCSQKCENVIGSYICKCAPGYLREPDGKTCRQNSNIEPY 3200
3201 LIFSNRYYLRNLTIDGYFYSLILEGLDNVVALDFDRVEKRLYWIDTQRQV 3250
3251 IERMFLNKTNKETIINHRLPAAESLAVDWVSRKLYWLDARLDGLFVSDLN 3300
3301 GGHRRMLAQHCVDANNTFCFDNPRGLALHPQYGYLYWADWGHRAYIGRVG 3350
3351 MDGTNKSVIISTKLEWPNGITIDYTNDLLYWADAHLGYIEYSDLEGHHRH 3400
3401 TVYDGALPHPFAITIFEDTIYWTDWNTRTVEKGNKYDGSNRQTLVNTTHR 3450
3451 PFDIHVYHPYRQPIVSNPCGTNNGGCSHLCLIKPGGKGFTCECPDDFRTL 3500
3501 QLSGSTYCMPMCSSTQFLCANNEKCIPIWWKCDGQKDCSDGSDELALCPQ 3550
3551 RFCRLGQFQCSDGNCTSPQTLCNAHQNCPDGSDEDRLLCENHHCDSNEWQ 3600
3601 CANKRCIPESWQCDTFNDCEDNSDEDSSHCASRTCRPGQFRCANGRCIPQ 3650
3651 AWKCDVDNDCGDHSDEPIEECMSSAHLCDNFTEFSCKTNYRCIPKWAVCN 3700
3701 GVDDCRDNSDEQGCEERTCHPVGDFRCKNHHCIPLRWQCDGQNDCGDNSD 3750
3751 EENCAPRECTESEFRCVNQQCIPSRWICDHYNDCGDNSDERDCEMRTCHP 3800
3801 EYFQCTSGHCVHSELKCDGSADCLDASDEADCPTRFPDGAYCQATMFECK 3850
3851 NHVCIPPYWKCDGDDDCGDGSDEELHLCLDVPCNSPNRFRCDNNRCIYSH 3900
3901 EVCNGVDDCGDGTDETEEHCRKPTPKPCTEYEYKCGNGHCIPHDNVCDDA 3950
3951 DDCGDWSDELGCNKGKERTCAENICEQNCTQLNEGGFICSCTAGFETNVF 4000
4001 DRTSCLDINECEQFGTCPQHCRNTKGSYECVCADGFTSMSDRPGKRCAAE 4050
4051 GSSPLLLLPDNVRIRKYNLSSERFSEYLQDEEYIQAVDYDWDPKDIGLSV 4100
4101 VYYTVRGEGSRFGAIKRAYIPNFESGRNNLVQEVDLKLKYVMQPDGIAVD 4150
4151 WVGRHIYWSDVKNKRIEVAKLDGRYRKWLISTDLDQPAAIAVNPKLGLMF 4200
4201 WTDWGKEPKIESAWMNGEDRNILVFEDLGWPTGLSIDYLNNDRIYWSDFK 4250
4251 EDVIETIKYDGTDRRVIAKEAMNPYSLDIFEDQLYWISKEKGEVWKQNKF 4300
4301 GQGKKEKTLVVNPWLTQVRIFHQLRYNKSVPNLCKQICSHLCLLRPGGYS 4350
4351 CACPQGSSFIEGSTTECDAAIELPINLPPPCRCMHGGNCYFDETDLPKCK 4400
4401 CPSGYTGKYCEMAFSKGISPGTTAVAVLLTILLIVVIGALAIAGFFHYRR 4450
4451 TGSLLPALPKLPSLSSLVKPSENGNGVTFRSGADLNMDIGVSGFGPETAI 4500
4501 DRSMAMSEDFVMEMGKQPIIFENPMYSARDSAVKVVQPIQVTVSENVDNK 4550
4551 NYGSPINPSEIVPETNPTSPAADGTQVTKWNLFKRKSKQTTNFENPIYAQ 4600
4601 MENEQKESVAATPPPSPSLPAKPKPPSRRDPTPTYSATEDTFKDTANLVK 4650
4651 EDSEV 4655
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.) |
Go back to the NucPred Home Page.