 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q96PZ7 from www.uniprot.org...
The NucPred score for your sequence is 0.39 (see score help below)
1 MTAWRRFQSLLLLLGLLVLCARLLTAAKGQNCGGLVQGPNGTIESPGFPH 50
51 GYPNYANCTWIIITGERNRIQLSFHTFALEEDFDILSVYDGQPQQGNLKV 100
101 RLSGFQLPSSIVSTGSILTLWFTTDFAVSAQGFKALYEVLPSHTCGNPGE 150
151 ILKGVLHGTRFNIGDKIRYSCLPGYILEGHAILTCIVSPGNGASWDFPAP 200
201 FCRAEGACGGTLRGTSSSISSPHFPSEYENNADCTWTILAEPGDTIALVF 250
251 TDFQLEEGYDFLEISGTEAPSIWLTGMNLPSPVISSKNWLRLHFTSDSNH 300
301 RRKGFNAQFQVKKAIELKSRGVKMLPSKDGSHKNSVLSQGGVALVSDMCP 350
351 DPGIPENGRRAGSDFRVGANVQFSCEDNYVLQGSKSITCQRVTETLAAWS 400
401 DHRPICRARTCGSNLRGPSGVITSPNYPVQYEDNAHCVWVITTTDPDKVI 450
451 KLAFEEFELERGYDTLTVGDAGKVGDTRSVLYVLTGSSVPDLIVSMSNQM 500
501 WLHLQSDDSIGSPGFKAVYQEIEKGGCGDPGIPAYGKRTGSSFLHGDTLT 550
551 FECPAAFELVGERVITCQQNNQWSGNKPSCVFSCFFNFTASSGIILSPNY 600
601 PEEYGNNMNCVWLIISEPGSRIHLIFNDFDVEPQFDFLAVKDDGISDITV 650
651 LGTFSGNEVPSQLASSGHIVRLEFQSDHSTTGRGFNITYTTFGQNECHDP 700
701 GIPINGRRFGDRFLLGSSVSFHCDDGFVKTQGSESITCILQDGNVVWSST 750
751 VPRCEAPCGGHLTASSGVILPPGWPGYYKDSLHCEWIIEAKPGHSIKITF 800
801 DRFQTEVNYDTLEVRDGPASSSPLIGEYHGTQAPQFLISTGNFMYLLFTT 850
851 DNSRSSIGFLIHYESVTLESDSCLDPGIPVNGHRHGGDFGIRSTVTFSCD 900
901 PGYTLSDDEPLVCERNHQWNHALPSCDALCGGYIQGKSGTVLSPGFPDFY 950
951 PNSLNCTWTIEVSHGKGVQMIFHTFHLESSHDYLLITEDGSFSEPVARLT 1000
1001 GSVLPHTIKAGLFGNFTAQLRFISDFSISYEGFNITFSEYDLEPCDDPGV 1050
1051 PAFSRRIGFHFGVGDSLTFSCFLGYRLEGATKLTCLGGGRRVWSAPLPRC 1100
1101 VAECGASVKGNEGTLLSPNFPSNYDNNHECIYKIETEAGKGIHLRTRSFQ 1150
1151 LFEGDTLKVYDGKDSSSRPLGTFTKNELLGLILNSTSNHLWLEFNTNGSD 1200
1201 TDQGFQLTYTSFDLVKCEDPGIPNYGYRIRDEGHFTDTVVLYSCNPGYAM 1250
1251 HGSNTLTCLSGDRRVWDKPLPSCIAECGGQIHAATSGRILSPGYPAPYDN 1300
1301 NLHCTWIIEADPGKTISLHFIVFDTEMAHDILKVWDGPVDSDILLKEWSG 1350
1351 SALPEDIHSTFNSLTLQFDSDFFISKSGFSIQFSTSIAATCNDPGMPQNG 1400
1401 TRYGDSREAGDTVTFQCDPGYQLQGQAKITCVQLNNRFFWQPDPPTCIAA 1450
1451 CGGNLTGPAGVILSPNYPQPYPPGKECDWRVKVNPDFVIALIFKSFNMEP 1500
1501 SYDFLHIYEGEDSNSPLIGSYQGSQAPERIESSGNSLFLAFRSDASVGLS 1550
1551 GFAIEFKEKPREACFDPGNIMNGTRVGTDFKLGSTITYQCDSGYKILDPS 1600
1601 SITCVIGADGKPSWDQVLPSCNAPCGGQYTGSEGVVLSPNYPHNYTAGQI 1650
1651 CLYSITVPKEFVVFGQFAYFQTALNDLAELFDGTHAQARLLSSLSGSHSG 1700
1701 ETLPLATSNQILLRFSAKSGASARGFHFVYQAVPRTSDTQCSSVPEPRYG 1750
1751 RRIGSEFSAGSIVRFECNPGYLLQGSTALHCQSVPNALAQWNDTIPSCVV 1800
1801 PCSGNFTQRRGTILSPGYPEPYGNNLNCIWKIIVTEGSGIQIQVISFATE 1850
1851 QNWDSLEIHDGGDVTAPRLGSFSGTTVPALLNSTSNQLYLHFQSDISVAA 1900
1901 AGFHLEYKTVGLAACQEPALPSNSIKIGDRYMVNDVLSFQCEPGYTLQGR 1950
1951 SHISCMPGTVRRWNYPSPLCIATCGGTLSTLGGVILSPGFPGSYPNNLDC 2000
2001 TWRISLPIGYGAHIQFLNFSTEANHDFLEIQNGPYHTSPMIGQFSGTDLP 2050
2051 AALLSTTHETLIHFYSDHSQNRQGFKLAYQAYELQNCPDPPPFQNGYMIN 2100
2101 SDYSVGQSVSFECYPGYILIGHPVLTCQHGINRNWNYPFPRCDAPCGYNV 2150
2151 TSQNGTIYSPGFPDEYPILKDCIWLITVPPGHGVYINFTLLQTEAVNDYI 2200
2201 AVWDGPDQNSPQLGVFSGNTALETAYSSTNQVLLKFHSDFSNGGFFVLNF 2250
2251 HAFQLKKCQPPPAVPQAEMLTEDDDFEIGDFVKYQCHPGYTLVGTDILTC 2300
2301 KLSSQLQFEGSLPTCEAQCPANEVRTGSSGVILSPGYPGNYFNSQTCSWS 2350
2351 IKVEPNYNITIFVDTFQSEKQFDALEVFDGSSGQSPLLVVLSGNHTEQSN 2400
2401 FTSRSNQLYLRWSTDHATSKKGFKIRYAAPYCSLTHPLKNGGILNRTAGA 2450
2451 VGSKVHYFCKPGYRMVGHSNATCRRNPLGMYQWDSLTPLCQAVSCGIPES 2500
2501 PGNGSFTGNEFTLDSKVVYECHEGFKLESSQQATAVCQEDGLWSNKGKPP 2550
2551 TCKPVACPSIEAQLSEHVIWRLVSGSLNEYGAQVLLSCSPGYYLEGWRLL 2600
2601 RCQANGTWNIGDERPSCRVISCGSLSFPPNGNKIGTLTVYGATAIFTCNT 2650
2651 GYTLVGSHVRECLANGLWSGSETRCLAGHCGSPDPIVNGHISGDGFSYRD 2700
2701 TVVYQCNPGFRLVGTSVRICLQDHKWSGQTPVCVPITCGHPGNPAHGFTN 2750
2751 GSEFNLNDVVNFTCNTGYLLQGVSRAQCRSNGQWSSPLPTCRVVNCSDPG 2800
2801 FVENAIRHGQQNFPESFEYGMSILYHCKKGFYLLGSSALTCMANGLWDRS 2850
2851 LPKCLAISCGHPGVPANAVLTGELFTYGAVVHYSCRGSESLIGNDTRVCQ 2900
2901 EDSHWSGALPHCTGNNPGFCGDPGTPAHGSRLGDDFKTKSLLRFSCEMGH 2950
2951 QLRGSPERTCLLNGSWSGLQPVCEAVSCGNPGTPTNGMIVSSDGILFSSS 3000
3001 VIYACWEGYKTSGLMTRHCTANGTWTGTAPDCTIISCGDPGTLANGIQFG 3050
3051 TDFTFNKTVSYQCNPGYVMEAVTSATIRCTKDGRWNPSKPVCKAVLCPQP 3100
3101 PPVQNGTVEGSDFRWGSSISYSCMDGYQLSHSAILSCEGRGVWKGEIPQC 3150
3151 LPVFCGDPGIPAEGRLSGKSFTYKSEVFFQCKSPFILVGSSRRVCQADGT 3200
3201 WSGIQPTCIDPAHNTCPDPGTPHFGIQNSSRGYEVGSTVFFRCRKGYHIQ 3250
3251 GSTTRTCLANLTWSGIQTECIPHACRQPETPAHADVRAIDLPTFGYTLVY 3300
3301 TCHPGFFLAGGSEHRTCKADMKWTGKSPVCKSKGVREVNETVTKTPVPSD 3350
3351 VFFVNSLWKGYYEYLGKRQPATLTVDWFNATSSKVNATFSEASPVELKLT 3400
3401 GIYKKEEAHLLLKAFQIKGQADIFVSKFENDNWGLDGYVSSGLERGGFTF 3450
3451 QGDIHGKDFGKFKLERQDPLNPDQDSSSHYHGTSSGSVAAAILVPFFALI 3500
3501 LSGFAFYLYKHRTRPKVQYNGYAGHENSNGQASFENPMYDTNLKPTEAKA 3550
3551 VRFDTTLNTVCTVV 3564
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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