 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q923L3 from www.uniprot.org...
The NucPred score for your sequence is 0.43 (see score help below)
1 MTAWRKFKSLLLPLVLAVLCAGLLTAAKGQNCGGLVQGPNGTIESPGFPH 50
51 GYPNYANCTWIIITGERNRIQLSFHTFALEEDFDILSVYDGQPQQGNLKV 100
101 RLSGFQLPSSIVSTGSLLTLWFTTDFAVSAQGFKAMYEVLPSHTCGNPGE 150
151 ILKGVLHGTRFNIGDKIRYSCLSGYILEGHAILTCIVSPGNGASWDFPAP 200
201 FCRAEGACGGTLRGTSGSISSPHFPSEYDNNADCTWTILAEPGDTIALVF 250
251 TDFQLEEGYDFLEISGTEAPSIWLTGMNLPSPVISSKNWLRLHFTSDSNH 300
301 RRKGFNAQFQVKKAIELKSRGVKMLPSKDSSHKNSVLTQGGVSLISDMCP 350
351 DPGIPDNGRRAGSDFRVGANVQFSCEDNYVLQGAKGITCQRVTETLAAWN 400
401 DHRPICRARTCGSNLRGPSGVITSPNYPVQYEDNAHCVWVITTTDPDKVI 450
451 KLAFEEFELERGYDTLTVGDAGKVGDTRSVLYVLTGSSVPDLIVSMSNQM 500
501 WLHLQSDDSIGSPGFKAVYQEIEKGGCGDPGIPAYGKRTGSSFLHGDTLT 550
551 FECQAAFELVGERVITCQKNNQWSGNKPSCVFSCFFNFTAPSGIILSPNY 600
601 PEEYGNNMNCVWLIISEPGSRIHLIFNDFDVEPQFDFLAVKDDGISDITV 650
651 LGTFSGNEVPAQLASSGHIVRLEFQSDHSTTGRGFNITYTTFGQNECHDP 700
701 GIPVNGRRFGDRFLLGSSVSFHCDDGFVKTQGSESITCILQDGNVVWSST 750
751 VPRCEAPCGGHLTASSGVILPPGWPGYYKDSLNCEWVIEAKPGHSIKITF 800
801 DRFQTEVNYDTLEVRDGPTSSSPLIGEYHGTQAPQFLISTGNYMYLLFTT 850
851 DSSRASVGFLIHYESVTLESDSCLDPGIPVNGQRHGSNFGIRSTVTFSCD 900
901 PGYTLSDDEPLVCEKNHQWNHALPSCDALCGGYIHGKSGTVLSPGFPDFY 950
951 PNSLNCTWTIEVSHGKGVQMNFHTFHLESSHDYLLITEDGSFSEPVARLT 1000
1001 GSVLPHTIKAGLFGNFTAQLRFISDFSISYEGFNITFAEYDLEPCDDPGV 1050
1051 PAFSRRIGFQFGVGDTLAFTCFQGYRLEGATKLTCLGGGRRVWSAPLPRC 1100
1101 VAECGASVKGNEGTLLSPNFPSHYDNNHECIYKIETEAGKGIHLRARTFQ 1150
1151 LFEGDTLKVYDGKDSSSRSLGVFTRSEFMGLVLNSTSNHLRLEFNTNGSD 1200
1201 TAQGFQLTYTSFDLVKCEDPGIPNYGYRIRDDGHFTDTVVLYSCNPGYAM 1250
1251 HGSSTLTCLSGDRRVWDKPMPSCVAECGGLVHAATSGRILSPGYPAPYDN 1300
1301 NLHCTWTIEADPGKTISLHFIVFDTETAHDILKVWDGPVDSNILLKEWSG 1350
1351 SALPEDIHSTFNSLTLQFDSDFFISKSGFSIQFSTSIASTCNDPGMPQNG 1400
1401 TRYGDSREPGDTITFQCDPGYQLQGPAKITCVQLNNRFFWQPDPPSCIAA 1450
1451 CGGNLTGPAGVILSPNYPQPYPPGKECDWRIKVNPDFVIALIFKSFSMEP 1500
1501 SYDFLHIYEGEDSNSPLIGSFQGSQAPERIESSGNSLFLAFRSDASVGLS 1550
1551 GFAIEFKEKPREACFDPGNIMNGTRIGTDFKLGSTVTYQCDSGYKIVDPS 1600
1601 SIECVTGADGKPSWDRALPACQAPCGGQYTGSEGVVLSPNYPHNYTAGQM 1650
1651 CIYSITVPKEFVVFGQFAYFQTALNDLAELFDGTHPQARLLSSLSGSHSG 1700
1701 ETLPLATSNQILLRFSAKSGASARGFHFVYQAVPRTSDTQCSSVPEPRYG 1750
1751 RRIGSEFSAGSIVRFECNPGYLLQGSTAIRCQSVPNALAQWNDTIPSCVV 1800
1801 PCSGNFTQRRGTILSPGYPEPYGNNLNCVWKIIVSEGSGIQIQVISFATE 1850
1851 QNWDSLEIHDGGDMTAPRLGSFSGTTVPALLNSTSNQLCLHFQSDISVAA 1900
1901 AGFHLEYKTVGLAACQEPALPSNGIKIGDRYMVNDVLSFQCEPGYTLQGR 1950
1951 SHISCMPGTVRRWNYPSPLCIATCGGTLTSMSGVILSPGFPGSYPNNLDC 2000
2001 TWKISLPIGYGAHIQFLNFSTEANHDYLEIQNGPYHSSPMMGQFSGPDLP 2050
2051 TSLLSTTHETLIRFYSDHSQNRQGFKLSYQAYELQNCPDPPAFQNGFMIN 2100
2101 SDYSVGQSISFECYPGYILLGHPVLTCQHGTDRNWNYPFPRCDAPCGYNV 2150
2151 TSQNGTIYSPGFPDEYPILKDCLWLVTVPPGHGVYINFTLLQTEAVNDYI 2200
2201 AVWDGPDQNSPQLGVFSGNTALETAYSSTNQVLLKFHSDFSNGGFFVLNF 2250
2251 HAFQLKRCPPPPAVPQADLLTEDEDFEIGDFVKYQCHPGYTLLGSDTLTC 2300
2301 KLSSQLLFQGSPPTCEAQCPANEVRTESSGVILSPGYPGNYFNSQTCAWS 2350
2351 IKVEPNFNITLFVDTFQSEKQFDALEVFDGSSGQSPLLVVLSGNHTEQSN 2400
2401 FTSRSNHLYLRWSTDHATSKKGFKIRYAAPYCSLTSTLRNGGILNKTAGA 2450
2451 VGSKVHYFCKPGYRMIGHSNATCRRNPVGVYQWDSMAPLCQAVSCGIPEA 2500
2501 PGNGSFTGNEFTLDSKVTYECNEGFKLDASQEATTVCQEDGLWSNRGKPP 2550
2551 TCKPVPCPSIEGQLSEHVLWRLVSGSLNEYGAQVLLSCSPGYFLQGQRLL 2600
2601 QCQANGTWSTEEDRPRCKVISCGSLSFPPNGNKIGTLTIYGATAIFTCNT 2650
2651 GYTLVGSHVRECLANGLWSGSETRCLAGHCGSPDPIVNGHISGDGFSYRD 2700
2701 TVVYQCNPGFRLVGTSVRICLQDHKWSGQTPVCVPITCGHPGNPAHGLTN 2750
2751 GTEFNLNDLVNFTCHTGYRLQGASRAQCRSNGQWSSPLPICRVVNCSDPG 2800
2801 SVENAVRHGQQNFPESFEYGTSVMYHCKTGFYLLGSSALTCMASGLWDRS 2850
2851 LPKCLAISCGHPGVPANAVLTGELFTYGATVQYSCKGGQILTGNSTRVCQ 2900
2901 EDSHWSGSLPHCSGNSPGFCGDPGTPAHGSRLGDEFKTKSLLRFSCEMGH 2950
2951 QLRGSAERTCLVNGSWSGVQPVCEAVSCGNPGTPTNGMILSSDGILFSSS 3000
3001 VIYACWEGYKTSGLMTRHCTANGTWTGTAPDCTIISCGDPGTLPNGIQFG 3050
3051 TDFTFNKTVSYQCNPGYLMEPPTSPTIRCTKDGTWNQSRPLCKAVLCNQP 3100
3101 PPVPNGKVEGSDFRWGASISYSCVDGYQLSHSAILSCEGRGVWKGEVPQC 3150
3151 LPVFCGDPGTPAEGRLSGKSFTFKSEVFIQCKPPFVLVGSSRRTCQADGI 3200
3201 WSGIQPTCIDPAHTACPDPGTPHFGIQNSSKGYEVGSTVFFRCRKGYHIQ 3250
3251 GSTTRTCLANLTWSGIQTECIPHACRQPETPAHADVRAIDLPAFGYTLVY 3300
3301 TCHPGFFLAGGSEHRTCKADMKWTGKSPVCKSKGVREVNETVTKTPVPSD 3350
3351 VFFINSVWKGYYEYLGKRQPATLTVDWFNATSSKVNATFTAASRVQLELT 3400
3401 GVYKKEEAHLLLKAFHIKGPADIFVSKFENDNWGLDGYVSSGLERGGFSF 3450
3451 QGDIHGKDFGKFKLERQDPSNSDADSSNHYQGTSSGSVAAAILVPFFALI 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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