 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O60494 from www.uniprot.org...
The NucPred score for your sequence is 0.39 (see score help below)
1 MMNMSLPFLWSLLTLLIFAEVNGEAGELELQRQKRSINLQQPRMATERGN 50
51 LVFLTGSAQNIEFRTGSLGKIKLNDEDLSECLHQIQKNKEDIIELKGSAI 100
101 GLPQNISSQIYQLNSKLVDLERKFQGLQQTVDKKVCSSNPCQNGGTCLNL 150
151 HDSFFCICPPQWKGPLCSADVNECEIYSGTPLSCQNGGTCVNTMGSYSCH 200
201 CPPETYGPQCASKYDDCEGGSVARCVHGICEDLMREQAGEPKYSCVCDAG 250
251 WMFSPNSPACTLDRDECSFQPGPCSTLVQCFNTQGSFYCGACPTGWQGNG 300
301 YICEDINECEINNGGCSVAPPVECVNTPGSSHCQACPPGYQGDGRVCTLT 350
351 DICSVSNGGCHPDASCSSTLGSLPLCTCLPGYTGNGYGPNGCVQLSNICL 400
401 SHPCLNGQCIDTVSGYFCKCDSGWTGVNCTENINECLSNPCLNGGTCVDG 450
451 VDSFSCECTRLWTGALCQVPQQVCGESLSGINGSFSYRSPDVGYVHDVNC 500
501 FWVIKTEMGKVLRITFTFFRLESMDNCPHEFLQVYDGDSSSAFQLGRFCG 550
551 SSLPHELLSSDNALYFHLYSEHLRNGRGFTVRWETQQPECGGILTGPYGS 600
601 IKSPGYPGNYPPGRDCVWIVVTSPDLLVTFTFGTLSLEHHDDCNKDYLEI 650
651 RDGPLYQDPLLGKFCTTFSVPPLQTTGPFARIHFHSDSQISDQGFHITYL 700
701 TSPSDLRCGGNYTDPEGELFLPELSGPFTHTRQCVYMMKQPQGEQIQINF 750
751 THVELQCQSDSSQNYIEVRDGETLLGKVCGNGTISHIKSITNSVWIRFKI 800
801 DASVEKASFRAVYQVACGDELTGEGVIRSPFFPNVYPGERTCRWTIHQPQ 850
851 SQVILLNFTVFEIGSSAHCETDYVEIGSSSILGSPENKKYCGTDIPSFIT 900
901 SVYNFLYVTFVKSSSTENHGFMAKFSAEDLACGEILTESTGTIQSPGHPN 950
951 VYPHGINCTWHILVQPNHLIHLMFETFHLEFHYNCTNDYLEVYDTDSETS 1000
1001 LGRYCGKSIPPSLTSSGNSLMLVFVTDSDLAYEGFLINYEAISAATACLQ 1050
1051 DYTDDLGTFTSPNFPNNYPNNWECIYRITVRTGQLIAVHFTNFSLEEAIG 1100
1101 NYYTDFLEIRDGGYEKSPLLGIFYGSNLPPTIISHSNKLWLKFKSDQIDT 1150
1151 RSGFSAYWDGSSTGCGGNLTTSSGTFISPNYPMPYYHSSECYWWLKSSHG 1200
1201 SAFELEFKDFHLEHHPNCTLDYLAVYDGPSSNSHLLTQLCGDEKPPLIRS 1250
1251 SGDSMFIKLRTDEGQQGRGFKAEYRQTCENVVIVNQTYGILESIGYPNPY 1300
1301 SENQHCNWTIRATTGNTVNYTFLAFDLEHHINCSTDYLELYDGPRQMGRY 1350
1351 CGVDLPPPGSTTSSKLQVLLLTDGVGRREKGFQMQWFVYGCGGELSGATG 1400
1401 SFSSPGFPNRYPPNKECIWYIRTDPGSSIQLTIHDFDVEYHSRCNFDVLE 1450
1451 IYGGPDFHSPRIAQLCTQRSPENPMQVSSTGNELAIRFKTDLSINGRGFN 1500
1501 ASWQAVTGGCGGIFQAPSGEIHSPNYPSPYRSNTDCSWVIRVDRNHRVLL 1550
1551 NFTDFDLEPQDSCIMAYDGLSSTMSRLARTCGREQLANPIVSSGNSLFLR 1600
1601 FQSGPSRQNRGFRAQFRQACGGHILTSSFDTVSSPRFPANYPNNQNCSWI 1650
1651 IQAQPPLNHITLSFTHFELERSTTCARDFVEILDGGHEDAPLRGRYCGTD 1700
1701 MPHPITSFSSALTLRFVSDSSISAGGFHTTVTASVSACGGTFYMAEGIFN 1750
1751 SPGYPDIYPPNVECVWNIVSSPGNRLQLSFISFQLEDSQDCSRDFVEIRE 1800
1801 GNATGHLVGRYCGNSFPLNYSSIVGHTLWVRFISDGSGSGTGFQATFMKI 1850
1851 FGNDNIVGTHGKVASPFWPENYPHNSNYQWTVNVNASHVVHGRILEMDIE 1900
1901 EIQNCYYDKLRIYDGPSIHARLIGAYCGTQTESFSSTGNSLTFHFYSDSS 1950
1951 ISGKGFLLEWFAVDAPDGVLPTIAPGACGGFLRTGDAPVFLFSPGWPDSY 2000
2001 SNRVDCTWLIQAPDSTVELNILSLDIESHRTCAYDSLVIRDGDNNLAQQL 2050
2051 AVLCGREIPGPIRSTGEYMFIRFTSDSSVTRAGFNASFHKSCGGYLHADR 2100
2101 GIITSPKYPETYPSNLNCSWHVLVQSGLTIAVHFEQPFQIPNGDSSCNQG 2150
2151 DYLVLRNGPDICSPPLGPPGGNGHFCGSHASSTLFTSDNQMFVQFISDHS 2200
2201 NEGQGFKIKYEAKSLACGGNVYIHDADSAGYVTSPNHPHNYPPHADCIWI 2250
2251 LAAPPETRIQLQFEDRFDIEVTPNCTSNYLELRDGVDSDAPILSKFCGTS 2300
2301 LPSSQWSSGEVMYLRFRSDNSPTHVGFKAKYSIAQCGGRVPGQSGVVESI 2350
2351 GHPTLPYRDNLFCEWHLQGLSGHYLTISFEDFNLQNSSGCEKDFVEIWDN 2400
2401 HTSGNILGRYCGNTIPDSIDTSSNTAVVRFVTDGSVTASGFRLRFESSME 2450
2451 ECGGDLQGSIGTFTSPNYPNPNPHGRICEWRITAPEGRRITLMFNNLRLA 2500
2501 THPSCNNEHVIVFNGIRSNSPQLEKLCSSVNVSNEIKSSGNTMKVIFFTD 2550
2551 GSRPYGGFTASYTSSEDAVCGGSLPNTPEGNFTSPGYDGVRNYSRNLNCE 2600
2601 WTLSNPNQGNSSISIHFEDFYLESHQDCQFDVLEFRVGDADGPLMWRLCG 2650
2651 PSKPTLPLVIPYSQVWIHFVTNERVEHIGFHAKYSFTDCGGIQIGDSGVI 2700
2701 TSPNYPNAYDSLTHCSSLLEAPQGHTITLTFSDFDIEPHTTCAWDSVTVR 2750
2751 NGGSPESPIIGQYCGNSNPRTIQSGSNQLVVTFNSDHSLQGGGFYATWNT 2800
2801 QTLGCGGIFHSDNGTIRSPHWPQNFPENSRCSWTAITHKSKHLEISFDNN 2850
2851 FLIPSGDGQCQNSFVKVWAGTEEVDKALLATGCGNVAPGPVITPSNTFTA 2900
2901 VFQSQEAPAQGFSASFVSRCGSNFTGPSGYIISPNYPKQYDNNMNCTYVI 2950
2951 EANPLSVVLLTFVSFHLEARSAVTGSCVNDGVHIIRGYSVMSTPFATVCG 3000
3001 DEMPAPLTIAGPVLLNFYSNEQITDFGFKFSYRIISCGGVFNFSSGIITS 3050
3051 PAYSYADYPNDMHCLYTITVSDDKVIELKFSDFDVVPSTSCSHDYLAIYD 3100
3101 GANTSDPLLGKFCGSKRPPNVKSSNNSMLLVFKTDSFQTAKGWKMSFRQT 3150
3151 LGPQQGCGGYLTGSNNTFASPDSDSNGMYDKNLNCVWIIIAPVNKVIHLT 3200
3201 FNTFALEAASTRQRCLYDYVKLYDGDSENANLAGTFCGSTVPAPFISSGN 3250
3251 FLTVQFISDLTLEREGFNATYTIMDMPCGGTYNATWTPQNISSPNSSDPD 3300
3301 VPFSICTWVIDSPPHQQVKITVWALQLTSQDCTQNYLQLQDSPQGHGNSR 3350
3351 FQFCGRNASAVPVFYSSMSTAMVIFKSGVVNRNSRMSFTYQIADCNRDYH 3400
3401 KAFGNLRSPGWPDNYDNDKDCTVTLTAPQNHTISLFFHSLGIENSVECRN 3450
3451 DFLEVRNGSNSNSPLLGKYCGTLLPNPVFSQNNELYLRFKSDSVTSDRGY 3500
3501 EIIWTSSPSGCGGTLYGDRGSFTSPGYPGTYPNNTYCEWVLVAPAGRLVT 3550
3551 INFYFISIDDPGDCVQNYLTLYDGPNASSPSSGPYCGGDTSIAPFVASSN 3600
3601 QVFIKFHADYARRPSAFRLTWDS 3623
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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