 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JLB4 from www.uniprot.org...
The NucPred score for your sequence is 0.41 (see score help below)
1 MASHFLWGFVTLLMVPGLDGETGTPEQKLQKRIADLHQPRMTTEEGNLVF 50
51 LTSSAQNIEFRTGSLGKIKLNDDDLGECLHQIQRNKDDIIDLKRNTTGLP 100
101 QNILSQVHQLNSKLVDLERDFQSLQQNVERKVCSSNPCHNGGTCVNLHDS 150
151 FICICPSQWKGLFCSEDVNECVLYAGTPFGCQSGSTCVNTMGSFRCDCTP 200
201 DTYGPQCASKYNDCEQGSQQLCKHGICEDLQRVYHGQLRFNCICDAGWTT 250
251 LPNGISCTEDKDECSLQPSPCSEHAQCFNTQGSFYCGACPKGWQGNGYQC 300
301 QDINECEINNGGCSQAPLVPCLNTPGSFTCGNCPAGFSGDGRVCTPLDIC 350
351 SIHNGGCHPDATCSSSSVLGSLLPVCTCPPGYTGNGYGSNGCVRLSNMCS 400
401 RHPCVNGQCIETVSSYFCKCDSGWFGQNCTENINECVSNPCLNGGTCIDG 450
451 VNGFTCDCTSSWTGYYCQTPQAACGGILSGTQGTFAYQSPNDTYVHNVNC 500
501 FWVVRTDEEKVLHITFTFFDLESASNCPREYLQIHDGDSSADFPLGRYCG 550
551 STPPQGVHSSANSLYFHLYSEYIKRGRGFTARWEAKLPECGGILTGNYGS 600
601 ITSPGYPGNYPPGRDCVWNLLVSPGSLITFTFGTLSLESHNDCSKDYLEI 650
651 RDGPFHHDPILGKFCTSLSTPPLQTTGPAARIHFHSDSETSDKGFHITYL 700
701 TTPSDLYCGGNYTDTEGELLLPPLTGPFSHSRQCVYLISQPQGEQIVINF 750
751 THVELESQRGCSHTFIEVGDHESLLRKICGNETLFPIRSISNNVWIRLRI 800
801 DALVQKASFRADYQVACGGELRGEGVIRSPFYPNAYAGRRTCRWTISQPP 850
851 REVVLLNFTDFQIGSSSSCDTDYIEIGPSSVLGSPGNEKFCGTNIPSFIT 900
901 SVYNVLYVTFVKSSSMENRGFMAMFSSEKLECGKVLTESTGIIESPGHPN 950
951 VYPSGVNCTWHIVVQRGQLIRLVFSSFYLEFHYNCANDYLEVYDTIAQTS 1000
1001 LGRYCGKSIPPSLTSSSHSIKLIFVSDSALAHEGFSINYEAINASSVCLY 1050
1051 DYTDNFGRLSSPNFPNNYPHNWNCVYRITVGLNQQIALHFTDFALEDYFG 1100
1101 PKCVDFVEIRDGGFETSPLIGIYCGSVFPPRIISHSNKLWLRFKSDTALT 1150
1151 ARGFSAYWDASSTGCGGNLTTPTGVLTSPNYPMPYYHSSECYWRLEASRG 1200
1201 SPFLLEFQDFHLEHHPNCSLDYLAVFDGPSTNSRLINKLCGDTPPAPIRS 1250
1251 SKDIVLLKLRTDAGQQGRGFEINYRQTCDNVVIVNKTSGILESINYPNPY 1300
1301 DKDQRCNWTIQATTGNTVNYTFLEFDVENYVNCSTDYLELYDGPQRIGRY 1350
1351 CGENIPPPGATTGSKLIVVFHTDGVDSGEKGFKMHWFIHGCGGEMSGTMG 1400
1401 SFSSPGYPNSYPHNKECIWNIRVAPGNSIQLTIHDFDVEYHASCKYDTLE 1450
1451 IYTGLDFHSPRIAQLCSRSPSANPMQISSTDNELAIRFKTDSSLNGRGFN 1500
1501 ASWRAVPGGCGGIFQVSRGEIHSPNYPNNYRANTECSWIIQVEKYHRVLL 1550
1551 NITDFDLEATDSCLMTYDGSSSANTRVATVCGRQQPPNSITSSGNSLFVR 1600
1601 FQSGSSSQSRGFRAQFRQECGAHIITDSSDSISSPLYPANYPNNQNCTWI 1650
1651 IEAQPPFNHIALSFTHFHLQSSTDCTRDFVEILDGRDSDAPVQGRYCGTS 1700
1701 LPHPIISFGNALTVRFVSDSVYGFDGFHAIYSASTSACGGTFYTGDGIFN 1750
1751 SPGYPEDYHSNTECVWNIASSPGNHLQLSFLSFQLENSLNCNKDFVEIRE 1800
1801 GNATGHLMGRYCGNSLPGNYSSIEGHNLWVRFVSDGSGTGMGFQARFKNI 1850
1851 FGNDNIVGTHGKIATPFWPGNYPLNSNYRWTVNVDSSHIIHGRILEMDIE 1900
1901 LTTNCFYDSLKIYDGFDIHSRLIGTYCGTQRESFSSSRNSLTFQFSSDSS 1950
1951 KSGRGFLLEWFAVDVSNVTLPTIAPGACGGYMVTGDTPVFFFSPGWPGPY 2000
2001 GNGADCIWIIYAPDSTVELNILSMDIEAQLSCSYDKLIIKDGDSRLSQQL 2050
2051 AVLCGRSVPGPIRSTGEYMYIRFTSDGSVTGAGFNASFQKSCGGYLHADR 2100
2101 GIITSPKYPDNYLPNLNCSWHVLVQSGLTIAVHFEQPFQIQNRDSSCSQG 2150
2151 DYLVLRNGPDNHSPPLGPSGGNGRFCGIYTPSTLFTSDNEMFIQFISDNS 2200
2201 NGGQGFKIRYEAKSLACGGTIYIHDANSDGYVTSPNYPANYPQHAECIWI 2250
2251 LEAPSGRSIQLQFEDQFNIEETPNCSASYLELRDGANSNAPVLSKLCGHT 2300
2301 LPRNWVSSRGLMYLKFHTEGGSGYMGFKAKYSIVSCGGTVSGDSGVIESV 2350
2351 GYPTRLYANNVFCQWHIQGLPGHYLTIRFEDFNLQSSPGCAKDFVEIWEN 2400
2401 HTSGILLGRYCGNSIPSSVDTSSNVASIRFVTDGSVTDSGFRLQFKSSRE 2450
2451 VCGGDLHGPTGTFTSPNYPNPNPHPRICEWTINVHEGRQIILTFTNLRLS 2500
2501 TQQSCNTEHLIVFNGIRNNSPRLQKLCSRVNVTNEFKSSGNTMKVIFFTD 2550
2551 GSRPYGGFTASYTSSEDAVCGGTLPSVSGGNFSSPGYNGIRDYARNLDCE 2600
2601 WTLSNPNRENSSISIHFLGLSLESHQDCTFDVLEFRVGNADGPLIEKFCS 2650
2651 LSAPRVPLVIPYPQVWIHFVSNERVEYTGFYVEYSFTNCGGIQTGENGVI 2700
2701 SSPNYPNLYSRWTQCSWLLEAPEGHTITLTFSDFSVENHPTCTSDSVTVR 2750
2751 NGDSPGSPIIGRYCGQSVPGPIQSGSNQLVVTFNTNNQGQSRGFYATWNT 2800
2801 NTLGCGGTLHSDNGTIKSPHWPQTFPENSRCSWTAVTHESKHWEISFDSN 2850
2851 FRIPSSDSQCRNSFVKVWEGMLETNDALLATSCGNVAPSPIVTLGNIFTA 2900
2901 VFQSEEMPAQGFSASFISRCGRTFNSSTGDIVSPNFPKHYDNNMNCNYYI 2950
2951 DVAPQSLVILTFVSFHLEDRSAVSGTCDYDGLHIIKGHNLSSTPLVTICG 3000
3001 SETLRPLTIDGPVMLNFYSDAYITDFGFKISYRVANCGGIYSGTYGVLNS 3050
3051 PSFSYTNYPNNVYCVYSLQVRNDRLILLRFNDFEIVPSNLCSHDYLEVFD 3100
3101 GPSIGNRSIGKFCGSTLPQVIKSTNNSLTLLFKTDSSQTARGWKVSFRET 3150
3151 IGPQQGCGGYLTEDSKSFVSPDHDSDGLYDKGLNCIWYIIAPENKLVKLT 3200
3201 FNAFTLEEPSSPGKCTFDYVQIADGASINSYLGGRFCGSSRPAPFISSGN 3250
3251 FLTVQFVSDISIQMRGFNATYTFVDMPCGGTYNATSMPQNTSSPQLSNIR 3300
3301 RPFSTCTWVIEAPPHQQVQITVWKLQLPSQDCSRSSLELQDSEQTNGNQV 3350
3351 TQFCGANYTTLPVFYSSGSTAVVVFKSDFLNRNSRVHFTYEIADCNREYN 3400
3401 QAFGNLKSPGWPQGYANNLDCSIILRAPQNHRISLFFYWFQLEDSRQCMN 3450
3451 DFLEVRNGSSSSSPLLGKYCSNLLPNPIFSQSNELYLHFHSDDSDTHHGY 3500
3501 EIIWASSPTGCGGTLLGNEGILANPGFPDSYPNNTHCEWTIVAPSGRPLS 3550
3551 VGFPFLSIDSPGGCDQNYLILFNGPDANSPPFGPFCGIDTVVAPFHASSN 3600
3601 RVFIRFHAEYATVSSGFEIMWSS 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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