 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O70244 from www.uniprot.org...
The NucPred score for your sequence is 0.42 (see score help below)
1 MSSQFLWGFVTLLMIAELDGKTGKPEQRGQKRIADLHQPRMTTEEGNLVF 50
51 LTSSTQNIEFRTGSLGKIKLNDEDLGECLHQIQRNKDDIIDLRKNTTGLP 100
101 QNILSQVHQLNSKLVDLERDFQNLQQNVERKVCSSNPCLNGGTCVNLHDS 150
151 FVCICPSQWKGLFCSEDVNECVVYSGTPFGCQSGSTCVNTVGSFRCDCTP 200
201 DTYGPQCASKYNDCEQGSKQLCKHGICEDLQRVHHGQPNFHCICDAGWTT 250
251 PPNGISCTEDKDECSLQPSPCSEHAQCFNTQGSFYCGACPKGWQGNGYEC 300
301 QDINECEINNGGCSQAPLVPCLNTPGSFSCGNCPAGFSGDGRVCTPVDIC 350
351 SIHNGGCHPEATCSSSPVLGSFLPVCTCPPGYTGNGYGSNGCVRLSNICS 400
401 RHPCVNGQCIETVSSYFCKCDSGWSGQNCTENINDCSSNPCLNGGTCIDG 450
451 INGFTCDCTSSWTGYYCQTPQAACGGILSGTQGTFAYHSPNDTYIHNVNC 500
501 FWIVRTDEEKVLHVTFTFFDLESASNCPREYLQIHDGDSSADFPLGRYCG 550
551 SRPPQGIHSSANALYFHLYSEYIRSGRGFTARWEAKLPECGGILTDNYGS 600
601 ITSPGYPGNYPPGRDCVWQVLVNPNSLITFTFGTLSLESHNDCSKDYLEI 650
651 RDGPFHQDPVLGKFCTSLSTPPLKTTGPAARIHFHSDSETSDKGFHITYL 700
701 TTQSDLDCGGNYTDTDGELLLPPLSGPFSHSRQCVYLITQAQGEQIVINF 750
751 THVELESQMGCSHTYIEVGDHDSLLRKICGNETLFPIRSVSNKVWIRLRI 800
801 DALVQKASFRADYQVACGGMLRGEGFFRSPFYPNAYPGRRTCRWTISQPQ 850
851 RQVVLLNFTDFQIGSSASCDTDYIEIGPSSVLGSPGNEKFCSSNIPSFIT 900
901 SVYNILYVTFVKSSSMENRGFTAKFSSDKLECGEVLTASTGIIESPGHPN 950
951 VYPRGVNCTWHVVVQRGQLIRLEFSSFYLEFHYNCTNDYLEIYDTAAQTF 1000
1001 LGRYCGKSIPPSLTSNSNSIKLIFVSDSALAHEGFSINYEAIDASSVCLY 1050
1051 DYTDNFGMLSSPNFPNNYPSNWECIYRITVGLNQQIALHFTDFTLEDYFG 1100
1101 SQCVDFVEIRDGGYETSPLVGIYCGSVLPPTIISHSNKLWLKFKSDAALT 1150
1151 AKGFSAYWDGSSTGCGGNLTTPTGVLTSPNYPMPYYHSSECYWRLEASHG 1200
1201 SPFELEFQDFHLEHHPSCSLDYLAVFDGPTTNSRLIDKLCGDTTPAPIRS 1250
1251 NKDVVLLKLRTDAGQQGRGFEINFRQRCDNVVIVNKTSGILESINYPNPY 1300
1301 DKNQRCNWTIQATTGNTVNYTFLGFDVESYMNCSTDYVELYDGPQWMGRY 1350
1351 CGNNMPPPGATTGSQLHVLFHTDGINSGEKGFKMQWFTHGCGGEMSGTAG 1400
1401 SFSSPGYPNSYPHNKECIWNIRVAPGSSIQLTIHDFDVEYHTSCNYDSLE 1450
1451 IYAGLDFNSPRIAQLCSQSPSANPMQVSSTGNELAIRFKTDSTLNGRGFN 1500
1501 ASWRAVPGGCGGIIQLSRGEIHSPNYPNNYRANTECSWIIQVERHHRVLL 1550
1551 NITDFDLEAPDSCLRLMDGSSSTNARVASVCGRQQPPNSIIASGNSLFVR 1600
1601 FRSGSSSQNRGFRAEFREECGGRIMTDSSDTIFSPLYPHNYLHNQNCSWI 1650
1651 IEAQPPFNHITLSFTHFQLQNSTDCTRDFVEILDGNDYDAPVQGRYCGFS 1700
1701 LPHPIISFGNALTVRFVTDSTRSFEGFRAIYSASTSSCGGSFYTLDGIFN 1750
1751 SPDYPADYHPNAECVWNIASSPGNRLQLSFLSFNLENSLNCNKDFVEIRE 1800
1801 GNATGHLIGRYCGNSLPGNYSSAEGHSLWVRFVSDGSGTGMGFQARFKNI 1850
1851 FGNNNIVGTHGKIASPFWPGKYPYNSNYKWVVNVDAYHIIHGRILEMDIE 1900
1901 PTTNCFYDSLKIYDGFDTHSRLIGTYCGTQTESFSSSRNSLTFQFSSDSS 1950
1951 VSGRGFLLEWFAVDVSDSTPPTIAPGACGGFMVTGDTPVHIFSPGWPREY 2000
2001 ANGADCIWIIYAPDSTVELNILSLDIEPQQSCNYDKLIVKDGDSDLSPEL 2050
2051 AVLCGVSPPGPIRSTGEYMYIRFTSDTSVAGTGFNASFHKSCGGYLHADR 2100
2101 GVITSPKYPDTYLPNLNCSWHVLVQTGLTIAVHFEQPFQIQNRDSFCSQG 2150
2151 DYLVLRNGPDNHSPPLGPSGRNGRFCGMYAPSTLFTSGNEMFVQFISDSS 2200
2201 NGGQGFKIRYEAKSLACGGTVYIHDADSDGYLTSPNYPANYPQHAECIWI 2250
2251 LEAPPGRSIQLQFEDQFNIEDTPNCSVSYLELRDGANSNARLVSKLCGHT 2300
2301 LPHSWVSSRERIYLKFHTDGGSSYMGFKAKYSIASCGGTVSGDSGVIESI 2350
2351 GYPTLPYANNVFCQWFIRGLPGHYLTLSFEDFNLQSSPGCTKDFVEIWEN 2400
2401 HTSGRVLGRYCGNSTPSSVDTSSNVASVKFVTDGSVTASGFRLQFKSSRQ 2450
2451 VCGGDLHGPTGTFTSPNYPNPNPHARICEWTITVQEGRRIVLTFTNLRLS 2500
2501 TQPSCNSEHLIVFNGIRSNSPLLQKLCSRVNVTNEFKSSGNTMKVVFFTD 2550
2551 GSRPYGGFTASYTSTEDAVCGGFLPSVSGGNFSSPGYNGIRDYARNLDCE 2600
2601 WTLSNPNRENSSISIYFLELSIESHQDCTFDVLEFRVGDADGPLIEKFCS 2650
2651 LSAPTAPLVIPYPQVWIHFVSNERVEYTGFYIEYSFTDCGGIRTGDNGVI 2700
2701 SSPNYPNLYSAWTHCSWLLKAPEGHTITLTFSDFLLEAHPTCTSDSVTVR 2750
2751 NGDSPGSPVIGRYCGQSVPRPIQSGSNQLIVTFNTNNQGQTRGFYATWTT 2800
2801 NALGCGGTFHSANGTIKSPHWPQTFPENSRCSWTVITHESKHWEISFDSN 2850
2851 FRIPSSDSQCQNSFVKVWEGRLMINKTLLATSCGDVAPSPIVTSGNIFTA 2900
2901 VFQSEEMAAQGFSASFISRCGRTFNTSPGDIISPNFPKQYDNNMNCTYLI 2950
2951 DADPQSLVILTFVSFHLEDRSAITGTCDHDGLHIIKGRNLSSTPLVTICG 3000
3001 SETLRPLTVDGPVLLNFYSDAYTTDFGFKISYRAITCGGIYNESSGILRS 3050
3051 PSYSYSNYPNNLYCVYSLHVRSSRVIIIRFNDFDVAPSNLCAHDFLEVFD 3100
3101 GPSIGNRSLGKFCGSTRPQTVKSTNSSLTLLFKTDSSQTARGWKIFFRET 3150
3151 IGPQQGCGGYLTEDNQSFVSPDSDSNGRYDKGLSCIWYIVAPENKLVKLT 3200
3201 FNVFTLEGPSSAGSCVYDYVQIADGASINSYLGGKFCGSRMPAPFISSGN 3250
3251 FLTFQFVSDVTVEMRGFNATYTFVDMPCGGTYNATSTPQNASSPHLSNIG 3300
3301 RPYSTCTWVIAAPPQQQVQITVWDLQLPSQDCSQSYLELQDSVQTGGNRV 3350
3351 TQFCGANYTTLPVFYSSMSTAVVVFKSGVLNRNSQVQFSYQIADCNREYN 3400
3401 QTFGNLKSPGWPQNYDNNLDCTIILRAPQNHSISLFFYWFQLEDSRQCMN 3450
3451 DFLEVRNGGSSTSPLLDKYCSNLLPNPVFSQSNELYLHFHSDHSVTNNGY 3500
3501 EIIWTSSAAGCGGTLLGDEGIFTNPGFPDSYPNNTHCEWTIVAPSGRPVS 3550
3551 VGFPFLSIDSSGGCDQNYLIVFNGPDANSPPFGPLCGINTGIAPFYASSN 3600
3601 RVFIRFHAEYTTRLSGFEIMWSS 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.) |
Go back to the NucPred Home Page.