 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JLC8 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 METKFQRWVRVTVLRGCVGCRTVAVPATATGRDLKERIFAETSFPVAEQR 50
51 LWRGDREVPDWIKIGDLTSKTCHLFVNLQSKGLKGGGRFGQTTPPLVDFL 100
101 KDILRRYPEGGQILKELIQNAEDAGATEVKFLYDETQYGTETLWSKDMAQ 150
151 YQGSALYVYNNAVFTPEDWHGIQEIARSRKKDDPLKVGRFGIGFNSVYHI 200
201 TDVPCIFSGDQIGMLDPHQTLFGPHESGQCWNLKDDIKEINELPDQFAPF 250
251 IGVFGSTKETFTNGSFPGTFFRFPLRLQPSQLSSNLYTKQKVLELFDSFR 300
301 ADADTVLLFLKSVQAVSLHVREADGTEKLVFRVTASENKALKHERPNSIK 350
351 ILGTAISNYCKKIPSNSVTCVTYHINIVLEDESTKDAQKTSWLVCNSVGG 400
401 RGISSKLDSLADELKFVPIIGLAMPLSGKDEENGAISDFSGKAFCFLPLP 450
451 PGEESRTGLPVHISGFFGLTDNRRSIKWRELDQWRDPAALWNEYLIVNVV 500
501 PKTYATLILDSIKRLETEKSSDFPLSVDTIYKLWPEASKVKAHWHPVLGP 550
551 LFSELFQHAVIYSIGGEWVKLEQVHFSELDGSLESTRSVLNYLQSSGKQI 600
601 AKVPGNLAAAVQLSAASATSSASPVRKVTPAWVRQVLRKCAHLGSAEEKL 650
651 HLLEFVLSDQAYSELLGLELLPLQSGAFVPFSSSVSDQDVVYITSEEFPR 700
701 SLFPGLEARLILENLKPHLLAALKEAAQTRGRPCTQLQLLNPERFARLIK 750
751 EVMNTFWPGRELVVQWYPFSEDKRHPSLSWLKMVWKNLYIHFSEDLTLFD 800
801 EMPLIPRTLLNEDQTCVELIRLRIPSVVILDDETEAQLPEFLADIVQKLG 850
851 GIVLKRLDTSIQHPLVKKYIHSPLPSAILQIMEKIPLQKLCNQIASLLPT 900
901 HKDALRKFLASLTDTSEKEKRIIQELTIFKRINHSSDQGISSYTKLKGCK 950
951 VLDHTAKLPTDLRLSVSVIDSSDEATIRLANMLKIEKLKTTSCLKFVLKD 1000
1001 IGNAFYTQEEVTQLMLWILENLSSLKNENSNVLDWLMPLKFIHMSQGHVV 1050
1051 AAGDLFDPDIEVLRDLFYNEEEACFPPTIFTSPDILHSLRQIGLKNESSL 1100
1101 KEKDVVQVARKIEALQVSSCQNQDVLMKKAKTLLLVLNKNQTLLQSSEGK 1150
1151 MALKKIKWVPACKERPPNYPGSLVWKGDLCNLCAPPDMCDAAHAVLVGSS 1200
1201 LPLVESVHVNLEQALSIFTKPTINAVLKHFKTVVDWYTSKTFSDEDYYQF 1250
1251 QHILLEIYGFMHDHLSEGKDSFKALKFPWVWTGKNFCPLAQAVIKPTHDL 1300
1301 DLQPYLYNVPKTMAKFHQLFKACGSIEELTSDHISMVIQKVYLKSDQELS 1350
1351 EEESKQNLHLMLNIMRWLYSNQIPASPNTPVPIYHSRNPSKLVMKPIHEC 1400
1401 CYCDIKVDDLNDLLEDSVEPIILVHEDIPMKTAEWLKVPCLSTRLINPEN 1450
1451 MGFEQSGQREPLTVRIKNILEEYPSVSDIFKELLQNADDANATECSFMID 1500
1501 MRRNMDIRENLLDPGMAACHGPALWSFNNSEFSDSDFLNITRLGESLKRG 1550
1551 EVDKVGKFGLGFNSVYHITDIPIIMSREFMIMFDPNINHISKHIKDRSNP 1600
1601 GIKINWSKQQKRLRKFPNQFKPFIDVFGCQLPLAVEAPYSYNGTLFRLSF 1650
1651 RTQQEAKVSEVSSTCYNTADIYSLVDEFSLCGHRLIIFTQSVNSMYLKYL 1700
1701 KIEETNPSLAQDTIIIKKKVCPSKALNAPVLSVLKEAAKLMKTCSSSNKK 1750
1751 LPTDVPKSSCILQITVEEFHHVFRRIADLQSPLFRGPDDDPATLFEMAKS 1800
1801 GQSKKPSDELPQKTVDCTTWLICTCMDTGEALKFSLNESGRRLGLVPCGA 1850
1851 VGVLLHETQEQKWTVKPHIGEVFCYLPLRIKTGLPIHINGCFAVTSNRKE 1900
1901 IWKTDTKGRWNTTFMRHVIVKAYLQALSVLRDLAIGGELTDYTYYAVWPD 1950
1951 PDLVHDDFSVICKGFYEDIAHGKGKELTRVFSDGSMWVSMKNVRFLDDSI 2000
2001 LQRKDVGSAAFKIFLKYLKKTGSKNLCAVELPSSVKAGFEEAGCKQILLE 2050
2051 NTFSEKQFFSEVFFPNIQEIEAELRDPLMNFVLNEKLDEFSGILRVTPCV 2100
2101 PCSLEGHPLVLPSRLIHPEGRVAKLFDTKDGRFPYGSTQDYLNPIILIKL 2150
2151 VQLGMAKDDILWDDMLERAESVAEINKSDHAAACLRSSILLSLIDEKLKI 2200
2201 KDPRAKDFAAKYQTIPFLPFLTKPAGFSLEWKGNSFKPETMFAATDIYTA 2250
2251 EYQDIVCLLQPILNENSHSFRGCGSVSLAVKEFLGLLKKPTVDLVINQLK 2300
2301 QVAKSVDDGITLYQENITNACYKYLHEAVLQNEMAKATIIEKLKPFCFIL 2350
2351 VENVYVESEKVSFHLNFEAAPYLYQLPNKYKNNFRELFESVGVRQSFTVE 2400
2401 DFALVLESIDQERGKKQITEENFQLCRRIISEGIWSLIREKRQEFCEKNY 2450
2451 GKILLPDTNLLLLPAKSLCYNDCPWIKVKDSTVKYCHADIPREVAVKLGA 2500
2501 IPKRHKALERYASNICFTALGTEFGQKEKLTSRIKSILNAYPSEKEMLKE 2550
2551 LLQNADDAKATEICFVFDPRQHPVDRIFDDKWAPLQGPALCVYNNQPFTE 2600
2601 DDVRGIQNLGKGTKEGNPCKTGHYGIGFNSVYHITDCPSFISGNDILGIF 2650
2651 DPHARYAPGATSVSPGRMFRDLDADFRTQFSDVLDLYLGNHFKLDNCTMF 2700
2701 RFPLRNAEMAQVSEISSVPSSDRMVQNLLDKLRSDGAELLMFLNHMEKIS 2750
2751 ICEIDKATGGLNVLYSVKGKITDGDRLKRKQFHASVIDSVTKKRQLKDIP 2800
2801 VQQITYTMDTEDSEGNLTTWLICNRSGFSSMEKVSKSVISAHKNQDITLF 2850
2851 PRGGVAACITHNYKKPHRAFCFLPLSLETGLPFHVNGHFALDSARRNLWR 2900
2901 DDNGVGVRSDWNNSLMTALIAPAYVELLIQLKKRYFPGSDPTLSVLQNTP 2950
2951 IHVVKDTLKKFLSFFPVNRLDLQPDLYCLVKALYSCIHEDMKRLLPVVRA 3000
3001 PNIDGSDLHSAVIITWINMSTSNKTRPFFDNLLQDELQHLKNADYNITTR 3050
3051 KTVAENVYRLKHLLLEIGFNLVYNCDETANLYHCLVDADIPVSYVTPADV 3100
3101 RSFLMTFSSPDTNCHIGKLPCRLQQTNLKLFHSLKLLVDYCFKDAEESEF 3150
3151 EVEGLPLLITLDSVLQIFDGKRPKFLTTYHELIPSRKDLFMNTLYLKYSS 3200
3201 VLLNCKVAKVFDISSFADLLSSVLPREYKTKNCAKWKDNFASESWLKNAW 3250
3251 HFISESVSVTDDQEEPKPAFDVIVDILKDWALLPGTKFTVSTSQLVVPEG 3300
3301 DVLIPLSLMHIAVFPNAQSDKVFHALMKAGCIQLALNKICSKDSALVPLL 3350
3351 SCHTANIDSPASILKAVHYMVQTSTFRTEKLMENDFEALLMYFNCNLSHL 3400
3401 MSQDDIKILKSLPCYKSISGRYMSIAKFGTCYVLTKSIPSAEVEKWTQSS 3450
3451 SSAFLEEKVHLKELYEVLGCVPVDDLEVYLKHLLPKIENLSYDAKLEHLI 3500
3501 YLKNRLASIEEPSEIKEQLFEKLESLLIIHDANNRLKQAKHFYDRTVRVF 3550
3551 EVMLPEKLFIPKEFFKKLEQVIKPKNQAAFMTSWVEFLRNIGLKYALSQQ 3600
3601 QLLQFAKEISVRANTENWSKETLQSTVDILLHHIFQERMDLLSGNFLKEL 3650
3651 SLIPFLCPERAPAEYIRFHPQYQEVNGTLPLIKFNGAQVNPKFKQCDVLQ 3700
3701 LLWTSCPILPEKATPLSIKEQEGSDLAPQEQLEQVLNMLNVNLDPPLDKV 3750
3751 INNCRNICNITTLDEEMVKTRAKVLRSIYEFLSAEKREFRFQLRGVAFVM 3800
3801 VEDGWKLLKPEEVVINLEYEADFKPYLYKLPLELGTFHQLFKHLGTEDII 3850
3851 STKQYVEVLSRIFKSSEGKQLDPNEMRTVKRVVSGLFKSLQNDSVKVRSD 3900
3901 LENARDLALYLPSQDGKLVKSSILVFDDAPHYKSRIQGNIGVQMLVDLSQ 3950
3951 CYLGKDHGFHTKLIMLFPQKLRPRLLSSILEEQLDEETPKVCQFGALCSL 4000
4001 QGRLQLLLSSEQFITGLIRIMKHENDNAFLANEEKAIRLCKALREGLKVS 4050
4051 CFEKLQTTLRVKGFNPIPHSRSETFAFLKRFGNAVILLYIQHSDSKDINF 4100
4101 LLALAMTLKSATDNLISDTSYLIAMLGCNDIYRISEKLDSLGVKYDSSEP 4150
4151 SKLELPMPGTPIPAEIHYTLLMDPMNVFYPGEYVGYLVDAEGGDIYGSYQ 4200
4201 PTYTYAIIVQEVEREDADNTSFLGKIYQIDIGYSEYKIVSSLDLYKFSRP 4250
4251 DESSQNRDSAPTTPTSPTEFLTPGLRSIPPLFSGKESHKSPSTKHHSPRK 4300
4301 LKVNALPEILKEVTSVVEQAWKLPESERKKIIRRLYLKWHPDKNPENHDI 4350
4351 ANEVFKHLQNEINRLEKQAFLDQNADRASRRTFSTSASRFQSDKYSFQRF 4400
4401 YTSWNQEATSHKSERQQQSKEKCPPSAGQTYSQRFFVPPTFKSVGNPVEA 4450
4451 RRWLRQARANFSAARNDLHKNANEWVCFKCYLSTKLALIAADYAVRGKSD 4500
4501 KDVKPTALAQKIEEYSQQLEGLTNDVHTLEAYGVDSLKTRYPDLLPFPQI 4550
4551 PNDRFTSEVAMRVMECTACIIIKLENFIQQKV 4582
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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