 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9NZJ4 from www.uniprot.org...
The NucPred score for your sequence is 0.84 (see score help below)
1 METKENRWVPVTVLPGCVGCRTVAALASWTVRDVKERIFAETGFPVSEQR 50
51 LWRGGRELSDWIKIGDLTSKNCHLFVNLQSKGLKGGGRFGQTTPPLVDFL 100
101 KDILRRYPEGGQILKELIQNAEDAGATEVKFLYDETQYGTETLWSKDMAP 150
151 YQGPALYVYNNAVFTPEDWHGIQEIARSRKKDDPLKVGRFGIGFNSVYHI 200
201 TDVPCIFSGDQIGMLDPHQTLFGPHESGQCWNLKDDSKEISELSDQFAPF 250
251 VGIFGSTKETFINGNFPGTFFRFPLRLQPSQLSSNLYNKQKVLELFESFR 300
301 ADADTVLLFLKSVQDVSLYVREADGTEKLVFRVTSSESKALKHERPNSIK 350
351 ILGTAISNYCKKTPSNNITCVTYHVNIVLEEESTKDAQKTSWLVCNSVGG 400
401 RGISSKLDSLADELKFVPIIGIAMPLSSRDDEAKGATSDFSGKAFCFLPL 450
451 PPGEESSTGLPVHISGFFGLTDNRRSIKWRELDQWRDPAALWNEFLVMNV 500
501 VPKAYATLILDSIKRLEMEKSSDFPLSVDVIYKLWPEASKVKVHWQPVLE 550
551 PLFSELLQNAVIYSISCDWVRLEQVYFSELDENLEYTKTVLNYLQSSGKQ 600
601 IAKVPGNVDAAVQLTAASGTTPVRKVTPAWVRQVLRKCAHLGCAEEKLHL 650
651 LEFVLSDQAYSELLGLELLPLQNGNFVPFSSSVSDQDVIYITSAEYPRSL 700
701 FPSLEGRFILDNLKPHLVAALKEAAQTRGRPCTQLQLLNPERFARLIKEV 750
751 MNTFWPGRELIVQWYPFDENRNHPSVSWLKMVWKNLYIHFSEDLTLFDEM 800
801 PLIPRTILEEGQTCVELIRLRIPSLVILDDESEAQLPEFLADIVQKLGGF 850
851 VLKKLDASIQHPLIKKYIHSPLPSAVLQIMEKMPLQKLCNQITSLLPTHK 900
901 DALRKFLASLTDSSEKEKRIIQELAIFKRINHSSDQGISSYTKLKGCKVL 950
951 HHTAKLPADLRLSISVIDSSDEATIRLANMLKIEQLKTTSCLKLVLKDIE 1000
1001 NAFYSHEEVTQLMLWVLENLSSLKNENPNVLEWLTPLKFIQISQEQMVSA 1050
1051 GELFDPDIEVLKDLFCNEEGTYFPPSVFTSPDILHSLRQIGLKNEASLKE 1100
1101 KDVVQVAKKIEALQVGACPDQDVLLKKAKTLLLVLNKNHTLLQSSEGKMT 1150
1151 LKKIKWVPACKERPPNYPGSLVWKGDLCNLCAPPDMCDVGHAILIGSSLP 1200
1201 LVESIHVNLEKALGIFTKPSLSAVLKHFKIVVDWYSSKTFSDEDYYQFQH 1250
1251 ILLEIYGFMHDHLNEGKDSFRALKFPWVWTGKKFCPLAQAVIKPIHDLDL 1300
1301 QPYLHNVPKTMAKFHQLFKVCGSIEELTSDHISMVIQKIYLKSDQDLSEQ 1350
1351 ESKQNLHLMLNIIRWLYSNQIPASPNTPVPIHHSKNPSKLIMKPIHECCY 1400
1401 CDIKVDDLNDLLEDSVEPIILVHEDIPMKTAEWLKVPCLSTRLINPENMG 1450
1451 FEQSGQREPLTVRIKNILEEYPSVSDIFKELLQNADDANATECSFLIDMR 1500
1501 RNMDIRENLLDPGMAACHGPALWSFNNSQFSDSDFVNITRLGESLKRGEV 1550
1551 DKVGKFGLGFNSVYHITDIPIIMSREFMIMFDPNINHISKHIKDKSNPGI 1600
1601 KINWSKQQKRLRKFPNQFKPFIDVFGCQLPLTVEAPYSYNGTLFRLSFRT 1650
1651 QQEAKVSEVSSTCYNTADIYSLVDEFSLCGHRLIIFTQSVKSMYLKYLKI 1700
1701 EETNPSLAQDTVIIKKKSCSSKALNTPVLSVLKEAAKLMKTCSSSNKKLP 1750
1751 SDEPKSSCILQITVEEFHHVFRRIADLQSPLFRGPDDDPAALFEMAKSGQ 1800
1801 SKKPSDELSQKTVECTTWLLCTCMDTGEALKFSLSESGRRLGLVPCGAVG 1850
1851 VQLSEIQDQKWTVKPHIGEVFCYLPLRIKTGLPVHINGCFAVTSNRKEIW 1900
1901 KTDTKGRWNTTFMRHVIVKAYLQVLSVLRDLATSGELMDYTYYAVWPDPD 1950
1951 LVHDDFSVICQGFYEDIAHGKGKELTKVFSDGSTWVSMKNVRFLDDSILK 2000
2001 RRDVGSAAFKIFLKYLKKTGSKNLCAVELPSSVKLGFEEAGCKQILLENT 2050
2051 FSEKQFFSEVFFPNIQEIEAELRDPLMIFVLNEKVDEFSGVLRVTPCIPC 2100
2101 SLEGHPLVLPSRLIHPEGRVAKLFDIKDGRFPYGSTQDYLNPIILIKLVQ 2150
2151 LGMAKDDILWDDMLERAVSVAEINKSDHVAACLRSSILLSLIDEKLKIRD 2200
2201 PRAKDFAAKYQTIRFLPFLTKPAGFSLDWKGNSFKPETMFAATDLYTAEH 2250
2251 QDIVCLLQPILNENSHSFRGCGSVSLAVKEFLGLLKKPTVDLVINQLKEV 2300
2301 AKSVDDGITLYQENITNACYKYLHEALMQNEITKMSIIDKLKPFSFILVE 2350
2351 NAYVDSEKVSFHLNFEAAPYLYQLPNKYKNNFRELFETVGVRQSCTVEDF 2400
2401 ALVLESIDQERGTKQITEENFQLCRRIISEGIWSLIREKKQEFCEKNYGK 2450
2451 ILLPDTNLMLLPAKSLCYNDCPWIKVKDTTVKYCHADIPREVAVKLGAVP 2500
2501 KRHKALERYASNVCFTTLGTEFGQKEKLTSRIKSILNAYPSEKEMLKELL 2550
2551 QNADDAKATEICFVFDPRQHPVDRIFDDKWAPLQGPALCVYNNQPFTEDD 2600
2601 VRGIQNLGKGTKEGNPYKTGQYGIGFNSVYHITDCPSFISGNDILCIFDP 2650
2651 HARYAPGATSISPGRMFRDLDADFRTQFSDVLDLYLGTHFKLDNCTMFRF 2700
2701 PLRNAEMAKVSEISSVPASDRMVQNLLDKLRSDGAELLMFLNHMEKISIC 2750
2751 EIDKSTGALNVLYSVKGKITDGDRLKRKQFHASVIDSVTKKRQLKDIPVQ 2800
2801 QITYTMDTEDSEGNLTTWLICNRSGFSSMEKVSKSVISAHKNQDITLFPR 2850
2851 GGVAACITHNYKKPHRAFCFLPLSLETGLPFHVNGHFALDSARRNLWRDD 2900
2901 NGVGVRSDWNNSLMTALIAPAYVELLIQLKKRYFPGSDPTLSVLQNTPIH 2950
2951 VVKDTLKKFLSFFPVNRLDLQPDLYCLVKALYNCIHEDMKRLLPVVRAPN 3000
3001 IDGSDLHSAVIITWINMSTSNKTRPFFDNLLQDELQHLKNADYNITTRKT 3050
3051 VAENVYRLKHLLLEIGFNLVYNCDETANLYHCLIDADIPVSYVTPADIRS 3100
3101 FLMTFSSPDTNCHIGKLPCRLQQTNLKLFHSLKLLVDYCFKDAEENEIEV 3150
3151 EGLPLLITLDSVLQTFDAKRPKFLTTYHELIPSRKDLFMNTLYLKYSNIL 3200
3201 LNCKVAKVFDISSFADLLSSVLPREYKTKSCTKWKDNFASESWLKNAWHF 3250
3251 ISESVSVKEDQEETKPTFDIVVDTLKDWALLPGTKFTVSANQLVVPEGDV 3300
3301 LLPLSLMHIAVFPNAQSDKVFHALMKAGCIQLALNKICSKDSAFVPLLSC 3350
3351 HTANIESPTSILKALHYMVQTSTFRAEKLVENDFEALLMYFNCNLNHLMS 3400
3401 QDDIKILKSLPCYKSISGRYVSIGKFGTCYVLTKSIPSAEVEKWTQSSSS 3450
3451 AFLEEKIHLKELYEVIGCVPVDDLEVYLKHLLPKIENLSYDAKLEHLIYL 3500
3501 KNRLSSAEELSEIKEQLFEKLESLLIIHDANSRLKQAKHFYDRTVRVFEV 3550
3551 MLPEKLFIPNDFFKKLEQLIKPKNHVTFMTSWVEFLRNIGLKYILSQQQL 3600
3601 LQFAKEISVRANTENWSKETLQNTVDILLHHIFQERMDLLSGNFLKELSL 3650
3651 IPFLCPERAPAEFIRFHPQYQEVNGTLPLIKFNGAQVNPKFKQCDVLQLL 3700
3701 WTSCPILPEKATPLSIKEQEGSDLGPQEQLEQVLNMLNVNLDPPLDKVIN 3750
3751 NCRNICNITTLDEEMVKTRAKVLRSIYEFLSAEKREFRFQLRGVAFVMVE 3800
3801 DGWKLLKPEEVVINLEYESDFKPYLYKLPLELGTFHQLFKHLGTEDIIST 3850
3851 KQYVEVLSRIFKNSEGKQLDPNEMRTVKRVVSGLFRSLQNDSVKVRSDLE 3900
3901 NVRDLALYLPSQDGRLVKSSILVFDDAPHYKSRIQGNIGVQMLVDLSQCY 3950
3951 LGKDHGFHTKLIMLFPQKLRPRLLSSILEEQLDEETPKVCQFGALCSLQG 4000
4001 RLQLLLSSEQFITGLIRIMKHENDNAFLANEEKAIRLCKALREGLKVSCF 4050
4051 EKLQTTLRVKGFNPIPHSRSETFAFLKRFGNAVILLYIQHSDSKDINFLL 4100
4101 ALAMTLKSATDNLISDTSYLIAMLGCNDIYRIGEKLDSLGVKYDSSEPSK 4150
4151 LELPMPGTPIPAEIHYTLLMDPMNVFYPGEYVGYLVDAEGGDIYGSYQPT 4200
4201 YTYAIIVQEVEREDADNSSFLGKIYQIDIGYSEYKIVSSLDLYKFSRPEE 4250
4251 SSQSRDSAPSTPTSPTEFLTPGLRSIPPLFSGRESHKTSSKHQSPKKLKV 4300
4301 NSLPEILKEVTSVVEQAWKLPESERKKIIRRLYLKWHPDKNPENHDIANE 4350
4351 VFKHLQNEINRLEKQAFLDQNADRASRRTFSTSASRFQSDKYSFQRFYTS 4400
4401 WNQEATSHKSERQQQNKEKCPPSAGQTYSQRFFVPPTFKSVGNPVEARRW 4450
4451 LRQARANFSAARNDLHKNANEWVCFKCYLSTKLALIAADYAVRGKSDKDV 4500
4501 KPTALAQKIEEYSQQLEGLTNDVHTLEAYGVDSLKTRYPDLLPFPQIPND 4550
4551 RFTSEVAMRVMECTACIIIKLENFMQQKV 4579
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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