 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8WXG9 from www.uniprot.org...
The NucPred score for your sequence is 0.25 (see score help below)
1 MSVFLGPGMPSASLLVNLLSALLILFVFGETEIRFTGQTEFVVNETSTTV 50
51 IRLIIERIGEPANVTAIVSLYGEDAGDFFDTYAAAFIPAGETNRTVYIAV 100
101 CDDDLPEPDETFIFHLTLQKPSANVKLGWPRTVTVTILSNDNAFGIISFN 150
151 MLPSIAVSEPKGRNESMPLTLIREKGTYGMVMVTFEVEGGPNPPDEDLSP 200
201 VKGNITFPPGRATVIYNLTVLDDEVPENDEIFLIQLKSVEGGAEINTSRN 250
251 SIEIIIKKNDSPVRFLQSIYLVPEEDHILIIPVVRGKDNNGNLIGSDEYE 300
301 VSISYAVTTGNSTAHAQQNLDFIDLQPNTTVVFPPFIHESHLKFQIVDDT 350
351 IPEIAESFHIMLLKDTLQGDAVLISPSVVQVTIKPNDKPYGVLSFNSVLF 400
401 ERTVIIDEDRISRYEEITVVRNGGTHGNVSANWVLTRNSTDPSPVTADIR 450
451 PSSGVLHFAQGQMLATIPLTVVDDDLPEEAEAYLLQILPHTIRGGAEVSE 500
501 PAELLFYIQDSDDVYGLITFFPMENQKIESSPGERYLSLSFTRLGGTKGD 550
551 VRLLYSVLYIPAGAVDPLQAKEGILNISRRNDLIFPEQKTQVTTKLPIRN 600
601 DAFLQNGAHFLVQLETVELLNIIPLIPPISPRFGEICNISLLVTPAIANG 650
651 EIGFLSNLPIILHEPEDFAAEVVYIPLHRDGTDGQATVYWSLKPSGFNSK 700
701 AVTPDDIGPFNGSVLFLSGQSDTTINITIKGDDIPEMNETVTLSLDRVNV 750
751 ENQVLKSGYTSRDLIILENDDPGGVFEFSPASRGPYVIKEGESVELHIIR 800
801 SRGSLVKQFLHYRVEPRDSNEFYGNTGVLEFKPGEREIVITLLARLDGIP 850
851 ELDEHYWVVLSSHGERESKLGSATIVNITILKNDDPHGIIEFVSDGLIVM 900
901 INESKGDAIYSAVYDVVRNRGNFGDVSVSWVVSPDFTQDVFPVQGTVVFG 950
951 DQEFSKNITIYSLPDEIPEEMEEFTVILLNGTGGAKVGNRTTATLRIRRN 1000
1001 DDPIYFAEPRVVRVQEGETANFTVLRNGSVDVTCMVQYATKDGKATARER 1050
1051 DFIPVEKGETLIFEVGSRQQSISIFVNEDGIPETDEPFYIILLNSTGDTV 1100
1101 VYQYGVATVIIEANDDPNGIFSLEPIDKAVEEGKTNAFWILRHRGYFGSV 1150
1151 SVSWQLFQNDSALQPGQEFYETSGTVNFMDGEEAKPIILHAFPDKIPEFN 1200
1201 EFYFLKLVNISGGSPGPGGQLAETNLQVTVMVPFNDDPFGVFILDPECLE 1250
1251 REVAEDVLSEDDMSYITNFTILRQQGVFGDVQLGWEILSSEFPAGLPPMI 1300
1301 DFLLVGIFPTTVHLQQHMRRHHSGTDALYFTGLEGAFGTVNPKYHPSRNN 1350
1351 TIANFTFSAWVMPNANTNGFIIAKDDGNGSIYYGVKIQTNESHVTLSLHY 1400
1401 KTLGSNATYIAKTTVMKYLEESVWLHLLIILEDGIIEFYLDGNAMPRGIK 1450
1451 SLKGEAITDGPGILRIGAGINGNDRFTGLMQDVRSYERKLTLEEIYELHA 1500
1501 MPAKSDLHPISGYLEFRQGETNKSFIISARDDNDEEGEELFILKLVSVYG 1550
1551 GARISEENTTARLTIQKSDNANGLFGFTGACIPEIAEEGSTISCVVERTR 1600
1601 GALDYVHVFYTISQIETDGINYLVDDFANASGTITFLPWQRSEVLNIYVL 1650
1651 DDDIPELNEYFRVTLVSAIPGDGKLGSTPTSGASIDPEKETTDITIKASD 1700
1701 HPYGLLQFSTGLPPQPKDAMTLPASSVPHITVEEEDGEIRLLVIRAQGLL 1750
1751 GRVTAEFRTVSLTAFSPEDYQNVAGTLEFQPGERYKYIFINITDNSIPEL 1800
1801 EKSFKVELLNLEGGVAELFRVDGSGSGDGDMEFFLPTIHKRASLGVASQI 1850
1851 LVTIAASDHAHGVFEFSPESLFVSGTEPEDGYSTVTLNVIRHHGTLSPVT 1900
1901 LHWNIDSDPDGDLAFTSGNITFEIGQTSANITVEILPDEDPELDKAFSVS 1950
1951 VLSVSSGSLGAHINATLTVLASDDPYGIFIFSEKNRPVKVEEATQNITLS 2000
2001 IIRLKGLMGKVLVSYATLDDMEKPPYFPPNLARATQGRDYIPASGFALFG 2050
2051 ANQSEATIAISILDDDEPERSESVFIELLNSTLVAKVQSRSIPNSPRLGP 2100
2101 KVETIAQLIIIANDDAFGTLQLSAPIVRVAENHVGPIINVTRTGGAFADV 2150
2151 SVKFKAVPITAIAGEDYSIASSDVVLLEGETSKAVPIYVINDIYPELEES 2200
2201 FLVQLMNETTGGARLGALTEAVIIIEASDDPYGLFGFQITKLIVEEPEFN 2250
2251 SVKVNLPIIRNSGTLGNVTVQWVATINGQLATGDLRVVSGNVTFAPGETI 2300
2301 QTLLLEVLADDVPEIEEVIQVQLTDASGGGTIGLDRIANIIIPANDDPYG 2350
2351 TVAFAQMVYRVQEPLERSSCANITVRRSGGHFGRLLLFYSTSDIDVVALA 2400
2401 MEEGQDLLSYYESPIQGVPDPLWRTWMNVSAVGEPLYTCATLCLKEQACS 2450
2451 AFSFFSASEGPQCFWMTSWISPAVNNSDFWTYRKNMTRVASLFSGQAVAG 2500
2501 SDYEPVTRQWAIMQEGDEFANLTVSILPDDFPEMDESFLISLLEVHLMNI 2550
2551 SASLKNQPTIGQPNISTVVIALNGDAFGVFVIYNISPNTSEDGLFVEVQE 2600
2601 QPQTLVELMIHRTGGSLGQVAVEWRVVGGTATEGLDFIGAGEILTFAEGE 2650
2651 TKKTVILTILDDSEPEDDESIIVSLVYTEGGSRILPSSDTVRVNILANDN 2700
2701 VAGIVSFQTASRSVIGHEGEILQFHVIRTFPGRGNVTVNWKIIGQNLELN 2750
2751 FANFSGQLFFPEGSLNTTLFVHLLDDNIPEEKEVYQVILYDVRTQGVPPA 2800
2801 GIALLDAQGYAAVLTVEASDEPHGVLNFALSSRFVLLQEANITIQLFINR 2850
2851 EFGSLGAINVTYTTVPGMLSLKNQTVGNLAEPEVDFVPIIGFLILEEGET 2900
2901 AAAINITILEDDVPELEEYFLVNLTYVGLTMAASTSFPPRLDSEGLTAQV 2950
2951 IIDANDGARGVIEWQQSRFEVNETHGSLTLVAQRSREPLGHVSLFVYAQN 3000
3001 LEAQVGLDYIFTPMILHFADGERYKNVNIMILDDDIPEGDEKFQLILTNP 3050
3051 SPGLELGKNTIALIIVLANDDGPGVLSFNNSEHFFLREPTALYVQESVAV 3100
3101 LYIVREPAQGLFGTVTVQFIVTEVNSSNESKDLTPSKGYIVLEEGVRFKA 3150
3151 LQISAILDTEPEMDEYFVCTLFNPTGGARLGVHVQTLITVLQNQAPLGLF 3200
3201 SISAVENRATSIDIEEANRTVYLNVSRTNGIDLAVSVQWETVSETAFGMR 3250
3251 GMDVVFSVFQSFLDESASGWCFFTLENLIYGIMLRKSSVTVYRWQGIFIP 3300
3301 VEDLNIENPKTCEAFNIGFSPYFVITHEERNEEKPSLNSVFTFTSGFKLF 3350
3351 LVQTIIILESSQVRYFTSDSQDYLIIASQRDDSELTQVFRWNGGSFVLHQ 3400
3401 KLPVRGVLTVALFNKGGSVFLAISQANARLNSLLFRWSGSGFINFQEVPV 3450
3451 SGTTEVEALSSANDIYLIFAENVFLGDQNSIDIFIWEMGQSSFRYFQSVD 3500
3501 FAAVNRIHSFTPASGIAHILLIGQDMSALYCWNSERNQFSFVLEVPSAYD 3550
3551 VASVTVKSLNSSKNLIALVGAHSHIYELAYISSHSDFIPSSGELIFEPGE 3600
3601 REATIAVNILDDTVPEKEESFKVQLKNPKGGAEIGINDSVTITILSNDDA 3650
3651 YGIVAFAQNSLYKQVEEMEQDSLVTLNVERLKGTYGRITIAWEADGSISD 3700
3701 IFPTSGVILFTEGQVLSTITLTILADNIPELSEVVIVTLTRITTEGVEDS 3750
3751 YKGATIDQDRSKSVITTLPNDSPFGLVGWRAASVFIRVAEPKENTTTLQL 3800
3801 QIARDKGLLGDIAIHLRAQPNFLLHVDNQATENEDYVLQETIIIMKENIK 3850
3851 EAHAEVSILPDDLPELEEGFIVTITEVNLVNSDFSTGQPSVRRPGMEIAE 3900
3901 IMIEENDDPRGIFMFHVTRGAGEVITAYEVPPPLNVLQVPVVRLAGSFGA 3950
3951 VNVYWKASPDSAGLEDFKPSHGILEFADKQVTAMIEITIIDDAEFELTET 4000
4001 FNISLISVAGGGRLGDDVVVTVVIPQNDSPFGVFGFEEKTVMIDESLSSD 4050
4051 DPDSYVTLTVVRSPGGKGTVRLEWTIDEKAKHNLSPLNGTLHFDETESQK 4100
4101 TIVLHTLQDTVLEEDRRFTIQLISIDEVEISPVKGSASIIIRGDKRASGE 4150
4151 VGIAPSSRHILIGEPSAKYNGTAIISLVRGPGILGEVTVFWRIFPPSVGE 4200
4201 FAETSGKLTMRDEQSAVIVVIQALNDDIPEEKSFYEFQLTAVSEGGVLSE 4250
4251 SSSTANITVVASDSPYGRFAFSHEQLRVSEAQRVNITIIRSSGDFGHVRL 4300
4301 WYKTMSGTAEAGLDFVPAAGELLFEAGEMRKSLHVEILDDDYPEGPEEFS 4350
4351 LTITKVELQGRGYDFTIQENGLQIDQPPEIGNISIVRIIIMKNDNAEGII 4400
4401 EFDPKYTAFEVEEDVGLIMIPVVRLHGTYGYVTADFISQSSSASPGGVDY 4450
4451 ILHGSTVTFQHGQNLSFINISIIDDNESEFEEPIEILLTGATGGAVLGRH 4500
4501 LVSRIIIAKSDSPFGVIRFLNQSKISIANPNSTMILSLVLERTGGLLGEI 4550
4551 QVNWETVGPNSQEALLPQNRDIADPVSGLFYFGEGEGGVRTIILTIYPHE 4600
4601 EIEVEETFIIKLHLVKGEAKLDSRAKDVTLTIQEFGDPNGVVQFAPETLS 4650
4651 KKTYSEPLALEGPLLITFFVRRVKGTFGEIMVYWELSSEFDITEDFLSTS 4700
4701 GFFTIADGESEASFDVHLLPDEVPEIEEDYVIQLVSVEGGAELDLEKSIT 4750
4751 WFSVYANDDPHGVFALYSDRQSILIGQNLIRSIQINITRLAGTFGDVAVG 4800
4801 LRISSDHKEQPIVTENAERQLVVKDGATYKVDVVPIKNQVFLSLGSNFTL 4850
4851 QLVTVMLVGGRFYGMPTILQEAKSAVLPVSEKAANSQVGFESTAFQLMNI 4900
4901 TAGTSHVMISRRGTYGALSVAWTTGYAPGLEIPEFIVVGNMTPTLGSLSF 4950
4951 SHGEQRKGVFLWTFPSPGWPEAFVLHLSGVQSSAPGGAQLRSGFIVAEIE 5000
5001 PMGVFQFSTSSRNIIVSEDTQMIRLHVQRLFGFHSDLIKVSYQTTAGSAK 5050
5051 PLEDFEPVQNGELFFQKFQTEVDFEITIINDQLSEIEEFFYINLTSVEIR 5100
5101 GLQKFDVNWSPRLNLDFSVAVITILDNDDLAGMDISFPETTVAVAVDTTL 5150
5151 IPVETESTTYLSTSKTTTILQPTNVVAIVTEATGVSAIPEKLVTLHGTPA 5200
5201 VSEKPDVATVTANVSIHGTFSLGPSIVYIEEEMKNGTFNTAEVLIRRTGG 5250
5251 FTGNVSITVKTFGERCAQMEPNALPFRGIYGISNLTWAVEEEDFEEQTLT 5300
5301 LIFLDGERERKVSVQILDDDEPEGQEFFYVFLTNPQGGAQIVEEKDDTGF 5350
5351 AAFAMVIITGSDLHNGIIGFSEESQSGLELREGAVMRRLHLIVTRQPNRA 5400
5401 FEDVKVFWRVTLNKTVVVLQKDGVNLVEELQSVSGTTTCTMGQTKCFISI 5450
5451 ELKPEKVPQVEVYFFVELYEATAGAAINNSARFAQIKILESDESQSLVYF 5500
5501 SVGSRLAVAHKKATLISLQVARDSGTGLMMSVNFSTQELRSAETIGRTII 5550
5551 SPAISGKDFVITEGTLVFEPGQRSTVLDVILTPETGSLNSFPKRFQIVLF 5600
5601 DPKGGARIDKVYGTANITLVSDADSQAIWGLADQLHQPVNDDILNRVLHT 5650
5651 ISMKVATENTDEQLSAMMHLIEKITTEGKIQAFSVASRTLFYEILCSLIN 5700
5701 PKRKDTRGFSHFAEVTENFAFSLLTNVTCGSPGEKSKTILDSCPYLSILA 5750
5751 LHWYPQQINGHKFEGKEGDYIRIPERLLDVQDAEIMAGKSTCKLVQFTEY 5800
5801 SSQQWFISGNNLPTLKNKVLSLSVKGQSSQLLTNDNEVLYRIYAAEPRII 5850
5851 PQTSLCLLWNQAAASWLSDSQFCKVVEETADYVECACSHMSVYAVYARTD 5900
5901 NLSSYNEAFFTSGFICISGLCLAVLSHIFCARYSMFAAKLLTHMMAASLG 5950
5951 TQILFLASAYASPQLAEESCSAMAAVTHYLYLCQFSWMLIQSVNFWYVLV 6000
6001 MNDEHTERRYLLFFLLSWGLPAFVVILLIVILKGIYHQSMSQIYGLIHGD 6050
6051 LCFIPNVYAALFTAALVPLTCLVVVFVVFIHAYQVKPQWKAYDDVFRGRT 6100
6101 NAAEIPLILYLFALISVTWLWGGLHMAYRHFWMLVLFVIFNSLQGLYVFM 6150
6151 VYFILHNQMCCPMKASYTVEMNGHPGPSTAFFTPGSGMPPAGGEISKSTQ 6200
6201 NLIGAMEEVPPDWERASFQQGSQASPDLKPSPQNGATFPSSGGYGQGSLI 6250
6251 ADEESQEFDDLIFALKTGAGLSVSDNESGQGSQEGGTLTDSQIVELRRIP 6300
6301 IADTHL 6306
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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