 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8VHN7 from www.uniprot.org...
The NucPred score for your sequence is 0.32 (see score help below)
1 MSVTSEPGMISSFLLVYLSTLFISFVFGEAEIRFTGQTEFFVNETSTTVI 50
51 RLVIERIGEPANVTAIVSLSGEDTGDFFDTYAAAFIPARGTNRTVYIAVC 100
101 DDDLPEPDETFTFHLTLQKPSANVKLGWPRAASVTILSNDNAFGIISFST 150
151 PSSISVIEPRSRNASVPLTLIREKGTYGMVTVTFDVSGGPNPPEEDLNPV 200
201 RGNITFPPGRATVIYNVTVLDDEVPENDELFLIQLRSVEGGAEINASRSS 250
251 VEIIVKKNDSPVNFMQSVYVVPEDDHVLTIPVLRGKDSDGNLIGSDETQV 300
301 SIRYKVMTWDSTAHAQQNVDFIDLQPDTTLVFPPFVHESHLKFQIIDDLI 350
351 PEIAESFHIMLLKNTLQGDAVLMGPSTVQVTIKPNDKPYGVLSFNSILFE 400
401 RPVIIDEDTASSSRFEEIAVVRNGGTHGNVSVSWVLTRNSSDPSPVTADI 450
451 TPASGTLQFAQGQMLAPISLVVFDDDLPEEAEAYLLTILPHTIQGGAEVS 500
501 EPAQLLFYIQDSDNVYGEIAFFPGESQKIESSPSERSLSLSLARRGGSKG 550
551 DVRVIYSALYIPAGAMDPLRAKDGILNTSRRSSLLFPEQNQQVSIKLPIR 600
601 NDAFLQNGAHFLVQLEAVVLVNIFPPIPPVSPRFGEIRNISLLVTPAIAN 650
651 GEIGFLSNLPIILHEPKDSSAEVVSIPLHRDGTDGQATVYWSLRPSGFNS 700
701 KAVTLDDAGPFNGSVVFLSGQNETSINITVKGDDIPELNETVTLSLDRVS 750
751 VDSDVLKSGYTSRDLIILENDDPGGIFEFSYDSRGPYVIKEGDAVELRIT 800
801 RSRGSLVKQFLRFHVEPRESNEFYGNMGVLEFTPGEREVVITLLTRLDGT 850
851 PELDEHFWVILSSHGERESKLGRATLVNITILKNDYPHGIIEFVSDGLSA 900
901 SIKESKGEDIYHAVYGVIRTRGNFGAVNVSWMVSPDFTQDVFPVQGTVCF 950
951 GDQEFFKNITVYSLVDEIPEEMEEFTIILLNATGGAQTGIRTTASLRILR 1000
1001 NDDPVYFAEPCVLRVQEGETANFTVLRNGSVDGACTVQYATVDGKASGEE 1050
1051 GDFAPVEKGETLVFEVGSREQSISVHVKDDGIPETDEPFYIVLFNSTGDT 1100
1101 VVYEYGVATVIIEANDDPNGVFSLEPIDKAVEEGKTNAFWILRHRGHFGN 1150
1151 VSVAWQLFQNASLQPGQEFYETSGTVNFTDGEETKPVILRAFPDRIPEFN 1200
1201 EFYILRLVNISGPGGQLAETNFQVTVMIPFNDDPFGIFILDPECLEREVA 1250
1251 EDVLSEDDMSYITSFTILRQQGVFGDVRVGWEVLSREFTAGLPPMIDFIL 1300
1301 LGSFPSTVPLQPHMRRHHSGTDVLYFSGLEGAFGTVDPKYQPFRNNTIAN 1350
1351 FTFSAWVMPNANTNGFLIAKDDSHGSIYYGVKIQTNETHVTLSLHYKTFG 1400
1401 SNVTYIAKSTVMKYLEEGVWLHVLIILDDGIIEFYLDGKAMPRGIKSLKG 1450
1451 EAITDGPGILRIGAGMDGGARFTGWMQDVRTYERKLTPEEIYELHAVPAR 1500
1501 TDLHPISGYLEFRQGESNKSFIVAARDDSEEEGEELFLLKLVSVDGGAQI 1550
1551 SKENTTARLRIQKSDNANGLFGFTGACIPEMTEEGSTVSCVVERTRGALG 1600
1601 YVHVFYTISQIESEGINYLVDDFANASGTITFLPWQRSEVLNLYVLDEDM 1650
1651 PELNEYFRVTLVSAVPGDGKLGSTPISGASIDPEKETTGITVKASDHPYG 1700
1701 LMQFSTGLPPQPEDSMSLPASSVPHITVQEEDGEIRLLVIRAQGLLGRVT 1750
1751 VGFRTVSLTAFSPEDYQSTAGTLEFQSGERYKYIFVNITDNSIPELEKSF 1800
1801 KVELLNLDGGVSDLFRVDGSGSGEADTDFFLPPVLPHASLGVASQILVTI 1850
1851 AASDHAHGVFEFSPESLFVSGTEPEDGYSTVVLNVTRTRGALSAVTLQWK 1900
1901 VDSDLDGDLAITSGNITFETGQRIASITVEILSDEEPELDKALTVSILNV 1950
1951 SSGSLGVLTNATLTILASDDPYGVFIFPNKTRPLSVEEATQNVALSIIRL 2000
2001 KGLMGEVAVSYATIDDMEKPPYFPPNLARATQGGDYISASGLALFRVNQT 2050
2051 EATITISILDDAEPERSESVFIELFNSSLVDKVQNRPIPHSPRLGPKVET 2100
2101 VAHLVIVANDDAFGTVQLSATSVHVAENHVGPIINVTRTGGTFADVSVKF 2150
2151 KAVPITAAAGEDYSIASSDVVLLEGETTKAVPIYIINDIYPELEETFLVQ 2200
2201 LLNETTGGATLGPLREAVITIEASDDPYGLFGFQNTKFIVEEPEFNSVRV 2250
2251 NVPIIRNSGTLGNVTVQWVAIINGQFATGDLRVVSGNVTFAPGETIQTLL 2300
2301 LEVLADDVPEIEEVVQVQLAAASGGGTIGLDRVANIVIPANDNPYGSVAF 2350
2351 VQSVFRVQEPLERSSYANITVRRSGGHFGRLLLCYGTSDIDVVARAVEEG 2400
2401 EDVLSYYESPTQGVPDPLWRTWVNVSAVEETQYTCATLCLKERACSAFSV 2450
2451 VSGAEGPRCFWMTSWVSGTVNSSDFQTYKKNMTRVASLFSGQAVAGSDYE 2500
2501 PVTRQWAVILEGDEFANLTVSVLPDDAPEMDESFLISLLEVHLMNISDSF 2550
2551 KNQPTIGHPNTSAVVIGLNGDAFGVFIIYSVSPNTSEDGLCVEVQEQPQT 2600
2601 SVELVIYRTGGSLGQVMVEWRVVGGTATEGLDFMGAGDILTFAEGETKKM 2650
2651 AILTILDDSEPEDNESILVRLGATEGGSRILPSSDTVTVNILANDNVAGI 2700
2701 VSFQTASRSVIGHEGEMLQFHVVRTPPGRGNVTVNWKVVGQNLEVNFANF 2750
2751 TGQLFFSEGTLNKTIFVHLLDDNIPEEKEVYQVVLYDVKTQGVSPAGVAL 2800
2801 LDAQGYAAVLTVEASDEPHGVLNFALSSRFVVLQEANVTIQLFVNREFGS 2850
2851 LGAINVTYATVPGIVSLKNNTEGNLAEPESDFIPVVGSLVLEEGETTAAI 2900
2901 SITVLEDDIPELKEYFLVNLTHVDLIMAPLTSSPPRLDSEGLTAQIVIDA 2950
2951 NDGAQGMIEWQRNRFEVNETDGVVTLVAQRSRAALGQVSLFMYAQNLEAQ 3000
3001 AGLDYMRTPQILHFTDGERFKHVDVMILDDDMPEGDERFQLLLTNPSPGL 3050
3051 ELGKNTIALITVLANDDGPGVLSFNNSGHIFLREPTSLYVQESVAVLVIV 3100
3101 REPAQGLFGTVAVQFVVTEVNSSTESKDLSPSKGFIVLEEGVRSKTLRIS 3150
3151 AILDTEPEMDEHFVCTLFNPTGGARLGAHVQTLITIFQNQAPLGLFSISA 3200
3201 VENSATSIDVEESNRSVYLNVSRTNGLDLTASVQWETVSETAFGMRGMDV 3250
3251 VFSIFQSFFDKTALDWCFFTVEGSVYGVMLRKSSLVVYRWQGTFVPVEDL 3300
3301 KVESPKTCEAFNIGVSPYLVITHGERSGEKPSINSVYMLTAGFRLVLIQT 3350
3351 IIISGSCQVRHFTSDSQDYFIIASRRNDSELTQVFRWNGNNFAWHQTLPV 3400
3401 RGVLGMALFSRGGSVFLAISQANIRQTSLLFTWSGTQFINFQELPISGIT 3450
3451 QVEALSSGDDVYLCFAKNTFLGNQNAIDIFVWEMGHSSLRYFQSLDFAAV 3500
3501 KRIRSFTPASGIVHILLTAQDGSALYCWNSELNAFSFVLEAPAAHDAAFV 3550
3551 TVKSLNSSKTLIALVGATDSHLYELTYVSSQSDFIPSLGELIFEPGDKEA 3600
3601 IIAVNVLDDTVPEKEESFRVQLKSPRGGAEIGINSSVRVTVLANDGAYGV 3650
3651 VAFAQNSLHKQLEELERDSLVTLNVERLRGTHGRITVAWEAAGSVSDVFP 3700
3701 TSGVISFTEDQAMSMITLTVLADDLPELSEAVVVTLTQIVTEGVEDPLKG 3750
3751 ATIDQSRSRSVLTILPSDSPYGAVRWHTESLFNRVPEPTENITVVQLHIV 3800
3801 RDKGLFGDISIHLIAKPNFLLHINNQATEDEDFVLQDSVIIMKENIKETH 3850
3851 AEVAILPDEVPELDEGLIVTIAAVNLVNPNFPAEQPRVQRPRMESAEILI 3900
3901 EENDDPRGIFNFHVVRDVGGVIIAHEGPPPLNVLQVPVVRMAGTFETVNV 3950
3951 YWKATPDSAGLEDFQPSHGMLQFADGQVIAPILVTIIDDSEFELLETFTI 4000
4001 SLVSVTGGGRLGDDVSVNVVIAPNDSPFGIFGFEKKTVMVDGPLLSDDPD 4050
4051 SYVTLTVVRSPGGKGAVRLHWAIEEKAKDDLSPLNGTLYFDETESQKSVI 4100
4101 LHTLKDGMVGEDRRFIIELTAADEVEISPVKGSASVIIRGDKSISEVGIA 4150
4151 SSSRHIIIGEPSATYNGTAIIDLVRGPGVSGEITVNWKILPPSRGEFVET 4200
4201 SGQLTMLDGQTAATVVIQVLNDDIPEEKCHYEFQLTEISEGRMLHEASVS 4250
4251 ARITMVASDAPYGRFSFSHEQLHVSKAAQRVNVTVVRSGGSFGRARVLYE 4300
4301 TGSRTAEAGWDFVPASGELLFEAREKMKSLYIDILDDDLPEGPEEFVLAI 4350
4351 TRVDLQGRGYDFTIQENGLQIDQPPEIGNISIVRIIIMKNDNAEGIIEFD 4400
4401 PKYTDISVEEDAGVITLPVLRLHGTYGHVSADFSSRGFSAVPGGYVLRGS 4450
4451 SVTFQHGQNLSFINVSIIDDNGSEFEKQFEILLIGATGGAILGRHLVSKI 4500
4501 TIAKSDSPFGIIRFLNQSKISLPNPSSTMALHLVLERTGGLLGEIQVSWE 4550
4551 VVGPDAEEPLPPHNGDFADPVSGTVSFGDGEGGVRSIILRVCPHEETEAE 4600
4601 ETFIVQLKPLREAKLDPRAKAVTLTIQKFGDPNGVIHFAPESLSKRRFSE 4650
4651 PPPSDGPLLVSFLVTRSKGTSGDIKVHWELSSEFDITRDFLSTRGFFTIA 4700
4701 DGESDANFDVHLLPDDVPEIEEEYAVQLVSVEGGAELDLGKCTARFSVSA 4750
4751 NDDPHGVFALYSDRQSVLIGQNLDRSIQINITRLAGAFGAVAVRVQILSD 4800
4801 NKEDPVATENEERQLVITDGARYKVGLVPLKNQVFLSLGSNFTLQLVSVR 4850
4851 LLSGPFYGIPTILQEAKNAILSVPEEAANSQVGFESAAFQLMDIKAGTSQ 4900
4901 VMVSRKGTYGRLSVAWTTGYAPGSEIPEPIVIGNMTPTLGSLSFVHGEER 4950
4951 KGVLLWTFPSPGRPEAFVLHLSGLRSSAAGGAQLRSGFTTAEIEPMGVFQ 5000
5001 FSPSSRNITVSEDAQTIRICVQRLFGFHGDLIKVSYETTAGSAKPPEDFE 5050
5051 AVQKGEVFFQRFQPEIDFEITIINDQLPEIEETYYINLTSVETRGLGKGG 5100
5101 VNWRPRLNPDLSVAVVTIVDNDDLTGAAVSVPVTAGTVAVDSTLLAMETG 5150
5151 STTHPNKSKITTIPYTTEVFAPVTETVTVSAIPEKLATAHSVISVKPDVV 5200
5201 PGTVVASVYGTLSIGPPIVYVSEEMKNGTLSTADILIQRMGGFAGNVTIT 5250
5251 VKTFGGRCAQKEPSVWPFQDVYGVGNLTTWAVEEEDFEEQLLTLTFLYGE 5300
5301 RERKIAVQILDDDDAEGQEFFYVFLTDPQGGAEIVRGKDSTGFSAFAVII 5350
5351 ISGSDLHNGIIGFSEESQRGLELREGADKNSQRLVVTRQPNRAFEEVQIF 5400
5401 WRVTLNQTVTILQEKGANLTDELRFVAGVTTCTGGQTRCFIHLELNPKKV 5450
5451 HQVEMPFFVELYDVTAGAAINNSARFAPIKLSKSGAPQSLVSFSVGSRLA 5500
5501 VAHKKSTLISLQVARDSGTGIMMSVNFITQELRSAETVGRVLISPAVSGK 5550
5551 DFVRTEGTLVFEPGQKSAVLDVVLTPEAGSLNKFPKRFQIVLFDPKGGAR 5600
5601 IDKVYGTANITLISDADSQAVWGLEDLLHRPLHEDILNRVLHNLNLRVAT 5650
5651 ESTDEQLSAVMLIMEKITMEGRNQAFSIKSRTLLYELLCVLINPKRKDTR 5700
5701 GFSHFVEVAEHFAFSLLTDVTCGSPGEKSKTILDSCPYLSILALHWNPQQ 5750
5751 INGHKFEGKEGDYIQIPERLLDVPEAEMLDGKNACTLVQFVEYSSQQWFI 5800
5801 AGDNLPALKDKVLSLNVKGQSAQPLPNNNEVLYRIHAAEPRVVPHTSRCL 5850
5851 LWNQAAASWLSDSQFCKVVEDASDYVECACSHMSVYAVYAQTDNSSSYNE 5900
5901 AFFSAGLICISGLCLAVVSHMFCARHSMFAAKLLTHMMVASLGTQILFLA 5950
5951 SAYASPHLSEESCSAVAAVAHYLYLCQFSWMLIQSVNFWYVLVVSDEHTE 6000
6001 RRCLLFCLLSWGLPSFVVILLILILRGIYHRSMPQIYGLIHGDLCFIPNI 6050
6051 YAALFTAALVPLMCLVVVFVVFIHAYQLKPQWKGYDDVFRGRTNAAEIPL 6100
6101 ILYLFALISMTWLWGGLHMAYGHFWMLVLFVIFNSLQGLYVFVVYFILHN 6150
6151 QTCCPMKASYTVEMNGHPGPSTAFFTPGSGIPPAGEINKSTQNLINAMEE 6200
6201 VPSDWERSSFQQTSQASPDLKTSPQNGASFPSSGGYGPGSLIADEESQEF 6250
6251 DDLIFALKTGAGLSVSDNESGQGSQEGGTLTDSQIVELRRIPIADTHL 6298
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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