 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9JHU4 from www.uniprot.org...
The NucPred score for your sequence is 0.80 (see score help below)
1 MSEPGGGEDGSAGLEVSAVQNVADVAVLQKHLRKLVPLLLEDGGDAPAAL 50
51 EAALEEKSALEQMRKFLSDPQVHTVLVERSTLKEDVGDEGEEEKEFISYN 100
101 INIDIHYGVKSNSLAFIKRAPVIDADKPVSSQLRVLTLSEDSPYETLHSF 150
151 ISNAVAPFFKSYIRESGKADRDGDKMAPSVEKKIAELEMGLLHLQQNIEI 200
201 PEISLPIHPIITNVAKQCYERGEKPKVTDFGDKVEDPTFLNQLQSGVNRW 250
251 IREIQKVTKLDRDPASGTALQEISFWLNLERALYRIQEKRESPEVLLTLD 300
301 ILKHGKRFHATVSFDTDTGLKQALETVNDYNPLMKDFPLNDLLSATELDK 350
351 IRQALVAIFTHLRKIRNTKYPIQRALRLVEAISRDLSSQLLKVLGTRKLM 400
401 HVAYEEFEKVMVACFEVFQTWDDEYEKLQVLLRDIVKRKREENLKMVWRI 450
451 NPAHRKLQARLDQMRKFRRQHEQLRAVIVRVLRPQVTAVAQQNQGEAPEP 500
501 QDMKVAEVLFDAADANAIEEVNLAYENVKEVDGLDVSKEGTEAWEAAMKR 550
551 YDERIDRVETRITARLRDQLGTAKNANEMFRIFSRFNALFVRPHIRGAIR 600
601 EYQTQLIQRVKDDIESLHDKFKVQYPQSQACKMSHVRDLPPVSGSIIWAK 650
651 QIDRQLTAYMKRVEDVLGKGWENHVEGQKLKQDGDSFRMKLNTQEIFDDW 700
701 ARKVQQRNLGVSGRIFTIESARVRGRTGNVLKLKVNFLPEIITLSKEVRN 750
751 LKWLGFRVPLAIVNKAHQANQLYPFAISLIESVRTYERTCEKVEERNTIS 800
801 LLVAGLKKEVQALIAEGIALVWESYKLDPYVQRLAETVFNFQEKVDDLLI 850
851 IEEKIDLEVRSLETCMYDHKTFSEILNRVQKAVDDLNLHSYSNLPIWVNK 900
901 LDMEIERILGVRLQAGLRAWTQVLLGQAEDKAEVDMDTDAPQVSHKPGGE 950
951 PKIKNVVHELRITNQVIYLNPPIEECRYKLYQEMFAWKMVVLSLPRIQSQ 1000
1001 RYQVGVHYELTEEEKFYRNALTRMPDGPVALEESYSAVMGIVTEVEQYVK 1050
1051 VWLQYQCLWDMQAENIYNRLGEDLNKWQALLVQIRKARGTFDNAETKKEF 1100
1101 GPVVIDYGKVQSKVNLKYDSWHKEVLSKFGQMLGSNMTEFHSQISKSRQE 1150
1151 LEQHSVDTASTSDAVTFITYVQSLKRKIKQFEKQVELYRNGQRLLEKQRF 1200
1201 QFPPSWLYIDNIEGEWGAFNDIMRRKDSAIQQQVANLQMKIVQEDRAVES 1250
1251 RTTDLLTDWEKTKPVTGNLRPEEALQALTIYEGKFGRLKDDREKCAKAKE 1300
1301 ALELTDTGLLSGSEERVQVALEELQDLKGVWSELSKVWEQIDQMKEQPWV 1350
1351 SVQPRKLRQNLDGLLNQLKNFPARLRQYASYEFVQRLLKGYMKINMLVIE 1400
1401 LKSEALKDRHWKQLMKRLHVNWVVSELTLGQIWDVDLQKNEAVVKDVLLV 1450
1451 AQGEMALEEFLKQIREVWNTYELDLVNYQNKCRLIRGWDDLFNKVKEHIN 1500
1501 SVSAMKLSPYYKVFEEDALSWEDKLNRIMALFDVWIDVQRRWVYLEGIFT 1550
1551 GSADIKHLLPVETQRFQSISTEFLALMKKVSKSPLVMDVLNIQGVQRSLE 1600
1601 RLADLLGKIQKALGEYLERERSSFPRFYFVGDEDLLEIIGNSKNVAKLQK 1650
1651 HFKKMFAGVSSIILNEDNSVVLGISSREGEEVMFKTPVSITEHPKINEWL 1700
1701 TLVEKEMRVTLAKLLAESVTEVEIFGKATSIDPNTYITWIDKYQAQLVVL 1750
1751 SAQIAWSENVENALSNVGGGGDVGPLQSVLSNVEVTLNVLADSVLMEQPP 1800
1801 LRRRKLEHLITELVHQRDVTRSLIKSKIDNAKSFEWLSQMRFYFDPKQTD 1850
1851 VLQQLSIQMANAKFNYGFEYLGVQDKLVQTPLTDRCYLTMTQALEARLGG 1900
1901 SPFGPAGTGKTESVKALGHQLGRFVLVFNCDETFDFQAMGRIFVGLCQVG 1950
1951 AWGCFDEFNRLEERMLSAVSQQVQCIQEALREHSNPNYDKTSAPITCELL 2000
2001 NKQVKVSPDMAIFITMNPGYAGRSNLPDNLKKLFRSLAMTKPDRQLIAQV 2050
2051 MLYSQGFRTAEVLANKIVPFFKLCDEQLSSQSHYDFGLRALKSVLVSAGN 2100
2101 VKRERIQKIKREKEERGEAVDEGEIAENLPEQEILIQSVCETMVPKLVAE 2150
2151 DIPLLFSLLSDVFPGVQYHRGEMTALREELKKVCQEMYLTYGDGEEVGGM 2200
2201 WVEKVLQLYQITQINHGLMMVGPSGSGKSMAWRVLLKALERLEGVEGVAH 2250
2251 IIDPKAISKDHLYGTLDPNTREWTDGLFTHVLRKIIDNVRGELQKRQWIV 2300
2301 FDGDVDPEWVENLNSVLDDNKLLTLPNGERLSLPPNVRIMFEVQDLKYAT 2350
2351 LATVSRCGMVWFSEDVLSTDMIFNNFLARLRSIPLDEGEDEAQRRRKGKE 2400
2401 DEGEEAASPMLQIQRDAATIMQPYFTSNGLVTKALEHAFKLEHIMDLTRL 2450
2451 RCLGSLFSMLHQACRNVAQYNANHPDFPMQIEQLERYIQRYLVYAILWSL 2500
2501 SGDSRLKMRAELGEYIRRITTVPLPTAPNVPIIDYEVSISGEWSPWQAKV 2550
2551 PQIEVETHKVAAPDVVVPTLDTVRHEALLYTWLAEHKPLVLCGPPGSGKT 2600
2601 MTLFSALRALPDMEVVGLNFSSATTPELLLKTFDHYCEYRRTPNGVVLAP 2650
2651 VQLGKWLVLFCDEINLPDMDKYGTQRVISFIRQMVEHGGFYRTSDQTWVK 2700
2701 LERIQFVGACNPPTDPGRKPLSHRFLRHVPVVYVDYPGPASLTQIYGTFN 2750
2751 RAMLRLIPSLRTYAEPLTAAMVEFYTMSQERFTQDTQPHYIYSPREMTRW 2800
2801 VRGIFEALRPLETLPVEGLIRIWAHEALRLFQDRLVEDEERRWTDENIDM 2850
2851 VALKHFPNIDKEKAMSRPILYSNWLSKDYIPVDQEELRDYVKARLKVFYE 2900
2901 EELDVPLVLFNEVLDHVLRIDRIFRQPQGHLLLIGVSGAGKTTLSRFVAW 2950
2951 MNGLSVYQIKVHRKYTGEDFDEDLRTVLRRSGCKNEKIAFIMDESNVLDS 3000
3001 GFLERMNTLLANGEVPGLFEGDEYATLMTQCKEGAQKEGLMLDSHEELYK 3050
3051 WFTSQVIRNLHVVFTMNPSSEGLKDRAATSPALFNRCVLNWFGDWSTEAL 3100
3101 YQVGKEFTSKMDLEKPNYIVPDYMPVVYDKLPQPPTHREAIVNSCVFVHQ 3150
3151 TLHQANARLAKRGGRTMAITPRHYLDFINHYANLFHEKRSELEEQQMHLN 3200
3201 VGLRKIKETVDQVEELRRDLRIKSQELEVKNAAANDKLKKMVKDQQEAEK 3250
3251 KKVMSQEIQEQLHKQQEVIADKQMSVKEDLDKVEPAVIEAQNAVKSIKKQ 3300
3301 HLVEVRSMANPPAAVKLALESICLLLGESTTDWKQIRSIIMRENFIPTIV 3350
3351 NFSAEEISDAIREKMKKNYMSNPSYNYEIVNRASLACGPMVKWAIAQLNY 3400
3401 ADMLKRVEPLRNELQKLEDDAKDNQQKANEVEQMIRDLEASIARYKEEYA 3450
3451 VLISEAQAIKADLAAVEAKVNRSTALLKSLSAERERWEKTSETFKNQMST 3500
3501 IAGDCLLSAAFIAYAGYFDQQMRQNLFTTWSHHLQQANIQFRTDIARTEY 3550
3551 LSNADERLRWQASSLPADDLCTENAIMLKRFNRYPLIIDPSGQATEFIMN 3600
3601 EYKDRKITRTSFLDDAFRKNLESALRFGNPLLVQDVESYDPVLNPVLNRE 3650
3651 VRRTGGRVLITLGDQDIDLSPSFVIFLSTRDPTVEFPPDLCSRVTFVNFT 3700
3701 VTRSSLQSQCLNEVLKAERPDVDEKRSDLLKLQGEFQLRLRQLEKSLLQA 3750
3751 LNEVKGRILDDDTIITTLENLKREAAEVTRKVEETDIVMQEVETVSQQYL 3800
3801 PLSTACSSIYFTMESLKQVHFLYQYSLQFFLDIYHNVLYENPNLKGATDH 3850
3851 TQRLSIITKDLFQVAFNRVARGMLHQDHITFAMLLARIKLKGTVGEPTYD 3900
3901 AEFQHFLRGKEIVLSAGSTPKIQGLTVEQAEAVVRLSCLPAFKDLIAKVQ 3950
3951 ADEQFGIWLDSSSPEQTVPYLWSEETPTTPIGQAIHRLLLIQAFRPDRLL 4000
4001 AMAHMFVSTNLGESFMSIMEQPLDLTHIVGTEVKPNTPVLMCSVPGYDAS 4050
4051 GHVEDLAAEQNTQITSIAIGSAEGFNQADKAINTAVKSGRWVMLKNVHLA 4100
4101 PGWLMQLEKKLHSLQPHACFRLFLTMEINPKVPVNLLRAGRIFVFEPPPG 4150
4151 VKANMLRTFSSIPVSRICKSPNERARLYFLLAWFHAIIQERLRYAPLGWS 4200
4201 KKYEFGESDLRSACDTVDTWLDDTAKGRQNISPDKIPWSALKTLMAQSIY 4250
4251 GGRVDNEFDQRLLNTFLERLFTTRSFDSEFKLACKVDGHKDIQMPDGIRR 4300
4301 EEFVQWVELLPDAQTPSWLGLPNNAERVLLTTQGVDMISKMLKMQMLEDE 4350
4351 DDLAYAETEKKARTDSTSDGRPAWMRTLHTTASNWLHLIPQTLSPLKRTV 4400
4401 ENIKDPLFRFFEREVKMGAKLLQDVRQDLADVVQVCEGKKKQTNYLRTLI 4450
4451 NELVKGILPRSWSHYTVPAGMTVIQWVSDFSERIKQLQNISQAAASGGAK 4500
4501 ELKNIHVCLGGLFVPEAYITATRQYVAQANSWSLEELCLEVNVTASQSAT 4550
4551 LDACSFGVTGLKLQGATCSNNKLSLSNAISTVLPLTQLRWVKQTSAEKKA 4600
4601 SVVTLPVYLNFTRADLIFTVDFEIATKEDPRSFYERGVAVLCTE 4644
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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