 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q14204 from www.uniprot.org...
The NucPred score for your sequence is 0.81 (see score help below)
1 MSEPGGGGGEDGSAGLEVSAVQNVADVSVLQKHLRKLVPLLLEDGGEAPA 50
51 ALEAALEEKSALEQMRKFLSDPQVHTVLVERSTLKEDVGDEGEEEKEFIS 100
101 YNINIDIHYGVKSNSLAFIKRTPVIDADKPVSSQLRVLTLSEDSPYETLH 150
151 SFISNAVAPFFKSYIRESGKADRDGDKMAPSVEKKIAELEMGLLHLQQNI 200
201 EIPEISLPIHPMITNVAKQCYERGEKPKVTDFGDKVEDPTFLNQLQSGVN 250
251 RWIREIQKVTKLDRDPASGTALQEISFWLNLERALYRIQEKRESPEVLLT 300
301 LDILKHGKRFHATVSFDTDTGLKQALETVNDYNPLMKDFPLNDLLSATEL 350
351 DKIRQALVAIFTHLRKIRNTKYPIQRALRLVEAISRDLSSQLLKVLGTRK 400
401 LMHVAYEEFEKVMVACFEVFQTWDDEYEKLQVLLRDIVKRKREENLKMVW 450
451 RINPAHRKLQARLDQMRKFRRQHEQLRAVIVRVLRPQVTAVAQQNQGEVP 500
501 EPQDMKVAEVLFDAADANAIEEVNLAYENVKEVDGLDVSKEGTEAWEAAM 550
551 KRYDERIDRVETRITARLRDQLGTAKNANEMFRIFSRFNALFVRPHIRGA 600
601 IREYQTQLIQRVKDDIESLHDKFKVQYPQSQACKMSHVRDLPPVSGSIIW 650
651 AKQIDRQLTAYMKRVEDVLGKGWENHVEGQKLKQDGDSFRMKLNTQEIFD 700
701 DWARKVQQRNLGVSGRIFTIESTRVRGRTGNVLKLKVNFLPEIITLSKEV 750
751 RNLKWLGFRVPLAIVNKAHQANQLYPFAISLIESVRTYERTCEKVEERNT 800
801 ISLLVAGLKKEVQALIAEGIALVWESYKLDPYVQRLAETVFNFQEKVDDL 850
851 LIIEEKIDLEVRSLETCMYDHKTFSEILNRVQKAVDDLNLHSYSNLPIWV 900
901 NKLDMEIERILGVRLQAGLRAWTQVLLGQAEDKAEVDMDTDAPQVSHKPG 950
951 GEPKIKNVVHELRITNQVIYLNPPIEECRYKLYQEMFAWKMVVLSLPRIQ 1000
1001 SQRYQVGVHYELTEEEKFYRNALTRMPDGPVALEESYSAVMGIVSEVEQY 1050
1051 VKVWLQYQCLWDMQAENIYNRLGEDLNKWQALLVQIRKARGTFDNAETKK 1100
1101 EFGPVVIDYGKVQSKVNLKYDSWHKEVLSKFGQMLGSNMTEFHSQISKSR 1150
1151 QELEQHSVDTASTSDAVTFITYVQSLKRKIKQFEKQVELYRNGQRLLEKQ 1200
1201 RFQFPPSWLYIDNIEGEWGAFNDIMRRKDSAIQQQVANLQMKIVQEDRAV 1250
1251 ESRTTDLLTDWEKTKPVTGNLRPEEALQALTIYEGKFGRLKDDREKCAKA 1300
1301 KEALELTDTGLLSGSEERVQVALEELQDLKGVWSELSKVWEQIDQMKEQP 1350
1351 WVSVQPRKLRQNLDALLNQLKSFPARLRQYASYEFVQRLLKGYMKINMLV 1400
1401 IELKSEALKDRHWKQLMKRLHVNWVVSELTLGQIWDVDLQKNEAIVKDVL 1450
1451 LVAQGEMALEEFLKQIREVWNTYELDLVNYQNKCRLIRGWDDLFNKVKEH 1500
1501 INSVSAMKLSPYYKVFEEDALSWEDKLNRIMALFDVWIDVQRRWVYLEGI 1550
1551 FTGSADIKHLLPVETQRFQSISTEFLALMKKVSKSPLVMDVLNIQGVQRS 1600
1601 LERLADLLGKIQKALGEYLERERSSFPRFYFVGDEDLLEIIGNSKNVAKL 1650
1651 QKHFKKMFAGVSSIILNEDNSVVLGISSREGEEVMFKTPVSITEHPKINE 1700
1701 WLTLVEKEMRVTLAKLLAESVTEVEIFGKATSIDPNTYITWIDKYQAQLV 1750
1751 VLSAQIAWSENVETALSSMGGGGDAAPLHSVLSNVEVTLNVLADSVLMEQ 1800
1801 PPLRRRKLEHLITELVHQRDVTRSLIKSKIDNAKSFEWLSQMRFYFDPKQ 1850
1851 TDVLQQLSIQMANAKFNYGFEYLGVQDKLVQTPLTDRCYLTMTQALEARL 1900
1901 GGSPFGPAGTGKTESVKALGHQLGRFVLVFNCDETFDFQAMGRIFVGLCQ 1950
1951 VGAWGCFDEFNRLEERMLSAVSQQVQCIQEALREHSNPNYDKTSAPITCE 2000
2001 LLNKQVKVSPDMAIFITMNPGYAGRSNLPDNLKKLFRSLAMTKPDRQLIA 2050
2051 QVMLYSQGFRTAEVLANKIVPFFKLCDEQLSSQSHYDFGLRALKSVLVSA 2100
2101 GNVKRERIQKIKREKEERGEAVDEGEIAENLPEQEILIQSVCETMVPKLV 2150
2151 AEDIPLLFSLLSDVFPGVQYHRGEMTALREELKKVCQEMYLTYGDGEEVG 2200
2201 GMWVEKVLQLYQITQINHGLMMVGPSGSGKSMAWRVLLKALERLEGVEGV 2250
2251 AHIIDPKAISKDHLYGTLDPNTREWTDGLFTHVLRKIIDSVRGELQKRQW 2300
2301 IVFDGDVDPEWVENLNSVLDDNKLLTLPNGERLSLPPNVRIMFEVQDLKY 2350
2351 ATLATVSRCGMVWFSEDVLSTDMIFNNFLARLRSIPLDEGEDEAQRRRKG 2400
2401 KEDEGEEAASPMLQIQRDAATIMQPYFTSNGLVTKALEHAFQLEHIMDLT 2450
2451 RLRCLGSLFSMLHQACRNVAQYNANHPDFPMQIEQLERYIQRYLVYAILW 2500
2501 SLSGDSRLKMRAELGEYIRRITTVPLPTAPNIPIIDYEVSISGEWSPWQA 2550
2551 KVPQIEVETHKVAAPDVVVPTLDTVRHEALLYTWLAEHKPLVLCGPPGSG 2600
2601 KTMTLFSALRALPDMEVVGLNFSSATTPELLLKTFDHYCEYRRTPNGVVL 2650
2651 APVQLGKWLVLFCDEINLPDMDKYGTQRVISFIRQMVEHGGFYRTSDQTW 2700
2701 VKLERIQFVGACNPPTDPGRKPLSHRFLRHVPVVYVDYPGPASLTQIYGT 2750
2751 FNRAMLRLIPSLRTYAEPLTAAMVEFYTMSQERFTQDTQPHYIYSPREMT 2800
2801 RWVRGIFEALRPLETLPVEGLIRIWAHEALRLFQDRLVEDEERRWTDENI 2850
2851 DTVALKHFPNIDREKAMSRPILYSNWLSKDYIPVDQEELRDYVKARLKVF 2900
2901 YEEELDVPLVLFNEVLDHVLRIDRIFRQPQGHLLLIGVSGAGKTTLSRFV 2950
2951 AWMNGLSVYQIKVHRKYTGEDFDEDLRTVLRRSGCKNEKIAFIMDESNVL 3000
3001 DSGFLERMNTLLANGEVPGLFEGDEYATLMTQCKEGAQKEGLMLDSHEEL 3050
3051 YKWFTSQVIRNLHVVFTMNPSSEGLKDRAATSPALFNRCVLNWFGDWSTE 3100
3101 ALYQVGKEFTSKMDLEKPNYIVPDYMPVVYDKLPQPPSHREAIVNSCVFV 3150
3151 HQTLHQANARLAKRGGRTMAITPRHYLDFINHYANLFHEKRSELEEQQMH 3200
3201 LNVGLRKIKETVDQVEELRRDLRIKSQELEVKNAAANDKLKKMVKDQQEA 3250
3251 EKKKVMSQEIQEQLHKQQEVIADKQMSVKEDLDKVEPAVIEAQNAVKSIK 3300
3301 KQHLVEVRSMANPPAAVKLALESICLLLGESTTDWKQIRSIIMRENFIPT 3350
3351 IVNFSAEEISDAIREKMKKNYMSNPSYNYEIVNRASLACGPMVKWAIAQL 3400
3401 NYADMLKRVEPLRNELQKLEDDAKDNQQKANEVEQMIRDLEASIARYKEE 3450
3451 YAVLISEAQAIKADLAAVEAKVNRSTALLKSLSAERERWEKTSETFKNQM 3500
3501 STIAGDCLLSAAFIAYAGYFDQQMRQNLFTTWSHHLQQANIQFRTDIART 3550
3551 EYLSNADERLRWQASSLPADDLCTENAIMLKRFNRYPLIIDPSGQATEFI 3600
3601 MNEYKDRKITRTSFLDDAFRKNLESALRFGNPLLVQDVESYDPVLNPVLN 3650
3651 REVRRTGGRVLITLGDQDIDLSPSFVIFLSTRDPTVEFPPDLCSRVTFVN 3700
3701 FTVTRSSLQSQCLNEVLKAERPDVDEKRSDLLKLQGEFQLRLRQLEKSLL 3750
3751 QALNEVKGRILDDDTIITTLENLKREAAEVTRKVEETDIVMQEVETVSQQ 3800
3801 YLPLSTACSSIYFTMESLKQIHFLYQYSLQFFLDIYHNVLYENPNLKGVT 3850
3851 DHTQRLSIITKDLFQVAFNRVARGMLHQDHITFAMLLARIKLKGTVGEPT 3900
3901 YDAEFQHFLRGNEIVLSAGSTPRIQGLTVEQAEAVVRLSCLPAFKDLIAK 3950
3951 VQADEQFGIWLDSSSPEQTVPYLWSEETPATPIGQAIHRLLLIQAFRPDR 4000
4001 LLAMAHMFVSTNLGESFMSIMEQPLDLTHIVGTEVKPNTPVLMCSVPGYD 4050
4051 ASGHVEDLAAEQNTQITSIAIGSAEGFNQADKAINTAVKSGRWVMLKNVH 4100
4101 LAPGWLMQLEKKLHSLQPHACFRLFLTMEINPKVPVNLLRAGRIFVFEPP 4150
4151 PGVKANMLRTFSSIPVSRICKSPNERARLYFLLAWFHAIIQERLRYAPLG 4200
4201 WSKKYEFGESDLRSACDTVDTWLDDTAKGRQNISPDKIPWSALKTLMAQS 4250
4251 IYGGRVDNEFDQRLLNTFLERLFTTRSFDSEFKLACKVDGHKDIQMPDGI 4300
4301 RREEFVQWVELLPDTQTPSWLGLPNNAERVLLTTQGVDMISKMLKMQMLE 4350
4351 DEDDLAYAETEKKTRTDSTSDGRPAWMRTLHTTASNWLHLIPQTLSHLKR 4400
4401 TVENIKDPLFRFFEREVKMGAKLLQDVRQDLADVVQVCEGKKKQTNYLRT 4450
4451 LINELVKGILPRSWSHYTVPAGMTVIQWVSDFSERIKQLQNISLAAASGG 4500
4501 AKELKNIHVCLGGLFVPEAYITATRQYVAQANSWSLEELCLEVNVTTSQG 4550
4551 ATLDACSFGVTGLKLQGATCNNNKLSLSNAISTALPLTQLRWVKQTNTEK 4600
4601 KASVVTLPVYLNFTRADLIFTVDFEIATKEDPRSFYERGVAVLCTE 4646
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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