 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q6JAN0 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MPAVLALSGLLLMLLTVSVRSESAELRFQGQTQFVVNESSRAIVRLVVER 50
51 VGDPINVTALVLLQGDDTGDFEATTAAAFLLSSESSKTIFIAVKDDDIPE 100
101 ADETFVFILRLQSSSNGVTVGTPNTATITILSNDNAFGIISFNSSSLITV 150
151 EESKGRSQYVPLTLLREKGTYGTVTVNFEIFGGPNPASEDLSPDMGNITF 200
201 PPGRSVVVFSIMIQDDKLPEDDEIFTVQLTEAAGGALLNPNRSSVQIKIS 250
251 RNDAPIRFSKSTLVVPENIGVISLTVTRGRTEDGLLIGSDDKTVSVAYAI 300
301 ITGNGAASATPLTDFVDLQTERMVVFLPGVHEADLRFSIRDDNIPEIAES 350
351 FQVVLLEETLLGDAVLVTPSLTLVTIEPNDKPYGVLSISPSPIQPHIINE 400
401 DLNLIYEGMIIVRNGGTHGAVSVQWNITRNSTDRSPVSADLNPAAGTLRF 450
451 SEGQMSAVLPLNITQDSLPEEAEAFLLKLIPGSVQGGAEVDEPMEMVFFI 500
501 QDSDDVYGRFGFHPRENQSIQSQPEGRFLSLSFLREGGTLGEVRLTLTAL 550
551 YIPARPLDPSRARDGVLNGTSVNTVLFSSGQSRAQLILPIRNDAFLQNGA 600
601 HFRIQLDSVELVNITPPIPSMSPRFAGALNISLIITPDIANGEIGFTSNQ 650
651 TVVALEPEDSNSSLITLQLRRDGTDGQAVVFWSLRPTGENKEDVTKGDIS 700
701 PFTGSVTFLSGQSEAVINLTVLADNIPEINETIILTLDRTNVDNQILKPG 750
751 FTSREIVILENDDPGGVFEFSPVSKGPWFINEGETVELRVIRAQGQLLNQ 800
801 LIRYTVIPSGTAQFYGATGILEFQPGEREVMVALVAKPDGIPELDETFSV 850
851 VLSSYSTPASRLGNRREVNITVRKSDDPFGVIEFIQPDLDFTINESKALG 900
901 CLLSILPPLEKSRGRFGNVSIFWILEPTYSGDVKPVQGEIVFAEGEYQKN 950
951 LTLSSVADEIPEKTENFTITLLNATGGARLGNILSARLSIRANDDPIYFA 1000
1001 EPVGQRVREGGVANFTILRAGLANFVTTVNYRFEYGDTSSEDFIPESNDT 1050
1051 MLVFHFGEWMKNISVAVVDDNIPETDEPFYIVLFNATGDAVVYGQITATV 1100
1101 VIEANDDANGIFSLDSAQKPGEEGKTNNFYVLRDRGHFGNVTIYWQLFAN 1150
1151 DTPLEPYQEFVNTSGFITFRTGEKTKPIVLEVISDKLPEFNEFYELRLMN 1200
1201 VSGGYPGEGGKLANRDLNASVLIPFNDDPFGVFAIAPDSLEREVAEDVLS 1250
1251 VNDMTSVTSLTILRQQGTFGDVRVAWEILSGAFPRGLPPMEDLILMASFP 1300
1301 SAVELQPHSRRRHAGTDALFFSGRPGAYGSISAETTLLVPQILANFTLSV 1350
1351 WLKPKPNTDGFVVSKGNGNGTVYYGVQVQTNDSHVTIMLHYTTIGSNSTH 1400
1401 VARATANTFVEDAWVHVIIAVEDGIIEFYLDGSPIPGGIKSLKGEAIVND 1450
1451 ATPIRIGSNPDGEQRFTGLLQDVRLYSSCLNRSQIHELHNQPAKTDLHNV 1500
1501 SGYLTYRQEEKEKSFLVEVRDDQEAEGEEVFYLQLVAVQGGARLPMPRPT 1550
1551 AILKVMKSDNANGLFSFTGACIPDIAEEGSMISCVVERTRGALDYVYVNY 1600
1601 TVTQLDSPADLSNASDFANATGFILFQPGQLSEVLNLLVVNDDLPEVDEH 1650
1651 FRVRLVSAKSGDGKPGSTPTSGASIDPEKAVNNVTVKASDHPYGLLQFQT 1700
1701 TPVPVGMIRPALEEARVTVQEEAGVVRLLVARAQGLLGRVMVGYRTSPFT 1750
1751 AAGSEDYEGFLDFLPGERFKYINVTIIDNSVPELDKVFRVELYNPNGGVD 1800
1801 PYFASEGSGSGESETDFFLPSFHYHHANLGAAARIIVTIAASDEAHGVFQ 1850
1851 FGADSLIVNGTEPEEGRSTVVLQVIRTFGALSNVTVYWEADAASEGELVY 1900
1901 RSGNVNFEVGQTVRSIYLLISQDDVPELDKTFKVRLTNASHGRLGKETTA 1950
1951 TLTVLASDDPYGLFVFSDNTRPVRVAEANALVALTIQRRKGLMGRVRVAY 2000
2001 RTLRDTDTVLYSTPGVGRASEGNDFIAVVDSVIFSANQSEVNVTLRVLDD 2050
2051 NEPERAESVFLELVSVTLIEGLQPRPVALSPRLGPRNVTIAQVIIEASDD 2100
2101 AFGVLQLSSSAVSVPEYYTGPIINVTRIGGIFADVSVKFRAVPLTARVGE 2150
2151 DYRVASSDVVLLEGESSKPVPILIINDVVPELEETFRIELLNQTTGGALL 2200
2201 GDLTQAIITILPSDDPFGLFVFQAAPITIEEPALTAFEVSVPIVRNAGTM 2250
2251 GDVAVQWRATVNGRPATGDLRPVSGEVMFSPGETLKTLKVEVLPDDVPEI 2300
2301 EEIIKVELVSATSGGNIGLEKVVDTIVPANDNPHGTVYFEQAVYRVQEPL 2350
2351 EGIYIANVTIRRSGGNFGMLEVVYSTLEVDIVSNALKEGRNFLVYYDSRL 2400
2401 AGVPSNAIRRPINITTSTNVLNFCAAFCLRERACQAFSFTNTTTPSCFWV 2450
2451 TSGVSQLSPSPQTFTYLKNTTATASLFSSQAVAGSDFITMTAQTTTMLDG 2500
2501 SGVANLTVPILTDSLPEMDESFIIKILKVSLVNVTATARNLPTIRQPDTA 2550
2551 LVTIGMNGDAFGIFLLYSINPNATQEGLYLEVREEPKTTVLLVIERRGGS 2600
2601 MGQVTVEWKYVGGSATPNADFNGTGETLIFAEGDVKKTLEFIITDDTEPE 2650
2651 NNETLQIGLVSTEGGSRILPSSDTVTILILANDNAAGVVGFHTASRSRIV 2700
2701 REGESVTLLVERTAPAIGNVAVDWRIEGPLVPTTFADTSGTLFFSEGILN 2750
2751 NTIVLKLLEDTTPEDREEYRVILSNIQTTGVTKTGIAALSAQGREAVVSV 2800
2801 EASDEPFGLLSIAPSSLQVTTDEKNTTIRIYINREFGASGAVNISYETVR 2850
2851 GSLQDLRQTEGALAQPGQDFRYVSNSVIMQDGQTSVSIPITIIDDDIPEL 2900
2901 QEFFLVNITSAVLITTLPTAPKLNTEGLVAEIIINANDGIRGIIGWQNID 2950
2951 YVVNETIGVLTLVAYRDAGTYGNVSLFFYAQNLEAQLGLDFNATPSMIYF 3000
3001 VDGERHKFIEVQILDDAVPEGGETFQLILANPSAGLQLGENTTATVMILA 3050
3051 NDDGHGIISFNNSEHFLLREPTSVSGLGTSVATLYIIRDPPQGTFGTVTV 3100
3101 QFTITDVNGSLYTDDLTPSSGFVVLEDGVRYKTLEIWAVLDAEPEMNETF 3150
3151 TVTLSNPTGGARLGVSLQTFITVLENQAPLGLFRISPSINRTLDTMTVEE 3200
3201 HMGTVFLTVSRSNGLESAVSVEWETRSGTAFGMRGEQPVLAVYQSIRDSF 3250
3251 ASVWCSVPSGDAALVLRLIKGLTQNQTVLYKWQGVFVPVEFVSIQNPKSC 3300
3301 VGFTVNGSSYVAVSHADNTVSLTTNISLFRVQADLNLTLEQTFSVSGFSV 3350
3351 KHFSTDLKQYLIASSEIFVWNRGSFFLHQSLELQDIIAAVPFRRGSSNVQ 3400
3401 HLAVCRNRTSAACFIYQWTDGRFQNPQPLALNTEVKQVESHQMGGDTFLF 3450
3451 IVTEGLNPACEVFLWGSQQTVFQQTQSILVPGLFSVHPFTTPSGIFHLLL 3500
3501 AGVNGSALYSWRSELRQFAEMLKSASAQEFLYLPVPSINSPKSLILASGK 3550
3551 SSSLVYELTSVSNQSDFIPSSGELFFQPGVQELEIAVNVIDDDVPEEEEH 3600
3601 FRVSLKNPKGGAEIGFRGQVTFFIPANDDTYGIIGFSQNSLMREVEELQS 3650
3651 DNPVSLSIERRRGRFGRLTVHWSAYGSLDDIFPTSGVVTFSESQAVATIS 3700
3701 LNVLADDIPELAEKVTIVLTKVTTIGIIDPSRGASIDYQRAQANLTIRAN 3750
3751 GSPYGVIGWHLDSQYFITPEPQKSPSNITLSIVRDQGSSGNVLVYYSTKP 3800
3801 ALHLLPLNQASGGTDYVAKEATVVMMENATVVLVFLTILPDDIPELAETF 3850
3851 FVNITRVEVLGGDTGAAQPSVKRPGLEIAEVTIQENDDPRGVLSFNVSKD 3900
3901 VSGAVLAFEVPSPGNVLRLAVMRMAGIFGRLVLYWETQSVTASTEDFTPS 3950
3951 SGNITFQDGQAMAYIEITIIDDTIVESTETFMVKLIRVIGGARLGVETSV 4000
4001 VVSIPANDSPFGRFGFEELKVSVSEPQFLNDPASVATLTVVRSSGGEGVV 4050
4051 HLIWLLQEESRDDLSPRNGTLIFNGTESKKTLVIQALADAVLEGEESFTI 4100
4101 QLLSPKNEPVIDPVRGVATVVIRPDVGALGTVGIADSSRNVLIGEPICSY 4150
4151 NGTALISLIRGPGIFGEIEIFWNITTAAVSEFEETSGKVVMKDRQSAATI 4200
4201 QLKALDDEIPEERRVYQLRLSSLTPGSVINPDRQFASITMAASDLPHGLF 4250
4251 SFSQASLRATEEDRAVNVTIVRSMGLFGSVWVSFHTEGRTAISGQDFGQS 4300
4301 SGRPLFRPGESSRVIPLVIFDDDLPEGPEEFFLNITLVELLNASSMDFTV 4350
4351 REYGLQIDQPPAIGNLSSLMVIIQKNDNAEGILEFDPKYVNNTVEEDVGT 4400
4401 LSIPVLRRVGSYGQVTVQFVSKGLTAQPDSDYILLNGSITFQHGQTLSYI 4450
4451 NVSIVDDTESEFNEIFELQLTGATGGAILGAQLIARITIAKSDSPNGVVR 4500
4501 FINQSAITIPNPNSTVRLSLFAERADGLLGDATVMWHIQGPNSNEVLPSM 4550
4551 NTDIEPVNGTFSFRDGEGGVRSIDLKILPHGEVEVTEKFIIMLSVISGEM 4600
4601 GVDPRAGSVTLTIEKFGDPNGIVQFTEQDLKERIYSEPSDSEGPLKVSVL 4650
4651 VTRREGVMGNITVFWEILSDADTSGDFAALRGSVIILAGQRLAEIILTLL 4700
4701 PDSVPELEETYTLRLTSVEGGAELDLNRSSTRLKVRANDEPHGVFVMYSQ 4750
4751 NQSVVVNAADRSRQLIISVNRLAGAFGNASVGYRISFTTPGQSFTEDTIT 4800
4801 GNILVKDGEREASIRVPVSSQVFFVTGFNFSVELTDVTLIGPLLGSPPRI 4850
4851 QLESKLAVVSVPEVAANPVVGFASLALRVLDIESGQCEALVTRTGLYGNI 4900
4901 TVRWSSGFPPGQTPPGYQPGEILPRSGSIMLAHGQRSELISFTALNNISV 4950
4951 VTAHAIYLTSVESESPGGARLRTGYTVAEVEPLGLYQFHPNSQHLVIEED 5000
5001 VQTITLYVQRFYGFRSNRSSVSYRTWPGTAQPDKDYVPVTDGQLLFDSRQ 5050
5051 TSASIRLSILDDTLTEPDEDFYVNLTSVRVLSTTLPLITAQPGIVQKNSI 5100
5101 STVTIRANDVVSGFLSIGPAVKLISEDSVEDSPQQKLQLRVRRTAGLSGV 5150
5151 VSAKIRAYAGLKTPLVDASQFHREHKGTWALEGEDFALETQSVTLLEGQN 5200
5201 EVEVTVIMLNDQEPEGQEAFFIYLSDAEGGAQIVSVPDELGFTSFAKIII 5250
5251 LGSDLQNGIVGFSLSSLSGQVLDEDSVNRTVTLVLQRQENRAFEDVLVFW 5300
5301 RVTFSTTDHALVSHGVDLSKELLQTSGTSIRRKGEVLCALKLEVQPDKNP 5350
5351 EYEVWFLVEVYKVGEGAAINQTARFANITMLESDDPRGLVYFAQGSRLPV 5400
5401 VTLKATSVSLQIYRDASTASSISVEYRMQELPKVESIGPSLVWPAVAGQD 5450
5451 FVMSEGTLTFEIGQSSAGLNIDLTPNIGSSNLTPKRFQVVLSDATGGARV 5500
5501 HPEFGLANVTLVSDTETQAVWALLDQLHQPLEETIINRVLHALINKVSRD 5550
5551 ITPEQLMAVLDASSKILSDAEQTPLKDSSRGLTYDLLCAMANPNRTDTQG 5600
5601 VSQLSEVAERFAYSLLTDIKCGAEGKRGITILDNCPYFTIAAHHWYPMQI 5650
5651 NGHTFIGKNTDTFTLPETLLEVPALPADSTAPSACYKVHFTEYSTEHWFL 5700
5701 TNKKPSALNGKVFSVSLYGRGSKPLSEGQEVVYRIHTPDRRGKPKPSQSL 5750
5751 CLLWNQAAESWLSDGQFCRLVDDTQNYVECACSHLSIYTAYAEIESLASF 5800
5801 NEAFYAAGFICISGFALAMVSHLMCARFLMFAAKLLTHMMVACLGTQICF 5850
5851 LVSAFRGRMFSEDSCAALGLFFHYFHLSQFGWMLVQAINFWQILVMNDEH 5900
5901 TERRYLLYFLLSWGLPALVIIVLVVVLLGGFGWSIHSVYGLVQGDLCFIP 5950
5951 NVYAALCTAALVPLICLVGVLVIFIHAYQVTQQWKAYDDIYRGRTNSSEV 6000
6001 PMMLYLFALVTLVCVWAGLHMAYRYIWMLILLVIFNIFLGLYVFSVYFVM 6050
6051 HNQLFWPGKATYTVEMNGHSSPDSIYQSTGAATVGGGEISKSTQNLISAM 6100
6101 EEISADWERASLRPSSQPSSIFKPSPQDEAYITEGGFINTSLVRDEESQE 6150
6151 FDDLIFALKTGSGLNVSDNESIHGSHDGGSMANSQIVELRRIPIADTHL 6199
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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