 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P37276 from www.uniprot.org...
The NucPred score for your sequence is 0.79 (see score help below)
1 MGDSLENPDTSVDPIVNLSIANYDAFANYLRKAVTILLPEDDVVPASLND 50
51 ALDDPVNQDTIRKFLSDPQVQALYVQRNCIKEDDSEQPAEGEDEKEQVTY 100
101 QISNDVHFTNSRMASLACIKRGLVVEADKSIHSQLRLINFSDGSPYETLH 150
151 AFISKSLAPYFKSYVKESGRADRDGDKMAPSVEKKLAELEMGLLHLQQNI 200
201 DIPEITLTAHQTVNNVIRKCAEENRKAKVADFGDKVEDSSFLNLLQNGVN 250
251 RWIAEIKKVTKLNRDPGSGTALQEISFWLNLERALYRIQEKRESPEVALT 300
301 LDILKHGKRFHATVSFDTDTGLKQALATVADYNPLMKDFPINDLLSATEL 350
351 EKIRPAVQQIFAHLRKVRNTKYPIQRCLKLIEAISRDLSQQLLKVLGTRR 400
401 LMHIPFDEFERVMNQCFEIFSCWDDEYDKLQGLLRDIVKKKRDEHLKMVW 450
451 RVSPAHKKLQTRMEHMRKFRRQHEQLRTVILRVLRPTKPAVGDDGNVVET 500
501 KQPYSLDAADANAIEEVNLAYENVKEVDCLDITKEGSEAWEAAVKRYEEK 550
551 IDRVETRITAHLRDQLGTAKNANEMFRIFSRFNALFVRPHIRGAIREYQT 600
601 QLIQRVKDDIEALHEKFKVQYPQSKSCRLSSVRDLPPVAGSIIWARQIDN 650
651 QLTMYLKRVEDVLGKGWETHIEGQKLKADGDSFRAKLSISDVFHEWARKV 700
701 QERNFGSTGRIFTIESTRSRIGRGNVLRLRVNFLPEIITLAKEVRNIKNL 750
751 GFRVPLTIVNKAHQANQIYPYAISLIESVRTYERTLEKIEDRASIVPLVA 800
801 GLRKDVLNLVSEGIGLIWESYKLDPYVIRLSECVTQFQEKVDDLLVVEEQ 850
851 LDVDVRSLETCPYSAATFVEILSKIQHAVDDLSLRQYSNLSVWVTRLDEE 900
901 VEKKLALRLQAGIQAWTEALTGNKKEVDTSMDTDAPAQPTHKLGGDPQIQ 950
951 NAVHEIRITNQQMYLYPSIEEARFQIMQQFFAWQAIVTSQVRLQSTRYQV 1000
1001 GLEKHVSQTYRNLLTKLPEGKILENAYGAIEQKVSEVRNYVDEWLRYQSL 1050
1051 WDLQADMLYGRLGEDVNLWIKCLNDIKQSRTTFDTSDTRRAYGPIIIDYA 1100
1101 KVQAKVTLKYDSWHKEALGKFGTLLGTEMTSFHSKVSKSRTDLEMQSIEA 1150
1151 ASTSDAVSFITYVQSLKKDMIAWDKQVEVFREAQRILERQRFQFPNTWLH 1200
1201 VDNIEGEWSAFNEIIKRKDTAIQTQVASLQAKIVAEDKAVETRTVDFLND 1250
1251 WEKTKPTGGKIRPDDALQQLQIFESKYSRLKEERDNVVKAKEALELQESA 1300
1301 VPNNSAERMNVALEELQDLRGVWSELSKVWTQIDETREKPWLSVQPRKLR 1350
1351 QQLEAMMAQLKELPARLRMYESYEYVKKLIQSYIKVNMLIVELKSDALKE 1400
1401 RHWKQLTKQLRVNWVLSDLSLGQVWDVNLQKNEGIVKDIILVAQGEMALE 1450
1451 EFLKQVRESWQNYELDLINYQNKCRIIRGWDDLFNKVKEHINSVAAMKLS 1500
1501 PYYKVFEEEALTWEEKLNRINALFDVWIDVQRRWVYLEGIFSGSADIKTL 1550
1551 LPVETSRFQSISSEFLGLMKKVTKSPKVMDVLNIPAVQRSLERLADLLGK 1600
1601 IQKALGEYLERERTSFPRFYFVGDEDLLEIIGNSKNIARLQKHFKKMFAG 1650
1651 VAAILLNEENNVILGISSREGEEVHFMNPVSTVEHPKINEWLSLVEKQMR 1700
1701 FTLASLLAQAVQDIKQFRDGKIDPQAYMEWCDKYQAQIVVLAAQILWSED 1750
1751 VESALQQASENNQSKPMQRVLGNVESTLNVLADSVLQEQPPLRRRKLEHL 1800
1801 INEFVHKRTVTRRLLNNGVTSPKSFQWLCEMRFYFDPRQTEVLQQLTIHM 1850
1851 ANARFFYGFEYLGVQDRLVQTPLTDRCYLTMTQALESRLGGSPFGPAGTG 1900
1901 KTESVKALGNQLGRFVLVFNCDETFDFQAMGRIFVGLCQVGAWGCFDEFN 1950
1951 RLEERMLSACSQQIQTIQEALKYEMDSNKESITVELVGKQVRVSPDMAIF 2000
2001 ITMNPGYAGRSNLPDNLKKLFRSLAMTTPDRQLIAEVMLFSQGFRSAEKL 2050
2051 ACKIVPFFKLCDEQLSNQSHYDFGLRALKSVLISAGNVKRDRIMKIKEQM 2100
2101 KQRGDENIDEASVAENLPEQEILIQSVCETMVPKLVAEDIPLLFSLLSDV 2150
2151 FPNVGYTRAEMKGLKEEIRKVCQEDYLVCGEGDEQGAAWMEKVLQLYQIS 2200
2201 NLNHGLMMVGPSGSGKSTAWKTLLKALERFEGVEGVAHVIDPKAISKEAL 2250
2251 YGVLDPNTREWTDGLFTHILRKIIDNVRGEINKRQWIIFDGDVDPEWVEN 2300
2301 LNSVLDDNKLLTLPNGERLSLPPNVRVMFEVQDLKFATLATVSRCGMVWF 2350
2351 SEDVLSTEMIFENYLSRLRSIPLEDGDEDFVGVIKPAKDKEEEVSPSLQV 2400
2401 QRDIALLLLPFFSADGIVVRTLEYAMDQEHIMDFTRLRALSSLFSMLNQA 2450
2451 ARNVLTFNAQHPDFPCSADQLEHYIPKALVYSVLWSFAGDAKLKVRIDLG 2500
2501 DFVRSVTTVPLPGAAGAPIIDYEVNMSGDWVPWSNKVPVIEVETHKVASP 2550
2551 DIVVPTLDTVRHESLLYTWLAEHKPLVLCGPPGSGKTMTLFSALRALPDM 2600
2601 EVVGLNFSSATTPELLLKTFDHYCEYRKTPNGVVLSPVQIGKWLVLFCDE 2650
2651 INLPDMDSYGTQRVISFLRQLVEHKGFYRASDQAWVSLERIQFVGACNPP 2700
2701 TDPGRKPLSHRFLRHVPIIYVDYPGETSLKQIYGTFSRAMLRLMPALRGY 2750
2751 AEPLTNAMVEFYLASQDRFTQDMQPHYVYSPREMTRWVRGICEAIRPLDS 2800
2801 LPVEGLVRLWAHEALRLFQDRLVDDSERRWTNENIDLVGQKHFPGINQEE 2850
2851 ALQRPILYSNWLSKDYMPVNREELREYVHARLKVFYEEELDVPLVLFDEV 2900
2901 LDHVLRIDRIFRQPQGHLLLIGVSGAGKTTLSRFVAWMNGLSIFQIKVHN 2950
2951 KYTSEDFDEDLRCVLRRSGCKDEKIAFILDESNVLDSGFLERMNTLLANG 3000
3001 EVPGLFEGDEYTTLMTQCKEGAQREGLMLDSSDELYKWFTQQVMRNLHVV 3050
3051 FTMNPSTDGLKDRAATSPALFNRCVLNWFGDWSDSALFQVGKEFTTRVDL 3100
3101 EKPNWHAPDFFPSVCPLVPANPTHRDAVINSCVYVHQTLHQANARLAKRG 3150
3151 GRTMAVTPRHYLDFIHHFVKLYNEKRSDLEEQQLHLNVGLNKIAETVEQV 3200
3201 EEMQKSLAVKKQELQAKNEAANAKLKQMFQDQQEAEKKKIQSQEIQIRLA 3250
3251 DQTVKIEEKRKYVMADLAQVEPAVIDAQAAVKSIRKQQLVEVRTMANPPS 3300
3301 VVKLALESICLLLGENATDWKSIRAVIMRENFINSIVSNFGTENITDDVR 3350
3351 EKMKSKYLSNPDYNFEKVNRASMACGPMVKWAIAQIEYADMLKRVEPLRE 3400
3401 ELRSLEEQADVNLASAKETKDLVEQLERSIAAYKEEYAQLISQAQAIKTD 3450
3451 LENVQAKVDRSIALLKSLNIERERWESTSETFKSQMSTIIGDVLLSAAFI 3500
3501 AYGGYFDQHYRLNLFTTWSQHLQAASIQYRADIARTEYLSNPDERLRWQA 3550
3551 NALPTDDLCTENAIMLKRFNRYPLIIDPSGQATTFLLNEYAGKKITKTSF 3600
3601 LDDSFRKNLESALRFGNPLLVQDVENYDPILNPVLNRELRRTGGRVLITL 3650
3651 GDQDIDLSPSFVIFLSTRDPTVEFPPDICSRVTFVNFTVTRSSLQSQCLN 3700
3701 QVLKAERPDIDEKRSDLLKLQGEFRLRLRQLEKSLLQALNDAKGKILDDD 3750
3751 SVITTLETLKKEAYDINQKVDETDKVIAEIETVSQQYLPLSVACSNIYFT 3800
3801 MDSLNQVHFLYQYSLKMFLDIFSTVLYNNPKLEGRTDHSERLGIVTRDLF 3850
3851 QVCYERVARGMIHIDRLTFALLMCKIHLKGTSESNLDAEFNFFLRSREGL 3900
3901 LANPTPVEGLSAEQIESVNRLALRLPIFRKLLEKVRSIPELGAWLQQSSP 3950
3951 EQVVPQLWDESKALSPIASSVHQLLLIQAFRPDRVIAAAHNVVNTVLGED 4000
4001 FMPNAEQELDFTSVVDKQLNCNTPALLCSVPGFDASGRVDDLAAEQNKQI 4050
4051 SSIAIGSAEGFNQAERAINMACKTGRWVLLKNVHLAPQWLVQLEKKMHSL 4100
4101 QPHSGFRLFLTMEINPKVPVNLLRAGRIFVFEPPPGIRANLLRTFSTVPA 4150
4151 ARMMKTPSERARLYFLLAWFHAIVQERLRYVPLGWAKKYEFNESDLRVAC 4200
4201 DTLDTWIDTTAMGRTNLPPEKVPWDALVTLLSQSIYGGKIDNDFDQRLLT 4250
4251 SFLKKLFTARSFEADFALVANVDGASGGLRHITMPDGTRRDHFLKWIENL 4300
4301 TDRQTPSWLGLPNNAEKVLLTTRGTDLVSKLLKMQQLEDDDELAYSVEDQ 4350
4351 SEQSAVGRGEDGRPSWMKTLHNSATAWLELLPKNLQVLKRTVENIKDPLY 4400
4401 RYFEREVTSGSRLLQTVILDLQDVVLICQGEKKQTNHHRSMLSELVRGII 4450
4451 PKGWKRYTVPAGCTVIQWITDFSNRVQQLQKVSQLVSQAGAKELQGFPVW 4500
4501 LGGLLNPEAYITATRQCVAQANSWSLEELALDVTITDAGLKNDQKDCCFG 4550
4551 VTGLKLQGAQCKNNELLLASTIMMDLPVTILKWIKISSEPRISKLTLPVY 4600
4601 LNSTRTELLFTVDLAVAAGQESHSFYERGVAVLTSTALN 4639
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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