 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q09221 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MNDYEIRDLTGSSQKSNNQKISNNSGESAHFNGTSENVKTESDDDSTCSS 50
51 SLNKLSNRHSEPMNHLSVGGFVAKDLLSSLTIPLPQTVIRTCQSIDIDTP 100
101 DRSRNDKTLTSFFYSSCKNLLLTTIIPDPLDLVAAAVGQSTAQALLVAQI 150
151 GWMVCVCCRCCLCKCIANGRGVRGQKKQIQEPTIADDDGRDLPYEVPGSI 200
201 HIPKPVYNKKLSKSRSSSVPHVSVPSLVSSSADYKRLSMWETSLNVESDV 250
251 EPLHTPPLVVGRKLDYIDKEGNNPSCCRERGTRKPGFRPLRGSGSPPPPD 300
301 PSTSTKVAVTSASEVKKIKDHKKQLKKEKEKKKKMDKKQSSSGGGFFTRW 350
351 FGGSGSNNSQQNLAEDVIDARSERKTAKQREQELLQRSERRSGGRTHSHE 400
401 EYRRHQQPSLMVNTNFDEDDYATIDRVRRSNNREMSMPASPRNVHFVDET 450
451 SGPLSRTMNESAFGDRAHLQYRPRAQKGAAGPTTRGAPTSSVNQLDEATL 500
501 DLLRLSTEPSPVPSRRALPKSASLSSVQQKQPIKTADGGQLKIGSVYTWD 550
551 QNSVDTTTDDERAKDFLRGDRSSRLSPQSERKNERQIQIRQQSSGPTNRR 600
601 ETEIEYEEKRQGPPVVRTTVEGKLKMEKIVGADLITVDSCISSAWTVRDT 650
651 VTNYKIKSTIGKKSLILEEIKDGQSKYKITLIENGETKMEREASLDVPDF 700
701 VNKKDYLAEVSKKLLSDLREDSESVSALTHIEVEIVEDVTNILKTYVIGE 750
751 RADDVLAEEQLRLHYEQTADKTPSPIPLEKVEKIYVDELQKDKIELEDPE 800
801 KADIHLIKDGRHFEGEGALKRIRRFETEESIEKPTVIRMEPRCAHAFADC 850
851 DVAKKDDTSNYTVKIAVPLVHTITFLLKKSKMMRQQKAAGYEMEQEGQRF 900
901 EDETTLRRIKRYETEEEEEKQHVAVIQHVEEVKVATMKTQREVEAEGGQY 950
951 EMSQEGIHLRGETTFKKRGKHLDSESSEERFMEREAEGGQYAMQMEGERL 1000
1001 LGEKKFRSKGRHYESESEESMASWNGGSPTLVDLVKKESSSIFEATFETA 1050
1051 NNHSPIVAEVRRPKLKKENTTIGCTISNQKATSANAELTTKHVNTEKGSG 1100
1101 KFRELGEEQAMMLCGFENQKSSKEEVTGSRQQKNEIKVAFAAGSAETENT 1150
1151 TISTTIFHDADSFAVEGSSKSANSTATYGRFKEMSEENASNMVYLQKSES 1200
1201 SSSNLSGAETKMKHIQRQSSEARFSEFKQVAESCAVMIKNTGVERGSTSS 1250
1251 TVAEAATDLRIRRKDAKGEIIVFVLFKRVFGNYVHASMRLASSGSGLLRE 1300
1301 NRSELREMRSEEKRSNSMHHEESHYAHSSYEHTSEHYSQSSFYHQESDEH 1350
1351 GLEPDQQVSKQLSSIERQLLKLDEVVIGNECTDVVEVKVTIKKREQNANQ 1400
1401 LIVVLEELLETAICGGIREIKRSIVDTSLESRVRKLEQSSLYSIIKKSSS 1450
1451 KESVVHCGKPPLERQASLEGRFRMEESWSAVECKRRSSSIDRASLKLRAS 1500
1501 QEEVCTGFWNTSKGESTRRTILQKTKSVESMCLKTKSASSTSIDMFSDCQ 1550
1551 RATVSQTISTEIASHRREIVAAAFGISTQSLERLLKFVEIIDWENITMNV 1600
1601 SQKEQLSANLQALTSAPVNLDIPSDLGRIVAPAEQDAYSDLIMRERAESR 1650
1651 VFAQLRASADEITIREVALGSMSQLEQAAFMSLLITSVSRCDLRTIAPSN 1700
1701 IIANTEIFYDVAEEKMSVSGVMKRESRKEHSSKKFTSSREEIIQGFWKGE 1750
1751 RDEERVVKTLKDRMVSFKHSLSVEAASSTSENVSLGMRKADQKSEFTISQ 1800
1801 KLSPKEIVSEAYGVSESKLDQFFQVMEKMDWSNIELGEKEHTALSANIRT 1850
1851 LAPEQKVCDGILGKLKAPKQQDESVGTQIQEIRRATAVMSVRASLLLTIS 1900
1901 SNSNFSKNISDSEKAVFSNLISVITSHNLSTIGTSSETTTASISYQDIPE 1950
1951 MLAASKLWISRNSEKLKKEIREPVIQTVESFWNTTNDQEKVAVLLNEKID 2000
2001 SIYSSLNTLAASMENEIISQELVRHTENAGKVNIKPISPRELVSSSFKIT 2050
2051 SSDLQQFFNVLEKMDWSQISLPAQEHAIISKNVRSLAPSASELSNILGKL 2100
2101 RAPEMQEETTDLKIRQAQQARVVLNVSESMSNTISSSETFSRIPENKKAT 2150
2151 LTNLVGIITTQDLSTLGASSENQTIEMDYTEAKEELEASKKLVDKNLSVL 2200
2201 KTEIRESGDEVVQGFWNTASDKEKVGAIICEKLKSIHHTLHTHAIRTVTE 2250
2251 SLSTDIQKEQQKLSTHHFVKLSTRDVVQAAFRISSESVDHLLTLVEKMDW 2300
2301 SKIDLVEPVYSNISSNVQALATSSSQSSGILGKLLPPAPEEETTSKEIRE 2350
2351 INTAKCILNVISSLNSTITSDASLDMISESEQAIFSNIIGVMASNNLTTS 2400
2401 SQSTSSSFGFNNVFELSEARRVLQESNKQNLIQKVRESSEELIHGIWSTA 2450
2451 SETEKVAIVVKEKLETVHQAMKTLAIQMATQSVNSELIGSEGNLASIKSI 2500
2501 TLPTREVISAAFGISNEVIQKMLEVLTKGEWSTISLPQSEYESISQNVRA 2550
2551 LAEPSFNCDSILGKINAPGPQTETTSHELIEKNTAAAVANVKAAVESVIS 2600
2601 KESSLAKLPADEKAIITNLAGLVLSKDVSSLCTSSETFGFDQRIFRNQNA 2650
2651 NISLGAVRSETLLQRLREPIENQVQGFWSTASNQEKASFIAKQKLDTMYD 2700
2701 TMKVIAIQMISQTIDGDFSSASISSETFKNIDKAAREVVAAEFGIANESV 2750
2751 QKALEVLNKVEWSDISLPEKLHQNISSNVRVLADSSYNCDSIIGKLNAPE 2800
2801 PQSAYVDQQISEHQTLEVIANIKSAVSSVISNDSVLSKRSEDEKAVISNI 2850
2851 ANLIISCDLASMSSTSNSFEFQRDILEPQNANVLLGTPASQVFTRNLQEP 2900
2901 IQSEVQGFWSTNSSQEKASLVIRQKLNVKYDAMKMMATEIAYQSLNSNIT 2950
2951 SEETNLESQKSFDKSIRESIMASFGVSSEHVQKTLEILSKSELSTIVLPA 3000
3001 VEYEATVQNIKALSEPNFNCESILGKLNAPDPQSAVMDIIMNEQRKLTVV 3050
3051 SNIQSAIESVISNDSSLEKLTEVDKSVILKISEIIVSHDLTALSSSCSDF 3100
3101 NLLQESNTQEKADIFLKTPNSQMLIERLREPIEKQVQGFWSTASNSEKQE 3150
3151 MFLKEKVETIHAMLQTFSASLVSETVQRDFMATVQSLAALRSIQLTPREI 3200
3201 LCAAFGISNEQIEQTFRGLDEVDWSHIDIPCALRDNLLVNLRIVNADAPN 3250
3251 VFGNLISRPDENQETSTVLEEKNRIQFLLNLQRSLDQTVQMTSSLTRSDE 3300
3301 RIEVKVSNIISLISSENLGDLISQAVQLAQPNMSDETEKSFEIPRNILLN 3350
3351 RVVPETSEESIQSFWKTSQLAEEASSTISEKLSMLQSEFVVSAAKQVSTS 3400
3401 LTLDYRRKIFNQNSEIIFGDLTRDVVKAAFSVSDETLNTLFSQLEQSDWS 3450
3451 QIKLSSKQKSILSANIKSLATSNLDTLIGQLMPREADSEQSEASLSERSS 3500
3501 VEMENTFKIQQIIFDTVVRKFGSDEERASIINLIGLLTSVNLKAAIQVAS 3550
3551 FERLPDSLDSTASITPVFKIQEHVRQPTEQVVQGFWSKERHPNETVEVVN 3600
3601 RKIEVLKSILNCYAVAECHKSLERSLEKPSQQDETCATRGVTEVVQNAFA 3650
3651 VTEQSYSKILKVLGSMQNFEIPEAVKETISKNLKILNLPPVDTLLVSKYK 3700
3701 ESHVSKTVEEQELVQFMFNFKSALNSEIEMSQNVSKCIEEETVVLPNLVA 3750
3751 ILASANLATIVPTESRQLSDSENILNFSIQHEESERSAATVRDEINILRH 3800
3801 YLQTFAIQKASSEVSRVIEIIKTSQSFHLVHTTMLEEIKTIQLRMEVLKR 3850
3851 VSMNQSTAEELLQEAFQNSEQQMETFQRIVQNVHWEETKLTEEIIRNLRI 3900
3901 NMTAIPRFESTVGSLKAPDDQHESADSYISSTRRAEAVINLMAAADNAIS 3950
3951 TSSTLSMMESDERLLHKTILKTMAVCNLTTPASRSETETMTQGFYQKVEQ 4000
4001 CQTEQELAERPKINCEMKVLEISDDEVHGIWSSRKEQESSQKILLASELE 4050
4051 KATLQTLASGDETNSIYTELASFGTREEIEKLVSIELRDIVEKSFGVSEE 4100
4101 SLNKLLNLIPKMDWSSLTMPIVQKELVIKNLALLLPAEALIMESVGTIQA 4150
4151 PPEEEAFAELDLKQAREAKVMMDIQECVFTTCTGYTEMNKPDDAEITTFS 4200
4201 ALLGTLSICDLVTMASSNIQVDSKYDYYRRPAPKNAETTFTESNADAFSL 4250
4251 ALQEAGEVTSSGIWSTVSNSDAAKTTVSDKLISISKTEMSMKAVSENAVN 4300
4301 QDHSLKKDSIEAVEMNIPDSRKEILQKNYSIDRSNSTVEMQGAIASEDIA 4350
4351 IQYSAPRVDNVSQTMRSQSQKDLLFGGSFGELEPPLPQEEDVETTARQSR 4400
4401 VYRSSSQVRAPSEESIQKTEALRRSESLESNARKTFVDRRRENVSLSRKA 4450
4451 SVERETNMEARVAKADQSIPVETLQKSKKQESIFSSVVESKDLNVCGSWT 4500
4501 TAKPPIGAKVSLQTKKVEKEVTSATMTVASASVECEVGLESKREQSRDAT 4550
4551 GSMVRAKSIEEVEREFGVEVTSTEKTLEKRDQIESWIKSIPQRLEVEESA 4600
4601 EFGNDEVIIGGVMGSLEAPLEQEEETEKLLTMKRSASEARSLKAATKESI 4650
4651 EEADEFSKIEADQEILTVQKELVKATGILNAAASREINASSKMEYSKVPS 4700
4701 EDVIELSLTESRRQSNSGQFKETKEEEIVGLWNTGTKGENASKVLPHKPP 4750
4751 IDTASMKAKAAKQNSIEMTGSLQKSPSAEAMGIVSQKVTEGANSKFGIAQ 4800
4801 GAVETTLTASAQSGVTCRDISVSNIGVASETVTQFSNKETGIGFGASNLI 4850
4851 APPPESAETEFTGKISNLSQTSLNKMAASDVGTTVESKIQAPGDNYGDVS 4900
4901 LLQKVASSDAITKAMQASRDSAISVDFRQDRETVSAEKSDLNFKSTNSET 4950
4951 QKLKLFESKEEESGIFIRSSHEYEETQKTLRHRSASRESASRTVTAPTNQ 5000
5001 EVQMNFDKKVEDSVAEGSLSIGIVRESSQSEVMQHAERTSELTKLSMNEE 5050
5051 VAGVRAVSETTNETFRGYQQGDVEVQTGAAMGRIEAPRPQRGEAEITQKL 5100
5101 RRTLSVERSAKASEMAESQTVTQIQKREDSLASEYSVRDTLLLKSSSVSH 5150
5151 VATEQMSEHLVMRSKSEHHISEKLQERLIEKESFSSQEFISENQGVHTHW 5200
5201 DVIDNNGDALICWKSSETEQKSLDAKQVTEELAGTTLDLSVRLLGGTDEE 5250
5251 KIAEHVIVGASEVLNISETRADMEMSRQDAFSDDATYVASDILEERSLST 5300
5301 VHEFGESEASTTFGVGKLVTKKPEKEEVGRSFSETRKLSQFSDITAISET 5350
5351 TTDMDSEILRLPECDQSVTLISHKNQQKDVKDLKATVENTADQVTLLEKR 5400
5401 EVREQSADVKKKKFEVEDIEEVLPMIRRWAEILSMKASTSVEIQAENKLS 5450
5451 KPEAQAESQKYLKTANLEINKINVNATHEEVMSATTALECKTSGQDVASI 5500
5501 RLRDKSRERVEKKYQENQWNLLSTNAEWETLLNDLEDSVTIAQSVQDSMA 5550
5551 FSVKASATVNLNSDTSIKKAQPEIGIQKSISQSNVEKTVGQFSSATIGNE 5600
5601 LLVTRLGMDLEEIETLVDEINREQAVGGRIREFGKSETGGGIYLVRRALP 5650
5651 KVKETSTHTVTVATSFRQIFSTMSAGDEISEANVELTIPSTSVGAEIKST 5700
5701 VARSDSMTFSTSHASEYTASTVADYARDIAVSASTAARKKAIPTERTSQK 5750
5751 LKEVGADGIEILSLWEGIETDLDATTQLVDLLRVKSSLQTIESSEEVERI 5800
5801 NQYLEMPEEKGLVIHLINVQNKETCERNFAVSICSLDTVHSRKSEIPPTY 5850
5851 SVSKVLTEKRVLRETWRVIESGDVRFNAVINLHRYSLSKPTLAQETVLRE 5900
5901 VVRIAAQPLFISAEPTYMRTKESSEDIVGVGIAYNVPEENLISAIKLDEK 5950
5951 ASGGCYELTTKAAGDEYKMISSILSKTQAVEKVKGKMIQKVVAKDEIRVL 6000
6001 ETTTESVNANFNYEIPEENFKIGIAKTCANNAAPFTMRLHECLDFYVKMF 6050
6051 YNMNKKDDFESLIKTIIISNGIESKTLSCTAAEHIEATRNPEFNRPAEFS 6100
6101 DIQKTVVDYNKIEGASLNTFEALNEKMALSTMLSRQDDYSRVEHIVKDKN 6150
6151 RGANLKFRFIESSEEKQTTFSAFEIDTEKEEIERTIDLVRQGGQFKLSTD 6200
6201 AAEENEITLHRDISKPIITHYDTLHLITLSNSAPHQILSTSGASNELHTI 6250
6251 STQLSKPAEWLNTELLIVDKNVEQPVTWRVLECEECVENLHPIYRRPDDI 6300
6301 FDLDEIWHIARNGGKFERRCKASGDEKEIFEQEIKSRGIKDDIIDKKFIV 6350
6351 GNQGEPTSFTTIQTSSVSASVSQDMSRLGEKEAIKKVLQNSNKGINVEKK 6400
6401 MIEATEYRETISEQFRKNDEFDKADLTVKDRRAGGSYELSTNASEQSSSS 6450
6451 VSNELICPRPSQLSIEKTFITAQTIIPAILSCKASESIGHTISEQWNRPN 6500
6501 DQFAISQIVKDCNKENEQFTFREAGEEYHTTNCFYTREQEEKVIAKTLHE 6550
6551 ARKGSGQTFETKHATDRSMDASQHLEKDRLAEATAEKLFVIGNTAGPVSY 6600
6601 SSKSSSDIQHSQVVNLSRPSPYVQVEIIRKGANLGTPTYFHARETTSKTE 6650
6651 NVATQLSRKEEKMEVSTTHTTPLLAEPVTFDSNASKSHSTSLDKNLVASE 6700
6701 DYELDAVVIIVDNNTEQPQFFRCSCTKEASTSAAAALSAGSKSESCKIVR 6750
6751 LASNLGHPTSLVLCESSSVQETNNVHYQRDEHHEHISETKSYPRDGGKFT 6800
6801 LDTKASTANEVRIDKDLEKKSDRELETEIKTIVRNEGEPVEIFVSATEES 6850
6851 AAGVTTSLSRANPFESANILLTSPNKGEPAYSRVTESSELTETNNVQLRR 6900
6901 EEEHQETEKIIEIAANGGSSLLRAGFADEKFADIEAKLGKDAQFESAQTI 6950
6951 RQIGNEDKTNLSIGASQETSVTFDETVQCNKSSCEETSITKVAKNIEPHV 7000
7001 IFRSTEASDMAVGIHYTLRSSEKVEETEEIKNIARNGGSATFSCFASGDE 7050
7051 SPDSVSAFLTRQPQEETTEKLFPTPMFDFIKFNSTAAEEFAVWNTTIFRR 7100
7101 KDNEGEVEKIFNTSEAGHNETFSANAAEDVSVTLDADLHFGVGYKEHRQI 7150
7151 TKDEANQGEGTGMHSGASEETIFNLGYDYCKQPTEFTTVCVTEDKLLIQG 7200
7201 AYGFRAAKEESITLDADLHFGVNYRDLLAMGLHASNNEIEGTGMRSTASE 7250
7251 ETIFNLAYDYCKQPTEFRTVFVSEDHQFVHGAFGFRAVGEEHIETQVLEL 7300
7301 QARMVEVMVEGSVHNLARRHEDEPFVLYTEVIEETIIRVDEQLEKKTTVI 7350
7351 ETEQASEVKMREKGEERRKEEKRVSFAAEVQEKTMEAIDKSLGLDTSMEV 7400
7401 EPAFQKPSIIKKPMKKERERRSRDLRQNAAPAFKPVRRNSLLQALAIGSP 7450
7451 HNIPHFKTLDDIVKAIKHAGLEYSNLIFGIDYTKSNFYQGERTFDKRPLH 7500
7501 TIDPAEMNPYQQVIQIVGKTLSSFDADGQIPAYGFGDEEFTDHGIFNIAE 7550
7551 RYDLEKDCNGFEEVLRVYNEVTPTIEMSGPTNFVPLIDRAIEICKEKHSY 7600
7601 HILVIVADGQVTNEKINQKAIAAASHYPLSIIMVGVGDGPWNMMGRFDDN 7650
7651 IPKRLFDNFHFVDFHKVMFNAPNADASFALNALMEIPDQYKAIKELGLLK 7700
7701 HSRRG 7705
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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