 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9NU22 from www.uniprot.org...
The NucPred score for your sequence is 0.93 (see score help below)
1 MEHFLLEVAAAPLRLIAAKNEKSRSELGRFLAKQVWTPQDRQCVLSTLAQ 50
51 LLLDKDCTVLVGRQLRPLLLDLLERNAEAIKAGGQINHDLHERLCVSMSK 100
101 LIGNHPDVLPFALRYFKDTSPVFQRLFLESSDANPVRYGRRRMKLRDLME 150
151 AAFKFLQQEQSVFRELWDWSVCVPLLRSHDTLVRWYTANCLALVTCMNEE 200
201 HKLSFLKKIFNSDELIHFRLRLLEEAQLQDLEKALVLANPEVSLWRKQKE 250
251 LQYLQGHLVSSDLSPRVTAVCGVVLPGQLPAPGELGGNRSSSREQELALR 300
301 SYVLVESVCKSLQTLAMAVASQNAVLLEGPIGCGKTSLVEYLAAVTGRTK 350
351 PPQLLKVQLGDQTDSKMLLGMYRCTDVPGEFVWQPGTLTQAATMGHWILL 400
401 EDIDYAPLDVVSVLIPLLENGELLIPGRGDCLKVAPGFQFFATRRLLSCG 450
451 GNWYRPLNSHATLLDKYWTKIHLDNLDKRELNEVLQSRYPSLLAVVDHLL 500
501 DIYIQLTGEKHHSWSDSSVGCEQAPEEVSEARRENKRPTLEGRELSLRDL 550
551 LNWCNRIAHSFDSSSLSASLNIFQEALDCFTAMLSEHTSKLKMAEVIGSK 600
601 LNISRKKAEFFCQLYKPEIVINELDLQVGRVRLLRKQSEAVHLQREKFTF 650
651 AATRPSSVLIEQLAVCVSKGEPVLLVGETGTGKTSTIQYLAHITGHRLRV 700
701 VNMNQQSDTADLLGGYKPVDHKLIWLPLREAFEELFAQTFSKKQNFTFLG 750
751 HIQTCYRQKRWHDLLRLMQHVHKSAVNKDGKDSETGLLIKEKWEAFGLRL 800
801 NHAQQQMKMTENTLLFAFVEGTLAQAVKKGEWILLDEINLAAPEILECLS 850
851 GLLEGSSGSLVLLDRGDTEPLVRHPDFRLFACMNPATDVGKRNLPPGIRN 900
901 RFTELYVEELESKEDLQVLIVDYLKGLSVNKNTVQGIINFYTALRKESGT 950
951 KLVDGTGHRPHYSLRTLCRALRFAASNPCGNIQRSLYEGFCLGFLTQLDR 1000
1001 ASHPIVQKLICQHIVPGNVKSLLKQPIPEPKGGRLIQVEGYWIAVGDKEP 1050
1051 TIDETYILTSSVKLNLRDIVRVVSAGTYPVLIQGETSVGKTSLIQWLAAA 1100
1101 TGNHCVRINNHEHTDIQEYIGCYTSDSSGKLVFKEGVLIDAMRKGYWIIL 1150
1151 DELNLAPTDVLEALNRLLDDNRELLVTETQEVVKAHPRFMLFATQNPPGL 1200
1201 YGGRKVLSRAFRNRFVELHFDELPSSELETILHKRCSLPPSYCSKLVKVM 1250
1251 LDLQSYRRSSSVFAGKQGFITLRDLFRWAERYRLAEPTEKEYDWLQHLAN 1300
1301 DGYMLLAGRVRKQEEIDVIQEVLEKHFKKKLCPQSLFSKENVLKLLGKLS 1350
1351 TQISTLECNFGHIVWTEGMRRLAMLVGRALEFGEPVLLVGDTGCGKTTIC 1400
1401 QVFAALANQKLYSVSCHLHMETSDFLGGLRPVRQKPNDKEEIDTSRLFEW 1450
1451 HDGPLVQAMKEDGFFLLDEISLADDSVLERLNSVLEVEKSLVLAEKGSPE 1500
1501 DKDSEIELLTAGKKFRILATMNPGGDFGKKELSPALRNRFTEIWCPQSTS 1550
1551 REDLIQIISHNLRPGLCLGRIDPKGSDIPEVMLDFIDWLTHQEFGRKCVV 1600
1601 SIRDILSWVNFMNKMGEEAALKRPEIISTVTSFVHAACLVYIDGIGSGVT 1650
1651 SSGFGTALLARKECLKFLIKRLAKIVRLTEYQKNELKIYDRMKAKEFTGI 1700
1701 DNLWGIHPFFIPRGPVLHRNNIADYALSAGTTAMNAQRLLRATKLKKPIL 1750
1751 LEGSPGVGKTSLVGALAKASGNTLVRINLSEQTDITDLFGADLPVEGGKG 1800
1801 GEFAWRDGPLLAALKAGHWVVLDELNLASQSVLEGLNACFDHRGEIYVPE 1850
1851 LGMSFQVQHEKTKIFGCQNPFRQGGGRKGLPRSFLNRFTQVFVDPLTVID 1900
1901 MEFIASTLFPAIEKNIVKKMVAFNNQIDHEVTVEKKWGQKGGPWEFNLRD 1950
1951 LFRWCQLMLVDQSPGCYDPGQHVFLVYGERMRTEEDKKKVIAVFKDVFGS 2000
2001 NSNPYMGTRLFRITPYDVQLGYSVLSRGSCVPHPSRHPLLLLHQSFQPLE 2050
2051 SIMKCVQMSWMVILVGPASVGKTSLVQLLAHLTGHTLKIMAMNSAMDTTE 2100
2101 LLGGFEQVDLIRPWRRLLEKVEGTVRALLRDSLLISADDAEVVLRAWSHF 2150
2151 LLTYKPKCLGEGGKAITMEIVNKLEAVLLLMQRLNNKINSYCKAEFAKLV 2200
2201 EEFRSFGVKLTQLASGHSHGTFEWVDSMLVQALKSGDWLLMDNVNFCNPS 2250
2251 VLDRLNALLEPGGVLTISERGMIDGSTPTITPNPNFRLFLSMDPVHGDIS 2300
2301 RAMRNRGLEIYISGEGDASTPDNLDLKVLLHSLGLVGNSVCDILLALHTE 2350
2351 TRSTVVGSPTSSVSTLIQTAILIVQYLQRGLSLDRAFSEACWEVYVCSQH 2400
2401 SPANRKLVQALLEKHVSSLRAHETWGDSILGMGLWPDSVPSALFATEDSH 2450
2451 LSTVRRDGQILVYCLNRMSMKTSSWTRSQPFTLQDLEKIMQSPSPENLKF 2500
2501 NAVEVNTYWIDEPDVLVMAVKLLIERATNQDWMLRVKWLYHLAKNIPQGL 2550
2551 ESIQIHLEASAASLRNFYSHSLSGAVSNVFKILQPNTTDEFVIPLDPRWN 2600
2601 MQALDMIRNLMDFDPQTDQPDQLFALLESAANKTIIYLDREKRVFTEANL 2650
2651 VSVGSKKLRESVLRMSFEFHQDPESYHTLPHEIVVNLAAFFELCDALVLL 2700
2701 WVQSSQGMVSDASANEILGSLRWRDRFWTVADTVKVDAPGLALLALHWHW 2750
2751 VLKHLVHQIPRLLMNYEDKYYKEVQTVSEHIQNCLGSQTGGFAGIKKLQK 2800
2801 FLGRPFPFKDKLVVECFSQLKVLNKVLAIREQMSALGESGWQEDINRLQV 2850
2851 VASQWTLKKSLLQAWGLILRANILEDVSLDELKNFVHAQCLELKAKGLSL 2900
2901 GFLEKKHDEASSLSHPDLTSVIHLTRSVQLWPAMEYLAMLWRYKVTADFM 2950
2951 AQACLRRCSKNQQPQINEEISHLISFCLYHTPVTPQELRDLWSLLHHQKV 3000
3001 SPEEITSLWSELFNSMFMSFWSSTVTTNPEYWLMWNPLPGMQQREAPKSV 3050
3051 LDSTLKGPGNLNRPIFSKCCFEVLTSSWRASPWDVSGLPILSSSHVTLGE 3100
3101 WVERTQQLQDISSMLWTNMAISSVAEFRRTDSQLQGQVLFRHLAGLAELL 3150
3151 PESRRQEYMQNCEQLLLGSSQAFQHVGQTLGDMAGQEVLPKELLCQLLTS 3200
3201 LHHFVGEGESKRSLPEPAQRGSLWVSLGLLQIQTWLPQARFDPAVKREYK 3250
3251 LNYVKEELHQLQCEWKTRNLSSQLQTGRDLEDEVVVSYSHPHVRLLRQRM 3300
3301 DRLDNLTCHLLKKQAFRPQLPAYESLVQEIHHYVTSIAKAPAVQDLLTRL 3350
3351 LQALHIDGPRSAQVAQSLLKEEASWQQSHHQFRKRLSEEYTFYPDAVSPL 3400
3401 QASILQLQHGMRLVASELHTSLHSSMVGADRLGTLATALLAFPSVGPTFP 3450
3451 TYYAHADTLCSVKSEEVLRGLGKLILKRSGGKELEGKGQKACPTREQLLM 3500
3501 NALLYLRSHVLCKGELDQRALQLFRHVCQEIISEWDEQERIAQEKAEQES 3550
3551 GLYRYRSRNSRTALSEEEEEEREFRKQFPLHEKDFADILVQPTLEENKGT 3600
3601 SDGQEEEAGTNPALLSQNSMQAVMLIHQQLCLNFARSLWYQQTLPPHEAK 3650
3651 HYLSLFLSCYQTGASLVTHFYPLMGVELNDRLLGSQLLACTLSHNTLFGE 3700
3701 APSDLMVKPDGPYDFYQHPNVPEARQCQPVLQGFSEAVSHLLQDWPEHPA 3750
3751 LEQLLVVMDRIRSFPLSSPISKFLNGLEILLAKAQDWEENASRALSLRKH 3800
3801 LDLISQMIIRWRKLELNCWSMSLDNTMKRHTEKSTKHWFSIYQMLEKHMQ 3850
3851 EQTEEQEDDKQMTLMLLVSTLQAFIEGSSLGEFHVRLQMLLVFHCHVLLM 3900
3901 PQVEGKDSLCSVLWNLYHYYKQFFDRVQAKIVELRSPLEKELKEFVKISK 3950
3951 WNDVSFWSIKQSVEKTHRTLFKFMKKFEAVLSEPCRSSLVESDKEEQPDF 4000
4001 LPRPTDGAASELSSIQNLNRALRETLLAQPAAGQATIPEWCQGAAPSGLE 4050
4051 GELLRRLPKLRKRMRKMCLTFMKESPLPRLVEGLDQFTGEVISSVSELQS 4100
4101 LKVEPSAEKEKQRSEAKHILMQKQRALSDLFKHLAKIGLSYRKGLAWARS 4150
4151 KNPQEMLHLHPLDLQSALSIVSSTQEADSRLLTEISSSWDGCQKYFYRSL 4200
4201 ARHARLNAALATPAKEMGMGNVERCRGFSAHLMKMLVRQRRSLTTLSEQW 4250
4251 IILRNLLSCVQEIHSRLMGPQAYPVAFPPQDGVQQWTERLQHLAMQCQIL 4300
4301 LEQLSWLLQCCPSVGPAPGHGNVQVLGQPPGPCLEGPELSKGQLCGVVLD 4350
4351 LIPSNLSYPSPIPGSQLPSGCRMRKQDHLWQQSTTRLTEMLKTIKTVKAD 4400
4401 VDKIRQQSCETLFHSWKDFEVCSSALSCLSQVSVHLQGLESLFILPGMEV 4450
4451 EQRDSQMALVESLEYVRGEISKAMADFTTWKTHLLTSDSQGGNQMLDEGF 4500
4501 VEDFSEQMEIAIRAILCAIQNLEERKNEKAEENTDQASPQEDYAGFERLQ 4550
4551 SGHLTKLLEDDFWADVSTLHVQKIISAISELLERLKSYGEDGTAAKHLFF 4600
4601 SQSCSLLVRLVPVLSSYSDLVLFFLTMSLATHRSTAKLLSVLAQVFTELA 4650
4651 QKGFCLPKEFMEDSAGEGATEFHDYEGGGIGEGEGMKDVSDQIGNEEQVE 4700
4701 DTFQKGQEKDKEDPDSKSDIKGEDNAIEMSEDFDGKMHDGELEEQEEDDE 4750
4751 KSDSEGGDLDKHMGDLNGEEADKLDERLWGDDDEEEDEEEEDNKTEETGP 4800
4801 GMDEEDSELVAKDDNLDSGNSNKDKSQQDKKEEKEEAEADDGGQGEDKIN 4850
4851 EQIDERDYDENEVDPYHGNQEKVPEPEALDLPDDLNLDSEDKNGGEDTDN 4900
4901 EEGEEENPLEIKEKPEEAGHEAEERGETETDQNESQSPQEPEEGPSEDDK 4950
4951 AEGEEEMDTGADDQDGDAAQHPEEHSEEQQQSVEEKDKEADEEGGENGPA 5000
5001 DQGFQPQEEEEREDSDTEEQVPEALERKEHASCGQTGVENMQNTQAMELA 5050
5051 GAAPEKEQGKEEHGSGAADANQAEGHESNFIAQLASQKHTRKNTQSFKRK 5100
5101 PGQADNERSMGDHNERVHKRLRTVDTDSHAEQGPAQQPQAQVEDADAFEH 5150
5151 IKQGSDAYDAQTYDVASKEQQQSAKDSGKDQEEEEIEDTLMDTEEQEEFK 5200
5201 AADVEQLKPEEIKSGTTAPLGFDEMEVEIQTVKTEEDQDPRTDKAHKETE 5250
5251 NEKPERSRESTIHTAHQFLMDTIFQPFLKDVNELRQELERQLEMWQPRES 5300
5301 GNPEEEKVAAEMWQSYLILTAPLSQRLCEELRLILEPTQAAKLKGDYRTG 5350
5351 KRLNIRKVIPYIASQFRKDKIWLRRTKPSKRQYQICLAIDDSSSMVDNHT 5400
5401 KQLAFESLAVIGNALTLLEVGQIAVCSFGESVKLLHPFHEQFSDYSGSQI 5450
5451 LRLCKFQQKKTKIAQFLESVANMFAAAQQLSQNISSETAQLLLVVSDGRG 5500
5501 LFLEGKERVLAAVQAARNANIFVIFVVLDNPSSRDSILDIKVPIFKGPGE 5550
5551 MPEIRSYMEEFPFPYYIILRDVNALPETLSDALRQWFELVTASDHP 5596
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.) |
Go back to the NucPred Home Page.