 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q91ZU8 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MAGYLSPAAYMYVEEQEYLQAYEDVLERYKDERDKVQKKTFTKWINQHLM 50
51 KVRKHVNDLYEDLRDGHNLISLLEVLSGDTLPREKGRMRFHRLQNVQIAL 100
101 DYLKRRQVKLVNIRNDDITDGNPKLTLGLIWTIILHFQISDIHVTGESED 150
151 MSAKERLLLWTQQATEGYAGVRCENFTTCWRDGKLFNAIIHKYRPDLIDM 200
201 NTVAVQSNLANLEHAFYVAEKIGVIRLLDPEDVDVSSPDEKSVITYVSSL 250
251 YDAFPKVPEGGEGIGANDVEVKWIEYQNMVNYLIQWIRHHVVTMSERTFP 300
301 NNPLELKALYNQYLQFKEKEIPPKEMEKSKIKRLYKLLEIWIEFGRIKLL 350
351 QGYHPNDIEKEWGKLIIAMLEREKALRPEVERLDMLQQIATRVQRDSVSC 400
401 EDKLILARNALQSDSKRLESGVQFQNEAEIAGYILECENLLRQHVIDVQI 450
451 LIDGKYYQADQLVQRVAKLRDEIMALRNECSSVYSKGRMLTTEQTKLMIS 500
501 GITQSLNSGFAQTLHPSLNSGLTQSLTPSLTSSSVTSGLSSGMTSRLTPS 550
551 VTPVYAPGFPSVVAPNFSLGVEPNSLQTLKLMQIRKPLLKSSLLDQNLTE 600
601 EEVNMKFVQDLLNWVDEMQVQLDRTEWGSDLPSVESHLENHKNVHRAIEE 650
651 FESSLKEAKISEIQMTAPLKLSYTDKLHRLESQYAKLLNTSRNQERHLDT 700
701 LHNFVTRATNELIWLNEKEESEVAYDWSERNSSVARKKSYHAELMRELEQ 750
751 KEESIKAVQEIAEQLLLENHPARLTIEAYRAAMQTQWSWILQLCQCVEQH 800
801 IQENSAYFEFFNDAKEATDYLRNLKDAIQRKYSCDRSSSIHKLEDLVQES 850
851 MEKEELLQYRSVVAGLMGRAKTVVQLKPRNPDNPLKTSIPIKAICDYRQI 900
901 EITIYKDDECVLANNSHRAKWKVISPTGNEAMVPSVCFTVPPPNKEAVDF 950
951 ANRIEQQYQSVLTLWHESHINMKSVVSWHYLVNEIDRIRASNVASIKTML 1000
1001 PGEHQQVLSNLQSRLEDFLEDSQESQIFSGSDISQLEKEVSVCRKYYQEL 1050
1051 LKSAEREEQEESVYNLYISEVRNIRLRLESCEDRLIRQIRTPLERDDLHE 1100
1101 SMLRITEQEKLKKELDRLKDDLGTITNKCEEFFSQAADSPSVPALRSELS 1150
1151 VVIQSLSQIYSMSSTYIEKLKTVNLVLKNTQAAEALVKLYETKLCEEEAV 1200
1201 IADKNNIENLMSTLKQWRSEVDEKREVFHALEDELQKAKAISDEMFKTHK 1250
1251 ERDLDFDWHKEKADQLVERWQSVHVQIDNRLRDLEGIGKSLKHYRDSYHP 1300
1301 LDDWIQHIETTQRKIQENQPENSKALALQLNQQKMLVSEIEVKQSKMDEC 1350
1351 QKYSEQYSAAVKDYELQTMTYRAMVESQQKSPVKRRRIQSSADLVIQEFM 1400
1401 DLRTRYTALVTLMTQYIKFAGDSLKRLEEEEKSLDEEKKQHIEKAKELQK 1450
1451 WVSNISKTLGDGEKAGKPLFSKQQMSSKEISTKKEQFSEALQTTQIFLAK 1500
1501 HGDKLTEEERSDLEKQVKTLQEGYNLLFSESLKQQELQPSGESKVPEKPD 1550
1551 KVIAGTINQTTGEVLSVFQAVLRGLIDYETGIRLLEAQLVITGLISPELR 1600
1601 KCFDLRDAESHGLIDEQVLRQLKELNRAKQLISTASPTSIPVLDSLAQGM 1650
1651 VSESMAIRVLEILLSAGPLLVPATGEHLTLQQAFQQNLISSALFSKVLER 1700
1701 QDTCKDLIDPCTSEKVSLTDMVQRSILQENTRMWLLPVRPQEAGRITLKC 1750
1751 GRSVSILRAAHEGLIDRETMFRLLGAQLLSGGLIDCNSGQKMTVEEAVAE 1800
1801 GVIDRDTASSILTYQVQTGGIVHSNPAKRLTVDEAVQCELITSSSALLVL 1850
1851 EAQRGYVGLIWPHSGEIFPTSSSLQQELITNELASKILNGRQKIAALYIP 1900
1901 ESSQVIGLDAAKQLGIIDNNTASVLKSVTLPDKMPDLGDLEDCKNAKRWL 1950
1951 SFCKLQPSTVHDYRQEEGGSDGEEPVTAQSSEQTKKLFLSYLMVNSYMDA 2000
2001 HTGQRLLLYDGDLDEAVGMLLESCGTELGADTSTRESLSVLTIPDAFPDC 2050
2051 ALSEEKHECSAAAAGPDKCHYSHPGHKESLENAKWDMNEAFCKMGNNDSN 2100
2101 GELPRPENLADTTVVQKGSESPSRVRVPKPTSSSTQPEGSVLRPESGSIL 2150
2151 KGCKSQSEPVTKKYPDGANHSHFLTSETSRPCDSNEREDEENIQKGPSVF 2200
2201 DYSPRLSALLSHDELRQSQGRFSDTSTPQNTGYLCEASTLSPSDQRVLAD 2250
2251 QSTREKFQDQFLGIAAISVSLQGAPCGQKPVDTECSSSQVHYHSEESMSD 2300
2301 ASAESGATRQTDESEKTGSKVEDNSCTMVPGGGSRNDNTSDCGPLSHKGA 2350
2351 IDAGDYETSLLAGQQSDTATDSDSDDYFYDTPLFEDEDHDSLILQGDDRD 2400
2401 CLQPEDYDTSLQEENDRTPPPDDIFYDVMKEKENPEFPHGGMDESLGVEN 2450
2451 KVCCPQGFPVGIEKPELYLAGEKEFNSGGSEQLVESVSESENPPGLWDSE 2500
2501 SDSLTEGEIIGRKERLGASLTPDGHWRGDREECDTSRESQSDTDGVGSIQ 2550
2551 SSESYRPYMSDGSDLDEEDNGGRSSEDSGDGRGGQGVADEGGEPQYQADP 2600
2601 TQLYTAIRKEHGGETQNVSDMIPLDKTHSYSPLETQHGAGVFQPESAGRG 2650
2651 GWDTERSSHPELTTEADEEDEASLSTHMATKGVSLSNAEGTASEEIRLVQ 2700
2701 GPDSTGILKAEDLENVSPEISPSSDNIVRSEAELGGGASEDGHLSFTGSD 2750
2751 RDQQGPGRGLVKGRDGQSDKLVDETSIREMGFQKEGVLMSSPEEGGEEER 2800
2801 DLEPFPNGSATESLNMGKSQVPPLLTHTEELSHRGAPHTTTMTTTMTLEG 2850
2851 EAKNVQTGLTESPVLLETLAEIFDTPASKVTRADLTSAVTASEMKSQVKE 2900
2901 DSLTGGPEKETGPCTSLGHCDKCIHVDMLEPNEHTPSCALVAPPTVKDNL 2950
2951 CSVNNAGEKSVRPQEDWPPAAEVRLSDACVEESISEGKAGILQFTPENSD 3000
3001 STLSRLPHQSVAGWGKSADSVQARLPVSGVRHTSADTLDVGCPQLESSRE 3050
3051 KASAEEEPHRERALSLKPQEREHHMLGFVEDGRSILKSSLDKVHMNLQEV 3100
3101 GDPSAGTGTKISIQNLIRRAILSELPNEVSNVPSHGISPISNSSEVRAES 3150
3151 GGDPFCITSFLHLLKQNQPPQETPGISELAKVLTQMDCDPEQRGLGSELL 3200
3201 PPQLKNAFYKLLFDGYATEKDQAEALGQTSCAVPKMAEEKPHVCSDLRNK 3250
3251 EGHHCPLNPQAVGEAEVEPFSVHIAALPGGEKLGELCSEPPEHSESTSGS 3300
3301 KERSSDSSSKEKCSNGLQQCLQHTEKMHEYLVLLQDMKPPLDNQASVESS 3350
3351 LEALKSQLKQLEAFELGLAPIAVFLRKDLKLAEEFLKSFPSDLPRRHHEE 3400
3401 LSKSHQRLQNAFSSLSSVSSERMKLIKLAINSEMSKLAVRHEDFLHKLTS 3450
3451 YSDWVSEKSRSVKAIQTVNVQDTELVKNSVKFLKNVLADLSHTKMQLETT 3500
3501 AFDVQSFISDYAQDLSPSQSRQLLRLLNTTQKGFLDLQELVTTEADRLEA 3550
3551 LLQLEQELGHQKVVAERQQEYREKLQGLCDLLTQTENRLISNQEAFVIGD 3600
3601 GTVELQKYQSKQEELQRDMQGSTQAMEEIVRNTELFLKESGDELSQADRA 3650
3651 LIEQKLNEVKMKCAQLNLKAEQSRKELDKAVTTALKEETEKVAAVRQLEE 3700
3701 SKTKIENLLNWLSNVEEDSEGVWTKHTQPMEQNGTYLHEGDSKLGAGEED 3750
3751 EVNGNLLETDAEGHSEATKGNLNQQYEKVKAQHGKIMAQHQAVLLATQSA 3800
3801 QVLLEKQGHYLSPEEKEKLQKNTQELKVHYEKVLAECEKKVKLTHSLQEE 3850
3851 LEKFDTDYSEFEHWLQQSEQELANLEAGADDLSGLMDKLTRQKSFSEDVI 3900
3901 SHKGDLRYITISGNRVIDAAKSCSKRDSDRIGKDSVETSATHREVQTKLD 3950
3951 QVTDRFRSLYSKCSVLGNNLKDLVDQYQQYEDASCGLLSGLQACEAKASK 4000
4001 HLREPIALDPKNLQRQLEETKALQGQISSQQVAVEKLKKTAEVLLDAKGS 4050
4051 LLPAKNDIQKTLDDIVGRYDDLSKCVNERNEKLQITLTRSLSVQDALDEM 4100
4101 LDWMGSVESSLVKPGQVPLNSTALQDLISKDTMLEQDITGRQSSINAMNE 4150
4151 KVKTFIETTDPSTASSLQAKMKDLSARFSEASQKHKEKLAKMVELKAKVE 4200
4201 QFEKLSDKLQTFLETQSQALTEVAMPGKDVPELSQHMQESTAKFLEHRKD 4250
4251 LEALHSLLKEISSHGLPGDKALVFEKTNNLSRKFKEMEDTIQEKKDALSS 4300
4301 CQEQLSAFQTLAQSLKTWIKETTKQVPVVKPSLGTEDLRKSLEETKKLQE 4350
4351 KWNLKAPEIHKANNSGVSLCNLLSALISPAKAIAAAKSGGVILNGEGTDT 4400
4401 NTQDFLANKGLTSIKKDMTDISHSYEDLGLLLKDKIVELNTKLSKLQKAQ 4450
4451 EESSAMMQWLEKMNKTASRWRQTPTPADTESVKLQVEQNKSFEAELKQNV 4500
4501 NKVQELKDKLSELLEENPEAPEAQSWKQALAEMDTKWQELNQLTMDRQQK 4550
4551 LEESSNNLTQFQTTEAQLKQWLMEKELMVSVLGPLSIDPNMLNTQKQQVQ 4600
4601 ILLQEFDTRKPQYEQLTAAGQGILSRPGEDPSLHGIVNEQLEAVTQKWDN 4650
4651 LTGQLRDRCDWIDQAIVKSTQYQSLLRSLSGTLTELDDKLSSGLTSGALP 4700
4701 DAVNQQLEAAQRLKQEIEQQAPKIKEAQEVCEDLSALVKEEYLKAELSRQ 4750
4751 LEGILKSFKDIEQKTENHVQHLQSACASSHQFQQMSKDFQAWLDAKKEEQ 4800
4801 RDSPPISAKLDVLESLLNSQKDFGKTFTEQSNIYEKTISEGENLLLKTQG 4850
4851 AEKAALQLQLNTMKTDWDRFRKQVKEREEKLKDSLEKALKYREQVETLRP 4900
4901 WIDRCQHSLDGVTFSLDPTESESSIAELKSLQKEMDHHFGMLELLNNTAN 4950
4951 SLLSVCEVDKEAVTEENQSLMEKVNRVTEQLQSKTVSLENMAQKFKEFQE 5000
5001 VSRDTQRQLQDTKEQLEVHHSLGPQAYSNKHLSVLQAQQKSLQTLKQQVD 5050
5051 EAKRLAQDLVVEAADSKGTSDVLLQAETLAEEHSELSQQVDEKCSFLETK 5100
5101 LQGLGHFQNTIREMFSQFTECDDELDGMAPVGRDAETLRKQKACMQTFLK 5150
5151 KLEALMASNDSANRTCKMMLATEETSPDLIGVKRDLEALSKQCNKLLDRA 5200
5201 KTREEQVDGATEKLEEFHRKLEEFSTLLQKAEEHEESQGPVGTETETINQ 5250
5251 QLDVFKVFQKEEIEPLQVKQQDVNWLGQGLIQSAAANTCTQGLEHDLDSV 5300
5301 NSRWKTLNKKVAQRTSQLQEALLHCGRFQDALESLLSWMADTEELVANQK 5350
5351 PPSAEFKVVKAQIQEQKLLQRLLEDRKSTVEVIKREGEKIAASAEPADRV 5400
5401 KLTRQLSLLDSRWEALLSRAEARNRQLEGISVVAQEFHETLEPLNEWLTA 5450
5451 VEKKLANSEPIGTQAPKLEEQISQHKALQEDILLRKQSVDQALLNGLELL 5500
5501 KQTTGDEVLIIQDKLEAIKARYKDITKLSADVAKTLEHALQLAGQLQSMH 5550
5551 KELCNWLDKVEVELLSYETQGLKGEAASQVQERQKELKNEVRSNKALVDS 5600
5601 LNEVSSALLELVPWRAREGLEKTIAEDNERYRLVSDTITQKVEEIDAAIL 5650
5651 RSQQFEQAADAELSWITETQKKLMSLGDIRLEQDQTSAQLQVQKAFTMDI 5700
5701 LRHKDIIDELVTSGHKIMTTSSEEEKQSMKKKLDKVLKKYDAVCQINSER 5750
5751 HLQLERAQSLVSQFWETYEELWPWLTETQRIISQLPAPALEYETLRRQQE 5800
5801 EHRQLRELIAEHKPHIDKMNKTGPQLLELSPKEGIYIQEKYVAADTLYSQ 5850
5851 IKEDVKKRAVVLDEAISQSTQFHDKIDQILESLERIAERLRQPPSISAEV 5900
5901 EKIKEQIGENKSVSVDMEKLQPLYETLRQRGEEMIARSEGTEKDVSARAV 5950
5951 QDKLDQMVFIWGSIHTLVEEREAKLLDVMELAEKFWCDHMSLVVTIKDTQ 6000
6001 DFIRDLEDPGIDPSVVKQQQEAAEAIREEIDGLQEELDMVITLGSELIAA 6050
6051 CGEPDKPIVKKSIDELNSAWDSLNKAWKDRVDRLEEAMQAAVQYQDGLQG 6100
6101 IFDWVDIAGNKLATMSPIGTDLETVKQQIEELKQFKSEAYQQQIEMERLN 6150
6151 HQAELLLKKVTEEADKHTVQDPLMELKLIWDSLDERIVSRQHKLEGALLA 6200
6201 LGQFQHALDELLAWLTHTKGLLSEQKPVGGDPKAIEIELAKHHVLQNDVL 6250
6251 AHQSTVEAVNKAGNDLIESSEGEEASNLQYKLRILNQRWQDILEKTDQRK 6300
6301 QQLDSALRQAKGFHGEIEDLQQWLTDTERHLLASKPLGGLPETAKEQLNA 6350
6351 HMEVCTAFAIKEETYKSLMLRGQQMLARCPRSAETNIDQDITNLKEKWES 6400
6401 VKSKLNEKKTKLEEALHLAMNFHNSLQDFINWLTQAEQTLNVASRPSLIL 6450
6451 DTILFQIDEHKVFANEVNSHREQIIELDKTGTHLKYFSQKQDVVLIKNLL 6500
6501 ISVQSRWEKVVQRLVERGRSLDEARKRAKQFHEAWSKLMEWLEESEKSLD 6550
6551 SELEIANDPDKIKAQLVQHKEFQKSLGGKHSVYDTTNRTGRSLKEKTSLA 6600
6601 DDNLKLDNMLSELRDKWDTICGKSVERQNKLEEALLFSGQFTDALQALID 6650
6651 WLYRVEPQLAEDQPVHGDIDLVMNLIDNHKVFQKELGKRTSSVQALKRSA 6700
6701 RELIEGSRDDSSWVRVQMQELSTRWETVCALSISKQTRLESALQQAEEFH 6750
6751 SVVHTLLEWLAEAEQTLRFHGALPDDEDALRTLIEQHKEFMKRLEEKRAE 6800
6801 LSKATGMGDALLAVCHPDSITTIKHWITIIQARFEEVLAWAKQHQQRLAG 6850
6851 ALAGLIAKQELLETLLAWLQWAETTLTEKDKEVIPQEIEEVKTLIAEHQT 6900
6901 FMEEMTRKQPDVDKVTKTYKRRATDPPSLQSHIPVLDKGRAGRKRFPASG 6950
6951 FYPSGSQTQIETKNPRVNLLVSKWQQVWLLALERRRKLNDALDRLEELRE 7000
7001 FANFDFDIWRKKYMRWMNHKKSRVMDFFRRIDKDQDGKITRQEFIDGILS 7050
7051 SKFPTSRLEMSAVADIFDRDGDGYIDYYEFVAALHPNKDAYKPITDADKI 7100
7101 EDEVTRQVAKCKCAKRFQVEQIGDNKYRFFLGNQFGDSQQLRLVRILRST 7150
7151 VMVRVGGGWMALDEFLVKNDPCRVHHHGSKMLRSESNSSITATQPTLAKG 7200
7201 RTNMELREKFILADGASQGMAAFRPRGRRSRPSSRGASPNRSTSASSHAC 7250
7251 QAASPPVPAAASTPKGTPIQGSKLRLPGYLSGKGFHSGEDSALITTAAAR 7300
7301 VRTQFAESRKTPSRPGSRAGSKAGSRASSRRGSDASDFDISEIQSVCSDV 7350
7351 ETVPQTHRPVPRAGSRPSTAKPSKIPTPQRRSPASKLDKSSKR 7393
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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