 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Y6V0 from www.uniprot.org...
The NucPred score for your sequence is 0.99 (see score help below)
1 MGNEASLEGEGLPEGLAAAAAAGGGASGAGSPSHTAIPAGMEADLSQLSE 50
51 EERRQIAAVMSRAQGLPKGSVPPAAAESPSMHRKQELDSSHPPKQSGRPP 100
101 DPGRPAQPGLSKSRTTDTFRSEQKLPGRSPSTISLKESKSRTDLKEEHKS 150
151 SMMPGFLSEVNALSAVSSVVNKFNPFDLISDSEASQEETTKKQKVVQKEQ 200
201 GKPEGIIKPPLQQQPPKPIPKQQGPGRDPLQQDGTPKSISSQQPEKIKSQ 250
251 PPGTGKPIQGPTQTPQTDHAKLPLQRDASRPQTKQADIVRGESVKPSLPS 300
301 PSKPPIQQPTPGKPPAQQPGHEKSQPGPAKPPAQPSGLTKPLAQQPGTVK 350
351 PPVQPPGTTKPPAQPLGPAKPPAQQTGSEKPSSEQPGPKALAQPPGVGKT 400
401 PAQQPGPAKPPTQQVGTPKPLAQQPGLQSPAKAPGPTKTPVQQPGPGKIP 450
451 AQQAGPGKTSAQQTGPTKPPSQLPGPAKPPPQQPGPAKPPPQQPGSAKPP 500
501 SQQPGSTKPPPQQPGPAKPSPQQPGSTKPPSQQPGSAKPSAQQPSPAKPS 550
551 AQQSTKPVSQTGSGKPLQPPTVSPSAKQPPSQGLPKTICPLCNTTELLLH 600
601 VPEKANFNTCTECQTTVCSLCGFNPNPHLTEVKEWLCLNCQMKRALGGDL 650
651 APVPSSPQPKLKTAPVTTTSAVSKSSPQPQQTSPKKDAAPKQDLSKAPEP 700
701 KKPPPLVKQPTLHGSPSAKAKQPPEADSLSKPAPPKEPSVPSEQDKAPVA 750
751 DDKPKQPKMVKPTTDLVSSSSATTKPDIPSSKVQSQAEEKTTPPLKTDSA 800
801 KPSQSFPPTGEKVSPFDSKAIPRPASDSKIISHPGPSSESKGQKQVDPVQ 850
851 KKEEPKKAQTKMSPKPDAKPMPKGSPTPPGPRPTAGQTVPTPQQSPKPQE 900
901 QSRRFSLNLGSITDAPKSQPTTPQETVTGKLFGFGASIFSQASNLISTAG 950
951 QPGPHSQSGPGAPMKQAPAPSQPPTSQGPPKSTGQAPPAPAKSIPVKKET 1000
1001 KAPAAEKLEPKAEQAPTVKRTETEKKPPPIKDSKSLTAEPQKAVLPTKLE 1050
1051 KSPKPESTCPLCKTELNIGSKDPPNFNTCTECKNQVCNLCGFNPTPHLTE 1100
1101 IQEWLCLNCQTQRAISGQLGDIRKMPPAPSGPKASPMPVPTESSSQKTAV 1150
1151 PPQVKLVKKQEQEVKTEAEKVILEKVKETLSMEKIPPMVTTDQKQEESKL 1200
1201 EKDKASALQEKKPLPEEKKLIPEEEKIRSEEKKPLLEEKKPTPEDKKLLP 1250
1251 EAKTSAPEEQKHDLLKSQVQIAEEKLEGRVAPKTVQEGKQPQTKMEGLPS 1300
1301 GTPQSLPKEDDKTTKTIKEQPQPPCTAKPDQVEPGKEKTEKEDDKSDTSS 1350
1351 SQQPKSPQGLSDTGYSSDGISSSLGEIPSLIPTDEKDILKGLKKDSFSQE 1400
1401 SSPSSPSDLAKLESTVLSILEAQASTLADEKSEKKTQPHEVSPEQPKDQE 1450
1451 KTQSLSETLEITISEEEIKESQEERKDTFKKDSQQDIPSSKDHKEKSEFV 1500
1501 DDITTRREPYDSVEESSESENSPVPQRKRRTSVGSSSSDEYKQEDSQGSG 1550
1551 EEEDFIRKQIIEMSADEDASGSEDDEFIRNQLKEISSSTESQKKEETKGK 1600
1601 GKITAGKHRRLTRKSSTSIDEDAGRRHSWHDEDDEAFDESPELKYRETKS 1650
1651 QESEELVVTGGGGLRRFKTIELNSTIADKYSAESSQKKTSLYFDEEPELE 1700
1701 MESLTDSPEDRSRGEGSSSLHASSFTPGTSPTSVSSLDEDSDSSPSHKKG 1750
1751 ESKQQRKARHRPHGPLLPTIEDSSEEEELREEEELLKEQEKQREIEQQQR 1800
1801 KSSSKKSKKDKDELRAQRRRERPKTPPSNLSPIEDASPTEELRQAAEMEE 1850
1851 LHRSSCSEYSPSIESDPEGFEISPEKIIEVQKVYKLPTAVSLYSPTDEQS 1900
1901 IMQKEGSQKALKSAEEMYEEMMHKTHKYKAFPAANERDEVFEKEPLYGGM 1950
1951 LIEDYIYESLVEDTYNGSVDGSLLTRQEEENGFMQQKGREQKIRLSEQIY 2000
2001 EDPMQKITDLQKEFYELESLHSVVPQEDIVSSSFIIPESHEIVDLGTMVT 2050
2051 STEEERKLLDADAAYEELMKRQQMQLTPGSSPTQAPIGEDMTESTMDFDR 2100
2101 MPDASLTSSVLSGASLTDSTSSATLSIPDVKITQHFSTEEIEDEYVTDYT 2150
2151 REIQEIIAHESLILTYSEPSESATSVPPSDTPSLTSSVSSVCTTDSSSPI 2200
2201 TTLDSITTVYTEPVDMITKFEDSEEISSSTYFPGSIIDYPEEISVSLDRT 2250
2251 APPDGRASADHIVISLSDMASSIIESVVPKPEGPVADTVSTDLLISEKDP 2300
2301 VKKAKKETGNGIILEVLEAYRDKKELEAERTKSSLSETVFDHPPSSVIAL 2350
2351 PMKEQLSTTYFTSGETFGQEKPASQLPSGSPSVSSLPAKPRPFFRSSSLD 2400
2401 ISAQPPPPPPPPPPPPPPPPPPPPPPLPPPTSPKPTILPKKKLTVASPVT 2450
2451 TATPLFDAVTTLETTAVLRSNGLPVTRICTTAPPPVPPKPSSIPSGLVFT 2500
2501 HRPEPSKPPIAPKPVIPQLPTTTQKPTDIHPKPTGLSLTSSMTLNLVTSA 2550
2551 DYKLPSPTSPLSPHSNKSSPRFSKSLTETYVVITLPSEPGTPTDSSASQA 2600
2601 ITSWPLGSPSKDLVSVEPVFSVVPPVTAVEIPISSEQTFYISGALQTFSA 2650
2651 TPVTAPSSFQAAPTSVTQFLTTEVSKTEVSATRSTAPSVGLSSISITIPP 2700
2701 EPLALDNIHLEKPQYKEDGKLQLVGDVIDLRTVPKVEVKTTDKCIDLSAS 2750
2751 TMDVKRQITANEVYGKQISAVQPSIINLSVTSSIVTPVSLATETVTFVTC 2800
2801 TASASYTTGTESLVGAEHAMTTPLQLTTSKHAEPPYRIPSDQVFPIAREE 2850
2851 APINLSLGTPAHAVTLAITKPVTVPPVGVTNGWTDSTVSQGITDGEVVDL 2900
2901 STTKSHRTVVTMDESTSSVMTKIIEDEKPVDLTAGRRAVCCDVVYKLPFG 2950
2951 RSCTAQQPATTLPEDRFGYRDDHYQYDRSGPYGYRGIGGMKPSMSDTNLA 3000
3001 EAGHFFYKSKNAFDYSEGTDTAVDLTSGRVTTGEVMDYSSKTTGPYPETR 3050
3051 QVISGAGISTPQYSTARMTPPPGPQYCVGSVLRSSNGVVYSSVATPTPST 3100
3101 FAITTQPGSIFSTTVRDLSGIHTADAVTSLPAMHHSQPMPRSYFITTGAS 3150
3151 ETDIAVTGIDISASLQTITMESLTAETIDSVPTLTTASEVFPEVVGDESA 3200
3201 LLIVPEEDKQQQQLDLERELLELEKIKQQRFAEELEWERQEIQRFREQEK 3250
3251 IMVQKKLEELQSMKQHLLFQQEEERQAQFMMRQETLAQQQLQLEQIQQLQ 3300
3301 QQLHQQLEEQKIRQIYQYNYDPSGTASPQTTTEQAILEGQYAALEGSQFW 3350
3351 ATEDATTTASAVVAIEIPQSQGWYTVQSDGVTQYIAPPGILSTVSEIPLT 3400
3401 DVVVKEEKQPKKRSSGAKVRGQYDDMGENMTDDPRSFKKIVDSGVQTDDE 3450
3451 DATDRSYVSRRRRTKKSVDTSVQTDDEDQDEWDMPTRSRRKARVGKYGDS 3500
3501 MTEADKTKPLSKVSSIAVQTVAEISVQTEPVGTIRTPSIRARVDAKVEII 3550
3551 KHISAPEKTYKGGSLGCQTEADSDTQSPQYLSATSPPKDKKRPTPLEIGY 3600
3601 SSHLRADSTVQLAPSPPKSPKVLYSPISPLSPGKALESAFVPYEKPLPDD 3650
3651 ISPQKVLHPDMAKVPPASPKTAKMMQRSMSDPKPLSPTADESSRAPFQYT 3700
3701 EGYTTKGSQTMTSSGAQKKVKRTLPNPPPEEISTGTQSTFSTMGTVSRRR 3750
3751 ICRTNTMARAKILQDIDRELDLVERESAKLRKKQAELDEEEKEIDAKLRY 3800
3801 LEMGINRRKEALLKEREKRERAYLQGVAEDRDYMSDSEVSSTRPTRIESQ 3850
3851 HGIERPRTAPQTEFSQFIPPQTQTESQLVPPTSPYTQYQYSSPALPTQAP 3900
3901 TSYTQQSHFEQQTLYHQQVSPYQTQPTFQAVATMSFTPQVQPTPTPQPSY 3950
3951 QLPSQMMVIQQKPRQTTLYLEPKITSNYEVIRNQPLMIAPVSTDNTFAVS 4000
4001 HLGSKYNSLDLRIGLEERSSMASSPISSISADSFYADIDHHTPRNYVLID 4050
4051 DIGEITKGTAALSTAFSLHEKDLSKTDRLLRTTETRRSQEVTDFLAPLQS 4100
4101 SSRLHSYVKAEEDPMEDPYELKLLKHQIKQEFRRGTESLDHLAGLSHYYH 4150
4151 ADTSYRHFPKSEKYSISRLTLEKQAAKQLPAAILYQKQSKHKKSLIDPKM 4200
4201 SKFSPIQESRDLEPDYSSYMTSSTSSIGGISSRARLLQDDITFGLRKNIT 4250
4251 DQQKFMGSSLGTGLGTLGNTIRSALQDEADKPYSSGSRSRPSSRPSSVYG 4300
4301 LDLSIKRDSSSSSLRLKAQEAEALDVSFSHASSSARTKPTSLPISQSRGR 4350
4351 IPIVAQNSEEESPLSPVGQPMGMARAAAGPLPPISADTRDQFGSSHSLPE 4400
4401 VQQHMREESRTRGYDRDIAFIMDDFQHAMSDSEAYHLRREETDWFDKPRE 4450
4451 SRLENGHGLDRKLPERLVHSRPLSQHQEQIIQMNGKTMHYIFPHARIKIT 4500
4501 RDSKDHTVSGNGLGIRIVGGKEIPGHSGEIGAYIAKILPGGSAEQTGKLM 4550
4551 EGMQVLEWNGIPLTSKTYEEVQSIISQQSGEAEICVRLDLNMLSDSENSQ 4600
4601 HLELHEPPKAVDKAKSPGVDPKQLAAELQKVSLQQSPLVLSSVVEKGSHV 4650
4651 HSGPTSAGSSSVPSPGQPGSPSVSKKKHGSSKPTDGTKVVSHPITGEIQL 4700
4701 QINYDLGNLIIHILQARNLVPRDNNGYSDPFVKVYLLPGRGQVMVVQNAS 4750
4751 AEYKRRTKHVQKSLNPEWNQTVIYKSISMEQLKKKTLEVTVWDYDRFSSN 4800
4801 DFLGEVLIDLSSTSHLDNTPRWYPLKEQTESIDHGKSHSSQSSQQSPKPS 4850
4851 VIKSRSHGIFPDPSKDMQVPTIEKSHSSPGSSKSSSEGHLRSHGPSRSQS 4900
4901 KTSVTQTHLEDAGAAIAAAEAAVQQLRIQPTKPPNHRPAESSVSTGSSGS 4950
4951 SFGSGYSVDSEGSSSTAGETNLFPIPRIGKMGQNGQEPVKQPGVGVGLAD 5000
5001 TEAKTQVMGEIKIALKKEMKTDGEQLIVEILQCRNITYKFKSPDHLPDLY 5050
5051 VKIYVMNISTQKKVIKKKTRVCRHDREPSFNETFRFSLSPAGHSLQILLF 5100
5101 SNGGKFMKKTLIGEACIWLDKVDLRKRIVNWHKLLVSPTQTH 5142
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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