 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q96T58 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MVRETRHLWVGNLPENVREEKIIEHFKRYGRVESVKILPKRGSEGGVAAF 50
51 VDFVDIKSAQKAHNSVNKMGDRDLRTDYNEPGTIPSAARGLDDTVSIASR 100
101 SREVSGFRGGGGGPAYGPPPSLHAREGRYERRLDGASDNRERAYEHSAYG 150
151 HHERGTGGFDRTRHYDQDYYRDPRERTLQHGLYYASRSRSPNRFDAHDPR 200
201 YEPRAREQFTLPSVVHRDIYRDDITREVRGRRPERNYQHSRSRSPHSSQS 250
251 RNQSPQRLASQASRPTRSPSGSGSRSRSSSSDSISSSSSTSSDSSDSSSS 300
301 SSDDSPARSVQSAAVPAPTSQLLSSLEKDEPRKSFGIKVQNLPVRSTDTS 350
351 LKDGLFHEFKKFGKVTSVQIHGTSEERYGLVFFRQQEDQEKALTASKGKL 400
401 FFGMQIEVTAWIGPETESENEFRPLDERIDEFHPKATRTLFIGNLEKTTT 450
451 YHDLRNIFQRFGEIVDIDIKKVNGVPQYAFLQYCDIASVCKAIKKMDGEY 500
501 LGNNRLKLGFGKSMPTNCVWLDGLSSNVSDQYLTRHFCRYGPVVKVVFDR 550
551 LKGMALVLYNEIEYAQAAVKETKGRKIGGNKIKVDFANRESQLAFYHCME 600
601 KSGQDIRDFYEMLAERREERRASYDYNQDRTYYESVRTPGTYPEDSRRDY 650
651 PARGREFYSEWETYQGDYYESRYYDDPREYRDYRNDPYEQDIREYSYRQR 700
701 ERERERERFESDRDRDHERRPIERSQSPVHLRRPQSPGASPSQAERLPSD 750
751 SERRLYSRSSDRSGSCSSLSPPRYEKLDKSRLERYTKNEKTDKERTFDPE 800
801 RVERERRLIRKEKVEKDKTDKQKRKGKVHSPSSQSSETDQENEREQSPEK 850
851 PRSCNKLSREKADKEGIAKNRLELMPCVVLTRVKEKEGKVIDHTPVEKLK 900
901 AKLDNDTVKSSALDQKLQVSQTEPAKSDLSKLESVRMKVPKEKGLSSHVE 950
951 VVEKEGRLKARKHLKPEQPADGVSAVDLEKLEARKRRFADSNLKAEKQKP 1000
1001 EVKKSSPEMEDARVLSKKQPDVSSREVILLREGEAERKPVRKEILKRESK 1050
1051 KIKLDRLNTVASPKDCQELASISVGSGSRPSSDLQARLGELAGESVENQE 1100
1101 VQSKKPIPSKPQLKQLQVLDDQGPEREDVRKNYCSLRDETPERKSGQEKS 1150
1151 HSVNTEEKIGIDIDHTQSYRKQMEQSRRKQQMEMEIAKSEKFGSPKKDVD 1200
1201 EYERRSLVHEVGKPPQDVTDDSPPSKKKRMDHVDFDICTKRERNYRSSRQ 1250
1251 ISEDSERTGGSPSVRHGSFHEDEDPIGSPRLLSVKGSPKVDEKVLPYSNI 1300
1301 TVREESLKFNPYDSSRREQMADMAKIKLSVLNSEDELNRWDSQMKQDAGR 1350
1351 FDVSFPNSIIKRDSLRKRSVRDLEPGEVPSDSDEDGEHKSHSPRASALYE 1400
1401 SSRLSFLLRDREDKLRERDERLSSSLERNKFYSFALDKTITPDTKALLER 1450
1451 AKSLSSSREENWSFLDWDSRFANFRNNKDKEKVDSAPRPIPSWYMKKKKI 1500
1501 RTDSEGKMDDKKEDHKEEEQERQELFASRFLHSSIFEQDSKRLQHLERKE 1550
1551 EDSDFISGRIYGKQTSEGANSTTDSIQEPVVLFHSRFMELTRMQQKEKEK 1600
1601 DQKPKEVEKQEDTENHPKTPESAPENKDSELKTPPSVGPPSVTVVTLESA 1650
1651 PSALEKTTGDKTVEAPLVTEEKTVEPATVSEEAKPASEPAPAPVEQLEQV 1700
1701 DLPPGADPDKEAAMMPAGVEEGSSGDQPPYLDAKPPTPGASFSQAESNVD 1750
1751 PEPDSTQPLSKPAQKSEEANEPKAEKPDATADAEPDANQKAEAAPESQPP 1800
1801 ASEDLEVDPPVAAKDKKPNKSKRSKTPVQAAAVSIVEKPVTRKSERIDRE 1850
1851 KLKRSNSPRGEAQKLLELKMEAEKITRTASKNSAADLEHPEPSLPLSRTR 1900
1901 RRNVRSVYATMGDHENRSPVKEPVEQPRVTRKRLERELQEAAAVPTTPRR 1950
1951 GRPPKTRRRADEEEENEAKEPAETLKPPEGWRSPRSQKTAAGGGPQGKKG 2000
2001 KNEPKVDATRPEATTEVGPQIGVKESSMEPKAAEEEAGSEQKRDRKDAGT 2050
2051 DKNPPETAPVEVVEKKPAPEKNSKSKRGRSRNSRLAVDKSASLKNVDAAV 2100
2101 SPRGAAAQAGERESGVVAVSPEKSESPQKEDGLSSQLKSDPVDPDKEPEK 2150
2151 EDVSASGPSPEATQLAKQMELEQAVEHIAKLAEASASAAYKADAPEGLAP 2200
2201 EDRDKPAHQASETELAAAIGSIINDISGEPENFPAPPPYPGESQTDLQPP 2250
2251 AGAQALQPSEEGMETDEAVSGILETEAATESSRPPVNAPDPSAGPTDTKE 2300
2301 ARGNSSETSHSVPEAKGSKEVEVTLVRKDKGRQKTTRSRRKRNTNKKVVA 2350
2351 PVESHVPESNQAQGESPAANEGTTVQHPEAPQEEKQSEKPHSTPPQSCTS 2400
2401 DLSKIPSTENSSQEISVEERTPTKASVPPDLPPPPQPAPVDEEPQARFRV 2450
2451 HSIIESDPVTPPSDPSIPIPTLPSVTAAKLSPPVASGGIPHQSPPTKVTE 2500
2501 WITRQEEPRAQSTPSPALPPDTKASDVDTSSSTLRKILMDPKYVSATSVT 2550
2551 STSVTTAIAEPVSAAPCLHEAPPPPVDSKKPLEEKTAPPVTNNSEIQASE 2600
2601 VLVAADKEKVAPVIAPKITSVISRMPVSIDLENSQKITLAKPAPQTLTGL 2650
2651 VSALTGLVNVSLVPVNALKGPVKGSVTTLKSLVSTPAGPVNVLKGPVNVL 2700
2701 TGPVNVLTTPVNATVGTVNAAPGTVNAAASAVNATASAVTVTAGAVTAAS 2750
2751 GGVTATTGTVTMAGAVIAPSTKCKQRASANENSRFHPGSMPVIDDRPADA 2800
2801 GSGAGLRVNTSEGVVLLSYSGQKTEGPQRISAKISQIPPASAMDIEFQQS 2850
2851 VSKSQVKPDSVTASQPPSKGPQAPAGYANVATHSTLVLTAQTYNASPVIS 2900
2901 SVKADRPSLEKPEPIHLSVSTPVTQGGTVKVLTQGINTPPVLVHNQLVLT 2950
2951 PSIVTTNKKLADPVTLKIETKVLQPANLGSTLTPHHPPALPSKLPTEVNH 3000
3001 VPSGPSIPADRTVSHLAAAKLDAHSPRPSGPGPSSFPRASHPSSTASTAL 3050
3051 STNATVMLAAGIPVPQFISSIHPEQSVIMPPHSITQTVSLSHLSQGEVRM 3100
3101 NTPTLPSITYSIRPEALHSPRAPLQPQQIEVRAPQRASTPQPAPAGVPAL 3150
3151 ASQHPPEEEVHYHLPVARATAPVQSEVLVMQSEYRLHPYTVPRDVRIMVH 3200
3201 PHVTAVSEQPRAADGVVKVPPASKAPQQPGKEAAKTPDAKAAPTPTPAPV 3250
3251 PVPVPLPAPAPAPHGEARILTVTPSNQLQGLPLTPPVVVTHGVQIVHSSG 3300
3301 ELFQEYRYGDIRTYHPPAQLTHTQFPAASSVGLPSRTKTAAQGPPPEGEP 3350
3351 LQPPQPVQSTQPAQPAPPCPPSQLGQPGQPPSSKMPQVSQEAKGTQTGVE 3400
3401 QPRLPAGPANRPPEPHTQVQRAQAETGPTSFPSPVSVSMKPDLPVSLPTQ 3450
3451 TAPKQPLFVPTTSGPSTPPGLVLPHTEFQPAPKQDSSPHLTSQRPVDMVQ 3500
3501 LLKKYPIVWQGLLALKNDTAAVQLHFVSGNNVLAHRSLPLSEGGPPLRIA 3550
3551 QRMRLEATQLEGVARRMTVETDYCLLLALPCGRDQEDVVSQTESLKAAFI 3600
3601 TYLQAKQAAGIINVPNPGSNQPAYVLQIFPPCEFSESHLSRLAPDLLASI 3650
3651 SNISPHLMIVIASV 3664
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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