 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9MBF8 from www.uniprot.org...
The NucPred score for your sequence is 0.64 (see score help below)
1 MEPGDEGKGHQLTADATCIAWVRSKLQLLKPESLGDSDGAEWLSSVWHND 50
51 VHTPVVSTFLMSVKATRMFAALDGGHEGGSSPKLVLALEVPKQFEQMVYF 100
101 VRDPSKFVTRENVGSVIFFGVMRGGDPLHSLLNIMHGLYVPVVVANTTWP 150
151 ETVKSDFTAQMHKFMANLTETVYEVKGKTILYIPQEDLRDPKAAAKQKDL 200
201 VQRLESTIIHWTRQVKELLNQQDSVDASEQAGPLAEIEFWRERSVDLSGI 250
251 RAQLDDGAVSSIVSVLEYAKSSYLAPFLSLRNLIHREAVAAEDNLKFLLC 300
301 LEEPCQQLASAHPQTIPSLLPPILNCIRMVWNLSRFYNTPERLSVLLRKL 350
351 SNEIINRCCSVISLPDVWSGDVDNVMVALRQSMEAGERWKELYKRTAAAV 400
401 AVRSPKPWDFDISSIFAHIDAFLQRCNDLLEVCEAQLQFAPRTPLPVFGG 450
451 TYGPEVKKSILDIQESFQGLVQGLQALKYDILDVKATRWHDDFNGFKGGV 500
501 KDLEVMMANVIQRAFDTQPCLAARGELLEGFQTMAKRDYIRRFVEKKTVE 550
551 FFALFNAEINTVKKLFDAVKRSQPKSPILPRYAGLAKYAMNLMRRLEQSH 600
601 KVVDSVRYTLPQVSEAADVMQQYELAHQAIEQYISNTHNDWFSTIESSIA 650
651 KELQACLLTQDKASGGLLSMNFHKDLLSMGQEVHFWERMRLAVPLVAMEI 700
701 NAQREKYRVLRDNILMVVRDYNKILTALDKEERKLFHDRIRYLDRRIMPG 750
751 VTKLQWTADKHALEFYYREARKFCRDADMAVGDYKTANSRLDAICRSISE 800
801 LVLVDVEKKKIYQHAEFANLQESHHAKIKDRLVSAVDEIRDIMASIHRVF 850
851 EQDSEEVQREWVRFTQKVDRKLEDALRHVIKKSLQELSRLLNGDNKTEVM 900
901 PIFHVTMVLERTNRVELRPTIQALFDTINSVARNLILVLQSVPRVALQLT 950
951 DKQRRDMEDAGLPLPKPLPTLYETISADEDAVLRTIMQITSGITSIIDKV 1000
1001 QAFLTYWEKKYRQVWEADRDAYIRRYEKAQKPLSSFEADISRYLQCIDEI 1050
1051 RGEDGATNMRFLRIDCGPLKLTLVGHCEAWVSKFTGLLGQLAATELRTLH 1100
1101 TYFRENKDSLMLAPSTLEQLAELVGLHRRLADERRRTEARFEPLRDKYKL 1150
1151 LERYEVGAKEEEAALLEGLEPAWTQFQALLDETAGKLERYKDNFREKVKS 1200
1201 LLDTFLKDVAQLCEDFSRDAPYSSEVPTPDALDFIQASKQADEDTRKRAA 1250
1251 EIKNGMDIFNIPQPQYKDLAAMEKDLDFLDRIWGLKDEWEQLYYGWKDGS 1300
1301 FTDIKVEEMEEAAVRIGKNVAKLGRDIRQWTVWSSLKDTLDAFKRTMPLI 1350
1351 TDLRNPAMRPRHWQNLQDHIGVRFDPHSRDFTLDSLVALRLDQHVEFVAE 1400
1401 LSVNATKELAIENNIKAIAATWSALGLDMAEYKSTFKLRSTEEIFTSLEE 1450
1451 NIVTLSTMKASKYFIVFEKDIAYWEKTLSHISETIEIILQVQRNWMYLEN 1500
1501 IFIGSEDIRKQLPQESQMFDAVHNNFMRLMKQLYSTANCLKACTAQGLLE 1550
1551 SFQDMNNKLERIQKSLDNYLENKRQQFPRFYFLSSDDLLEILGQAKDPLN 1600
1601 VQPHLKKCFEGIKKLDMHLPGEDRKQTISVGITSPDGEYLPFANPVITEG 1650
1651 RPEEWLNRVEDAMFLTTKKHLYKVLEESKAQKKEKWVKENQGQMIITAGQ 1700
1701 IVWTHECEKALADADSARKNLKLLKKKWISYLNKLTAVTRSKLNKIERNK 1750
1751 VVALITIEVHARDVIEKLGKSNCSSTNDFEWVSQLRFYWDREKNDCIVKQ 1800
1801 VLSVFYYGYEYQGNNGRLVITPLTDRCYMTLGAAMFTRRGGNPLGPAGTG 1850
1851 KTETVKDFGKALARYVIVFNCSDGVDYKMTGKMFSGLAQTGAWACLDEFN 1900
1901 RIEVEVLSVVATQIAAVMQAIKESKKRFLFLGQEIRLNPSCGIFVTMNPG 1950
1951 YAGRSELPDNLKAMLRPVSMMVPDFTLIAEIMMFSEGFSSAKVLAKKMIA 2000
2001 IMELSQQQLSKQDHYDYGLRSFVIPIARAAGSLKRLDPEGSEEVILYRTM 2050
2051 LDLIKPKLVYLDLPLFMALLSDLFPGVELPPADGGSLRRAIEAELRESNL 2100
2101 QIVPEFVTKIIQVFDCKVARHGNMIVGRTGSGKSEAWKCLQRALGRLRKE 2150
2151 EPDDDRFQKVHVHTINPLALSNDELYGCFEAATHEWQDGVLARIMRTVCK 2200
2201 DETHEQKWILFDGPVDTLWIESMNTTLDDNKLLTLLSGERIAMTPAVSLL 2250
2251 FEVEDLSQASPATVSRAGMIYLNVEDLGWRPFITSWLAAKQAAPGADAAI 2300
2301 IDQVSKLVDKYMEAALEHKRLHCRELVPTDRLSCVRAFTRLWDALAVPEN 2350
2351 GVGTMPVDESAGPPGSKAAAAAAAAAAAAAPPEETSGGTGGNLVEMWFLF 2400
2401 CLIWGIGGPLDEEGRKKFDAFMREMDTRYPSSDTVFEYFVEPKAKSWLAW 2450
2451 ETKLTGAFKPAMDQPFFKILVPTVDTVRNRFVGSALVRVSQHTLIVGNVG 2500
2501 VGKTMIVGSLLEGLPGDRMSSMTINFSAQTSSNSLQDTIEGKLEKRTKGV 2550
2551 FAPAGGKRLVCFIDDLNMPQKSKFGFIPPLELLKLWVDNGFWYDRAKCEV 2600
2601 KHIKDMQLLAAMAPPGGGRNAFSQRVQACFATLNVTAPNDNQLKRIFGTI 2650
2651 LNAKLADFDDEVKPLSEPITMATIGIYRAVSKELLPTPSKSHYLFNTRDL 2700
2701 AKIIQGMMQATKAFYNSKEEVLQLWCHECMRIIADRMWDHADKEWLVRQL 2750
2751 DEKLGTTFSTSFGTLFEAYNETVPPFVTFMRQNVDVPVYEAVRDMVALKD 2800
2801 LLTERLEDYALEPGHSAMDLVLFRDALSHVCRIHRILGQPRGNALLVGVG 2850
2851 GSGRKSLARLAAFVAELKCFTIEITKNYRQTEFREDLKGLYRQAGVANKP 2900
2901 TVFLFDETQIVYETFLEDVNNILTSGEVPNLFPKDELGSVLDELRPAAKA 2950
2951 AGAGETADALYGFLLERVRTNLHVVLCLSPVGEAFRERCRMFPGLVNCTT 3000
3001 IDWFTEWPADALFEVAQKQLMDVDLGSTEVKTAVCKVFVTAHQSVENTSA 3050
3051 KMFAALKRRNYVTPTNYLETVRGYKGLLAEKRTELGEKAAKLQGGLHKLD 3100
3101 ETSVQVAAMKKVAEEKKVVVAQAKADCEELLVEIVQDKRVADEQEKQVNA 3150
3151 EAQKIGKEAEEANIIAAQVQQELDKALPALREAEAALDVLTKKDMSELKA 3200
3201 YAKPPEKVEMTLNAVLTVLRRPPNWDEAKKRLSDANFMQSLKEFDKDKLD 3250
3251 DSLLKKIGKFTANPDFTYEKINTVSAAASGMCKWVHAMETYGYVAKDVAP 3300
3301 KRAKLKSAQDTLARKQAALALAQEQLAVVLAKVQALKDKYDTSIARKQAL 3350
3351 EEELADLEGKLERAEKLVTGLAGERVRWEASISEYNIALGCLPGDVVVAA 3400
3401 AFMSYAGPFPSEYRDELVKHTWLPQVKALNIPASEHFDFALFLANPAMVR 3450
3451 DWNIQGLPSDSFSTENGVMVTRGRRWPLMIDPQGQANKWIKNMEGRGGRL 3500
3501 KVLNLQMSDMARQIENAIQFGQPVLMQDILQEIDPILEPVLAKSFIKRGN 3550
3551 QTLIKLGDKEVDYNFDFRLYLTTKLANPLYTPEISTKVMIVNFAVKEQGL 3600
3601 EAQLLATVVKNERPDLDKQKNDLVVKVAAGKRTQAELEDTILHLLSTATG 3650
3651 SLLDNVTLINTLDQSKTTWEEVNASLAVAEETQKKIEAASQLYRPCSVRA 3700
3701 SVLYFVLNDLSTIDPMYQFSLDAYNDLFLLSIKNSPKNDNLAERIKSLND 3750
3751 FHTYAVYKYTSRGLFERHKLLLSLQMCVRILQTANQVNTEEWQFFLRGGT 3800
3801 VLDRSSQPNNPSQEWISEEAWDNITELDALPNFKGVVSSFESNLGEWEAW 3850
3851 YRKGDPEASELPAEWESKCNELQRLILVRCLRPDRVIFAATTYVSNALGR 3900
3901 KYVEPPVLDLAETLKDSTALSPLIFVLSAGVDPTDNLRKLATEKGMTSRF 3950
3951 FTVALGQGQAPTATRLIEDGLREGNWVFLANCHLMTSWLPTLDKIIEGFE 4000
4001 TKQPHENFRLWLSSNPSPSFPIAILQRGIKMTTEPPKGLRANLLRLYNSV 4050
4051 SDASYAQCKTQIKYQKLLFALTYFHSVLLERRKFRTLGFNIPYDFNDTDF 4100
4101 SVSDDLLKSYLDSYEQTPWDALKYLIAEANYGGRVTDELDRRVLASYLNK 4150
4151 FYCEDALAVPGYLLSPLSTYYVPENGPLQSFKDYILTLPAGDRPEAFGQH 4200
4201 PNAEISYLIEDSKVLLDSLLSLQPRTEGAAGGAGTRREDVVMAIATDLLD 4250
4251 QVPQPFNLEEVMKAKADDPSALHVVLFQEVERYNALLVAVRRSCVELQRG 4300
4301 IKGLVVMSADLDLIFESLYAAKVPAAWLKTYPSLKPLGPWTRDLLQRIEQ 4350
4351 LATWVEETYPRVYWLSGFTYPTGFLTAVLQTTARKASVPIDTLSFEFSII 4400
4401 NLDEREINAPPKEGVYIKGLFLEGAGWDFENGCLCEPNPMELIVPMPILL 4450
4451 FRPVENKKRTAKGIYTCPLYLYPLRTGTRERPSFMINVDLRSGSADPDHW 4500
4501 IMRGTALLLSLAT 4513
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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