 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q39610 from www.uniprot.org...
The NucPred score for your sequence is 0.51 (see score help below)
1 MSIFWEVPNAQGEAPCPRSGHSFTVLGERFVLFGGCGRKDGKAAAFNDLY 50
51 ELDTSDPDEYKWKELVVANAPPPRARHAAIALDDKRLLVFGGLNKRIRYN 100
101 DVWLFNYDDKSWTCMEVEGAAPEPRAHFTATRFGSRVFIFGGYGGSGQVY 150
151 NEMWVLHFGEDGFRWQNITESIEGTGPAPRFDHSAFIYPVTPNSDTYDKL 200
201 LIMGGRDLSQMYQDSHMLDLNKMAWENETQPPTLPYEICNNVCDGIESVP 250
251 YHKVFSFGGRKGMMQYLNTVEVMDCGTQMWSTPPVDHGVAPVGREDTAWV 300
301 FDVKTCSLLIFGGWANRWLGDLHKLNVSPIIGPPYACTAIQPEMGPVFGS 350
351 TELVIRGLRFRDGKVQVKFGLSEKNEVVVEGTYVDQETIRVQTPNYEQFG 400
401 ALTVDVRVSINGEGWTVNKIKYAYFANTAARNCIAYGPGLLAETISGVEV 450
451 PFIIQAKDTLNDKRTSGGDVFKVTVVSADGKNEGVSRVRDLQNGQYEVQY 500
501 AAPTAGPYLIHVAFNELGTSDFVPIRGSPFTVKCTDSWTKHRVMGAAPAK 550
551 RKGATICTMGNELVLYGGDKSGVTVLNTEGAEWRWSPATVSGSTPPDRTA 600
601 HSTVVLSDGELVVFGGINLADQNDLNDIYYLRKQGEGWVWSCPSESRPYI 650
651 RHPKGAAAVSAEPSAEPAAEPAAEPAAEPDADAPAAEPAAEGEEGAVPAE 700
701 GEEGAEGATGSRPVSAKPAPAAAAPAAEALPELPVSARNSHVAVAIDKDL 750
751 YVMMGDHDGDLMTELAMVDTSDRTCAHWLEPILKGDVPVPRKACAAAATG 800
801 NTIVLFGGQTQNADGEATVTGDLVIMEVTGPNSIQCAVNPAAPGASGSPA 850
851 ARYGAVMQEFSNGKLFLHGGMDAASKPLNDGWLFDVPSKTWQCVYVGSSD 900
901 VVLPTGQLATLSGNRIVLVSAAVGSPKLDSVQSLDFQELRDQAGVHAKMR 950
951 ASTETLLKGLEDWVDTQAHGMELARSPEKLSKDFENGLRKVMDALFQVKS 1000
1001 QRSQTDLLIDQLHEAFAQLAEEKVPGINKMEKRLEAAAHKWDEIKKAQPQ 1050
1051 VKTDVEPIQAAKGEDIKKEIETFAAKVRNYRADFRRRGFFKYATGFDGAY 1100
1101 PLLDAAAHELAELKKECDRLSELASVFEFPQAIEPVTVAIKETVEDLVMV 1150
1151 KDVWDTAVLCELQFQDWRQTLWSDIRTDIMEEGAKQFVKEVKSLHKKVRD 1200
1201 EDVFRGVDQVVKNFLVSVPLVADLRSPAMRDRHWEQLMATTKMTFNVKDP 1250
1251 NFKLDDLLALELHKFEEEVGEIVDRAQKEEKMEIAIRKLNDTWTRVEFQF 1300
1301 HRHKDYDVHTVKMAEEDFEALEDNQVQVQGMIANRYMATFKDEILGWQKK 1350
1351 LNDVADVNQIMAEIQRTWAYLESLFIHSEEVKKELPQATERFAAIDTEVK 1400
1401 KVLREFQQLKNCVSCCNREGLYANLETQERELEICKKALNDYMESKRRAF 1450
1451 PRFYFVSSADLLDILSNGNNPMRVQIHMNKCFQAIDNVRLDSEEVVPGRR 1500
1501 PKALGMESCVGIEYVPFSSLPLENKVEQYMNDIIAKMRNDVRMVLKASVE 1550
1551 DYPSKPRDKWLFDWPSQIILVVNQIYWCLEVEQAFTEMARGDKGAMSKYN 1600
1601 EFQVKQLTKLIEVTRTDLSKPDRQKIMNMITIDAHSRDMVLAGADQPDSF 1650
1651 QWVSQLRSYWDRDISDCRIRICDASFPYGYEYLGNGPRLVITPLTDRIYI 1700
1701 TATQACWLSLGTAPAGPAGTGKTETTKDLSAQLGKSVYVFNCSPEMDYRT 1750
1751 MGDIFKGLAASGSWGCFDEFNRLVPEVLSVCSVQYKCVTDSQKKKTMLPG 1800
1801 RGLEYIKDGVKHPAVEHWSFIAADGVEMPLEEGTSAFITMNPGYIGRAEL 1850
1851 PESLKALFRPITVMVPDRQLIMENMLMAEGFVEAKMLAKKFASLYYLLED 1900
1901 LLSPQKHYDWGLRAIKSVLVVAGSLLRAEAGQVEADVLFRALRDFNIPKI 1950
1951 LAQDMVIFMGLLNDLFPGIDPPRKRDMEFEDVIVSTIKDLGLTPEDDFVL 2000
2001 RVVQFSELLAIRHCVFLMGPTGTGRTECYRVLAKAITKGCNNPVNDYLKM 2050
2051 TNKKKVVIRDINPKSISTYELYGQVNQATREWKDGLLSYYMRDVANMPGD 2100
2101 DPKWLLLDGDLDANWIESMNSVMDDNRLLTLPSNERIRVLPHMKLIFEIR 2150
2151 DLKFATPATATRAGILYISEGQQWHNMAMSWINRVVKPYAERAKWKDPQL 2200
2201 PCTWLREMFDKYIPPTLLEMKKSYSHITPLAQMNFISTLVNIMEGVLKPE 2250
2251 NLSNKADQAMFEMYFVFAMIWAFGGGLVEKDGIPYRRNFDKWFKQTWTTV 2300
2301 KIPGKGTVYDYFVNPKTQKFQPWAELVTDIDYDGSRPMSTVFVPTAETSS 2350
2351 LRFFLDMMVDLRKPIMFVGGAGVGKTQLVKGKLGSLNEEQISLSISFNYF 2400
2401 TDVVSFQKVLESPLEKQPAGINYGPPGTKQLIYFVDDLNMPKLDLYETAM 2450
2451 PISLIRQHLGWGHWFDRAKLTPKNINNTQYVACMNPTAGSFIINPRLQRL 2500
2501 FMTLAVDFPGQDSLMKIYGTFLQGHLKKFSESIQDMGTKILQAALALHDR 2550
2551 VSQTFRKTAINFHYEFTVRHLANVFQGLLMSTPEAFNSPTKWGKLWLHES 2600
2601 ERVYADRLVSLYDLDAYNKAATAIAKKYFSVADIDDYYKKKDPKPLIFCH 2650
2651 FARGLADKAYDEVADYTSLYKTLTEALNEYNETNAAMDLVLFEDAMKHVC 2700
2701 RISRIVSNPSGHALLVGVGGSGKQSLARLAAHICGYATQMIVISGSYSMN 2750
2751 NFKEDIQKMYKRTGVKGEGVMFLFTDSQIVDERMLVYINDLLSSGEIPDL 2800
2801 FPQEDRDEIVNALRSETKSLGLLDTAENCWATFIQKVKTNLHMVFTASPV 2850
2851 GENFRVRSQRFLATVTSTVIDWFQPWPESSLFSVAKRFLDEVDLGEDAVA 2900
2901 NAVVEFMPYSFQLVNKVSIKFREQERRYNYTTPKTFLELIKLYKNVLAAK 2950
2951 RKANQDNTERLENGLHKLHKVQADVDILVEEAKVKAVEVEHKVASANIFA 3000
3001 EQVGVEKEKVNAENAAAQVEAEKCAVIAKEVSEKQASCEKDLAAAEPLVA 3050
3051 EAMAALETVTKKDLGEAKSLKKPPPGVDDITAVVIILLENNPKDKSWQAA 3100
3101 QKLMNNVDKFLERVKSFKSVIDAGQVARKTVDACRPYLALEWFNREAIGK 3150
3151 KSAAAAGLCEWAVNIIKYYDVVQEVEPKRQELAAANAKLEEANVTLAAVE 3200
3201 EKVALLNAKVQELEQQYKEANDDKEAAIRESERCQRKLELANRLINALAS 3250
3251 EGERWALTVEQLRKSYEVLTGDMLLAAAFVSYAGPFTAKFRAHVIDDWIL 3300
3301 FLRERHMPMTEGITDPLKVLVDDALVAGWIREGLPSDPTSVQNGTILTNS 3350
3351 ERWSLMMDPQLQGILWIKERESKNNLQVTRMGASNMLQVMERAIEAGHSV 3400
3401 LVENMGETIDAVLNPIITRSTFKKGRSLYVKLGDKECEYNKNFRLFLHTK 3450
3451 LSNPHYPPEIQAETTLINFTVTEAGLEDQLLALVVNKERPDLEETKTQLI 3500
3501 IQNTEFTIKLKELEDGLLLKLSTAEGDITEDVALIESLEDAKRVSTEISE 3550
3551 KVKESRETEAAINENRNKYRTVAARGAMLFFLLNSLNKIHAFYQFSLNAF 3600
3601 VTVFSRGLDLAPGGRKKGKGLKKTPSLRDQPMDHQSLMEKARRSSGVGDR 3650
3651 RPSQEGLPGPEASQASLAESQGGRGSQVGDAEDEDDESFAMAPEALEQRL 3700
3701 VNLLETCTFTVYNYTRRGLFDRDKLIVLSLLTFTILLRSQAVDASEYEAL 3750
3751 CRGMRNPTPPPITDDLSRWMAESQWAALDVLTTLPCFAHLAKDMEKNSDD 3800
3801 WFNWCNNEAAERAPMPGEWGKLTEFRQLLIIRALRPDRITNALQNFCEHM 3850
3851 MGSDYVNQDAFSPAAMMDESSSATPIFFILFPGYSPSKEIEVYANKCGYS 3900
3901 VANGRLCLISMGQGQEAPAEAVLDKYTREGGWVFLDNVHLMQGWIPKLER 3950
3951 KLEIAAESAHPDFRCFFSAEPINGAPHANIIPESILQTCIKISNEPPSDM 4000
4001 KSNMRRAFAAFTPEQCDRPSTPAKRVAFRAILFGLCFYHSLLLGRKKFGV 4050
4051 GIGTGSGSGLGFCRGYSFNIGDLTTCGDVLYNYLEAYEQIPWRDLQYMFG 4100
4101 EVFYGGHITDSMDRRCCTTYLEVLIRNEILPKGNPDEVEAWEAPTLELAP 4150
4151 GFFAPKPVDYPTLKEYIETSLPAESPVVYGMHPNAELSLLTSLGETLFKT 4200
4201 VVEVAGGGGGGGGGGGGGENAVRQALETFKERLPEPFNMVEVELRVKEKT 4250
4251 PFVVVALQEATRMNALLSEMKRSMEELQLGLDGALNMSDNMEKLAKGIAS 4300
4301 NTVPELWMSCMSTRVQEVYTLTAWYQDVVKRHDQLSAWTAGDIITPHSVW 4350
4351 LPGLFNPKAFLTAVMQTFARANKLPLDVMKFMTEVTRMTSPEQVTEAAPL 4400
4401 GVYVHGLVLEGARWDREDGCLRDSKPNELHPAMPVLQVKPVTADQFNLEG 4450
4451 YYECPVYTNMQRANVYSPVVSTFTLRTQDMPAKWVLASVALLLQDDLAG 4499
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.