 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P47024 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MATPVRGETRNVIDDNISARIQSKVKTNDTVRQTPSSLRKVSIKDEQVRQ 50
51 YQRNLNRFKTILNGLKAEEEKLSEADDIQMLAEKLLKLGETIDKVENRIV 100
101 DLVEKIQLLETNENNNILHEHIDATGTYYLFDTLTSTNKRFYPKDCVFDY 150
151 RTNNVENIPILLNNFKKFIKKYQFDDVFENDIIEIDPRENEILCKIIKEG 200
201 LGESLDIMNTNTTDIFRIIDGLKKQNIEVCMVEMSELEPGEKVLVDTTCR 250
251 NSALLMNKLQKLVLMEKWIFSKCCQDCPNLKDYLQEAIMGTLHESLRNSV 300
301 KQRLYNIPHDVGIDHEEFLINTVIETVIDLSPIADDQIENSCMYCKSVFH 350
351 CSINCKKKPNRELGLTRPISQKPIIYKVHRDNNHLSPVQNEQKSWNKTQK 400
401 RSNKVYNSKKLVIIDTGSGVNITNDKTLLHNYEDSNRSTRFFGIGKNSSV 450
451 SVKGYGYIKIKNGHNNTDNKCLLTYYVPEEESTIISCYDLAKKTKMVLSR 500
501 KYTRLGNKIIKIKTKIVNGVIHVKMNELIERPSDDSKINAIKPTSSPGFK 550
551 LNKRSITLEDAHKRMGHTGIQQIENSIKHNHYEESLDLIKEPNEFWCQTC 600
601 KISKATKRNHYTGSMNNHSTDHEPGSSWCMDIFGPVSSSNADTKRYMLIM 650
651 VDNNTRYCMTSTHFNKNAETILAQVRKNIQYVETQFDRKVREINSDRGTE 700
701 FTNDQIEEYFISKGIHHILTSTQDHAANGRAERYIRTIITDATTLLRQSN 750
751 LRVKFWEYAVTSATNIRNYLEHKSTGKLPLKAISRQPVTVRLMSFLPFGE 800
801 KGIIWNHNHKKLKPSGLPSIILCKDPNSYGYKFFIPSKNKIVTSDNYTIP 850
851 NYTMDGRVRNTQNINKSHQFSSDNDDEEDQIETVTNLCEALENYEDDNKP 900
901 ITRLEDLFTEEELSQIDSNAKYPSPSNNLEGDLDYVFSDVEESGDYDVES 950
951 ELSTTNNSISTDKNKILSNKDFNSELASTEISISGIDKKGLINTSHIDED 1000
1001 KYDEKVHRIPSIIQEKLVGSKNTIKINDENKISDRIRSKNIGSILNTGLS 1050
1051 RCVDITDESITNKDESMHNAKPELIQEQLKKTNHETSFPKEGSIGTNVKF 1100
1101 RNTNNEISLKTGDTSLPIKTLESINNHHSNDYSTNKVEKFEKENHHPPPI 1150
1151 EDIVDMSDQTDMESNCQDGNNLKELKVTDKNVPTDNGTNVSPRLEQNIEA 1200
1201 SGSPVQTVNKSAFLNKEFSSLNMKRKRKRHDKNNSLTSYELERDKKRSKK 1250
1251 NRVKLIPDNMETVSAPKIRAIYYNEAISKNPDLKEKHEYKQAYHKELQNL 1300
1301 KDMKVFDVDVKYSRSEIPDNLIVPTNTIFTKKRNGIYKARIVCRGDTQSP 1350
1351 DTYSVITTESLNHNHIKIFLMIANNRNMFMKTLDINHAFLYAKLEEEIYI 1400
1401 PHPHDRRCVVKLNKALYGLKQSPKEWNDHLRQYLNGIGLKDNSYTPGLYQ 1450
1451 TEDKNLMIAVYVDDCVIAASNEQRLDEFINKLKSNFELKITGTLIDDVLD 1500
1501 TDILGMDLVYNKRLGTIDLTLKSFINRMDKKYNEELKKIRKSSIPHMSTY 1550
1551 KIDPKKDVLQMSEEEFRQGVLKLQQLLGELNYVRHKCRYDIEFAVKKVAR 1600
1601 LVNYPHERVFYMIYKIIQYLVRYKDIGIHYDRDCNKDKKVIAITDASVGS 1650
1651 EYDAQSRIGVILWYGMNIFNVYSNKSTNRCVSSTEAELHAIYEGYADSET 1700
1701 LKVTLKELGEGDNNDIVMITDSKPAIQGLNRSYQQPKEKFTWIKTEIIKE 1750
1751 KIKEKSIKLLKITGKGNIADLLTKPVSASDFKRFIQVLKNKITSQDILAS 1800
1801 TDY 1803
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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