 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q3ZJ90 from www.uniprot.org...
The NucPred score for your sequence is 0.96 (see score help below)
1 MFIKKKKKKVSSFEFSSLRSTFSFVHWAKLKHQTSSIIENHSDQISPIQI 50
51 DFYNRPFEKGRLKALVSWSISYFGEKKTVDLVENLKSIGYAYATKAGISL 100
101 GIDDLKIPPSKKDYITKAEQILEFTNQDVKKGYLTSIEYFSKVIETWNKT 150
151 SESLKEEVISNFKKTDELNPVFIMAFSGARGNISQVRQLTSMRGLMSDPQ 200
201 GRIINFPIQSNFREGLTLTEYLISCYGARKGVVDTALRTATSGYLTRRLV 250
251 DVAHHVIVRGFNCGTKKGLYLSDLKKGDKILLSLKNRIIGRRLAEDIYNS 300
301 KKQLIATKNQEISSNLSSLITKNKTSIFVRSPLTCQDKNYVCQLCYGWSL 350
351 ANHRLVPSGEAIGIIAAQSIGEPGTQLTMRTFHTGGVFSGEVTDEIKAPF 400
401 RGIVFFNTSIPGKLIRTTYGQIAFLTKQESFLTLIPENPKQQWLDSSKKL 450
451 GFSKQKEPDFQKDSLFLQKKVEFKIPAYTLLFVKNNQLVEKNQVLGEAST 500
501 FLTRQNQSIESYQTIYSEFSGEVKFQHSKGVQVLKKELDFKIDDQELSSV 550
551 LKNKLRKLKSLFNPSANSSTGEFWILSAQKQTISKPVNLLVKPGDFLHQK 600
601 ALLYLAKKASYSNYFETIALDPSHNLPTLPLPILFYDFGVNSLAKNLNKN 650
651 AFFNLALLNEQLYLRKGNLFLMQDRSTQKKANRSTIKILPSLQKQTLQNF 700
701 KKVCSDLPFVSRSLFPISESSRDKLNSIQNGVKLQSTIVGKHRFLSLNII 750
751 FTQIAKKKFFKMNLNSEVKIKSLKQFDLNHESNEMKQIGVFNKDKTPTGG 800
801 FGIQEIFYTSFESKLKTSPLFLLAQFSSDKTLIDLTKQNVQDFKFKNLNY 850
851 QYFHPFLVLASDPPFCVSEKTLHFMQSCGQSLNEIDPNPKIETFFSKSAP 900
901 SFLDKNRRNKRFKSLKKTMEKNHLLSLYYFLTQPLSDDNDDTIMNSFNRK 950
951 SKLNKFSTKRLNKKQIVFGKLNHHFFADKGGDDFHFKNFIHGGLSFKEVC 1000
1001 EVDLYLKERVEVFNWKIATLGTPVSDIITKAEKFVLFHSPTNACGSFILE 1050
1051 QPLKNPCFYAFGKIGFLRGKTDYGQPNKIFSSNLFNLKKGWKTKTEGRFT 1100
1101 NRPNKQKFKSFINSVEKSGPLNKKIFQNEGPFALNDFTKIQPIVGNQLNE 1150
1151 NFFTNAAELLKKRPGAAKQEFQLTSFLEKFINRHNRLFWFPKDQNILNFA 1200
1201 DQQSKGPTNLLFQRKFLKDLTLFSDGDPSFSCRSFVFQTKFYLSKKLFEN 1250
1251 LPRHNFENYDIISGPINFQEFEILQKIPFSFYPLSSRSTQKFGPFFILPR 1300
1301 EVTKKQSFQIFKYPLLFELKTYSPTFFSTVFFFIRNFGPKFHKKRERKLI 1350
1351 KAIFRPLPRLIGFSTLKSPLDSGQTTTEKAGLQKPVYRFIKKSELFFSIF 1400
1401 DYQILKNYRRLASNLILKQNGLNLLNYSPILNSLLKSALLFNIASAKKNS 1450
1451 VFEKLGLKISAAYSESKTFGIATEKNSLNSNSVVLFQKKQSGFLIRELNP 1500
1501 FHNTLGSPFFDRSKKVDNPQSSLTFDRPQSANERKQILKKARQKLRLFPL 1550
1551 NLNEKKNRFSSVTLDLLRDQTTLHKMQSCGEAESGNLKTKETLFKKVKRE 1600
1601 NKKITEIFTFCPFCPQLKSKGKRKSKGDQLFQEPCNLNLGENFLSCLPFG 1650
1651 FEYPVKARRRLVKEQRLPFSLSLILPDKLNYYQSLTSFPFSKQMGNRLLK 1700
1701 NLTQLNFLNVGCIRLSQLDSKILRSDSFNPQRKELKSKKFNNFSINELSD 1750
1751 SFMNLGWTGFSQKNLILKYLDTDQIFKKGGPLKSNLQDSQLVECFFKTKK 1800
1801 VHFLFISKLLKESSLNSFFYNFYYSGSSFNSFADIQKFENKASFFQTKKK 1850
1851 LYNFEKKDQWQKKMSFLFFIQNKKTHLFLPNSKGFFLKKKKDISCERFST 1900
1901 NVILSKSQSKNETKELKKASLANQNLKKHSTISTENALKNKMNQSFSFSH 1950
1951 FNKEKKPLSDCRAQTKGAIQKFSTKLPPTQTGWIFAILNPKIYLNKHNCV 2000
2001 EFAGNSNLSDISFDNYVTLTCFLTIRFVNTFFELHANVDFWLKTLNTFHL 2050
2051 GLLKQSSFDFKASKIKLQKTPFLFPSFPPNYIELVFVKKAILRRVAKKDS 2100
2101 TFCNLYSFENFLLDKRFQKFKTHFNLQSHLKLAPSFCVDLQGSKGLENFA 2150
2151 WTGKLRSQSQASWILETNLFSPCFADKQSKSPLLEFENKKCPAFAKGKLQ 2200
2201 KSKIEKFEFTYNDIKNKLVYFNKSFFLTSDQRPVFQRSFPKKIHRKTLFI 2250
2251 THPSIIWKSRFESSLKSLTFFKMKAKQNGTGSLSFFEKKSLETSLNKTSV 2300
2301 LFKKVLAIKTFLLFSFSSLNLVSGKLNFLSSVDQNKKSNLKIQSIFENFA 2350
2351 YQQSKGSNEINFSRSAALHSMQSFVFSEKLRKTLSLRISNFHKIQKYANQ 2400
2401 NLESGIGFFSFFQKSLSIVFFDSAYHFSPQNTSTKDSFEIKKENKTVINN 2450
2451 YCRFSNFKLLKLPVFKNRFLNKYSLFKSFLNYLSYSFDVKTSKQILVRLP 2500
2501 LLKETCFHFNKNSRFKPKLLILNQANSQQLLATCFVQPYSEFSNSSFVFN 2550
2551 SKSAHLHSQTAVKHRQNFEKKSKIIFDERKTFSFISSSTQVLFVSKKATH 2600
2601 YLNENFRSQNYKKKTYDFIDNANVLKNRFFERLSPVEFHRKREGFLSKDQ 2650
2651 KQMTFKYQNMQGGLIPALDSTSTFAPFARSSKARGSAKAIFSQAQRLWGE 2700
2701 ESFINDKQKSIRNQIIFAKNSRFKNLLILNNKNENEKLFYLNLKKKVSEQ 2750
2751 STMNFFVPALYKKLFYTKQSISKFLEVKIQPNLQIQWTFFNSNISKHEKQ 2800
2801 QKFLLPLFDETFNIQGSNLKNGLNFGMLSLSYSTLDPFFECLKKRVNSSW 2850
2851 FFNGKQTFKKKKKIAKEGAFFNHSFFLDAKKSKQLNKKIQKKFTKRLQTL 2900
2901 NFSEIKKGFFISEKFKTRLSCLIKKPFLISTFFLSYRLKKPKLALNFNYQ 2950
2951 SLGNNSKKFSLIRLNSIDFNLSKSQRGWFHNQNVSKQFRFFKHNRSVNLF 3000
3001 QIHFDFENSCDPCFAMQKQTTSSKPVLFLYNLKPLKTDFFQKGFQTTSQL 3050
3051 LFKHINHSIVPLKDDANHLSSFLNQANFRGAFEPKAKTIADKLISQNVAI 3100
3101 TKPNLPKSNFSSLKGEVFFVTNSRQFKLVDLSVFKKSSREIQLLTNLDLI 3150
3151 TFRIKNRNFPSKHIEEQKPNLIEKQLAQSKNKLQIYIGQLLRYGKEISPG 3200
3201 IGLNQSGQILILQSNKLVLRYAKPFLLASGGICDLVQGDFVKNQSPLLNL 3250
3251 KYKSLKTEDIVQGIPKIEQLFEARENFQDELGINNLLKNKFLVYKTLYHP 3300
3301 KEAVRKSFEFIQHYIIDGIQYVYQSQGVNISDKHIEIIVKQMTSKVRILE 3350
3351 PRNSGLLRGDVVDLDWIERINLDILTGKKAQYEPIVLGITKASLDRRGFI 3400
3401 SAASFQETIKVLTKATILQRRDYLRGLKENVILGHLINSGTGSTLYSILK 3450
3451 EKKSNFLNRFLQ 3462
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.