 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O95613 from www.uniprot.org...
The NucPred score for your sequence is 0.97 (see score help below)
1 MEVEQEQRRRKVEAGRTKLAHFRQRKTKGDSSHSEKKTAKRKGSAVDASV 50
51 QEESPVTKEDSALCGGGDICKSTSCDDTPDGAGGAFAAQPEDCDGEKRED 100
101 LEQLQQKQVNDHPPEQCGMFTVSDHPPEQHGMFTVGDHPPEQRGMFTVSD 150
151 HPPEQHGMFTVSDHPPEQRGMFTISDHQPEQRGMFTVSDHTPEQRGIFTI 200
201 SDHPAEQRGMFTKECEQECELAITDLESGREDEAGLHQSQAVHGLELEAL 250
251 RLSLSNMHTAQLELTQANLQKEKETALTELREMLNSRRAQELALLQSRQQ 300
301 HELELLREQHAREKEEVVLRCGQEAAELKEKLQSEMEKNAQIVKTLKEDW 350
351 ESEKDLCLENLRKELSAKHQSEMEDLQNQFQKELAEQRAELEKIFQDKNQ 400
401 AERALRNLESHHQAAIEKLREDLQSEHGRCLEDLEFKFKESEKEKQLELE 450
451 NLQASYEDLKAQSQEEIRRLWSQLDSARTSRQELSELHEQLLARTSRVED 500
501 LEQLKQREKTQHESELEQLRIYFEKKLRDAEKTYQEDLTLLQQRLQGARE 550
551 DALLDSVEVGLSCVGLEEKPEKGRKDHVDELEPERHKESLPRFQAELEES 600
601 HRHQLEALESPLCIQHEGHVSDRCCVETSALGHEWRLEPSEGHSQELPWV 650
651 HLQGVQDGDLEADTERAARVLGLETEHKVQLSLLQTELKEEIELLKIENR 700
701 NLYGKLQHETRLKDDLEKVKHNLIEDHQKELNNAKQKTELMKQEFQRKET 750
751 DWKVMKEELQREAEEKLTLMLLELREKAESEKQTIINKFELREAEMRQLQ 800
801 DQQAAQILDLERSLTEQQGRLQQLEQDLTSDDALHCSQCGREPPTAQDGE 850
851 LAALHVKEDCALQLMLARSRFLEERKEITEKFSAEQDAFLQEAQEQHARE 900
901 LQLLQERHQQQLLSVTAELEARHQAALGELTASLESKQGALLAARVAELQ 950
951 TKHAADLGALETRHLSSLDSLESCYLSEFQTIREEHRQALELLRADFEEQ 1000
1001 LWKKDSLHQTILTQELEKLKRKHEGELQSVRDHLRTEVSTELAGTVAHEL 1050
1051 QGVHQGEFGSEKKTALHEKEETLRLQSAQAQPFHQEEKESLSLQLQKKNH 1100
1101 QVQQLKDQVLSLSHEIEECRSELEVLQQRRERENREGANLLSMLKADVNL 1150
1151 SHSERGALQDALRRLLGLFGETLRAAVTLRSRIGERVGLCLDDAGAGLAL 1200
1201 STAPALEETWSDVALPELDRTLSECAEMSSVAEISSHMRESFLMSPESVR 1250
1251 ECEQPIRRVFQSLSLAVDGLMEMALDSSRQLEEARQIHSRFEKEFSFKNE 1300
1301 ETAQVVRKHQELLECLKEESAAKAELALELHKTQGTLEGFKVETADLKEV 1350
1351 LAGKEDSEHRLVLELESLRRQLQQAAQEQAALREECTRLWSRGEATATDA 1400
1401 EAREAALRKEVEDLTKEQSETRKQAEKDRSALLSQMKILESELEEQLSQH 1450
1451 RGCAKQAEAVTALEQQVASLDKHLRNQRQFMDEQAAEREHEREEFQQEIQ 1500
1501 RLEGQLRQAAKPQPWGPRDSQQAPLDGEVELLQQKLREKLDEFNELAIQK 1550
1551 ESADRQVLMQEEEIKRLEEMNINIRKKVAQLQEEVEKQKNIVKGLEQDKE 1600
1601 VLKKQQMSSLLLASTLQSTLDAGRCPEPPSGSPPEGPEIQLEVTQRALLR 1650
1651 RESEVLDLKEQLEKMKGDLESKNEEILHLNLKLDMQNSQTAVSLRELEEE 1700
1701 NTSLKVIYTRSSEIEELKATIENLQENQKRLQKEKAEEIEQLHEVIEKLQ 1750
1751 HELSLMGPVVHEVSDSQAGSLQSELLCSQAGGPRGQALQGELEAALEAKE 1800
1801 ALSRLLADQERRHSQALEALQQRLQGAEEAAELQLAELERNVALREAEVE 1850
1851 DMASRIQEFEAALKAKEATIAERNLEIDALNQRKAAHSAELEAVLLALAR 1900
1901 IRRALEQQPLAAGAAPPELQWLRAQCARLSRQLQVLHQRFLRCQVELDRR 1950
1951 QARRATAHTRVPGAHPQPRMDGGAKAQVTGDVEASHDAALEPVVPDPQGD 2000
2001 LQPVLVTLKDAPLCKQEGVMSVLTVCQRQLQSELLLVKNEMRLSLEDGGK 2050
2051 GKEKVLEDCQLPKVDLVAQVKQLQEKLNRLLYSMTFQNVDAADTKSLWPM 2100
2101 ASAHLLESSWSDDSCDGEEPDISPHIDTCDANTATGGVTDVIKNQAIDAC 2150
2151 DANTTPGGVTDVIKNWDSLIPDEMPDSPIQEKSECQDMSLSSPTSVLGGS 2200
2201 RHQSHTAEAGPRKSPVGMLDLSSWSSPEVLRKDWTLEPWPSLPVTPHSGA 2250
2251 LSLCSADTSLGDRADTSLPQTQGPGLLCSPGVSAAALALQWAESPPADDH 2300
2301 HVQRTAVEKDVEDFITTSFDSQETLSSPPPGLEGKADRSEKSDGSGFGAR 2350
2351 LSPGSGGPEAQTAGPVTPASISGRFQPLPEAMKEKEVRPKHVKALLQMVR 2400
2401 DESHQILALSEGLAPPSGEPHPPRKEDEIQDISLHGGKTQEVPTACPDWR 2450
2451 GDLLQVVQEAFEKEQEMQGVELQPRLSGSDLGGHSSLLERLEKIIREQGD 2500
2501 LQEKSLEHLRLPDRSSLLSEIQALRAQLRMTHLQNQEKLQHLRTALTSAE 2550
2551 ARGSQQEHQLRRQVELLAYKVEQEKCIAGDLQKTLSEEQEKANSVQKLLA 2600
2601 AEQTVVRDLKSDLCESRQKSEQLSRSLCEVQQEVLQLRSMLSSKENELKA 2650
2651 ALQELESEQGKGRALQSQLEEEQLRHLQRESQSAKALEELRASLETQRAQ 2700
2701 SSRLCVALKHEQTAKDNLQKELRIEHSRCEALLAQERSQLSELQKDLAAE 2750
2751 KSRTLELSEALRHERLLTEQLSQRTQEACVHQDTQAHHALLQKLKEEKSR 2800
2801 VVDLQAMLEKVQQQALHSQQQLEAEAQKHCEALRREKEVSATLKSTVEAL 2850
2851 HTQKRELRCSLEREREKPAWLQAELEQSHPRLKEQEGRKAARRSAEARQS 2900
2901 PAAAEQWRKWQRDKEKLRELELQRQRDLHKIKQLQQTVRDLESKDEVPGS 2950
2951 RLHLGSARRAAGSDADHLREQQRELEAMRQRLLSAARLLTSFTSQAVDRT 3000
3001 VNDWTSSNEKAVMSLLHTLEELKSDLSRPTSSQKKMAAELQFQFVDVLLK 3050
3051 DNVSLTKALSTVTQEKLELSRAVSKLEKLLKHHLQKGCSPSRSERSAWKP 3100
3101 DETAPQSSLRRPDPGRLPPAASEEAHTSNVKMEKLYLHYLRAESFRKALI 3150
3151 YQKKYLLLLIGGFQDSEQETLSMIAHLGVFPSKAERKITSRPFTRFRTAV 3200
3201 RVVIAILRLRFLVKKWQEVDRKGALAQGKAPRPGPRARQPQSPPRTRESP 3250
3251 PTRDVPSGHTRDPARGRRLAAAASPHSGGRATPSPNSRLERSLTASQDPE 3300
3301 HSLTEYIHHLEVIQQRLGGVLPDSTSKKSCHPMIKQ 3336
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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