 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q92824 from www.uniprot.org...
The NucPred score for your sequence is 0.70 (see score help below)
1 MGWGSRCCCPGRLDLLCVLALLGGCLLPVCRTRVYTNHWAVKIAGGFPEA 50
51 NRIASKYGFINIGQIGALKDYYHFYHSRTIKRSVISSRGTHSFISMEPKV 100
101 EWIQQQVVKKRTKRDYDFSRAQSTYFNDPKWPSMWYMHCSDNTHPCQSDM 150
151 NIEGAWKRGYTGKNIVVTILDDGIERTHPDLMQNYDALASCDVNGNDLDP 200
201 MPRYDASNENKHGTRCAGEVAAAANNSHCTVGIAFNAKIGGVRMLDGDVT 250
251 DMVEAKSVSFNPQHVHIYSASWGPDDDGKTVDGPAPLTRQAFENGVRMGR 300
301 RGLGSVFVWASGNGGRSKDHCSCDGYTNSIYTISISSTAESGKKPWYLEE 350
351 CSSTLATTYSSGESYDKKIITTDLRQRCTDNHTGTSASAPMAAGIIALAL 400
401 EANPFLTWRDVQHVIVRTSRAGHLNANDWKTNAAGFKVSHLYGFGLMDAE 450
451 AMVMEAEKWTTVPRQHVCVESTDRQIKTIRPNSAVRSIYKASGCSDNPNR 500
501 HVNYLEHVVVRITITHPRRGDLAIYLTSPSGTRSQLLANRLFDHSMEGFK 550
551 NWEFMTIHCWGERAAGDWVLEVYDTPSQLRNFKTPGKLKEWSLVLYGTSV 600
601 QPYSPTNEFPKVERFRYSRVEDPTDDYGTEDYAGPCDPECSEVGCDGPGP 650
651 DHCNDCLHYYYKLKNNTRICVSSCPPGHYHADKKRCRKCAPNCESCFGSH 700
701 GDQCMSCKYGYFLNEETNSCVTHCPDGSYQDTKKNLCRKCSENCKTCTEF 750
751 HNCTECRDGLSLQGSRCSVSCEDGRYFNGQDCQPCHRFCATCAGAGADGC 800
801 INCTEGYFMEDGRCVQSCSISYYFDHSSENGYKSCKKCDISCLTCNGPGF 850
851 KNCTSCPSGYLLDLGMCQMGAICKDGEYVDEHGHCQTCEASCAKCQGPTQ 900
901 EDCTTCPMTRIFDDGRCVSNCPSWKFEFENQCHPCHHTCQRCQGSGPTHC 950
951 TSCGADNYGREHFLYQGECGDSCPEGHYATEGNTCLPCPDNCELCHSVHV 1000
1001 CTRCMKGYFIAPTNHTCQKLECGQGEVQDPDYEECVPCEEGCLGCSLDDP 1050
1051 GTCTSCAMGYYRFDHHCYKTCPEKTYSEEVECKACDSNCGSCDQNGCYWC 1100
1101 EEGFFLLGGSCVRKCGPGFYGDQEMGECESCHRACETCTGPGHDECSSCQ 1150
1151 EGLQLLRGMCVHATKTQEEGKFWNDILRKLQPCHSSCKTCNGSATLCTSC 1200
1201 PKGAYLLAQACVSSCPQGTWPSVRSGSCENCTEACAICSGADLCKKCQMQ 1250
1251 PGHPLFLHEGRCYSKCPEGSYAEDGICERCSSPCRTCEGNATNCHSCEGG 1300
1301 HVLHHGVCQENCPERHVAVKGVCKHCPEMCQDCIHEKTCKECTPEFFLHD 1350
1351 DMCHQSCPRGFYADSRHCVPCHKDCLECSGPKADDCELCLESSWVLYDGL 1400
1401 CLEECPAGTYYEKETKECRDCHKSCLTCSSSGTCTTCQKGLIMNPRGSCM 1450
1451 ANEKCSPSEYWDEDAPGCKPCHVKCFHCMGPAEDQCQTCPMNSLLLNTTC 1500
1501 VKDCPEGYYADEDSNRCAHCHSSCRTCEGRHSRQCHSCRPGWFQLGKECL 1550
1551 LQCREGYYADNSTGRCERCNRSCKGCQGPRPTDCLSCDRFFFLLRSKGEC 1600
1601 HRSCPDHYYVEQSTQTCERCHPTCDQCKGKGALNCLSCVWSYHLMGGICT 1650
1651 SDCLVGEYRVGEGEKFNCEKCHESCMECKGPGAKNCTLCPANLVLHMDDS 1700
1701 HCLHCCNTSDPPSAQECCDCQDTTDECILRTSKVRPATEHFKTALFITSS 1750
1751 MMLVLLLGAAVVVWKKSRGRVQPAAKAGYEKLADPNKSYSSYKSSYREST 1800
1801 SFEEDQVIEYRDRDYDEDDDDDIVYMGQDGTVYRKFKYGLLDDDDIDELE 1850
1851 YDDESYSYYQ 1860
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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