 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9P2R3 from www.uniprot.org...
The NucPred score for your sequence is 0.26 (see score help below)
1 MAEEEVAKLEKHLMLLRQEYVKLQKKLAETEKRCALLAAQANKESSSESF 50
51 ISRLLAIVADLYEQEQYSDLKIKVGDRHISAHKFVLAARSDSWSLANLSS 100
101 TKELDLSDANPEVTMTMLRWIYTDELEFREDDVFLTELMKLANRFQLQLL 150
151 RERCEKGVMSLVNVRNCIRFYQTAEELNASTLMNYCAEIIASHWDDLRKE 200
201 DFSSMSAQLLYKMIKSKTEYPLHKAIKVEREDVVFLYLIEMDSQLPGKLN 250
251 EADHNGDLALDLALSRRLESIATTLVSHKADVDMVDKSGWSLLHKGIQRG 300
301 DLFAATFLIKNGAFVNAATLGAQETPLHLVALYSSKKHSADVMSEMAQIA 350
351 EALLQAGANPNMQDSKGRTPLHVSIMAGNEYVFSQLLQCKQLDLELKDHE 400
401 GSTALWLAVQHITVSSDQSVNPFEDVPVVNGTSFDENSFAARLIQRGSHT 450
451 DAPDTATGNCLLQRAAGAGNEAAALFLATNGAHVNHRNKWGETPLHTACR 500
501 HGLANLTAELLQQGANPNLQTEEALPLPKEAASLTSLADSVHLQTPLHMA 550
551 IAYNHPDVVSVILEQKANALHATNNLQIIPDFSLKDSRDQTVLGLALWTG 600
601 MHTIAAQLLGSGAAINDTMSDGQTLLHMAIQRQDSKSALFLLEHQADINV 650
651 RTQDGETALQLAIRNQLPLVVDAICTRGADMSVPDEKGNPPLWLALANNL 700
701 EDIASTLVRHGCDATCWGPGPGGCLQTLLHRAIDENNEPTACFLIRSGCD 750
751 VNSPRQPGANGEGEEEARDGQTPLHLAASWGLEETVQCLLEFGANVNAQD 800
801 AEGRTPIHVAISSQHGVIIQLLVSHPDIHLNVRDRQGLTPFACAMTFKNN 850
851 KSAEAILKRESGAAEQVDNKGRNFLHVAVQNSDIESVLFLISVHANVNSR 900
901 VQDASKLTPLHLAVQAGSEIIVRNLLLAGAKVNELTKHRQTALHLAAQQD 950
951 LPTICSVLLENGVDFAAVDENGNNALHLAVMHGRLNNIRVLLTECTVDAE 1000
1001 AFNLRGQSPLHILGQYGKENAAAIFDLFLECMPGYPLDKPDADGSTVLLL 1050
1051 AYMKGNANLCRAIVRSGARLGVNNNQGVNIFNYQVATKQLLFRLLDMLSK 1100
1101 EPPWCDGSYCYECTARFGVTTRKHHCRHCGRLLCHKCSTKEIPIIKFDLN 1150
1151 KPVRVCNICFDVLTLGGVS 1169
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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