 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O74501 from www.uniprot.org...
The NucPred score for your sequence is 0.73 (see score help below)
1 MSKKKPIVQYYDPFDLWPSIATDIESKIPLRNLQWVESYQYKKHTITSLD 50
51 LEFLPWLEEFSSQDTSKDPTLLNIPLVNMLFLPEQDGEIYKTQHKPLATK 100
101 WFQKVSKNENQSWMIVLVAEAQQAKRSNSLRRTHSRSSSYFAVKSTIDRV 150
151 RGDFNTGNINHCVRLDYVRFGTPDAEGWNEFLKCLEWAVLSSLDSRFLQL 200
201 SEQIRSIKLFDSSIFSDYLLFFLQKERLAASFFDLVLYRESLEQCEDMFS 250
251 TLSKIISAIDGKKENISKYLGEGSDAQPDLKDFNSSFLILGSRYNTLHNL 300
301 LADGDFTLFHFTLFLLSKIILLNLKISDYEKAFSTLQQALLFLTSSFFNE 350
351 FVQNNLEIIAAFNYDCYTLLFKKITEAIHNNDTPIPLCCAKILLYARRQL 400
401 TRMARWNNLIDDSSPLLWREPSFPLIEHHDNSLLDNRFQKLSETISSTSS 450
451 FLSEYYRLSSEALSIFKAYKNRYSFVNTACDTGLAQYLLEDYENAYASLK 500
501 NVDVQTAWSNDPLEEKWSKFYVDLLEKLEKFDEALEFASAISKGKVSIYN 550
551 KNKILELSKKTAKSKVWNLDDFFTIEIPHMMAVVPHDDGINLSFMCESKV 600
601 FDLESVDSVTCNYVLSSSKNPMNLLFTLHNSITDGKLNVHCNDIIPGRYH 650
651 LKTIVFKLKDSVLSFQKDCLHSNKQIEVLNPTKHSNHLYILQSKISQQFQ 700
701 NDGIFACIPIMFGERFYKASCSEIKLSYIENSGFTNLEKSFLSEESHCGL 750
751 ESKTDSLVIKNIQKVPGVAKLYIPLTNTPVEGNEFLINLSYILESGQTVR 800
801 FGKVLHIENAGIVEANCEKTNNMRDPLTSTIFLHIQPKEPIIFCSWNCMR 850
851 KTKQGTYDNVFDIPYNIPVLSPMDCFTSCHFDSDMSIDLKFSMNCLRLAD 900
901 VVFMKLCVNIWERFDFIDIEHIMPLLDHACQQISLSNITQGTVVLPEEIR 950
951 KLNGKKILNSFFDNEELRNEFSEFIQHLESEVSFNSLRSFVQEFEKKERL 1000
1001 KNLPFYFSLHQTIGPLPFKDEMQSTTIEYIWKIPQKVLLNKPTEITLTFL 1050
1051 RSLTHSKKEKQGQSFEIFYNFPAYTTTILFAGPTQRKIVWEKNQTTAQEQ 1100
1101 VTAVFLTAGRVMLPEVVVHSKDDDISILKNTKYTLVGRPLNDQPDLDYAP 1150
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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