 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O55111 from www.uniprot.org...
The NucPred score for your sequence is 0.21 (see score help below)
1 MARSPGDRCALLLLVQLLAVVCLDFGNGLHLEVFSPRNEGKPFPKHTHLV 50
51 RQKRAWITAPVALREGEDLSRKNPIAKIHSDLAEEKGIKITYKYTGKGIT 100
101 EPPFGIFVFDRNTGELNITSILDREETPYFLLTGYALDSRGNNLEKPLEL 150
151 RIKVLDINDNEPVFTQEVFVGSIEELSAAHTLVMKITATDADDPETLNAK 200
201 VSYRIVSQEPANSHMFYLNKDTGEIYTTSFTLDREEHSSYSLTVEARDGN 250
251 GQITDKPVQQAQVQIRILDVNDNIPVVENKMYEGTVEENQVNVEVMRIKV 300
301 TDADEVGSDNWLANFTFASGNEGGYFHIETDTQTNEGIVTLVKEVDYEEM 350
351 KKLDLSIIVTNKAAFHKSILSKYKATPIPITVKVKNVVEGIHFKSSVVSF 400
401 RASEAMDRSSLSRSIGNFQVFDEDTGQAAKVTYVKVQDTDNWVSVDSVTS 450
451 EIKLVKIPDFESRYVQNGTYTAKVVAISKEHPQKTITGTIVITVEDVNDN 500
501 CPVLVDSVRSVCEDEPYVNVTAEDLDGAQNSAPFSFSIIDQPPGTAQKWK 550
551 ITHQESTSVLLQQSERKRGRSEIPFLISDSQGFSCPERQVLQLTVCECLK 600
601 GGGCVAAQYDNYVGLGPAAIALMILALLLLLLVPLLLLICHCGGGAKGFT 650
651 PIPGTIEMLHPWNNEGAPPEDKVVPSLLVADHAESSAVRGGVGGAMLKEG 700
701 MMKGSSSASVTKGQHELSEVDGRWEEHRSLLTAGATHHVRTAGTIAANEA 750
751 VRTRATGSSRDMSGARGAVAVNEEFLRSYFTEKAASYNGEDDLHMAKDCL 800
801 LVYSQEDTASLRGSVGCCSFIEGELDDLFLDDLGLKFKTLAEVCLGRKID 850
851 LDVDIEQRQKPVREASVSAASGSHYEQAVTSSESAYSSNTGFPAPKPLHE 900
901 VHTEKVTQEIVTESSVSSRQSQKVVPPPDPVASGNIIVTETSYAKGSAVP 950
951 PSTVLLAPRQPQSLIVTERVYAPTSTLVDQHYANEEKVLVTERVIQPNGG 1000
1001 IPKPLEVTQHLKDAQYVMVRERESILAPSSGVQPTLAMPSVAAGGQNVTV 1050
1051 TERILTPASTLQSSYQIPSETSITARNTVLSSVGSIGPLPNLDLEESDRP 1100
1101 NSTITTSSTRVTKHSTMQHSYS 1122
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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