 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P46056 from www.uniprot.org...
The NucPred score for your sequence is 0.08 (see score help below)
1 MLRSISIASRAAGPVRPVARARTAVGVVAPPRVCCCAPAVGNYGQVDGKA 50
51 TLNSLRRLPAGYRPRSCPSSRSPDVKKVLVVGSGGLSIGQAGEFDYSGSQ 100
101 AIKALRESNIETILINPNIATIQTSHHLASEIYFLPVTADYVAYVLEKER 150
151 PDGILLTFGGQSALNVGIQLEKMGVLERLGVQVLGTPIRTLEISEDRDLF 200
201 VQALNEIDIPAAQSTAVSTIQDALDAAKTIGYPIILRSAFSLGGLGSFPH 250
251 DEEELRNLAAKSLSLSPQVLIEKSLKGWKEVEYEVVRDAADNTIICCNME 300
301 NFDPLGTHTGDSIVVAPSQTLTDEYHMLRSAAIKIVRHVGVVGECNVQYA 350
351 LDPNSRDYRVIEMNARLSRSSALASKATGYPLAYTAAKIALGHTLRELPN 400
401 AVTKSTTACFEPSLDYIVTKIPKWDLAKFQHVERNVGSAMKSVGEVMAIG 450
451 RTFEESLQKAIRQVDPNFAGFEAYWKPEDMITALTNNNDRRLFAIAHAML 500
501 NLDYSVDYLHDLTKIDKWFLYKLENIVAVHKQLRSTPFEQLDKELVMTAK 550
551 KTGFSDLQIAQLTGKTEAEVRTLRKQFGVTPFVKRIDTLAAEFPAYTNYL 600
601 YTSYNATTHDVKFDNGTMVLGSGVYRIGSSVEFDWCAVTCSRAIRDLGKK 650
651 TIMINYNPETVSTDFDEADRLYFEELGFERVMDIYDLEGASGVVVSVGGQ 700
701 LPQNIALRLKKAGVQVLGTDPEMIDSAEDRHKFSSILDSIGVDQPAWTEA 750
751 SSLASAKEFANRVGYPVLIRPSYVLSGAAMNVVWDEAQLEHNLTLATNVS 800
801 PDHPVVISQFIDNAQEIDVDAVAHKGKLLVHAVSEHVENAGVHSGDATLV 850
851 LPPFSVNQHDLGRLKTIAEKVAQAFQISGPFNMQIIRKPPTGEEDAELKV 900
901 IECNLRASRSFPFVSKVLGHNFIDTASAAIMDTNVPAPIDLMAQKRDYVA 950
951 IKVPQFSWTRLPGADPFLGVEMASTGEVASFGKDIYDAYWAALLSVNGMK 1000
1001 LPKANSGILLGGDITRPEMTEVAKNLINLGFSLYTYDPKVEAHINDQPYL 1050
1051 SIKKILVPVKDKKKLREILEEHEIQTVINMARSRAATTLDEDYAARRAAV 1100
1101 DFGIPLINNPKLAVLFTETLEKKFVKNNPIPYSEGFKPSEVGSWRDFVGE 1150
1151 AATTKLEGIAWTGEAYCIILIPGIGV 1176
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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