 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P93526 from www.uniprot.org...
The NucPred score for your sequence is 0.34 (see score help below)
1 MSSSRPAHSSSSSSRTRQSSQARILAQTTLDAELNAEYEESGDSFDYSKL 50
51 VEAQRSTPSEQQGRSGKVIAYLQHIQRGKLIQPFGCLLALDEKSFRVIAF 100
101 SENAPEMLTTVSHAVPNVDDPPKLGIGTNVRSLFTDPGATALQKALGFAD 150
151 VSLLNPILVQCKTSGKPFYAIVHRATGCLVVDFEPVKPTEFPATAAGALQ 200
201 SYKLAAKAISKIQSLPGGSMEALCNTVVKEVFELTGYDRVMAYKFHEDEH 250
251 GEVFAEITKPGIEPYLGLHYPATDIPQAARFLFMKNKVRMICDCRAKSVK 300
301 IIEDEALSIDISLCGSTLRAPHSCHLQYMENMNSIASLVMAVVVNENEED 350
351 DEPGPEQPPQQQKKKRLWGLIVCHHESPRYVPFPLRYACEFLAQVFAVHV 400
401 NKEFELEKQIREKSILRMQTMLSDMLFKEASPLSIVSGSPNIMDLVKCDG 450
451 AALLYGDKVWRLQTAPTESQIRDIAFWLSEVHGDSTGLSTDSLQDAGYPG 500
501 AASLGDMICGMAVAKITSKDILFWFRSHTAAEIKWGGAKHDPSDKDDNRR 550
551 MHPRLSFKAFLEVVKMKSLPWSDYEMDAIHSLQLILRGTLNDALKPVQAS 600
601 GLDNQIGDLKLDGLAELQAVTSEMVRLMETATVPILAVDGNGLVNGWNQK 650
651 VAELSGLRVDEAIGRHILTLVEDSSVSIVQRMLYLALQGKEEKEVRFELK 700
701 THGSKRDDGPVILVVNACASRDLHDHVVGVCFVAQDMTVHKLVMDKFTRV 750
751 EGDYKAIIHNPNPLIPPIFGADQFGWCSEWNVAMTKLTGWHRDEVIDKML 800
801 LGEVFDSSNASCLLKSKDDFVRLCIIINSALAGEEAENAPFGLFDRNGKY 850
851 IECLLSVNRKVNADGVVTGVFCFIHVPSDDLQHALHVQQASEQTAQRRLK 900
901 AFSYMRHAINKPLSGMLYSRETLKSTGLNEEQMRQVHVADSCHRQLNKIL 950
951 ADLDQDNITDKSSCLDLDMAEFVLEDVVVSAVSQVLIGCQGKGIRVACNL 1000
1001 PERFMKQKVYGDGIRLQQILSDFLFVSVKFSPVGGSVDISSKLTKNSIGE 1050
1051 NLHLIDFELRIKHQGAGVPAEILSQMYEEDNKEPSEEGLSLLVSRNLLRL 1100
1101 MNGNIRHIREAGMSTFILTAELAAAPSAVGQ 1131
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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