 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O13046 from www.uniprot.org...
The NucPred score for your sequence is 0.75 (see score help below)
1 MPAIKKNMRYGHPEGHTDVCFDDSGNFLVTCGSDGDIRIWESLDDDDPKS 50
51 ISIGEKAYSFALKNGKVVTAASNNAIQLHTFPDGEPDGILTRFTTNANHV 100
101 VFNTDGTRIAAGSGDFLVKVLQVEDSTQQKTLRGHSAPVLSVSFDPKDIY 150
151 LASASCDGSVRIWKISDQTCEAVLPLLEKCNDVFNAKSICRLAWQPKSGK 200
201 FVAIPVGKAVHLYDRDSLKNICTLSDDFITQPVNIVAWSPCGQYLVAGSV 250
251 DGCIVAWNIATKACLERIKHEKGYTICALAWHPHLPQIAYTDNEGNLGLL 300
301 EDVCQGDVKQPSAKVSSAETKDYDELFDGDDDEDFLNGDMIGHEGAVNDE 350
351 DDDDNFTALTGRPRNRGAIFDDDISSDVPSLKLVGNENPVVEDDQASSVQ 400
401 NFTSVASVKLSYNGPMPTPQQKPFQSGSTPVHLMHRFMVWNSVGVIRCYN 450
451 DEQDNAIDVEFHDTSIHHAIHLTNSLNHTLADVSQEAVLLACETTEELAS 500
501 KLQCLHFSSWDTSKEWMVDMPKGENIQAICLGQGWVACATSALLIRIFSV 550
551 GGVQKELISLFGPVVCMASHGEQLIVVYHRGMGFDGDQCLGVQLLELGKK 600
601 KKQVLHGDPLPLSRKSYLSWLGFTAEGSPCYVDSEGIVRLLNRSLGDTWV 650
651 PICNTREHCKGKSDHYWVVGIHENPQQVRCIPCKGSRFPPTLPRPAVAVL 700
701 PFNLPYCQITTEKGQMEEQYWRSQIFSNHSDYLSKHGYECDENFKAEAQK 750
751 MQQELLMKMFALSCKLEREFRCMELAEFMTQNVMNLAIKYASRSKRLILA 800
801 QRLSEMALEKAAEQASAEQNEEEDEEEEDFRSRLTAGYSRTATEWGDSRA 850
851 KPVKQDQYEENNEEEMEEEEKEQEEALESNTPTANPFNKSVKTPDVSESK 900
901 LGAILSSNQGRVNPFKVSASQKSPAFASNSSRSTSILDNMGKFPRKPSAS 950
951 GSPSTSKSDSVIIKPLAPKAKSKQGQATLFHSVQAKPIAKKTTEEKKAVP 1000
1001 TPPKAAADIAENKKPKTGFQLWLDENRPSILSENAGLDESEIIKEGMSRF 1050
1051 RMLTSEERMLWTEKAKGDYPGEDGADAKKRKRPEQENMASNGCPQENTDS 1100
1101 GIAAAKKHKPLGQSANNKLSAFAFKKE 1127
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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