 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02280 from www.uniprot.org...
The NucPred score for your sequence is 0.61 (see score help below)
1 MPGGRRGLVAPQNTFLENIIRRSNSQPDSSFLLANAQIVDFPIVYCNESF 50
51 CKISGYNRAEVMQKSCRYVCGFMYGELTDKETVGRLEYTLENQQQDQFEI 100
101 LLYKKNNLQCGCALSQFGKAQTQETPLWLLLQVAPIRNERDLVVLFLLTF 150
151 RDITALKQPIDSEDTKGVLGLSKFAKLARSVTRSRQFSAHLPTLKDPTKQ 200
201 SNLAHMMSLSADIMPQYRQEAPKTPPHILLHYCAFKAIWDWVILCLTFYT 250
251 AIMVPYNVAFKNKTSEDVSLLVVDSIVDVIFFIDIVLNFHTTFVGPGGEV 300
301 VSDPKVIRMNYLKSWFIIDLLSCLPYDVFNAFDRDEDGIGSLFSALKVVR 350
351 LLRLGRVVRKLDRYLEYGAAMLILLLCFYMLVAHWLACIWYSIGRSDADN 400
401 GIQYSWLWKLANVTQSPYSYIWSNDTGPELVNGPSRKSMYVTALYFTMTC 450
451 MTSVGFGNVAAETDNEKVFTICMMIIAALLYATIFGHVTTIIQQMTSATA 500
501 KYHDMLNNVREFMKLHEVPKALSERVMDYVVSTWAMTKGLDTEKVLNYCP 550
551 KDMKADICVHLNRKVFNEHPAFRLASDGCLRALAMHFMMSHSAPGDLLYH 600
601 TGESIDSLCFIVTGSLEVIQDDEVVAILGKGDVFGDQFWKDSAVGQSAAN 650
651 VRALTYCDLHAIKRDKLLEVLDFYSAFANSFARNLVLTYNLRHRLIFRKV 700
701 ADVKREKELAERRKNEPQLPQNQDHLVRKIFSKFRRTPQVQAGSKELVGG 750
751 SGQSDVEKGDGEVERTKVFPKAPKLQASQATLARQDTIDEGGEVDSSPPS 800
801 RDSRVVIEGAAVSSATVGPSPPVATTSSAAAGAGVSGGPGSGGTVVAIVT 850
851 KADRNLALERERQIEMASSRATTSDTYDTGLRETPPTLAQRDLIATVLDM 900
901 KVDVRLELQRMQQRIGRIEDLLGELVKRLAPGAGSGGNAPDNSSGQTTPG 950
951 DEICAGCGAGGGGTPTTQAPPTSAVTSPVDTVITISSPGASGSGSGTGAG 1000
1001 AGSAVAGAGGAGLLNPGATVVSSAGGNGLGPLMLKKRRSKSRKAPAPPKQ 1050
1051 TLASTAGTATAAPAGVAGSGMTSSAPASADQQQQHQSTADQSPTTPGAEL 1100
1101 LHLRLLEEDFTAAQLPSTSSGGAGGGGGSGSGATPTTPPPTTAGGSGSGT 1150
1151 PTSTTATTTPTGSGTATRGKLDFL 1174
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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