 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P10569 from www.uniprot.org...
The NucPred score for your sequence is 0.48 (see score help below)
1 MAYTSKHGVDDMVMLTSISNDAINDNLKKRFAADLIYTYIGHVLISVNPY 50
51 KQINNLYTERTLKDYRGKYRYELPPHVYALADDMYRTMLSESEDQCVIIS 100
101 GESGAGKTEASKKIMQYIAAVSGATGDVMRVKDVILEAFGNAKTIRNNNS 150
151 SRFGKYMEIQFDLKGDPVGGRISNYLLEKSRVVYQTNGERNFHIFYQLLA 200
201 ARARRPEAKFGLQTPDYYFYLNQGKTYTVDGMDDNQEFQDTWNAMKVIGF 250
251 TAEEQHEIFRLVTAILYLGNVQFVDDGKGGSTIADSRPVAVETALLYRTI 300
301 TTGEQGRGRSSVYSCPQDPLGAIYSRDALSKALYSRMFDYIIQRVNDAMY 350
351 IDDPEALTTGILDIYGFEIFGKNGFEQLCINFVNEKLQQIFIQLTLKAEQ 400
401 EEYGAEGIQWENIDYFNNKICCDLIEEKRPPGLMTILDDVCNFPKGTDDK 450
451 FREKLLGAFPTHAHLAATSQPDEFVIKHYAGDVVYNVDGFCDKNKDLLFK 500
501 DLIGLAECTSSTFFAGLFPEAKEVATSKKKPTTAGFKIKESINILVATLS 550
551 KCTPHYIRCIKPNEKKAANAFNNSLVLHQVKYLGLLENVRIRRAGYAYRQ 600
601 SYDKFFYRYRVVCPKTWSGWNGDMVSGAEAILNHVGMSLGKEYQKGKTKI 650
651 FIRQPESVFSLEELRDRTVFSYANKIQRFLRKTAMRKYYYEVKKGGNDAL 700
701 VNKKERRRLSLERPFKTDYINYRQNFKLKDCIGDKGTEKVLFADLCNNLD 750
751 KSFWGSKVERRIMVLTSNAMFLVAIDPNKDKIEKKVKPFLYVLKRRIDFN 800
801 KIGSITLSPLQDNFMLISVNGEHSNLLECRRKTELIGVLLKHNPSVRIQF 850
851 ADTFNVTLKGGKTCVVKFIRDPQGGDGKVKGTKVSVAPGLPPSSAPNIQA 900
901 PQETSGGASFTVAEQSYKDQILGAKGGGGGGGRGRGGPSPSGAVSPRPSP 950
951 GGGGGGPSPFGGRPSPSGPPAAASAPGPEQARALYDFAAENPDELTFNEG 1000
1001 AVVTVINKSNPDWWEGELNGQRGVFPASYVELIPRAAAPAPGPSGGPRPA 1050
1051 PPGGKSGRAAPMGGPGPMRGRGGPAPGGPGRGGAPPPGAGRAGPPGGRGM 1100
1101 PAPGGAAPRGRGAPPPGAGGPPGGGRGGAPPPGGMRGRGGPGPAPPGGMA 1150
1151 RGGMMPPRGRAGPPPPGM 1168
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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