 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P13806 from www.uniprot.org...
The NucPred score for your sequence is 0.41 (see score help below)
1 MAAGRPLAWTLTLWQAWLILIGPSSEEPFPSAVTIKSWVDKMQEDLVTLA 50
51 KTASGVHQLVDIYEKYQDLYTVEPNNARQLVEIAARDIEKLLSNRSKALV 100
101 RLALEAEKVQAAHQWREDFASNEVVYYNAKDDLDPEKNDSEPGSQRIKPV 150
151 FIDDANFRRQVSYQHAAVHIPTDIYEGSTIVLNELNWTSALDDVFKKNRE 200
201 EDPSLLWQVFGSATGLARYYPASPWVDNSRTPNKIDLYDVRRRPWYIQGA 250
251 ASPKDMLILVDVSGSVSGLTLKLIRTSVSEMLETLSDDDFVNVASFNSNA 300
301 QDVSCFQHLVQANVRNKKVLKDAVNNITAKGITDYKKGFSFAFEQLLNYN 350
351 VSRANCNKIIMLFTDGGEERAQEIFAKYNKDKKVRVFTFSVGQHNYDRGP 400
401 IQWMACENKGYYYEIPSIGAIRINTQEYLDVLGRPMVLAGDKAKQVQWTN 450
451 VYLDALELGLVITGTLPVFNITGQFENKTNLKNQLILGVMGVDVSLEDIK 500
501 RLTPRFTLCPNGYYFAIDPNGYVLLHPNLQPKPIGVGIPTINLRKRRPNV 550
551 QNPKSQEPVTLDFLDAELENDIKVEIRNKMIDGESGEKTFRTLVKSQDER 600
601 YIDKGNRTYTWTPVNGTDYSSLALVLPTYSFYYIKAKIEETITQARYSET 650
651 LKPDNFEESGYTFLAPRDYCSDLKPSDNNTEFLLNFNEFIDRKTPNNPSC 700
701 NTDLINRVLLDAGFTNELVQNYWSKQKNIKGVKARFVVTDGGITRVYPKE 750
751 AGENWQENPETYEDSFYKRSLDNDNYVFTAPYFNKSGPGAYESGIMVSKA 800
801 VEIYIQGKLLKPAVVGIKIDVNSWIENFTKTSIRDPCAGPVCDCKRNSDV 850
851 MDCVILDDGGFLLMANHDDYTNQIGRFFGEIDPSLMRHLVNISVYAFNKS 900
901 YDYQSVCEPGAAPKQGAGHRSAYVPSIADILQIGWWATAAAWSILQQFLL 950
951 SLTFPRLLEAADMEDDDFTASMSKQSCITEQTQYFFDNDSKSFSGVLDCG 1000
1001 NCSRIFHVEKLMNTNLIFIMVESKGTCPCDTRLLIQAEQTSDGPDPCDMV 1050
1051 KQPRYRKGPDVCFDNNVLEDYTDCGGVSGLNPSLWSIIGIQFVLLWLVSG 1100
1101 SRHCLL 1106
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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