 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P07856 from www.uniprot.org...
The NucPred score for your sequence is 0.72 (see score help below)
1 MRFVLCCTLIALAALSVKAFGHHPGNRDTVEVKNRKYNAASSESSYLNKD 50
51 NDSISAGAHRAKSVEQSQDKSKYTSGPEGVSYSGRSQNYKDSKQAYADYH 100
101 SDPNGGSASAGQSRDSSLRERNVHYVSDGEAVAASSDARDENRSAQQNAQ 150
151 ANWNADGSYGVSADRSGSASSRRRQANYYSDKDITAASKDDSRADSSRRS 200
201 NAYYNRDSDGSESAGLSDRSASSSKNDNVFVYRTKDSIGGQAKSSRSSHS 250
251 QESDAYYNSSPDGSYNAGTRDSSISNKKKASSTIYADKDQIRAANDRSSS 300
301 KQLKQSSAQISSGPEGTSVSSKDRQYSNDKRSKSDAYVGRDGTVAYSNKD 350
351 SEKTSRQSNTNYADQNSVRSDSAASDQTSKSYDRGYSDKNIVAHSSGSRG 400
401 SQNQKSSSYRADKDGFSSSTNTEKSKFSSSNSVVETSDGASASRESSAED 450
451 TKSSNSNVQSDEKSASQSSSSRSSQESASYSSSSSSSTLSEDSSEVDIDL 500
501 GNLGWWWNSDNKVQRAAGGATKSGASSSTQATTVSGADDSADSYTWWWNP 550
551 RRSSSSSSSASSSSSGSNVGGSSQSSGSSTSGSNARGHLGTVSSTGSTSN 600
601 TDSSSKSAGSRTSGGSSTYGYSSSHRGGSVSSTGSSSNTDSSTKNAGSST 650
651 SGGSSTYGYSSSHRGGSVSSTGSSSNTDSSTKSAGSSTSGGSSTYGYSSR 700
701 HRGGRVSSTGSSSTTDASSNSVGSSTSGGSSTYGYSSNSRDGSVSSTGSS 750
751 SNTDSNSNSAGSSTSGGSSTYGYSSNSRDGSVSSTGSSSNTDSNSNSAGS 800
801 STSGGSSTYGYSSNSRDGSVSSTGSSSNTDASTDLTGSSTSGGSSTYGYS 850
851 SDSRDGSVSSTGSSSNTDASTDLAGSSTSGGSSTYGYSSDCGDGSVSSTG 900
901 SSSNTDASTDLAGSSTSGGSSTYGYSSDSRDGSVSSTGSSSNTDASTDLA 950
951 GSSTSGGSSTYGYSSNSRDGSVSSTGSSSNTDASTDLTGSSTSGGSSTYG 1000
1001 YSSSNRDGSVLATGSSSNTDASTTEESTTSAGSSTEGYSSSSHDGSVTST 1050
1051 DGSSTSGGASSSSASTAKSDAASSEDGFWWWNRRKSGSGHKSATVQSSTT 1100
1101 DKTSTDSASSTDSTSSTSGASTTTSGSSSTSGGSSTSDASSTSSSVSRSH 1150
1151 HSGVNRLLHKPGQGKICLCFENIFDIPYHLRKNIGV 1186
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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