 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q2VLG6 from www.uniprot.org...
The NucPred score for your sequence is 0.28 (see score help below)
1 MSKLRMVPHGNSGSADFRRCFALLCPSAVAVVSILSTCLMTNSLGRADKE 50
51 MRLTDGEDNCSGRVEVKVQEEWGTVCNNGWGMDEVSVICRQLGCPTAIKA 100
101 AGWANSRAGSGRIWMDHVSCRGNESALWDCKHDGWGKHNCSHQQDAGVTC 150
151 SDGSSLEMRLMNGGNQCSGRIEVKFQGQWGTVCDDNFNIDHASVVCKQLE 200
201 CGSAVSFSGSANFGEGSGPIWFDDLVCSGNESALWNCKHEGWGKHNCDHA 250
251 EDVGVICLDGADLSLRLVDGVTECSGRLEVKFQGEWGTVCDDGWDSNDAA 300
301 VVCKQLGCPTAVTAIGRVNASEGSGHIWLDNLSCQGDESALWQCRHHEWG 350
351 KHYCNHNEDAGVTCSDGSDLELRLVGGGSRCAGTVEVEIQKLLGKVCDRG 400
401 WGLKEADVVCKQLGCGSALKTSYQRYSKVKATNTWLFLSRCSGNETSLWD 450
451 CKNWQWGGLSCDHYEEAKVTCSAHREPRLVGGDIPCSGRVEVKHGDTWGT 500
501 VCDSDFSLEAASVLCRELQCGTVISILGGAHFGEGNGQIWAEEFQCEGQE 550
551 SHLSLCSVASRPDGTCSHSRDVGVVCSRYTEIRLVNGQSPCEGRVELKIL 600
601 GNWGSLCNSHWDIEDAHVFCQQLKCGVALSIPGGAHFGKGSGQIWRHMFH 650
651 CTGTEQHMGDCPVTALGATLCSAGQVASVICSGNQSQTLSPCNSTSLDPT 700
701 RSTTSEESAVACIASGQLRLVNGGGRCAGRIEVYHEGSWGTICDDSWDLS 750
751 DAHVVCRQLGCGVAINATGSAHFGEGTGPIWLDEVNCNGKESHIWQCRSH 800
801 GWGQHNCRHKEDAGVICSEFMSLRLIDETSRDICAGRLEVFYNGAWGSVG 850
851 KSNMSATTVEVVCRQLGCADKGSINPASSDKPMSRHMWVDNVQCPKGPDT 900
901 LWQCPSSPWKQRVASSSEETWITCANKIRLQEGTSNCSGRVELWHGGSWG 950
951 TVCDDSWDLEDAQVVCRQLGCGPALEALKEAAFGQGTGPIWLNDVKCKGN 1000
1001 ESSLWDCPARPWGHSDCGHKEDAAVRCSEIAMAQRSSNPRGHSSLVALGI 1050
1051 FGVILLAFLIALLLWTQRRRQQQRLTVSLRGENSVHQIQYREMNSSLKAD 1100
1101 DLDVLTSSEYPNESDDFNDAGLISVSKSLPISG 1133
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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