 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P35448 from www.uniprot.org...
The NucPred score for your sequence is 0.29 (see score help below)
1 MKGIFLLLMLVMPQTHQAAESGNDDNSVFDLFELTGYNRKAGSRKPQGLH 50
51 LVKGPDPSSPAYRIEDADLIPPLPEDKFQDLLDAIRADRGFILLATLRQA 100
101 KKSRGALLSVERKDGGGHIFSLISNGRARTLDLSLSGERKQQVVSVEDAV 150
151 LATGNWTNITLFVQEDRAQLYVGCNKMENAELDVPIQKIFTENLASTAHL 200
201 RVAKGGVKDNFQGVLQNVRFVFGTTLEAILRNKGCLSMTNSVITLDNPVN 250
251 GSSPAIRTNYIGHKTKDLQAVCGFSCDDLSKLFAEMKGLRTLVTTLKDQV 300
301 TKETEKNELIAQIVTRTPGVCLHNGVLHKNRDEWTVDSCTECTCQNSATI 350
351 CRKVSCPLMPCTNATIPDGECCPRCWPSDSADDDWSPWSDWTPCSVTCGH 400
401 GIQQRGRSCDSLNNPCEGSSVQTRSCQIQDCDKRFKQDGGWSHWSPWSSC 450
451 SVTCGSGQITRIRLCNSPVPQLNGKQCEGEGRENKPCQKDPCPINGQWGP 500
501 WSLWDTCTVTCGGGMQKRERLCNNPKPQYEGKDCIGEPTDSQICNKQDCP 550
551 IDGCLSNPCFAGVKCTSFIDGSWKCGSCPPGYRGNGITCKDIDECKEVPD 600
601 ACFTLNGVHRCENTEPGYNCLPCPPRFTGTQPFGKGIEEAKANKQVCKPR 650
651 NPCADGTHDCHKNARCIYLGHYSDPMFRCECRPGYAGNGIICGEDTDLDG 700
701 WPNENLTCVDNATYHCLKDNCPNLPNSGQEDYDKDGMGDACDKDDDNDGI 750
751 LDDRDNCQFVYNPAQYDYDRDDVGDRCDNCPYNHNPDQADTDRNGEGDAC 800
801 SVDIDGDGILNERDNCAYVYNVDQKDTDKDGVGDQCDNCPLEHNPEQTDS 850
851 DSDLIGDKCDNNQDIDEDGHQNNLDNCPYIPNANQADHDKDGKGDACDHD 900
901 DDNDGVPDDKDNCRLVPNPDQTDTNGDGRGDACQYDFDDDSIPDAEDVCP 950
951 ENVEISTTDFRKFQMVPLDPKGTSQIDPNWVVRHQGKELVQTVNCDPGIA 1000
1001 VGFDEFSAVDFSGTFFINTERDDDYAGFVFGYQSSSRFYVVMWKQITQTY 1050
1051 WDTTPTVAQGYSGLSIKVVNSTSGPGEHLRNALWHTGNTPGQVRTLWHDP 1100
1101 HQKGWKDFTAYRWHLTHRPKTGFIRVVMYEGKRVMADSGPIYDKTYAGGR 1150
1151 LGLFVFSQEMVFFSDLKYECRDS 1173
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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