 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q8BHG1 from www.uniprot.org...
The NucPred score for your sequence is 0.44 (see score help below)
1 MLRRVAVAAVCVTGRKLRCEAGRELTALGRIEARGLCEESSKPFPTLTMP 50
51 GRNKAKSTCSCPDLQPNGQDLGESGRLARLGADESEEEGRSFSNVGDPEI 100
101 IKSPSDPKQYRYIKLQNGLQALLISDLSNVEGKTGNATDEEEEEEEEEEE 150
151 EDDDDDDDDDDDDEDSGAEIQDDDEEGFDDEEEFDDDDDDEHDDDDLENE 200
201 ENELEELEERVEARKKTTEKQSAAALCVGVGSFADPDDLPGLAHFLEHMV 250
251 FMGSLKYPDENGFDAFLKKHGGSDNASTDCERTVFQFDVQRKYFKEALDR 300
301 WAQFFIHPLMIRDAIDREVEAVDSEYQLARPSDANRKEMLFGSLARPGHP 350
351 MGKFFWGNAETLKHEPKKNNIDTHARLREFWMRYYSAHYMTLVVQSKETL 400
401 DTLEKWVTEIFSQIPNNGLPKPNFSHLTDPFDTPAFNKLYRVVPIRKIHA 450
451 LTITWALPPQQQHYRVKPLHYISWLVGHEGKGSILSYLRKKCWALALFGG 500
501 NGETGFEQNSTYSVFSISITLTDEGYEHFYEVAHTVFQYLKMLQKLGPEK 550
551 RVFEEIQKIEDNEFHYQEQTDPVEYVENMCENMQLYPRQDFLTGDQLLFE 600
601 YKPEVIAEALNQLVPQKANLVLLSGANEGRCDLKEKWFGTQYSIEDIENS 650
651 WTELWKSNFDLNPDLHLPAENKYIATDFTLKAFDCPETEYPAKIVNTAQG 700
701 CLWYKKDNKFKIPKAYIRFHLISPLIQKSAANVVLFDIFVNILTHNLAEP 750
751 AYEADVAQLEYKLVAGEHGLIIRVKGFNHKLPLLFQLIIDYLTEFSSTPA 800
801 VFTMITEQLKKTYFNILIKPETLAKDVRLLILEYSRWSMIDKYQALMDGL 850
851 SLDSLLNFVKDFKSQLFVEGLVQGNVTSTESMDFLKYVVDKLNFAPLERE 900
901 MPVQFQVVELPSGHHLCKVRALNKGDANSEVTVYYQSGTRSLREYTLMEL 950
951 LVMHMEEPCFDFLRTKQTLGYHVYPTCRNTSGILGFSVTVGTQATKYNSE 1000
1001 TVDKKIEEFLSSFEEKIENLTEDAFNTQVTALIKLKECEDTHLGEEVDRN 1050
1051 WNEVVTQQYLFDRLAHEIEALKSFSKSDLVSWFKAHRGPGSKMLSVHVVG 1100
1101 YGKYELEEDGAPFGEDSNSREGMQLTYLPPSPVLAESTTPITDIRAFTAT 1150
1151 LSLFPYHKIVK 1161
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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