 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5R4H6 from www.uniprot.org...
The NucPred score for your sequence is 0.51 (see score help below)
1 MLRKVTVAAVCATRRKLCEAGRELAALWGIETRGRCEDSAAVRPFPILAM 50
51 PGRNKAKSTCSCPDLQPNGQDLGENSRVARLGADESEEEGRRGSLSNAGD 100
101 PEIVKSPSDPKQYRYIKLQNGLQALLISDLSNMEGKTGNTTDDEEEEEVE 150
151 EEEEDDDEDSGAEIEDDDEEGFDDEDEFDDEHDDDLDTEDNELEELEERA 200
201 EARKKKTTEKQSAAALCVGVGSFADPDDLPGLAHFLEHMVFMGSLKYPDE 250
251 NGFDAFLKKHGGSDNASTDCERTVFQFDVQRKYFKEALDRWAQFFIHPLM 300
301 IRDAIDREVEAVDSEYQLARPSDANRKEMLFGSLARPGHPMGKFFWGNAE 350
351 TLKHEPKKNNIDTHARLREFWLRYYSAHYMTLVVQSKETLDTLEKWVTEI 400
401 FSQIPNNGLPRPNFGHLTDPFDTPAFNKLYRVVPIRKIHALTITWALPPQ 450
451 QQHYRVKPLHYISWLVGHEGKGSILSFLRKKCWALALFGGNGETGFEQNS 500
501 TYSVFSISITLTDEGYEHFYEVAYTVFQYLKMLQKLGPEKRIFEEIQKIE 550
551 DNEFHYQEQTDPVEYVENMCENMQPYPLQDILTGDQLLFEYKPEVIGEAL 600
601 NQLVPQKANLVLLSGANEGKCDLKEKWFGTQYSIEDIENSWAELWNSNFE 650
651 LNPDLHLPAENKYIATDFTLKAFDCPETEYPVKIVNTPQGCLWYKKDNKF 700
701 KIPKAYIRFHLISPLIQRSAANVVLFDIFANILTHNLAEPAYEADVAQLE 750
751 YKLVAGEHGLIIRVKGFNHKLPLLFQLIVDYLAEFNSTPAVFTMITEQLK 800
801 KTYFNILIKPETLAKDVRLLILEYARWSMIDKYQALMDGLSLESLLSFVK 850
851 EFKSQLFVEGLVQGNVTSTESMDFLKYVVDKLNFKPLEQEMPVQFQVVEL 900
901 PSGHHLCKVKALNKGDANSEVTVYYQSGTRSLREYTLMELLVMHMEEPCF 950
951 DFLRTKQTLGYHVYPTCRSTSGILGFSVTVGTQATKYNSEVVDKKIEEFL 1000
1001 SSFEEKIENLTEEAFNTQVTALIKLKECEDTHLGEEVDRNWNEVVTQQYL 1050
1051 FDRLAHEIEALKSFSKSDLVNWFKAHRGPGSKMLSVHVVGYGKYELEEDG 1100
1101 TPSSEDSNSSCEVMQLTYLPTSPLLADCIIPITDIRAFTTTLNLLPYHKI 1150
1151 VK 1152
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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