 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P46023 from www.uniprot.org...
The NucPred score for your sequence is 0.39 (see score help below)
1 MATMSGTTIVCLIYLTTMLGNSQGVNLKIESPSPPTLCSVEGTFHCDDGM 50
51 LQCVLMGSKCDGVSDCENGMDESVETCGCLQSEFQCNHTTCIDKILRCDR 100
101 NDDCSNGLDERECDIYICPLGTHVKWHNHFCVPRDKQCDFLDDCGDNSDE 150
151 KICERRECVATEFKCNNSQCVAFGNLCDGLVDCVDGSDEDQVACDSDKYF 200
201 QCAEGSLIKKEFVCDGWVDCKLTFADELNCKLCDEDDFRCSDTRCIQKSN 250
251 VCDGYCDCKTCDDEEVCANNTYGCPMDTKYMCRSIYGEPRCIDKDNVCNM 300
301 INDCRDGNVGTDEYYCSNDSECKNFQAAMGFFYCPEERCLAKHLYCDLHP 350
351 DCINGEDEQSCLAPPKCSQDEFQCHHGKCIPISKRCDSVHDCVDWSDEMN 400
401 CENHQCAANMKSCLSGHCIEEHKWCNFHRECPDGSDEKDCDPRPVCEANQ 450
451 FRCKNGQCIDPLQVCVKGDKYDGCADQSHLINCSQHICLEGQFRCRKSFC 500
501 INQTKVCDGTVDCLQGMWDENNCRYWCPHGQAICQCEGVTMDCTGQKLKE 550
551 MPVQQMEEDLSKLMIGDNLLNLTSTTFSATYYDKVTYLDLSRNHLTEIPI 600
601 YSFQNMWKLTHLNLADNNITSLKNGSLLGLSNLKQLHINGNKIETIEEDT 650
651 FSSMIHLTVLDLSNQRLTHVYKNMFKGLKQITVLNISRNQINSIDNGAFN 700
701 NLANVRLIDLSGNVIKDIGQKVFMGLPRLVELKTDSYRFCCLAPEGVKCS 750
751 PKQDEFSSCEDLMSNHVLRVSIWVLGVIALVGNFVVIFWRVRDFRGGKVH 800
801 SFLITNLAIGDFLMGVYLLIIATADTYYRGVYISHDENWKQSGLCQFAGF 850
851 VSTFSSELSVLTLSTITLDRLICILFPLRRTRLGLRQAIIVMSCIWVLVF 900
901 LLAVLPLLGFSYFENFYGRSGVCLALHVTPDRRPGWEYSVGVFILLNLLS 950
951 FVLIASSYLWMFSVAKKTRSAVRTAESKNDNAMARRMTLIVMTDFCCWVP 1000
1001 IIVLGFVSLAGARADDQVYAWIAVFVLPLNSATNPVIYTLSTAPFLGNVR 1050
1051 KRANRFRKSFIHSFTGDTKHSYVDDGTTHSYCEKKSPYRQLELKRLRSLN 1100
1101 SSPPMYYNTELHSDS 1115
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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