 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q53T03 from www.uniprot.org...
The NucPred score for your sequence is 0.72 (see score help below)
1 MRRSKADVERYVASVLGLTPSPRQKSMKGFYFAKLYYEAKEYDLAKKYIC 50
51 TYINVQERDPKAHRFLGLLYELEENTEKAVECYRRSVELNPTQKDLVLKI 100
101 AELLCKNDVTDGRAKYWVERAAKLFPGSPAIYKLKEQLLDCEGEDGWNKL 150
151 FDLIQSELYVRPDDVHVNIRLVELYRSTKRLKDAVAHCHEAERNIALRSS 200
201 LEWNSCVVQTLKEYLESLQCLESDKSDWQATNTDLLLAYANLMLLTLSTR 250
251 DVQENRELLESFDSALQSAKSSLGGNDELSATFLEMKGHFYMYAGSLLLK 300
301 MGQHGNNVQWRALSELAALCYLIAFQVPRPKIKLREGKAGQNLLEMMACD 350
351 RLSQSGHMLLSLSRGKQDFLKEVVETFANKIGQSALYDALFSSQSPKDTS 400
401 FLGSDDIGKIDVQEPELEDLARYDVGAIRAHNGSLQHLTWLGLQWNSLPA 450
451 LPGIRKWLKQLFHRLPHETSRLETNAPESICILDLEVFLLGVVYTSHLQL 500
501 KEKCNSHHSSYQPLCLPFPVCKQLCTERQKSWWDAVCTLIHRKAVPGNLA 550
551 KLRLLVQHEINTLRAQEKHGLQPALLVHWAKYLQKTGSGLNSFYGQLEYI 600
601 GRSVHYWKKVLPLLKIIKKNSIPEPIDPLFKHFHSVDIQASEIVEYEEDA 650
651 HITFAMLDAVNGNIEDAVTAFESIKSVVSYWNLALIFHRKAEDIENDALS 700
701 PEEQEECRNYLTKTRDYLIKIIDDGDSNLSVVKKLPVPLESVKQMLNSVM 750
751 QELEDYSEGGPLYKNGSLRNADSEIKHSTPSPTKYSLSPSKSYKYSPETP 800
801 PRWTEDRNSLLNMICQQVEAIKKEMQELKLNSSKSASRHRWPTENYGPDS 850
851 VPDGYQGSQTFHGAPLTVATTGPSVYYSQSPAYNSQYLLRPAANVTPTKG 900
901 SSNTEFKSTKEGFSIPVSADGFKFGISEPGNQEKKREKPLENDTGFQAQD 950
951 ISGRKKGRGVIFGQTSSTFTFADVAKSTSGEGFQFGKKDLNFKGFSGAGE 1000
1001 KLFSSRYGKMANKANTSGDFEKDDDAYKTEDSDDIHFEPVVQMPEKVELV 1050
1051 TGEEGEKVLYSQGVKLFRFDAEVRQWKERGLGNLKILKNEVNGKLRMLMR 1100
1101 REQVLKVCANHWITTTMNLKPLSGSDRAWMWSASDFSDGDAKLERLAAKF 1150
1151 KTPELAEEFKQKFEECQRLLLDIPLQTPHKLVDTGRAAKLIQRAEEMKSG 1200
1201 LKDFKTFLTNDQTKVTEEENKGSGTGAAGASDTTIKPNAENTGPTLEWDN 1250
1251 YDLREDALDDSVSSSSVHASPLASSPVRKNLFRFDESTTGSNFSFKSALS 1300
1301 LSKSPAKLNQSGTSVGTDEESVVTQEEERDGQYFEPVVPLPDLVEVSSGE 1350
1351 ENEQVVFSHRAEIYRYDKDVGQWKERGIGDIKILQNYDNKQVRIVMRRDQ 1400
1401 VLKLCANHRITPDMSLQNMKGTERVWVWTACDFADGERKVEHLAVRFKLQ 1450
1451 DVADSFKKIFDEAKTAQEKDSLITPHVSRSSTPRESPCGKIAVAILEETT 1500
1501 RERTDVIQGDDVADAASEVEVSSTSETTTKAVVSPPKFVFVSESVKRIFS 1550
1551 SEKSKPFVFGNSSATGSLFGFSFNAPLKSNNSETSSVAQSGSESKVEPKK 1600
1601 CELSKNSDIEQSSDSKVKNLSASFPTEESSINYTFKTPEKEPPLWHAEFT 1650
1651 KEELVQKLRSTTKSADHLNGLLREIEATNAVLMEQIKLLKSEIRRLERNQ 1700
1701 EREKSAANLEYLKNVLLQFIFLKPGSERERLLPVINTMLQLSPEEKGKLA 1750
1751 AVAQDEEENASRSSG 1765
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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