 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5BDW4 from www.uniprot.org...
The NucPred score for your sequence is 0.91 (see score help below)
1 MARHGDTRSPSPVGSTYSSSRRSRRDDDRYERKRDDGRSYRRSRSPERRY 50
51 RERDRDRDSYRRRDHSVDRRDSHRDEDNYRRRDRSRDRRRSRDRDHDRDY 100
101 RRRSRSRDRDYRSRRDDSRDRVRRRTDDSADLKRKSRRDDSRDRTRGAEP 150
151 KSREASTPAIPTRTGPTDDEKRAERLAKLEAWKQKQAAEKERKQKEAEAS 200
201 GGPRNILEEIDRKSGLSPAVSSPQSPATQGVDAAPAAYAGKFDPKAIAKN 250
251 AAQTPAAPSVLGNDVAVPSSAKTSNAQTARVQASKASGNAPSPAVLKAKG 300
301 NVGSFGLGTKQVADNEKSIATKTLGFGEEESTRRKLERLPTPPLDDADAS 350
351 KTAETNADDDDDVDMQDGETEEDAAAAARVAAERREERLQNESLTKTTNG 400
401 NTTAKAEEADKMEVDAQEEELDPLDAFMSELAESAPPKKKAGAKFSKAQE 450
451 PEAIFGDEHDVSMTAVGEGDAEDFLAIASKAKKKKDIPTVDHNKVEYEPF 500
501 RRKFYTEPSDLAQMSEEEAANLRLELDGIKVRGLDVPKPVQKWSQCGLGI 550
551 QTLDVIDKLGFASLTSIQAQAIPAIMSGRDVIGVAKTGSGKTMAFLIPMF 600
601 RHIKDQRPLENMEGPIGLIMTPTRELATQIHKDCKPFLKALNLRAVCAYG 650
651 GAPIKDQIAELKRGAEIIVCTPGRMIDLLAANAGRVTNLRRVTYVVLDEA 700
701 DRMFDMGFEPQVMKILSNVRPDRQTVLFSATFPRNMEALARKTLTKPIEI 750
751 VVGGRSVVAPEITQIVEVCNEEKKFVRLLELLGNLYSTDENEDARSLIFV 800
801 DRQEAADTLLRELMRKGYPCMSIHGGKDQIDRDSTIEDFKAGIFPVLIAT 850
851 SVAARGLDVKQLKLVVNYDAPNHLEDYVHRAGRTGRAGNTGTAVTFLTED 900
901 QERYSVDIAKALKQSGQEVPEAVQKLVDSFLEKVKAGKEKASNSGFGGKG 950
951 LERLDQERDAARMRERRTYKTGEEGEDEEEKDEKKNEQAEEQFNKVLSAV 1000
1001 QSTSAQLPGVPKGIDLDGKITVHKREVDPNAPNNPLDKVGSAVADIHARL 1050
1051 SRAGVMRSGVPIDNRGPDAGAFHATLEINDFPQKARWAVTNRTNVAKILE 1100
1101 ATGTSITTKGSFYPAGKEPGPGENPKLYILVEGETELSVTNAMRELMRLL 1150
1151 KEGTIAAVDSESRAPASGRYSVV 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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