 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q12965 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MGSKGVYQYHWQSHNVKHSGVDDMVLLSKITENSIVENLKKRYMDDYIFT 50
51 YIGSVLISVNPFKQMPYFGEKEIEMYQGAAQYENPPHIYALADNMYRNMI 100
101 IDRENQCVIISGESGAGKTVAAKYIMSYISRVSGGGTKVQHVKDIILQSN 150
151 PLLEAFGNAKTVRNNNSSRFGKYFEIQFSPGGEPDGGKISNFLLEKSRVV 200
201 MRNPGERSFHIFYQLIEGASAEQKHSLGITSMDYYYYLSLSGSYKVDDID 250
251 DRREFQETLHAMNVIGIFAEEQTLVLQIVAGILHLGNISFKEVGNYAAVE 300
301 SEEFLAFPAYLLGINQDRLKEKLTSRQMDSKWGGKSESIHVTLNVEQACY 350
351 TRDALAKALHARVFDFLVDSINKAMEKDHEEYNIGVLDIYGFEIFQKNGF 400
401 EQFCINFVNEKLQQIFIELTLKAEQEEYVQEGIRWTPIEYFNNKIVCDLI 450
451 ENKVNPPGIMSILDDVCATMHAVGEGADQTLLQKLQMQIGSHEHFNSWNQ 500
501 GFIIHHYAGKVSYDMDGFCERNRDVLFMDLIELMQSSELPFIKSLFPENL 550
551 QADKKGRPTTAGSKIKKQANDLVSTLMKCTPHYIRCIKPNETKKPRDWEE 600
601 SRVKHQVEYLGLKENIRVRRAGYAYRRIFQKFLQRYAILTKATWPSWQGE 650
651 EKQGVLHLLQSVNMDSDQFQLGRSKVFIKAPESLFLLEEMRERKYDGYAR 700
701 VIQKSWRKFVARKKYVQMREEASDLLLNKKERRRNSINRNFIGDYIGMEE 750
751 HPELQQFVGKREKIDFADTVTKYDRRFKGVKRDLLLTPKCLYLIGREKVK 800
801 QGPDKGLVKEVLKRKIEIERILSVSLSTMQDDIFILHEQEYDSLLESVFK 850
851 TEFLSLLAKRYEEKTQKQLPLKFSNTLELKLKKENWGPWSAGGSRQVQFH 900
901 QGFGDLAVLKPSNKVLQVSIGPGLPKNSRPTRRNTTQNTGYSSGTQNANY 950
951 PVRAAPPPPGYHQNGVIRNQYVPYPHAPGSQRSNQKSLYTSMARPPLPRQ 1000
1001 QSTSSDRVSQTPESLDFLKVPDQGAAGVRRQTTSRPPPAGGRPKPQPKPK 1050
1051 PQVPQCKALYAYDAQDTDELSFNANDIIDIIKEDPSGWWTGRLRGKQGLF 1100
1101 PNNYVTKI 1108
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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