 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P26234 from www.uniprot.org...
The NucPred score for your sequence is 0.44 (see score help below)
1 MPVFHTRTIESILEPVAQQISHLVIMHEEGEVDGKAIPDLTAPVAAVQAA 50
51 VSNLVRVGKETVQTTEDQILKRDMPPAFIKVENACTKLVQAAQMLQSDPY 100
101 SVPARDYLIDGSRGILSGTSDLLLTFDEAEVRKIIRVCKGILEYLTVAEV 150
151 VETMEDLVTYTKNLGPGMTKMAKMIDERQQELTHQEHRVMLVNSMNTVKE 200
201 LLPVLISAMKIFVTTKNSKNQGIEEALKNRNFTVEKMSAEINEIIRVLQL 250
251 TSWDEDAWASKKDTEAMKRALASIDSKLNQAKGWLRDPTASPGDAGEQAI 300
301 RQILDEAGKVGELCAGKERREILGTCKMLGQMTDQVADLRARGQGASPVA 350
351 MQKAQQVSQGLDVLTAKVENAARKLEAMTNSKQSIAKKIDAAQNWLADPN 400
401 GGPEGEEQIRGALAEARKIAELCDDPKERDDILRSLGEISALTSKLADLR 450
451 RQGKGDSPEARALAKQVATALQNLQTKTNRAVANSRPAKAAVHLEGKIEQ 500
501 AQRWIDNPTVDDRGVGQAAIRGLVAEGHRLANVMMGPYRQDLLAKCDRVD 550
551 QLTAQLADLAARGEGESPQARALASQLQDSLKDLKARMQEAMTQEVSDVF 600
601 SDTTTPIKLLAVAATAPPDAPNREEVFDERAANFENHSGRLGATAEKAAA 650
651 VGTANKSTVEGIQASVKTARELTPQVVSAARILLRNPGNQAAYEHFETMK 700
701 NQWIDNVEKMTGLVDEAIDTKSLLDASEEAIKKDLDKCKVAMANIQPQML 750
751 VAGATSIARRANRILLVAKREVENSEDPKFREAVKAASDELSKTISPMVM 800
801 DAKAVAGNISDPGLQKSFLDSGYRILGAVAKVREAFQPQEPDFPPPPPDL 850
851 EQLRLTDELAPPKPPLPEGEVPPPRPPPPEEKDEEFPEQKAGEVINQPMM 900
901 MAARQLHDEARKWSSKPGNPAAKVGIGVVAEADAADAVGFPVPSDMEDDY 950
951 EPELLLMPSSQPVNQPILAAAQSLHREATKWSSKGNDIIAAAKRMALLMA 1000
1001 EMSRLVRGGSGTKRALIQCAKDIAKASDEVTRLAKEVAKQCTDKRIRTNL 1050
1051 LQVCERIPTISTQLKILSTVKATMLGRTNISDEESEQATEMLVHNAQNLM 1100
1101 QSVKETVREAEAASIKIRTDAGFTLRWVRKTPWYQ 1135
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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