 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P97789 from www.uniprot.org...
The NucPred score for your sequence is 0.93 (see score help below)
1 MGVPKFYRWISERYPCLSEVVKEHQIPEFDNLYLDMNGIIHQCSHPNDDD 50
51 VHFRISDDKIFTDIFHYLEVLFRIIKPRKVFFMAVDGVAPRAKMNQQRGR 100
101 RFRSAKEAEDKIKKAIEKGETLPTEARFDSNCITPGTEFMARLHEHLKYF 150
151 VNMKISTDKSWQGVTIYFSGHETPGEGEHKIMEFIRSEKAKPDHDPNTRH 200
201 CLYGLDADLIMLGLTSHEAHFSLLREEVRFGGKKTQRVCAPEETTFHLLH 250
251 LSLMREYIDYEFSALKEKITFKYDIEKIIDDWILMGFLVGNDFIPHLPHL 300
301 HINHDALPLLYGTYIAILPELGGYINESGHLNLPRFERYLVKLSDFDREH 350
351 FSEVFVDLKWFESKVGNKYLNEAAGAAAEEAKNCKEKRKPKGQENSLSWA 400
401 ALDKSEGEGVASRDNFEDETEDDDLFETEFRQYKRTYYMTKMGVDVVSDE 450
451 FLANQAACYVQAIQWILHYYYHGVQSWSWYYPYHYAPFLSDIRSISTLKI 500
501 HFELGKPFKPFEQLLAVLPSASKNLLPTCYQHLMTSEDSPIIEYYPPDFK 550
551 TDLNGKQQEWEAVVLIPFIDETRLLEAMETCNHSLKKEERKRNQHSECLM 600
601 CWYDRDTEFTYSSPWPEKFPAIERCCTRYKMISLDAWRVDINKNKITRVD 650
651 QKALYFCGFPTLKHIKHKFFLKKSGVQVFQQSSRGENLMLEISVNAEPDE 700
701 LRIENIASAVLGKAVFVNWPHLEEARVVAVSDGETKFYIEEPPGTQKVYL 750
751 GKTAPPSKVIQLTDKEQSNWTKEIQGISEQYLRRKGIIINETSAVVYAQL 800
801 LTGRKYQISQNGEVRLEKQWSKQILPFVYQTIVKDIRAFDSRFSNIKTLD 850
851 DLFPPRTMVFMLGTPYYGCTGEVQDSGDLITEGRIRVVFSIPCEPNLDAL 900
901 IQNQHKYSIKYNPGYVLAGRLGVSGYLVSRFTGSIFIGRGSRRNPHGDHK 950
951 ANVGLNLKFNKKNEEVPGYTKKVGNEWMYSSAAEQLLAEYIERAPELFSY 1000
1001 IAKNSQEDVFYEDDIWPGENENGAEKVQEIITWLKGHPVSTLSRSSCDLH 1050
1051 ILDAAIVEKIEEEVEKCKQRKSNKKVRVTVKPHLMYRPLEQQHGVIPDRD 1100
1101 AEFRLFDRVVNVRESFSVPVGLRGTVIGIKGASREADVLFEVLFDEEFPG 1150
1151 GLTIRCSPGRGYRLPTSALVNLSHGSRCETGNQKLTAIVKPQPSVSHCSA 1200
1201 APSGHLGGLNHSPQSPFLPTQVPTKGDDEFCNIWQSLQGAGKTQHLQPTV 1250
1251 QEKGAVLPQEISQVTEGHKSGFTDHSVRHQQRKHDSQRKFKEEYKSPKAE 1300
1301 CQSQKLSSKQTSGGSARCSIKLLKRNESPGTSEAQKVVTSYPNAVHKPPS 1350
1351 GIENFLASLNLSKENEAQLPHHGEPPDEADLSPQSFAMKGTRMLKEILKI 1400
1401 DSPDTRDSKNDMKKSDNEATVSSRRDERGVSAHPKPTCHMNKPHGTNEFQ 1450
1451 NVASVDSVCWPGQMPPVSTPVTELSRICSLVGMPQPDFSFLRTTQTMTVC 1500
1501 QVKLSNGLLVHGPQCHSESEAKERAALFALQQLGSLGVSFPLPPPIFTNY 1550
1551 PPAVPPGAVPPVFTQPTANIMPSSSHLFGSVSWRPPVPVAGNAFHYPSYP 1600
1601 GTMPLAGGVPGGVHSQFIPLQVTKKRVANRKNFENKEAQSSQATPLQTNK 1650
1651 PGSSEATKMTPQESPPASSSSSQAAQPVSSHVETASQGHVGSQPRSAPSS 1700
1701 SKRKSRKLAVNFSVSKPSE 1719
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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