 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5XKL5 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MARCGEGSAAPMVLLGSAGVCSKGLQRKGPCERRRLKATVSEQLSQDLLR 50
51 LLREEFHTDVTFSVGCTLFKAHKAVLLARVPDFYFHTIGQTSNSLTNQEP 100
101 IAVENVEALEFRTFLQIIYSSNRNIKNYEEEILRKKIMEIGISQKQLDIS 150
151 FPKCENSSDCSLQKHEIPEDISDRDDDFISNDNYDLEPASELGEDLLKLY 200
201 VKPCCPDIDIFVDGKRFKAHRAILSARSSYFAAMLSGCWAESSQEYVTLQ 250
251 GISHVELNVMMHFIYGGTLDIPDKTNVGQILNMADMYGLEGLKEVAIYIL 300
301 RRDYCNFFQKPVPRTLTSILECLIIAHSVGVESLFADCMKWIVKHFARFW 350
351 SERSFANIPPEIQKSCLNMLIQSLNDKNAAFLLMESDRLIISLPRVKWTE 400
401 AALTMASQLQEKCIAFIVDNFSKIIQSENFALLLQSQAMSSTADLLDTIL 450
451 KAIEENITTENSCSLLMALDTLLNSDSTKEMGFTCKIQALRDKLWIFLVQ 500
501 SFYAVRHTESWKLMSTDDQQKIQAAAFDKGDDRRLGKKPIFSSSQQRKQV 550
551 SDSGDIKIKSWRGNNKKECWSYLSTNKKMKSDGLGASGHSSSTNRNSINK 600
601 TLKQDDVKEKDGTKIASKITKELKTGGKNVSGKPKTVTKSKTENGDKARL 650
651 ENMSPRQVVERSATAAAAATGQKNLLNGKGVRNQEGQISGARPKVLTGNL 700
701 NVQAKAKPLKKATGKDSPCLSIAGPSSRSTDSSMEFSISTECLDEPKENG 750
751 STEEEKPSGHKLSFCDSPGQMMKNSVDSVKNSTVAIKSRPVSRVTNGTSN 800
801 KKSIHEQDTNVNNSVLKKVSGKGCSEPVPQAILKKRGTSNGCTAAQQRTK 850
851 STPSNLTKTQGSQGESPNSVKSSVSSRQSDENVAKLDHNTTTEKQAPKRK 900
901 MVKQVHTALPKVNAKIVAMPKNLNQSKKGETLNNKDSKQKMPPGQVISKT 950
951 QPSSQRPLKHETSTVQKSMFHDVRDNNNKDSVSEQKPHKPLINLASEISD 1000
1001 AEALQSSCRPDPQKPLNDQEKEKLALECQNISKLDKSLKHELESKQICLD 1050
1051 KSETKFPNHKETDDCDAANICCHSVGSDNVNSKFYSTTALKYMVSNPNEN 1100
1101 SLNSNPVCDLDSTSAGQIHLISDRENQVGRKDTNKQSSIKCVEDVSLCNP 1150
1151 ERTNGTLNSAQEDKKSKVPVEGLTIPSKLSDESAMDEDKHATADSDVSSK 1200
1201 CFSGQLSEKNSPKNMETSESPESHETPETPFVGHWNLSTGVLHQRESPES 1250
1251 DTGSATTSSDDIKPRSEDYDAGGSQDDDGSNDRGISKCGTMLCHDFLGRS 1300
1301 SSDTSTPEELKIYDSNLRIEVKMKKQSNNDLFQVNSTSDDEIPRKRPEIW 1350
1351 SRSAIVHSRERENIPRGSVQFAQEIDQVSSSADETEDERSEAENVAENFS 1400
1401 ISNPAPQQFQGIINLAFEDATENECREFSATKKFKRSVLLSVDECEELGS 1450
1451 DEGEVHTPFQASVDSFSPSDVFDGISHEHHGRTCYSRFSRESEDNILECK 1500
1501 QNKGNSVCKNESTVLDLSSIDSSRKNKQSVSATEKKNTIDVLSSRSRQLL 1550
1551 REDKKVNNGSNVENDIQQRSKFLDSDVKSQERPCHLDLHQREPNSDIPKN 1600
1601 SSTKSLDSFRSQVLPQEGPVKESHSTTTEKANIALSAGDIDDCDTLAQTR 1650
1651 MYDHRPSKTLSPIYEMDVIEAFEQKVESETHVTDMDFEDDQHFAKQDWTL 1700
1701 LKQLLSEQDSNLDVTNSVPEDLSLAQYLINQTLLLARDSSKPQGITHIDT 1750
1751 LNRWSELTSPLDSSASITMASFSSEDCSPQGEWTILELETQH 1792
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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