 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q5SQE2 from www.uniprot.org...
The NucPred score for your sequence is 0.72 (see score help below)
1 MKKFRKVLDGLTTSSPVNPGGSPGCGSAAGTPSAAPTPRELEIQETLMSE 50
51 HFQICKTVRHGFPYQPTALAFDPVQKILAIGSRSGGIRMYPFLSFPHLGR 100
101 PGVDCHSQHESGAAVLQMQFLINEGALVTACADDTLHLWSLRQRLPAILH 150
151 SLKFNRERITFCHLPFQSKWLYVGTERGNTHIVNIESFILSGYVIMWNKA 200
201 IELSTKTHPGPVVHLSDSPKDEGKLLIGFESGTIVMWDLRAKRADFRIYY 250
251 DEAIHSVSWHHEGRQFMCSHSDGSLSMWNMRNTAKPFQVTFPHGKTQRDG 300
301 RKESCKPILKVEYKTSRNSSEAFVIFSGGLSYDKAGRRPTLTIMHGKAIT 350
351 VLEMDYPIVDFMVLCETPYLNEVQEPYAVVVLLEKDFVVVDLTQSNFPIF 400
401 ENPYPMDVHESPVTCTAYFADCPPDIIPVLYSIGAKHKKTGYSHKEWPVS 450
451 GGTWTVGSQTYPEIIITGHADGSIKFWDATAITLQMLYKLKTSKVFEKPK 500
501 TGDMGRSADLVEEDPYAVQMISWCPQSRIFCVVGISAHVILYRFSKHDAN 550
551 TIITSLELRLQCEMEDVISPSDTENTPCFSDPSGHSPQPQPPSPRSNTPD 600
601 SVRDSIPCLKVKDRMIRMPPGYQAELVVQLLWVDGEPPQQITCLDLNSAY 650
651 GLLALGNCNGLAVVDYLQKTILLCMSTLELYGSADPFQRLTRSPRKNRQS 700
701 TSGLTELSDNQVSLDLERSKSPTSDHVNGHCTSPTSQPCPSGRARVPGGP 750
751 EGPRLARRGPGRPPFRKAQSAACMEVSLPVSSLTEENSFSRSRSSSVSSI 800
801 DRETKEAVTTLQFAESYGRKSDSLPTPCLWVGTSLGLVLIIPMSIPTDEQ 850
851 ERQEDPVTVAPTGTVLMLKGSVLRFGFLDCGGALINSPYEVWRDQHAPDD 900
901 PDRPRKRKLVNFSPSSSQEACGDGHLAVVCSERQAKVFYMPSQACLYVHN 950
951 ITESSFVLRADVVSVSNSVCLACFCANGHIMILSLPSLRPLLDVSYLPLT 1000
1001 DMRIARTFCFTNGGQALYLCSPTEIQRITYSQEMCETLGELFTPIETPEA 1050
1051 QNRGFLKGFFGGNAQTFDREELFGEASAGKASRSLAQHIPGQGGIEGMKA 1100
1101 AAGGVVGDLARARIALDERGQRLGELEERTALMMTSAETFSKHAHELMLK 1150
1151 CKDKKWYQF 1159
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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