 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9Y2K9 from www.uniprot.org...
The NucPred score for your sequence is 0.62 (see score help below)
1 MKKFNFRKVLDGLTASSPGSGSSSGSNSGGGAGSGSVHPAGTAGVLREEI 50
51 QETLTSEYFQICKTVRHGFPHQPTALAFDPVQKILAIGTRTGAIRILGRP 100
101 GVDCYCQHESGAAVLQLQFLINEGALVSASSDDTLHLWNLRQKRPAILHS 150
151 LKFNRERITYCHLPFQSKWLYVGTERGNTHIVNIESFILSGYVIMWNKAI 200
201 ELSTKTHPGPVVHLSDSPRDEGKLLIGYENGTVVFWDLKSKRAELRVYYD 250
251 EAIHSIDWHHEGKQFMCSHSDGSLTLWNLKSPSRPFQTTIPHGKSQREGR 300
301 KSESCKPILKVEYKTCKNSEPFIIFSGGLSYDKACRRPSLTIMHGKAITV 350
351 LEMDHPIVEFLTLCETPYPNEFQEPYAVVVLLEKDLIVVDLTQSNFPIFE 400
401 NPYPMDIHESPVTCTAYFADCPPDLILVLYSIGVKHKKQGYSNKEWPISG 450
451 GAWNLGAQTYPEIIITGHADGSIKFWDASAITLQMLYKLKTSKVFEKQKV 500
501 GEGKQTCEIVEEDPFAIQMIYWCPESRIFCVSGVSAYVIIYKFSRHEITT 550
551 EIVSLEVRLQYDVEDIITPEPETSPPFPDLSAQLPSSRSLSGSTNTVASE 600
601 GVTKDSIPCLNVKTRPVRMPPGYQAELVIQLVWVDGEPPQQITSLAVSSA 650
651 YGIVAFGNCNGLAVVDFIQKTVLLSMGTIDLYRSSDLYQRQPRSPRKNKQ 700
701 FIADNFCMRGLSNFYPDLTKRIRTSYQSLTELNDSPVPLELERCKSPTSD 750
751 HVNGHCTSPTSQSCSSGKRLSSADVSKVNRWGPGRPPFRKAQSAACMEIS 800
801 LPVTTEENRENSYNRSRSSSISSIDKDSKEAITALYFMDSFARKNDSTIS 850
851 PCLFVGTSLGMVLIISLNLPLADEQRFTEPVMVLPSGTFLSLKGAVLTFS 900
901 CMDRMGGLMQPPYEVWRDPNNIDENEKSWRRKVVMNSSSASQEIGDHQYT 950
951 IICSEKQAKVFSLPSQTCLYVHNITETSFILQANVVVMCSSACLACFCAN 1000
1001 GHIMIMSLPSLRPMLDVNYLPLTDMRIARTFCFTNEGQALYLVSPTEIQR 1050
1051 LTYSQEMCDNLQDMLGDLFTPIETPEAQNRGFLKGLFGGSGQTFDREELF 1100
1101 GEASAGKASRSLAQHIPGPGSIEGMKGAAGGVMGELTRARIALDERGQRL 1150
1151 GELEEKTAGMMTSAEAFSKHAHELMLKYKDKKWYQF 1186
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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