 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9SQV1 from www.uniprot.org...
The NucPred score for your sequence is 0.88 (see score help below)
1 MATTEDTPASAGPRYAPEDPTLPQPWKGLIDGSTGILYYWNPETNVTQYE 50
51 RPSAPPPHSATTPKLAQIPVPSSGQGHQAQHEQAKPVGHVSQQHGFQQQP 100
101 QQFPSQHVRPQMMQQHPAQQMPQQSGQQFPQQQSQSMVPHPHGHPSVQTY 150
151 QPTTQQQQQGMQNQHSQMPQQLSHQYAHSQQHYMGFRPHMQTQGLQNSHQ 200
201 TPQGGPHGQQFPSQQEYNSLAPKREGDEFHGGKKTGFSQPHLPNSERSPS 250
251 QNTHFEANAASQKTNANLAMAQKCNGPQANAAVTQFQQPGANLIHQQLGP 300
301 RAPNQMDQTMLHQKSHVSPFQSNNTYENNLQSRPGNDSYVNMRMEVPVRG 350
351 AQPLHPAAMPKDIRISGGPPTNADPAMGQTGHGTYGHAGPAFPNKSLVRP 400
401 HFVTSPDVPHLSPVEIYRKQHEVTTTGENIPAPYITFESSGLPPEILREL 450
451 LSAGFPSPTPIQAQTWPIALQSRDIVAIAKTGSGKTLGYLIPAFILLRHC 500
501 RNDSRNGPTVLILAPTRELATQIQDEALRFGRSSRISCTCLYGGAPKGPQ 550
551 LKELERGADIVVATPGRLNDILEMKMIDFQQVSLLVLDEADRMLDMGFEP 600
601 QIRKIVNEIPPRRQTLMYTATWPKEVRKIASDLLVNPVQVNIGRVDELAA 650
651 NKAITQYVEVVPQMEKERRLEQILRSQERGSKVIIFCSTKRLCDHLARSV 700
701 GRHFGAVVIHGDKTQGERDWVLNQFRSGKSCVLIATDVAARGLDIKDIRV 750
751 VINYDFPTGVEDYVHRIGRTGRAGATGVAFTFFTEQDWKYAPDLIKVLEG 800
801 ANQQVPPQVRDIAMRGGGGGGPGYSQDRRGMVNRFDSGGGGTRWDSGGGF 850
851 GGRGGGFSGREGGFGGREGGFGGREGGFGGRGGRFGMRDDSFGRGGNRGR 900
901 GFTGPDAGHMNVGGRGGFGRFGNNNNMESRGFGRGSGRGFGRGVGRFDNR 950
951 RGRSRSRSPDLVRPRRRSSSYSRSRSRSGSYSRSRSRSRSWSRSRSRSPR 1000
1001 HSRDRGGHNRSRSYSRSPSPVYERRDRAPRVSGFDIKPPVESVVNLDMNA 1050
1051 AAAIENVVPTSLSERQGNGVVESEVEAALVRPVVDEEP 1088
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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