 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UVU5 from www.uniprot.org...
The NucPred score for your sequence is 0.48 (see score help below)
1 MPGLVEAPVEPYARPVRTLVQLALDPDDGLEYVSLNSQVYELIYGDLSDN 50
51 KTRFVSLQLLGSPLFVEYQLFRAELDSELPQDSIELHSPRLSSKYGSDFT 100
101 LEKCIVVPVTKVLSLTSVIMSFPHDVYRLIEGISKERLLEIVASERGSDA 150
151 GHILIRKTDFLKTLHGEVIHCEPVDQGFLSSDTNLVIVKQEGLKRGVNGN 200
201 VNGSHPDPSEIPVSTPVDFSVSHLGLGDLTFDDTSIFSNLTLQPLELQIG 250
251 FLKYSMLMDCTHILESSHEFDHEDDQLFACVCSSVLQKLGCFSGDLVQIQ 300
301 TCDCGAGIVCACDKSARRKITIRVFSLADPNDFDPEKLYLSPIFLNSIGN 350
351 PRIVFVKRLASQRPNGDFVCEKLENHVPVAKEVVIARVASPITLDRTMQH 400
401 LFLSNLKTYFESKHRVIVKDQYIPVPIDTVLAKSLFSTYNASGDETQPEI 450
451 IPQGIPNELAWFKITDGTTETDDGKSLVAGKQYIIDPAKTRMIQSGVCSD 500
501 KVPLSDGISYSQLRDYFELPRQFAYPCLRISNVLTFPYANQLRKIVSVAF 550
551 RIRDNSYSRSRVQTTILLSSMARCVGKATLVRRIATEFGANLLELDAYDL 600
601 LNQASVSKTIGTIRGKSDRVVDSCCSVILYIRHIEALAKKPDPNQQQKDS 650
651 MSLRLAELIDEYTSKGAIFIGSTNDADAISELIRSKFKFDISINVPTEPE 700
701 RKLILTDLLDDMKTKDKTPVVLRPDVSLDTLALQSAGLTANDLVSIVDNT 750
751 ITIAIERLERLSEEQKVNWDQLLSFNGGRIKLTPEDFETSINDARNKFSD 800
801 MIGAPRIPDVKWEDVGGLDVVKDEILDTIEMPLKHPELFSKGMKKRSGIL 850
851 FYGPPGTGKTLLAKAIATNFALNFFSVKGPELLNMYIGESEANVRRVFQK 900
901 ARDAKPCVIFFDELDSVAPKRGNQGDSGGVMDRIVSQLLAELDGMSGAEG 950
951 GDGVFVVGATNRPDLLDEALLRPGRFDKMLYLGIADTHEKQAKIIQALTR 1000
1001 KFQLDPSVDLGRIAETCPFTYTGADFYALCSDAMLNAMTRTAGAVEKKIN 1050
1051 EYNCNREEGDKISTRFWFDNIAKPEDTQVLVKSEDFAKARDELVPSVSAE 1100
1101 ELQHYLSVRENFEGGKTQDVMHTVPDGADIIISHD 1135
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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