 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P02845 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MRGIILALVLTLVGSQKFDIDPGFNSRRSYLYNYEGSMLNGLQDRSLGKA 50
51 GVRLSSKLEISGLPENAYLLKVRSPQVEEYNGVWPRDPFTRSSKITQVIS 100
101 SCFTRLFKFEYSSGRIGNIYAPEDCPDLCVNIVRGILNMFQMTIKKSQNV 150
151 YELQEAGIGGICHARYVIQEDRKNSRIYVTRTVDLNNCQEKVQKSIGMAY 200
201 IYPCPVDVMKERLTKGTTAFSYKLKQSDSGTLITDVSSRQVYQISPFNEP 250
251 TGVAVMEARQQLTLVEVRSERGSAPDVPMQNYGSLRYRFPAVLPQMPLQL 300
301 IKTKNPEQRIVETLQHIVLNNQQDFHDDVSYRFLEVVQLCRIANADNLES 350
351 IWRQVSDKPRYRRWLLSAVSASGTTETLKFLKNRIRNDDLNYIQTLLTVS 400
401 LTLHLLQADEHTLPIAADLMTSSRIQKNPVLQQVACLGYSSVVNRYCSQT 450
451 SACPKEALQPIHDLADEAISRGREDKMKLALKCIGNMGEPASLKRILKFL 500
501 PISSSSAADIPVHIQIDAITALKKIAWKDPKTVQGYLIQILADQSLPPEV 550
551 RMMACAVIFETRPALALITTIANVAMKESNMQVASFVYSHMKSLSKSRLP 600
601 FMYNISSACNIALKLLSPKLDSMSYRYSKVIRADTYFDNYRVGATGEIFV 650
651 VNSPRTMFPSAIISKLMANSAGSVADLVEVGIRVEGLADVIMKRNIPFAE 700
701 YPTYKQIKELGKALQGWKELPTETPLVSAYLKILGQEVAFININKELLQQ 750
751 VMKTVVEPADRNAAIKRIANQIRNSIAGQWTQPVWMGELRYVVPSCLGLP 800
801 LEYGSYTTALARAAVSVEGKMTPPLTGDFRLSQLLESTMQIRSDLKPSLY 850
851 VHTVATMGVNTEYFQHAVEIQGEVQTRMPMKFDAKIDVKLKNLKIETNPC 900
901 REETEIVVGRHKAFAVSRNIGELGVEKRTSILPEDAPLDVTEEPFQTSER 950
951 ASREHFAMQGPDSMPRKQSHSSREDLRRSTGKRAHKRDICLKMHHIGCQL 1000
1001 CFSRRSRDASFIQNTYLHKLIGEHEAKIVLMPVHTDADIDKIQLEIQAGS 1050
1051 RAAARIITEVNPESEEEDESSPYEDIQAKLKRILGIDSMFKVANKTRHPK 1100
1101 NRPSKKGNTVLAEFGTEPDAKTSSSSSSASSTATSSSSSSASSPNRKKPM 1150
1151 DEEENDQVKQARNKDASSSSRSSKSSNSSKRSSSKSSNSSKRSSSSSSSS 1200
1201 SSSSRSSSSSSSSSSNSKSSSSSSKSSSSSSRSRSSSKSSSSSSSSSSSS 1250
1251 SSKSSSSRSSSSSSKSSSHHSHSHHSGHLNGSSSSSSSSRSVSHHSHEHH 1300
1301 SGHLEDDSSSSSSSSVLSKIWGRHEIYQYRFRSAHRQEFPKRKLPGDRAT 1350
1351 SRYSSTRSSHDTSRAASWPKFLGDIKTPVLAAFLHGISNNKKTGGLQLVV 1400
1401 YADTDSVRPRVQVFVTNLTDSSKWKLCADASVRNAHKAVAYVKWGWDCRD 1450
1451 YKVSTELVTGRFAGHPAAQVKLEWPKVPSNVRSVVEWFYEFVPGAAFMLG 1500
1501 FSERMDKNPSRQARMVVALTSPRTCDVVVKLPDIILYQKAVRLPLSLPVG 1550
1551 PRIPASELQPPIWNVFAEAPSAVLENLKARCSVSYNKIKTFNEVKFNYSM 1600
1601 PANCYHILVQDCSSELKFLVMMKSAGEATNLKAINIKIGSHEIDMHPVNG 1650
1651 QVKLLVDGAESPTANISLISAGASLWIHNENQGFALAAPGHGIDKLYFDG 1700
1701 KTITIQVPLWMAGKTCGICGKYDAECEQEYRMPNGYLAKNAVSFGHSWIL 1750
1751 EEAPCRGACKLHRSFVKLEKTVQLAGVDSKCYSTEPVLRCAKGCSATKTT 1800
1801 PVTVGFHCLPADSANSLTDKQMKYDQKSEDMQDTVDAHTTCSCENEECST 1850
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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