 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q09733 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MDVGTMGSRAELLAISIQFGGLWVFRDLLQMNPLFEQDHVFCTFNHPNMG 50
51 LVLISFAKVDGHILAAYALSKLGPDTFSICYPFSSKLDAFAQTLDLEWQI 100
101 ASMHKEAAACVYEFLCVESVCLHTGENWRESVIEPEYASYIFDTINVFQS 150
151 SSLTRELYSLGEPNGVREFVVDAIFDIKVDNSWWEDPSNSYWKTVIGSRE 200
201 MFEDSRKKTSSPSPSFASSKDAGTIPAIQKKKSLLIEMMETESTYVERLR 250
251 HLVNDYALPLRDLAKSSKKLLGLYELNTLFPPCLNKLIQLNSAFLDEFEA 300
301 IMSDLNFEDIDEKKFEEIDLRLACCFESHFFAFSQHYPRYLEQSNDFGNV 350
351 LKMASKIPKFVEFHDQVKLNANMNVGLSQLIMEPVQRIPRYSLFLDQIIL 400
401 LTQEGECQHTYVRSVEIIKNIAEMPTVDAEERSRIFAGLQHIIPDLAPNM 450
451 ISNSRNFIDCIDVTREFLKNGQLHLIPYTLILFNDRICLVQRRSKSSIAS 500
501 TILDLRKQNPRNSYSKEKRAQYIGSNMNEAVELTRSMVEENTIFLISKYA 550
551 SSPSFFNEYPILKFRCDFENVRTMDRFYQSFQKALSMNKSQPSCLSFSKL 600
601 NDFVVFFNNYSRFEYEKESKRSDIVCICTNDANVDKHKFLQDGNIVITFF 650
651 QQDEDFHLSFDSWLGVSLPTEAVIAKEDLREACLNYLINIKRLLLCPFSN 700
701 RNFSSLDLYSNLIQHLLSANSSPRKSRLSFGGRPGSPSKISLSLNRFYNQ 750
751 GGLSKSCATLPSQMYNLDHNNISQKSLKFNTHNTSKASAEKTVEHLEAFK 800
801 GGFKYHTDLKNLLYPLSEKEKIEGDELYDNILKETFNEELLSHYPPNIIY 850
851 ATFQKYLSSFINRKFGVLLSSSFIQQLNTVENLNLSFNSTDAVYHLKKIL 900
901 QDLPESSLKILENIFSIASDLLLRLPLKDQCDFVTKQLAIALAPSMFGSN 950
951 AVELVYYLAYHSDRIFGTVEELPTPVSPANSNNDKQLDESKFQAIAMKEM 1000
1001 PERHPKEILPGQIEREAYEDLRRKYHLTLARLAQMTRLNEDSKKSIPLLY 1050
1051 DRFNHDLKLIKQSVQASLIRKQCELDTAKWTLEEYESKLNAKEGCQTNIF 1100
1101 I 1101
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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