 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9BXL7 from www.uniprot.org...
The NucPred score for your sequence is 0.86 (see score help below)
1 MPGGGPEMDDYMETLKDEEDALWENVECNRHMLSRYINPAKLTPYLRQCK 50
51 VIDEQDEDEVLNAPMLPSKINRAGRLLDILHTKGQRGYVVFLESLEFYYP 100
101 ELYKLVTGKEPTRRFSTIVVEEGHEGLTHFLMNEVIKLQQQMKAKDLQRC 150
151 ELLARLRQLEDEKKQMTLTRVELLTFQERYYKMKEERDSYNDELVKVKDD 200
201 NYNLAMRYAQLSEEKNMAVMRSRDLQLEIDQLKHRLNKMEEECKLERNQS 250
251 LKLKNDIENRPKKEQVLELERENEMLKTKNQELQSIIQAGKRSLPDSDKA 300
301 ILDILEHDRKEALEDRQELVNRIYNLQEEARQAEELRDKYLEEKEDLELK 350
351 CSTLGKDCEMYKHRMNTVMLQLEEVERERDQAFHSRDEAQTQYSQCLIEK 400
401 DKYRKQIRELEEKNDEMRIEMVRREACIVNLESKLRRLSKDSNNLDQSLP 450
451 RNLPVTIISQDFGDASPRTNGQEADDSSTSEESPEDSKYFLPYHPPQRRM 500
501 NLKGIQLQRAKSPISLKRTSDFQAKGHEEEGTDASPSSCGSLPITNSFTK 550
551 MQPPRSRSSIMSITAEPPGNDSIVRRYKEDAPHRSTVEEDNDSGGFDALD 600
601 LDDDSHERYSFGPSSIHSSSSSHQSEGLDAYDLEQVNLMFRKFSLERPFR 650
651 PSVTSVGHVRGPGPSVQHTTLNGDSLTSQLTLLGGNARGSFVHSVKPGSL 700
701 AEKAGLREGHQLLLLEGCIRGERQSVPLDTCTKEEAHWTIQRCSGPVTLH 750
751 YKVNHEGYRKLVKDMEDGLITSGDSFYIRLNLNISSQLDACTMSLKCDDV 800
801 VHVRDTMYQDRHEWLCARVDPFTDHDLDMGTIPSYSRAQQLLLVKLQRLM 850
851 HRGSREEVDGTHHTLRALRNTLQPEEALSTSDPRVSPRLSRASFLFGQLL 900
901 QFVSRSENKYKRMNSNERVRIISGSPLGSLARSSLDATKLLTEKQEELDP 950
951 ESELGKNLSLIPYSLVRAFYCERRRPVLFTPTVLAKTLVQRLLNSGGAME 1000
1001 FTICKSDIVTRDEFLRRQKTETIIYSREKNPNAFECIAPANIEAVAAKNK 1050
1051 HCLLEAGIGCTRDLIKSNIYPIVLFIRVCEKNIKRFRKLLPRPETEEEFL 1100
1101 RVCRLKEKELEALPCLYATVEPDMWGSVEELLRVVKDKIGEEQRKTIWVD 1150
1151 EDQL 1154
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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