 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O54747 from www.uniprot.org...
The NucPred score for your sequence is 0.71 (see score help below)
1 MDGKRRQAPSSGVPPKRACKGLWDEDEPSQFEENLALLEEIEAENRLQEA 50
51 EEELQLPPEGIVGGQFSTADIDPRWLRPTPLALDPSTEPLIFQQLEIDHY 100
101 VGTSPPLPEGPPASRNSVPILRAFGVTDEGFSVCCHIHGFAPYFYTPAPP 150
151 GFGAEHLSELQRELNAAISRDQRGGKELSGPAVLAIELCSRESMFGYHGH 200
201 GPSPFLRITLALPRLMAPARRLLEQGIRVPGLGTPSFAPYEANVDFEIRF 250
251 MVDADIVGCNWLELPAGKYVRRAEKKATLCQLEVDVLWSDVISHPPEGQW 300
301 QRIAPLRVLSFDIECAGRKGIFPEPERDPVIQICSLGLRWGEPEPFLRLA 350
351 LTLRPCAPILGAKVQSYEREEDLLQAWATFILAMDPDVITGYNIQNFDLP 400
401 YLISRAQTLKVDRFPFLGRVTGLRSNIRDSSFQSRQVGRRDSKVVSMVGR 450
451 VQMDMLQVLLREYKLRSYTLNAVSFHFLGEQKEDVQHSIITDLQNGNEQT 500
501 RRRLAVYCLKDAFLPLRLLERLMVLVNNVEMARVTGVPLGYLLSRGQQVK 550
551 VVSQLLRQAMREGLLMPVVKTEGGEDYTGATVIEPLKGYYDVPIATLDFS 600
601 SLYPSIMMAHNLCYTTLLRPGAAQKLGLKPDEFIKTPTGDEFVKASVRKG 650
651 LLPQILENLLSARKRAKAELAQETDPLRRQVLDGRQLALKVSPNSVYGFT 700
701 GAQVGKLPCLEISQSVTGFGRQMIEKTKQLVETKYTLENGYDANAKVVYG 750
751 DTDSVMCRFGVSSVAEAMSLGREAANWVSSHFPSPIRLEFEKVYFPYLLI 800
801 SKKRYAGLLFSSRSDAHDRMDCKGLEAVRRDNCPLVANLVTSSLRRILVD 850
851 RDPDGAVAHAKDVISDLLCNRIDISQLVITKELTRAAADYAGKQAHVELA 900
901 ERMRKRDPGSAPNLGDRVPYVIIGAAKGVAAYMKSEDPLFVLEHSLPIDT 950
951 QYYLEQQLAKPLLRIFEPILGEGRAESVLLRGDHTRCKTVLTSKVGGLLA 1000
1001 FTKRRNSCIGCRSVIDHQGAVCKFCQPRESELYQKEVSHLNALEERFSRL 1050
1051 WTQCQRCQGSLHEDVICTSRDCPIFYMRKKVRKDLEDQERLLQRFGPPGP 1100
1101 EAW 1103
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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