Reference : Discovering putative prion sequences in complete proteomes using probabilistic repres...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/16675
Discovering putative prion sequences in complete proteomes using probabilistic representations of Q/N-rich domains.
English
Espinosa Angarica, Vladimir mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Ventura, Salvador [> >]
Sancho, Javier [> >]
2013
BMC genomics
14
316
Yes (verified by ORBilu)
International
1471-2164
1471-2164
England
[en] Prion domain ; Protein aggregation ; Amyloid fibrils ; Prion prediction ; Prion database
[en] BACKGROUND: Prion proteins conform a special class among amyloids due to their ability to transmit aggregative folds. Prions are known to act as infectious agents in neurodegenerative diseases in animals, or as key elements in transcription and translation processes in yeast. It has been suggested that prions contain specific sequential domains with distinctive amino acid composition and physicochemical properties that allow them to control the switch between soluble and beta-sheet aggregated states. Those prion-forming domains are low complexity segments enriched in glutamine/asparagine and depleted in charged residues and prolines. Different predictive methods have been developed to discover novel prions by either assessing the compositional bias of these stretches or estimating the propensity of protein sequences to form amyloid aggregates. However, the available algorithms hitherto lack a thorough statistical calibration against large sequence databases, which makes them unable to accurately predict prions without retrieving a large number of false positives. RESULTS: Here we present a computational strategy to predict putative prion-forming proteins in complete proteomes using probabilistic representations of prionogenic glutamine/asparagine rich regions. After benchmarking our predictive model against large sets of non-prionic sequences, we were able to filter out known prions with high precision and accuracy, generating prediction sets with few false positives. The algorithm was used to scan all the proteomes annotated in public databases for the presence of putative prion proteins. We analyzed the presence of putative prion proteins in all taxa, from viruses and archaea to plants and higher eukaryotes, and found that most organisms encode evolutionarily unrelated proteins with susceptibility to behave as prions. CONCLUSIONS: To our knowledge, this is the first wide-ranging study aiming to predict prion domains in complete proteomes. Approaches of this kind could be of great importance to identify potential targets for further experimental testing and to try to reach a deeper understanding of prions' functional and regulatory mechanisms.
http://hdl.handle.net/10993/16675
10.1186/1471-2164-14-316
http://www.biomedcentral.com/1471-2164/14/316

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