Abstract
BackgroundPrion 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 β-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.ResultsHere 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.ConclusionsTo 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.
Highlights
Prion proteins conform a special class among amyloids due to their ability to transmit aggregative folds
Starting from a list of 29 proteins reported experimentally to exhibit conformational conversion and amyloid formation in yeast [38], we have developed a probabilistic model of prionogenic domains (PrD) to discover Q/N-rich prionogenic proteins in complete proteomes
Unlike previous approaches [36,37], this model allows us to obtain a representation of prionogenic domains accounting for the relative statistical significance of each residue in the scoring function
Summary
Prion proteins conform a special class among amyloids due to their ability to transmit aggregative folds. Studies from different groups have concluded that both amino acid composition and the length of such regions play important roles in prion induction [28,29,30] Additional sequential requirements such as the number and distribution of prolines and charged residues have been recently found to be relevant in the formation of prionic aggregates [30]. Mutational studies, in which the sequence of yeast prions Ure2p and Sup35p were randomly shuffled, proved that the [PSI+] phenotype is mainly determined by the amino acid composition of the domain independently of the primary sequence, as most of the shuffled species generated were able to form prions in vivo [28,29] This knowledge has been used to try to predict putative prions in biological sequence databases, though the available methodologies to carry out the task are just a few. This kind of methods, based on more or less complex models of parallel β-sheets, have proven quite ineffective for coping with Q/N-rich stretches since these domains do not share the common characteristics of β-sheet-amyloid forming peptides [35] –e.g. high hydrophobicity
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