Discovering putative prion sequences in complete proteomes using probabilistic representations of Q/N-rich domains

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.

Dicer and Hsp104 Function in a Negative Feedback Loop to Confer Robustness to Environmental Stress

“…Because these cannot be cured in S. pombe, this could result in uncontrolled and irreversible propagation of aggregates, which has detrimental effects for the cell. Notably, endogenous prions have not yet been found and the number of putative prions predicted for S. pombe is significantly lower than for other fungi (Espinosa Angarica et al, 2013;Harrison et al, 2007). It is possible that the inability of S. pombe to cope with prionogenic proteins resulted in negative selection on prions while positively selecting Dcr1/Hsp104 feedback regulation.…”

Amyloids or prions? That is the question

“…Only five amino acid residues seem to unite these 2 essential properties, i.e., amyloid propensity and structural disorder, according to FoldIndex 29 and Waltz 30 algorithms: N, Q, Y, S and W. (Figure 2). Interestingly enough N, Q, Y, S are, in this order, the most over-represented residues in bona-fide prion domains, relative to their frequency in the protein universe, 22 with odds ratios of 5.70, 4.13, 1.72 and 1.66, respectively. In this context, N and Q residues show medium amyloid propensity, allowing the formation of amyloids with moderate strength, while at the same time are the amyloidogenic residues that more benefice disorder.…”

“…30 The four more over-represented residues in yeast PFDs are circled by discontinuous lines, red indicates odd ratios > 4.0 and blue odd ratios > 1.5, relative to the composition of the protein universe. 22 …”

“…These results are at the core of several algorithms for prion domain prediction, all relying on the analysis of amino acid sequences. 17,[21][22][23][24] These programs are constructed on 2 alternative models for amyloid formation by prion-like domains ( Figure 1): (1) The compositional model relying on the establishment of a large number of weak interactions 17 and (2) our model, which proposes 'classical' nucleation by short amyloidogenic stretches, whose amyloid propensity is modulated by the structural context. 24 Despite the mechanistic difference between algorithms, the advent of accurate computational tools to detect yeast prion domains opens new and exciting possibilities, allowing the exploration of proteomes for the discovery of novel and hitherto unexpected Q/N-enriched domains that may drive conformational conversion in novel prion proteins.…”

“…Despite its aromatic character, W is one of the most under-represented residues in prion domains with and odd ratio of 0.091, only C, which is able to crosslink covalently polypeptide chains, being less frequent. 22 The absence of W in PFDs is best explained by its particular structure, wherein the indole group may not be easily placed in b-cross structures due to of steric impediments, being indeed depleted relative to F and Y in functional and pathogenic amyloids. 34 The two alternative models used to identify prion domains (Figure 1) coincide in the requirement of a relatively large disordered region in yeast PFDs.…”