Dataset Information

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes.

ABSTRACT: We have derived a novel method to assess compositional biases in biological sequences, which is based on finding the lowest-probability subsequences for a given residue-type set. As a case study, the distribution of prion-like glutamine/asparagine-rich ((Q+N)-rich) domains (which are linked to amyloidogenesis) was assessed for budding and fission yeasts and four other eukaryotes. We find more than 170 prion-like (Q+N)-rich regions in budding yeast, and, strikingly, many fewer in fission yeast. Also, some residues, such as tryptophan or isoleucine, are unlikely to form biased regions in any eukaryotic proteome.

SUBMITTER: Harrison PM

PROVIDER: S-EPMC193619 | biostudies-literature | 2003

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes.

Harrison Paul M PM Gerstein Mark M

Genome biology 20030530 6

We have derived a novel method to assess compositional biases in biological sequences, which is based on finding the lowest-probability subsequences for a given residue-type set. As a case study, the distribution of prion-like glutamine/asparagine-rich ((Q+N)-rich) domains (which are linked to amyloidogenesis) was assessed for budding and fission yeasts and four other eukaryotes. We find more than 170 prion-like (Q+N)-rich regions in budding yeast, and, strikingly, many fewer in fission yeast. A ...[more]

PMID: 12801414

Similar Datasets

Project description: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.

Dataset Information

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes.

Publications

A method to assess compositional bias in biological sequences and its application to prion-like glutamine/asparagine-rich domains in eukaryotic proteomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets