Dataset Information

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.

ABSTRACT: As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date, accurate fold prediction from a sequence has been dominated by Monte Carlo (MC)-based methods such as Rosetta. To overcome the limitations of current approaches in IDP simulation using Rosetta while maintaining its utility for modeling folded domains, we developed PyRosetta-based algorithms that allow for the accurate de novo prediction of proteins across all degrees of foldedness along with structural ensembles of disordered proteins. Our simulations have accuracy comparable to state-of-the-art MD with vastly reduced computational demands.

SUBMITTER: Ferrie JJ

PROVIDER: S-EPMC7725001 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.

Ferrie John J JJ Petersson E James EJ

The journal of physical chemistry. B 20200611 27

As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date, ...[more]

PMID: 32525675

Similar Datasets

Project description:Most models of protein evolution are based upon proteins that form relatively rigid 3D structures. A significant fraction of proteins, the so-called disordered proteins, do not form rigid 3D structures and sample a broad conformational ensemble. Disordered proteins do not typically maintain long-range interactions, so the constraints on their evolution should be different than ordered proteins. To test this hypothesis, we developed and compared models of evolution for disordered and ordered proteins. Substitution matrices were constructed using the sequences of putative homologs for sets of experimentally characterized disordered and ordered proteins. Separate matrices, at three levels of sequence similarity (>85%, 85-60%, and 60-40%), were inferred for each type of protein structure. The substitution matrices for disordered and ordered proteins differed significantly at each level of sequence similarity. The disordered matrices reflected a greater likelihood of evolutionary changes, relative to the ordered matrices, and these changes involved nonconservative substitutions. Glutamic acid and asparagine were interesting exceptions to this result. Important differences between the substitutions that are accepted in disordered proteins relative to ordered proteins were also identified. In general, disordered proteins have fewer evolutionary constraints than ordered proteins. However, some residues like tryptophan and tyrosine are highly conserved in disordered proteins. This is due to their important role in forming protein-protein interfaces. Finally, the amino acid frequencies for disordered proteins, computed during the development of the matrices, were compared with amino acid frequencies for different categories of secondary structure in ordered proteins. The highest correlations were observed between the amino acid frequencies in disordered proteins and the solvent-exposed loops and turns of ordered proteins, supporting an emerging structural model for disordered proteins.

Dataset Information

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.

Publications

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets