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On the characterization of protein native state ensembles.

ABSTRACT: Describing and understanding the biological function of a protein requires a detailed structural and thermodynamic description of the protein's native state ensemble. Obtaining such a description often involves characterizing equilibrium fluctuations that occur beyond the nanosecond timescale. Capturing such fluctuations remains nontrivial even for very long molecular dynamics and Monte Carlo simulations. We propose a novel multiscale computational method to exhaustively characterize, in atomistic detail, the protein conformations constituting the native state with no inherent timescale limitations. Applications of this method to proteins of various folds and sizes show that thermodynamic observables measured as averages over the native state ensembles obtained by the method agree remarkably well with nuclear magnetic resonance data that span multiple timescales. By characterizing equilibrium fluctuations at atomistic detail over a broad range of timescales, from picoseconds to milliseconds, our method offers to complement current simulation techniques and wet-lab experiments and can impact our understanding and description of the relationship between protein flexibility and function.

SUBMITTER: Shehu A

PROVIDER: S-EPMC1796840 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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On the characterization of protein native state ensembles.

Shehu Amarda A Kavraki Lydia E LE Clementi Cecilia C

Biophysical journal 20061208 5

Describing and understanding the biological function of a protein requires a detailed structural and thermodynamic description of the protein's native state ensemble. Obtaining such a description often involves characterizing equilibrium fluctuations that occur beyond the nanosecond timescale. Capturing such fluctuations remains nontrivial even for very long molecular dynamics and Monte Carlo simulations. We propose a novel multiscale computational method to exhaustively characterize, in atomist ...[more]

PMID: 17158570

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Project description:As genome sequencing efforts are unveiling the genetic diversity of the biosphere with an unprecedented speed, there is a need to accurately describe the structural and functional properties of groups of extant species whose genomes have been sequenced, as well as their inferred ancestors, at any given taxonomic level of their phylogeny. Elaborate approaches for the reconstruction of ancestral states at the sequence level have been developed, subsequently augmented by methods based on gene content. While these approaches of sequence or gene-content reconstruction have been successfully deployed, there has been less progress on the explicit inference of functional properties of ancestral genomes, in terms of metabolic pathways and other cellular processes. Herein, we describe PathTrace, an efficient algorithm for parsimony-based reconstructions of the evolutionary history of individual metabolic pathways, pivotal representations of key functional modules of cellular function. The algorithm is implemented as a five-step process through which pathways are represented as fuzzy vectors, where each enzyme is associated with a taxonomic conservation value derived from the phylogenetic profile of its protein sequence. The method is evaluated with a selected benchmark set of pathways against collections of genome sequences from key data resources. By deploying a pangenome-driven approach for pathway sets, we demonstrate that the inferred patterns are largely insensitive to noise, as opposed to gene-content reconstruction methods. In addition, the resulting reconstructions are closely correlated with the evolutionary distance of the taxa under study, suggesting that a diligent selection of target pangenomes is essential for maintaining cohesiveness of the method and consistency of the inference, serving as an internal control for an arbitrary selection of queries. The PathTrace method is a first step towards the large-scale analysis of metabolic pathway evolution and our deeper understanding of functional relationships reflected in emerging pangenome collections.

Dataset Information

On the characterization of protein native state ensembles.

Publications

On the characterization of protein native state ensembles.

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