Dataset Information

Proton channel models filling the gap between experimental data and the structural rationale.

ABSTRACT: Voltage-gated proton channels are integral membrane proteins with the capacity to permeate elementary particles in a voltage- and pH-dependent manner. These proteins have been found in several species and are involved in various physiological processes. Although their primary topology is known, lack of details regarding their structures in the open conformation has limited analyses toward a deeper understanding of the molecular determinants of their function and regulation. Consequently, the function–structure relationships have been inferred based on homology models. In the present work, we review the existing proton channel models, their assumptions, predictions, and the experimental facts that support them. Modeling proton channels is not a trivial task due to the lack of a close homolog template. Hence, there are important differences between published models. This work attempts to critically review existing proton channel models toward the aim of contributing to a better understanding of the structural features of these proteins.

SUBMITTER: Pupo A

PROVIDER: S-EPMC4203746 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Proton channel models filling the gap between experimental data and the structural rationale.

Pupo Amaury A Baez-Nieto David D Martínez Agustín A Latorre Ramón R González Carlos C

Channels (Austin, Tex.) 20140101 3

Voltage-gated proton channels are integral membrane proteins with the capacity to permeate elementary particles in a voltage- and pH-dependent manner. These proteins have been found in several species and are involved in various physiological processes. Although their primary topology is known, lack of details regarding their structures in the open conformation has limited analyses toward a deeper understanding of the molecular determinants of their function and regulation. Consequently, the fun ...[more]

PMID: 24755912

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Project description:Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

Dataset Information

Proton channel models filling the gap between experimental data and the structural rationale.

Publications

Proton channel models filling the gap between experimental data and the structural rationale.

Similar Datasets

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