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OpenStructure: an integrated software framework for computational structural biology.

ABSTRACT: Research projects in structural biology increasingly rely on combinations of heterogeneous sources of information, e.g. evolutionary information from multiple sequence alignments, experimental evidence in the form of density maps and proximity constraints from proteomics experiments. The OpenStructure software framework, which allows the seamless integration of information of different origin, has previously been introduced. The software consists of C++ libraries which are fully accessible from the Python programming language. Additionally, the framework provides a sophisticated graphics module that interactively displays molecular structures and density maps in three dimensions. In this work, the latest developments in the OpenStructure framework are outlined. The extensive capabilities of the framework will be illustrated using short code examples that show how information from molecular-structure coordinates can be combined with sequence data and/or density maps. The framework has been released under the LGPL version 3 license and is available for download from http://www.openstructure.org.

SUBMITTER: Biasini M

PROVIDER: S-EPMC3640466 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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OpenStructure: an integrated software framework for computational structural biology.

Biasini M M Schmidt T T Bienert S S Mariani V V Studer G G Haas J J Johner N N Schenk A D AD Philippsen A A Schwede T T

Acta crystallographica. Section D, Biological crystallography 20130419 Pt 5

Research projects in structural biology increasingly rely on combinations of heterogeneous sources of information, e.g. evolutionary information from multiple sequence alignments, experimental evidence in the form of density maps and proximity constraints from proteomics experiments. The OpenStructure software framework, which allows the seamless integration of information of different origin, has previously been introduced. The software consists of C++ libraries which are fully accessible from ...[more]

PMID: 23633579

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Project description:BACKGROUND: Plant cell walls are complex structures that full-fill many diverse functions during plant growth and development. It is therefore not surprising that thousands of gene products are involved in cell wall synthesis and maintenance. However, functional association for the majority of these gene products remains obscure. One useful approach to infer biological associations is via transcriptional coordination, or co-expression of genes. This approach has proved useful for several biological processes. Nevertheless, combining co-expression with other large-scale measurements may improve the biological inferences. RESULTS: In this study, we used a combined approach of co-expression and cell wall metabolomics to obtain new insight into cell wall synthesis in rice. We initially created a weighted gene co-expression network from publicly available datasets, and then established a comprehensive cell wall dataset by determining cell wall compositions from 29 tissues that almost cover the whole life cycle of rice. We subsequently combined the datasets through the conversion of co-expressed gene modules into eigen-vectors, representing expression profiles for the genes in the modules, and performed comparative analyses against the cell wall contents. Here, we made three major discoveries. First, we confirmed our approach by finding primary and secondary wall cellulose biosynthesis modules, respectively. Second, we found co-expressed modules that strongly correlated with re-organization of the secondary cell walls and with modifications and degradation of hemicellulosic structures. Third, we inferred that at least one module is likely to play a regulatory role in the production of G-rich lignification. CONCLUSIONS: Here, we integrated transcriptomic associations and cell wall metabolism and found that certain co-expressed gene modules are positively correlated with distinct cell wall characteristics. We propose that combining multiple data-types, such as coordinated transcription and cell wall analyses, may be a useful approach to glean new insight into biological processes. The combination of multiple datasets, as illustrated here, can further improve the functional inferences that typically are generated via a single type of datasets. In addition, our data extend the typical co-expression approach to allow deeper insight into cell wall biology in rice.

Dataset Information

OpenStructure: an integrated software framework for computational structural biology.

Publications

OpenStructure: an integrated software framework for computational structural biology.

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