What question did this study set out to answer?

To review recent updates to the Bio3D structural bioinformatics suite and introduce an ensemble difference distance matrix analysis method for comparing large sets of biomolecular structures.

July 31, 2020Protein ScienceOpen Access

The Bio3D packages for structural bioinformatics

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Authors

BGBarry J. GrantUniversity of California, San Diego LSLars SkjærvenTV 2 (Norway)XYXin‐Qiu YaoUniversity of Nebraska at Omaha

Discussion

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Implication

Methodological review reveals modular Bio3D structural bioinformatics tools in R, highlighting automated identification of functional protein residues via ensemble difference distance matrix analysis.

Key Points

To review recent updates to the Bio3D structural bioinformatics suite and introduce an ensemble difference distance matrix analysis method for comparing large sets of biomolecular structures.
Modularized the core Bio3D architecture into distinct open-source R packages focused on sequence, structure, and dynamic analysis.
Developed the ensemble difference distance matrix (eDDM) workflow to calculate and compare atomic distance matrices across homologous experimental and simulation-derived structures.
Implemented automated workflows linking distance matrix computations with multivariate analyses and sequence-structure-function tools.
The partitioned Bio3D package ecosystem provides automated and reproducible toolsets for normal mode analysis, correlation network modeling, and database mining.
The eDDM approach systematically uncovers residue-level determinants of specific functional states across large homologous protein families.
Bio3D-eddm seamlessly integrates both experimental structures and theoretical molecular simulations within a standardized, platform-independent R framework.

Cite This Study

Grant et al. (2020) studied this question.

synapsesocial.com/papers/69d97a56387cf7069868438c https://doi.org/10.1002/pro.3923

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July 31, 2020Protein ScienceOpen Access

The Bio3D packages for structural bioinformatics

View Full Paper

Ask AI

Bookmark

Authors

BGBarry J. GrantUniversity of California, San Diego LSLars SkjærvenTV 2 (Norway)XYXin‐Qiu YaoUniversity of Nebraska at Omaha

Discussion

Member takes

Implication

Key Points

To review recent updates to the Bio3D structural bioinformatics suite and introduce an ensemble difference distance matrix analysis method for comparing large sets of biomolecular structures.
Modularized the core Bio3D architecture into distinct open-source R packages focused on sequence, structure, and dynamic analysis.
Developed the ensemble difference distance matrix (eDDM) workflow to calculate and compare atomic distance matrices across homologous experimental and simulation-derived structures.
Implemented automated workflows linking distance matrix computations with multivariate analyses and sequence-structure-function tools.
The partitioned Bio3D package ecosystem provides automated and reproducible toolsets for normal mode analysis, correlation network modeling, and database mining.
The eDDM approach systematically uncovers residue-level determinants of specific functional states across large homologous protein families.
Bio3D-eddm seamlessly integrates both experimental structures and theoretical molecular simulations within a standardized, platform-independent R framework.

Cite This Study

Grant et al. (2020) studied this question.

synapsesocial.com/papers/69d97a56387cf7069868438c https://doi.org/10.1002/pro.3923

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