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Mining the protein data bank to differentiate error from structural variation in clustered static structures: an examination of HIV protease.


ABSTRACT: The Protein Data Bank (PDB) contains over 71,000 structures. Extensively studied proteins have hundreds of submissions available, including mutations, different complexes, and space groups, allowing for application of data-mining algorithms to analyze an array of static structures and gain insight about a protein's structural variation and possibly its dynamics. This investigation is a case study of HIV protease (PR) using in-house algorithms for data mining and structure superposition through generalized formulæ that account for multiple conformations and fractional occupancies. Temperature factors (B-factors) are compared with spatial displacement from the mean structure over the entire study set and separately over bound and ligand-free structures, to assess the significance of structural deviation in a statistical context. Space group differences are also examined.

SUBMITTER: Venkatakrishnan B 

PROVIDER: S-EPMC3347031 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Mining the protein data bank to differentiate error from structural variation in clustered static structures: an examination of HIV protease.

Venkatakrishnan Balasubramanian B   Palii Miorel-Lucian ML   Agbandje-McKenna Mavis M   McKenna Robert R  

Viruses 20120305 3


The Protein Data Bank (PDB) contains over 71,000 structures. Extensively studied proteins have hundreds of submissions available, including mutations, different complexes, and space groups, allowing for application of data-mining algorithms to analyze an array of static structures and gain insight about a protein's structural variation and possibly its dynamics. This investigation is a case study of HIV protease (PR) using in-house algorithms for data mining and structure superposition through g  ...[more]

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