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Viruses 2012, 4(3), 348-362; https://doi.org/10.3390/v4030348

Mining the Protein Data Bank to Differentiate Error from Structural Variation in Clustered Static Structures: An Examination of HIV Protease

Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL 32610, USA
These authors contributed equally to this work.
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Received: 5 February 2012 / Revised: 29 February 2012 / Accepted: 1 March 2012 / Published: 5 March 2012
(This article belongs to the Special Issue HIV Dynamics and Evolution)
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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. View Full-Text
Keywords: B-factor and spatial variation; data mining; HIV protease; structure superposition B-factor and spatial variation; data mining; HIV protease; structure superposition
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Venkatakrishnan, B.; Palii, M.-L.; Agbandje-McKenna, M.; McKenna, R. Mining the Protein Data Bank to Differentiate Error from Structural Variation in Clustered Static Structures: An Examination of HIV Protease. Viruses 2012, 4, 348-362.

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