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.
*
Author to whom correspondence should be addressed.
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)
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
Figure 1
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
Share & Cite This Article
MDPI and ACS Style
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.