title: 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.
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL 32610, USA. balavenkat@ufl.edu
authors.forename: Balasubramanian
authors.initials: B
authors.lastname: Venkatakrishnan
authors.forename: Miorel-Lucian
authors.initials: ML
authors.lastname: Palii
authors.forename: Mavis
authors.initials: M
authors.lastname: Agbandje-McKenna
authors.forename: Robert
authors.initials: R
authors.lastname: McKenna
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D018360:Crystallography, X-Ray; D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D014644:Genetic Variation; D006678:HIV / Q000502:physiology; D016333:HIV Protease / Q000737:chemistry*; D008958:Models, Molecular; D011487:Protein Conformation; D017433:Protein Structure, Secondary
chemical_list: D016333:HIV Protease