title: Comparing chemistry to outcome: the development of a chemical distance metric, coupled with clustering and hierarchal visualization applied to macromolecular crystallography.
abstract: Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications related to crystallography appear promising and the distance coefficient, clustering, and hierarchal visualization of results undoubtedly have applications in wider fields.
authors: Center for Computational Research, State University of New York (SUNY), Buffalo, New York, United States of America.
authors: Andrew E
authors: AE
authors: Bruno
authors: Center for Computational Research, State University of New York (SUNY), Buffalo, New York, United States of America.
authors: Amanda M
authors: AM
authors: Ruby
authors: Hauptman-Woodward Medical Research Institute, Buffalo, New York, United States of America; SUNY Buffalo Dept. of Structural Biology, Buffalo, New York, United States of America.
authors: Joseph R
authors: JR
authors: Luft
authors: Hauptman-Woodward Medical Research Institute, Buffalo, New York, United States of America.
authors: Thomas D
authors: TD
authors: Grant
authors: Department of Biological Sciences, The Northeast Structural Genomics Consortium, Columbia University, New York, New York, United States of America.
authors: Jayaraman
authors: J
authors: Seetharaman
authors: Northeast Structural Genomics Consortium, Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine and Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States of America.
authors: Gaetano T
authors: GT
authors: Montelione
authors: Department of Biological Sciences, The Northeast Structural Genomics Consortium, Columbia University, New York, New York, United States of America.
authors: John F
authors: JF
authors: Hunt
authors: Hauptman-Woodward Medical Research Institute, Buffalo, New York, United States of America; SUNY Buffalo Dept. of Structural Biology, Buffalo, New York, United States of America.
authors: Edward H
authors: EH
authors: Snell
mesh_terms: D001426:Bacterial Proteins / Q000737:chemistry*; D001441:Bacteroides fragilis / Q000378:metabolism; D020134:Catalytic Domain; D016000:Cluster Analysis; D003460:Crystallization; D018360:Crystallography, X-Ray; D006863:Hydrogen-Ion Concentration; D046911:Macromolecular Substances / Q000737:chemistry*; D008962:Models, Theoretical; D010710:Phosphates / Q000737:chemistry; D011092:Polyethylene Glycols / Q000737:chemistry; D011188:Potassium / Q000737:chemistry; D012964:Sodium / Q000737:chemistry
chemical_list: D001426:Bacterial Proteins; D046911:Macromolecular Substances; D010710:Phosphates; D011092:Polyethylene Glycols; D012964:Sodium; D011188:Potassium