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.
Highlights
Many high-throughput bioscience methods sample a large and diverse range of chemistries
Similarity between different chemical compounds associated with these chemistries is often perceived intuitively based on judgment with multiple approaches being developed to improve this judgment [1]
X-ray crystallography is a key technique in providing three-dimensional structural detail of biological macromolecules and crystallization is a critical step in this process
Summary
Many high-throughput bioscience methods sample a large and diverse range of chemistries. The cocktails used to probe solubility and induce crystallization are often comprised of several components At coarse granularity these components can be chemically described, based on how they are thought to work in the crystallization process, as buffers, salts, organic solvents, polymers and additives. The chemical relationships within these screens can be obvious, for example the Slice pH screen (Hampton Research, Aliso Viejo, CA) has 96 cocktails that finely sample pH with different chemical buffer types. The relationship between these cocktails is well defined and any result can rapidly be interpreted in terms of pH effects. A measure of similarity between the cocktails can be used to automate at least part of the analysis of large datasets, put the individual results into context, and guide the optimization processes
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