-Lactoglobulin (LG) is a major milk whey protein containing primarily a calyx for vitamin D 3 binding, although the existence of another site beyond the calyx is controversial. Using fluorescence spectral analyses in the previous study, we showed the binding stoichiometry for vitamin D 3 to LG to be 2:1 and a stoichiometry of 1:1 when the calyx was "disrupted" by manipulating the pH and temperature, suggesting that a secondary vitamin D binding site existed. To help localize this secondary site using X-ray crystallography in the present study, we used bioinformatic programs (Insight II, Q-SiteFinder, and GEMDOCK) to identify the potential location of this site. We then optimized the occupancy and enhanced the electron density of vitamin D 3 in the complex by altering the pH and initial ratios of vitamin D 3 /LG in the cocrystal preparation. We conclude that GEMDOCK can aid in searching for an extra density map around potential vitamin D binding sites. Both pH (8) and initial ratio of vitamin D 3 /LG (3:1) are crucial to optimize the occupancy and enhance the electron density of vitamin D 3 in the complex for rational-designed crystallization. The strategy in practice may be useful for future identification of a ligand-binding site in a given protein.

BackgroundThe increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.ResultsUsing the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.ConclusionBy representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment.