High-Throughput Liquid Chromatographic Analysis Using a Segmented Flow Injector with a 1 s Cycle Time.

High-throughput screening (HTS) workflows are revolutionizing many fields, including drug discovery, reaction discovery and optimization, diagnostics, sensing, and enzyme engineering. Liquid chromatography (LC) is commonly deployed during HTS to reduce matrix effects, distinguish isomers, and preconcentrate prior to detection, but LC separation time often limits throughput. Although subsecond LC separations have been demonstrated, they are rarely utilized during HTS due to limitations associated with the speed of common autosamplers. In this work, these limits are overcome by utilizing droplet microfluidics for sample introduction. In the method, a train of samples segmented by air are continuously pumped into the inlet of an LC injection valve that is actuated once each sample fills the sample loop. Coupled with 2.1 mm diameter × 5 mm long columns packed with 2.7 µm superficially porous C18 particles operated at 5 mL/min, the injector enabled separation of 3 components at 1 s/sample and analysis of a 96-well plate in 1.6 min with <2% peak area relative standard deviation. Analyte-dependent carryover was minimized by including wash droplets composed of organic solvent in between sample droplets. High-throughput LC coupled with mass spectrometric detection using the segmented flow injector was applied to a screen of inhibitors of a cytochrome P450-catalyzed hydroxylation reaction. Measurements of the reaction substrate and product concentrations made using fast LC with the segmented flow injector correlated well with measurements made using a more conventional, 3 min LC method. These results demonstrate the potential for droplet microfluidics to be used for sample introduction during high-throughput LC analysis.

Carbohydrate-Aromatic Interactions in Proteins.

Protein-carbohydrate interactions play pivotal roles in health and disease. However, defining and manipulating these interactions has been hindered by an incomplete understanding of the underlying fundamental forces. To elucidate common and discriminating features in carbohydrate recognition, we have analyzed quantitatively X-ray crystal structures of proteins with noncovalently bound carbohydrates. Within the carbohydrate-binding pockets, aliphatic hydrophobic residues are disfavored, whereas aromatic side chains are enriched. The greatest preference is for tryptophan with an increased prevalence of 9-fold. Variations in the spatial orientation of amino acids around different monosaccharides indicate specific carbohydrate C-H bonds interact preferentially with aromatic residues. These preferences are consistent with the electronic properties of both the carbohydrate C-H bonds and the aromatic residues. Those carbohydrates that present patches of electropositive saccharide C-H bonds engage more often in CH-π interactions involving electron-rich aromatic partners. These electronic effects are also manifested when carbohydrate-aromatic interactions are monitored in solution: NMR analysis indicates that indole favorably binds to electron-poor C-H bonds of model carbohydrates, and a clear linear free energy relationships with substituted indoles supports the importance of complementary electronic effects in driving protein-carbohydrate interactions. Together, our data indicate that electrostatic and electronic complementarity between carbohydrates and aromatic residues play key roles in driving protein-carbohydrate complexation. Moreover, these weak noncovalent interactions influence which saccharide residues bind to proteins, and how they are positioned within carbohydrate-binding sites.

Quantifying additive interactions of the osmolyte proline with individual functional groups of proteins: comparisons with urea and glycine betaine, interpretation of m-values.

To quantify interactions of the osmolyte l-proline with protein functional groups and predict their effects on protein processes, we use vapor pressure osmometry to determine chemical potential derivatives dµ2/dm3 = µ23, quantifying the preferential interactions of proline (component 3) with 21 solutes (component 2) selected to display different combinations of aliphatic or aromatic C, amide, carboxylate, phosphate or hydroxyl O, and amide or cationic N surface. Solubility data yield µ23 values for four less-soluble solutes. Values of µ23 are dissected using an ASA-based analysis to test the hypothesis of additivity and obtain α-values (proline interaction potentials) for these eight surface types and three inorganic ions. Values of µ23 predicted from these α-values agree with the experiment, demonstrating additivity. Molecular interpretation of α-values using the solute partitioning model yields partition coefficients (Kp) quantifying the local accumulation or exclusion of proline in the hydration water of each functional group. Interactions of proline with native protein surfaces and effects of proline on protein unfolding are predicted from α-values and ASA information and compared with experimental data, with results for glycine betaine and urea, and with predictions from transfer free energy analysis. We conclude that proline stabilizes proteins because of its unfavorable interactions with (exclusion from) amide oxygens and aliphatic hydrocarbon surfaces exposed in unfolding and that proline is an effective in vivo osmolyte because of the osmolality increase resulting from its unfavorable interactions with anionic (carboxylate and phosphate) and amide oxygens and aliphatic hydrocarbon groups on the surface of cytoplasmic proteins and nucleic acids.

CH-π Interactions in Glycan Recognition.

Carbohydrate recognition is crucial for biological processes ranging from development to immune system function to host-pathogen interactions. The proteins that bind glycans are faced with a daunting task: to coax these hydrophilic species out of water and into a binding site. Here, we examine the forces underlying glycan recognition by proteins. Our previous bioinformatic study of glycan-binding sites indicated that the most overrepresented side chains are electron-rich aromatic residues, including tyrosine and tryptophan. These findings point to the importance of CH-π interactions for glycan binding. Studies of CH-π interactions show a strong dependence on the presence of an electron-rich π system, and the data indicate binding is enhanced by complementary electronic interactions between the electron-rich aromatic ring and the partial positive charge of the carbohydrate C-H protons. This electronic dependence means that carbohydrate residues with multiple aligned highly polarized C-H bonds, such as ß-galactose, form strong CH-π interactions, whereas less polarized residues such as α-mannose do not. This information can guide the design of proteins to recognize sugars and the generation of ligands for proteins, small molecules, or catalysts that bind sugars.

Glycan-Modified Virus-like Particles Evoke T Helper Type 1-like Immune Responses.

Dendritic cells (DCs) are highly effective antigen-presenting cells that shape immune responses. Vaccines that deliver antigen to the DCs can harness their power. DC surface lectins recognize glycans not typically present on host tissue to facilitate antigen uptake and presentation. Vaccines that target these surface lectins should offer improved antigen delivery, but their efficacy will depend on how lectin targeting influences the T cell subtypes that result. We examined how antigen structure influences uptake and signaling from the C-type lectin DC-SIGN (dendritic cell-specific intercellular adhesion molecule-3-grabbing nonintegrin or CD209). Virus-like particles (VLPs) were engineered from bacteriophage Qß to present an array of mannoside ligands. The VLPs were taken up by DCs and efficiently trafficked to endosomes. The signaling that ensued depended on the ligand displayed on the VLP: only those particles densely functionalized with an aryl mannoside, Qß-Man540, elicited DC maturation and induced the expression of the proinflammatory cytokines characteristic of a T helper type 1 (TH1)-like immune response. This effect was traced to differential binding to DC-SIGN at the acidic pH of the endosome. Mice immunized with a VLP bearing the aryl mannoside, and a peptide antigen (Qß-Ova-Man540) had antigen-specific responses, including the production of CD4+ T cells producing the activating cytokines interferon-γ and tumor necrosis factor-α. A TH1 response is critical for intracellular pathogens (e.g., viruses) and cancer; thus, our data highlight the value of targeting DC lectins for antigen delivery and validate the utility of DC-targeted VLPs as vaccine vehicles that induce cellular immunity.