Hydration, charge, size, and shape characteristics of peptides from their CZE analyses

Photoinduced reconfiguration to control the protein-binding affinity of azobenzene-cyclized peptides

The impact of next-generation biorecognition elements (ligands) will be determined by the ability to remotely control their binding activity for a target biomolecule in complex environments. Compared to conventional mechanisms for regulating binding affinity (pH, ionic strength, or chaotropic agents), light provides higher accuracy and rapidity, and is particularly suited for labile targets. In this study, we demonstrate a general method to develop azobenzene-cyclized peptide ligands with light-controlled affinity for target proteins. Light triggers a cis/trans isomerization of the azobenzene, which results in a major structural rearrangement of the cyclic peptide from a non-binding to a binding configuration. Critical to this goal are the ability to achieve efficient photo-isomerization under low light dosage and the temporal stability of both cis and trans isomers. We demonstrated our method by designing photo-switchable peptides targeting vascular cell adhesion marker 1 (VCAM1), a cell marker implicated in stem cell function. Starting from a known VCAM1-binding linear peptide, an ensemble of azobenzene-cyclized variants with selective light-controlled binding were identified by combining in silico design with experimental characterization via spectroscopy and surface plasmon resonance. Variant cycloAZOB[G-VHAKQHRN-K] featured rapid, light-controlled binding of VCAM1 (KD,trans/KD,cis ∼ 130). Biotin-cycloAZOB[G-VHAKQHRN-K] was utilized to label brain microvascular endothelial cells (BMECs), showing co-localization with anti-VCAM1 antibodies in cis configuration and negligible binding in trans configuration.

Determination of effective charge of small ions, polyelectrolytes and nanoparticles by capillary electrophoresis

In this paper, a systematic and comparative study related to the effective charge determination of three kinds of solutes (small ions, polyelectrolytes and nanoparticles) was performed. Four approaches were compared regarding their conditions of validity and their advantages/disadvantages. Three of them allow the effective charge determination from the electrophoretic mobility and the hydrodynamic radius of the solutes using electrophoretic mobility modelings based on Nernst-Einstein (NE), O'Brien-White-Ohshima (OWO) and Yoon and Kim (YK) equations. Electrophoretic mobility and hydrodynamic radius were determined by capillary electrophoresis and Taylor dispersion analysis, respectively, using the same instrumentation in similar conditions, on a large set of samples. A fourth experimental approach based on the sensitivity of detection in indirect UV detection mode (IUV) was compared to the previously mentioned methods. OWO and YK modelings are well adapted for the effective charge determination of small ions and nanoparticles, while IUV is the only method adapted for polyelectrolytes.

Modeling the electrophoresis of oligolysines

CE is used to measure the electrophoretic mobility of low molecular mass oligo-L-lysines (n=1-8) in aqueous LiH₂PO₄ buffer, BGE, at pH 2.5 over a range of temperatures (25-50 °C) and ionic strengths (10-100 mM). Mobilities are corrected for Joule heating and under the conditions of the experiment, interaction of the peptides with the capillary walls can be ignored. A "coarse grained" bead modeling methodology (BMM) (H. Pei et al., J. Chromatogr. A 2009, 1216, 1908-1916) is used to model the mobilities. This model partially accounts for peptide conformation as well as the assumed form of its secondary structure. For highly charged oligolysines, it is necessary to properly account for the relaxation effect. In the present study, the BMM approach tends to overestimate oligolysine mobility and that effect tends to increase with increasing ionic strength and peptide length. It is proposed that association between the oligolysines and buffer components (H₂PO₄⁻ in this case) that go beyond classical electrostatic interactions are responsible for this discrepancy. A simple binding model is introduced that illustrates how this association can reconcile model and experiment.