(634f) Speed and Sensitivity Optimization of the Protein-Ligand Binding with Steric Hindrance Effect in a Microfluidic Microbead Array | AIChE

(634f) Speed and Sensitivity Optimization of the Protein-Ligand Binding with Steric Hindrance Effect in a Microfluidic Microbead Array

Authors 

Pauchard, V. - Presenter, City College of New York
Manafirasi, S. - Presenter, City College of New York,Levich Institute and Chemical Engineering
Maldarelli, C. - Presenter, The City College of New York

Analytical tools which screen the binding interactions of a protein target against a library of probe molecules displayed on a surface are central to the clinical diagnostic identification of disease markers.Typically the protein target is much large than the surface probes, which are smaller ligands, and for this case the surface density of the probes relative to the size of the protein target becomes critical in balancing speed with sensitivity in completing the assay. The larger the surface density of the probes, the greater is the density of the bound proteins and its corresponding surface signal, for a given time of exposure of the protein analyte to the probe surface. However, with large probe densities, bound protein obscures open ligand sites, and can also hinder binding of new molecules to open sites. As a result the binding can be less than the case in which the probes are widely spaced apart.  In order to understand the importance of the ligand density, a random sequential adsorption model is used to simulate the kinetic binding of protein to surface receptors as a function of the ratio of the area per molecule of the receptor, to the molecular area of the binding face of the protein. Experiments are also undertaken in a microfluidic microbead array as an illustrative screening platform, with biotin probes on the microbead surface and Neutravidin, biotin’s binding partner, streamed over the array as the target protein of an assay. Binding is quantified by fluorescence labeling of the protein. Taken together the experiments and random sequential model provide a basis for the correct prediction of the accumulation of the protein target on the surface as a function of the probe density, and provide guidelines on how this density affects the speed and sensitivity of the assay.