 Nano-motion Dynamics are Determined by Surface-Tethered Selectin Mechanokinetics and Bond FormationBrian J Schmidt, Jason A Papin and Michael B Lawrence PLOS Computational Biology, 2009, vol. 5, issue 12, 1-19 Abstract: The interaction of proteins at cellular interfaces is critical for many biological processes, from intercellular signaling to cell adhesion. For example, the selectin family of adhesion receptors plays a critical role in trafficking during inflammation and immunosurveillance. Quantitative measurements of binding rates between surface-constrained proteins elicit insight into how molecular structural details and post-translational modifications contribute to function. However, nano-scale transport effects can obfuscate measurements in experimental assays. We constructed a biophysical simulation of the motion of a rigid microsphere coated with biomolecular adhesion receptors in shearing flow undergoing thermal motion. The simulation enabled in silico investigation of the effects of kinetic force dependence, molecular deformation, grouping adhesion receptors into clusters, surface-constrained bond formation, and nano-scale vertical transport on outputs that directly map to observable motions. Simulations recreated the jerky, discrete stop-and-go motions observed in P-selectin/PSGL-1 microbead assays with physiologic ligand densities. Motion statistics tied detailed simulated motion data to experimentally reported quantities. New deductions about biomolecular function for P-selectin/PSGL-1 interactions were made. Distributing adhesive forces among P-selectin/PSGL-1 molecules closely grouped in clusters was necessary to achieve bond lifetimes observed in microbead assays. Initial, capturing bond formation effectively occurred across the entire molecular contour length. However, subsequent rebinding events were enhanced by the reduced separation distance following the initial capture. The result demonstrates that vertical transport can contribute to an enhancement in the apparent bond formation rate. A detailed analysis of in silico motions prompted the proposition of wobble autocorrelation as an indicator of two-dimensional function. Insight into two-dimensional bond formation gained from flow cell assays might therefore be important to understand processes involving extended cellular interactions, such as immunological synapse formation. A biologically informative in silico system was created with minimal, high-confidence inputs. Incorporating random effects in surface separation through thermal motion enabled new deductions of the effects of surface-constrained biomolecular function. Important molecular information is embedded in the patterns and statistics of motion.Author Summary: The binding of a receptor on one cell to a ligand on another is a process of broad biological interest, important to cell adhesion and signaling. Interactions between cell surfaces can be called “two-dimensional” because the reactive groups on interacting molecular pairs are constrained to move 100 nm or less in the direction perpendicular to the surfaces. The molecular reactive groups are attached to their respective cellular surfaces through a molecular tether embedded in the cell membrane. There are many parameters that might affect the observed binding kinetics, such as the distance between the cell surfaces, the length of the molecular tether, and the freedom of the reactive groups to move about on their molecular tether. A well-studied case of two-dimensional interactions is that through which circulating leukocytes capture to the endothelium and exit the blood into the tissues. Leukocyte capture presents an additional complexity: bonds that restrain leukocytes against the shearing force exerted by the blood must be capable of withstanding the force trying to pull the receptor and ligand apart at their noncovalent interface. New models have been proposed to explain the behavior of individual receptors and ligands, raising the question: which molecular behaviors have an effect on function? Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1000612 DOI: 10.1371/journal.pcbi.1000612 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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