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Brownian dynamics simulations of the B-cell activation

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IIT-22-039C_poster.pdf (147.4Kb)
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García Sánchez, Miguel
Castro Ponce, Mario
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info:eu-repo/semantics/draft
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The binding between B cell receptors (BCRs) and their ligands is a fundamental step in the B cell activation. This binding consists of a series of states and processes which include: diffusion, orientation and molecular binding [1]. Once the bond is formed, it is mechanically stressed by tensile forces coming from the environment and the internal reorganization of the cell membrane. Interestingly, it has been observed in many biological systems that the applied force changes the bond mean lifetime. Naively, one could expect that the larger the force, the shorter the time (as the two molecules are pulled away). This mechanism is called slip-bond unbinding. However, it has been empirically observed that for some systems, the opposite happens (at least for small tensions). This is called catch-bond mechanism, and it has been reported in a wide variety of systems, from elastic fibers to T-cells [2].Microscopically, the three steps in Fig. 1 are stochastic in nature due to thermal fluctuations at the protein level. In this work, we model all those previous processes as Brownian motions, either in a cell membrane (diffusion), extracellular space (orientation), or a potential landscape (binding rupture). Using Montecarlo simulations, we investigate the so-called ”Two-pathways model” of the catch bond [3] and reproduce the orientation and catch bond dynamics. We find that the affinity of two BCRs with an antigen can drastically change if it is measured in solution (absence of force, f = 0) or in a cell-to-cell set up as in physiological conditions (see Fig.2).Our results highlight the dangers of uncontrolled in-vitro experiments to rank the affinity of antibodies as those experiments are unable to capture the true underlying kinetic rates and, thus, can mislead the quest to find specific therapeutic treatments based on antibodies.
 
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http://hdl.handle.net/11531/66817
Brownian dynamics simulations of the B-cell activation
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