This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Mechanosensitivity of Cell Membranes: Role of Lipid-Protein Interactions PI: Mirianas Chachisvilis Abstract The goal of this project is to perform simulations at molecular dynamics (MD) and ab initio levels to support NIH grant R01 HL86943-3 (Mechanosensitivity of Cell Membranes: Role of Lipid-Protein Interactions, PI: M. Chachisvilis) and NSF grant MCB 0721396 (The Role of Dipole Potential In Mechanosensing, PI: M. Chachisvilis). The central hypothesis is that the plasma membrane of endothelial cell acts as a mechanosensitive element;i.e. changes in physical properties of the membrane under mechanical stress can regulate activity of membrane proteins coupled to intracellular signaling pathways. Due to rather basic nature of the specific aims of the parent grants, computational modeling would enable to link experimentally observed correlations between mechanically induced changes in the properties of lipid bilayer membrane and conformational changes in the receptor conformation using mechanistic molecular models. Computational modeling at the MD level will be used to model changes in lateral diffusion of lipid probes and conformational response of the G protein coupled receptor (GPCR) to specific changes in the lipid bilayer properties thereby enabling to confirm existence of the causative relationship between the conformational response and changes in bilayer properties under mechanical stress. Such theoretical confirmation would enable to draw more definite conclusions about the role of the plasma membrane in mechanosensing. Modeling capability will also enable to guide experimental work in designing and optimizing new FRET sensors for detection of GPCR and G protein activity which will significantly accelerate experimental work by enabling us to exclude from experimental construction unoptimal sensor configurations. Validation of the MD simulations by comparison with our experimental data will enable faster research progress in the future as it can eliminate the need for some expensive and time consuming experiments;validation of computational approach will also offer an efficient tool that can be used to test mechansosensitivity of many other potential mechanosensors during future research at the LJBI. More generally computational simulations will help to better understand processes underlying mechanochemical response by providing a visual representation of molecular geometries, spatial alignments and energetics that contribute to experimentally observed mechanosensitive conformational transitions.

National Institute of Health (NIH)
National Center for Research Resources (NCRR)
Biotechnology Resource Grants (P41)
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Carnegie-Mellon University
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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