The goal of this research is to combine quantitative models and experiments to understand the interdomain coordination that underlies the mechanochemistry of kinesin. Despite a body of structural and functional experiments, there are a number of unresolved questions regarding the force-generating transition that drives kinesin motility. Kinesin processivity, defined as the ability to walk many steps along its filament before detaching, is achieved through chemical and mechanical coordination between the two motor domains. However, the coordination mechanisms are difficult to probe experimentally, and there is a great need for quantitative theoretical models that realistically describe the interactions between the two heads and incorporate both concerted conformational changes and diffusion-based search mechanisms. In the proposed work, stochastic models of kinesin stepping will be developed to understand the underlying molecular mechanism. Predictions from these models will be tested experimentally using single-molecule fluorescence assays and optical tweezer experiments. These predictions will then be verified using modern statistical techniques including hidden Markov models and maximum likelihood methods. The underlying hypothesis is that a significant fraction of the 16-nm that the tethered kinesin head moves during each mechanical step can be modeled as free diffusion constrained by a spring-like tether with the constraints determined by conformational changes. By modeling the interconversion of chemical energy and mechanical work, this investigation will illuminate the principles underlying other mechanical processes in the cell such as mechanotransduction and cell crawling. Secondly, because members of the kinesin family play vital roles in cell division, developing a deeper understanding of kinesin mechanochemistry will help in the design of antitumor drugs that target these motors. Third, the integrated approach of using models to drive experiments and applying modern statistical methods to verify experimental data sets a standard for similar integration and verification across a variety of future biomathematical research projects. Using this integrated approach, students will be better prepared to tackle a variety of cutting edge problems in their research careers.
|Effective start/end date||9/1/07 → 8/31/13|
- National Science Foundation: $577,126.00
- National Science Foundation: $567,000.00