  The molecular motor protein kinesin plays a key role in fundamental cellular
processes such as intracellular transport, mitotic spindle formation, and
cytokinesis, with important implications for neurodegenerative and cancer
disease pathways. Recently, kinesin has been studied as a paradigm for the
tailored design of nano-bio sensor and other nanoscale systems. As it processes
along a microtubule within the cell, kinesin undergoes a cycle of chemical
state and physical conformation transitions that enable it to take ~100 regular
8.2-nm steps before ending its processive walk. Despite an extensive body of
experimental and theoretical work, a unified microscopic model of kinesin
mechanochemistry does not yet exist. Here we present a methodology that
optimizes a kinetic model for kinesin constructed with a minimum of a priori
assumptions about the underlying processive mechanism. Kinetic models are
preferred for numerical calculations since information about the kinesin
stepping mechanism at all levels, from the atomic to the microscopic scale, is
fully contained within the particular states of the cycle: how states
transition, and the rate constants associated with each transition. We combine
Markov chain calculations and simulated annealing optimization to determine the
rate constants that best fit experimental data on kinesin speed and
processivity.
