Analysis of kinesin mechanochemistry via simulated annealing

TL;DR

This paper introduces a novel method combining Markov chain calculations and simulated annealing to optimize a kinetic model of kinesin, providing a unified microscopic understanding of its mechanochemistry based on experimental data.

Contribution

It presents a new approach to model kinesin's mechanochemistry with minimal assumptions, using optimization techniques to fit experimental data.

Findings

Optimized rate constants match experimental kinesin speed and processivity data.

The model offers a unified microscopic view of kinesin's stepping mechanism.

Methodology can be applied to other molecular motors for mechanistic insights.

Abstract

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.