Using Stochastic Force Inference to Predict Stall Forces and Rate Kinetics in Molecular Motors with a Focus on Kinesin-1

Motor proteins constitutite a broad class of subcecullar macromolecules that have demonstrated the ability to perform positive work in the presence of random thermal fluctuations. Motor protein motility is essential for active transport within cellular structures. The relationship between the rate kinetics and force-generation properties have been the subject of several decades of research. Many different mechanisms have been proposed to explain for the observed time scale differences between the motor force generation and observed stepping dynamics, and the reaction kinetics that governs the hydrolysis of molecules such as ATP. This has made it difficult to specify the exact nature of the coupling between the mechanical and chemical degrees of freedom of the motor.

The work presented here uses the computational method of stochastic force inference to determine the force-generation of Kinesin-1, a widely studied motor protein. This method assumes only that the molecule is goverened by an overdamped Langevin equation. Several different external forces are investigated, and both the diffusion and stochastic force fields are reconstructuted based on similated trainign data and experimental data sets. Moleculaer force generation is related directly to the entropy priduction of the chemical process, providing a direct link between the mechanical and chemical degrees of freedom.