A stochastic automaton model for simulating kinesin processivity.

Area of Science:

• Biophysics
• Molecular Motors
• Cellular Mechanics

Background:

• Kinesin-1 is an ATP-driven motor protein that moves along microtubules (MTs).
• Its mechanical processivity, the number of steps per MT encounter, is crucial but not fully understood.
• Factors influencing kinesin-1's stepping behavior require detailed investigation.

Purpose of the Study:

• To model and simulate the mechanical processivity of kinesin-1.
• To investigate how cellular interactions and ATP dynamics affect kinesin-1 stepping.
• To elucidate the mechanisms behind reduced processivity under load.

Main Methods:

• Developed a probabilistic timed automaton model for kinesin-1.
• Simulated kinesin-1's mechanical processivity using the developed model.
• Analyzed the influence of ATP hydrolysis and synthesis on stepping behavior.

Main Results:

• Backward stepping is primarily powered by ATP hydrolysis, especially at high loads and ATP concentrations.
• Kinesin-1 processivity is determined by the number of ATP kinetic cycles before inactivation.
• Backward stepping cycles at high loads are shorter than forward stepping cycles.

Conclusions:

• The frequency of backward stepping powered by ATP hydrolysis contributes to reduced kinesin-1 processivity under load.
• Understanding ATP dynamics is key to predicting kinesin-1's mechanical behavior.
• Model provides insights into motor protein processivity and load dependence.