二聚体转运马达中的信息驱动步进
Information-driven stepping in dimeric transport motors
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中文总结 AI 辅助
本研究提出二聚体转运马达的信息驱动步进理论模型，揭示其作为纯信息引擎的运行机制，重现实验行为并计算驻留时间分布，为分子马达性能提供新解释。
中文摘要 AI 辅助
二聚体转运马达是沿细胞骨架丝移动的纳米级蛋白复合物。本文为其步进动力学引入了一个理论模型，其中两个马达头部做布朗运动，迁移率以依赖位置的方式周期性切换，使得每次仅一个头部移动。该机制通过消耗化学自由能产生定向运动。我们在稳态下表征了马达的平均速度及其能量和信息流，发现该马达作为纯信息引擎运行，仅在其组分间转导信息，无能量交换。对于局域切换，该模型给出了热力学一致的平均马达速度表达式，重现了实验观察到的行为，并捕捉了紧密耦合马达的 stall force（阻滞力）特性。最后，将模型粗粒化为单个力学自由度产生二阶非马尔可夫动力学，由此计算出可在单分子实验中直接观测的四种不同驻留时间分布。我们的发现强调，通过作为麦克斯韦妖的隐式运行实现的信息转导，可能是这些分子马达卓越性能的基础。
英文摘要
Dimeric transport motors are nanoscale protein complexes that move along cytoskeletal filaments. Here we introduce a theoretical model for their stepping dynamics, in which the two motor heads undergo Brownian motion with mobilities periodically switching in a position-dependent manner so that only one head moves at a time. Through consumption of chemical free energy, this mechanism produces directed motion. We characterize at steady state the motor's mean velocity and its energy and information flows. The motor operates as a pure information engine, where only information is transduced between its components, without energy exchange. For localized switching, the model yields a thermodynamically consistent expression for the mean motor velocity that reproduces experimentally observed behavior, and captures the stall force characteristic of tightly coupled motors. Finally, coarse-graining the model to a single mechanical degree of freedom produces a second-order non-Markovian dynamics, from which we compute the four distinct dwell-time distributions that can be directly observed in single-molecule experiments. Our findings highlight how information transduction, via implicit operation as a Maxwell demon, may underlie the remarkable performance of these molecular motors.
