分子动力学能否用于模拟生物分子识别？
Can molecular dynamics be used to simulate biomolecular recognition?
AI总结：
本文探讨了分子动力学在模拟生物分子识别中的局限性，以LacI与DNA结合转换过程为基准，指出仅偏置部分自由度无法实现可逆转换，为评估机器学习寻找集体变量的方法提供了重要参考。
摘要：
生物化学中存在许多难以通过实验研究的问题。由于可以直接获取随时间变化的原子坐标，模拟方法极具吸引力。然而，直接的分子模拟受到系统尺寸和描述相关运动所需时间尺度的挑战。理论上，增强采样算法有助于克服分子模拟的一些局限性。在此，我们讨论了生物化学中一个对增强采样方法构成重大挑战的问题，因此它可以作为基准，用于比较利用机器学习寻找合适集体变量的方法。更具体地说，我们研究了LacI在非特异性结合与特异性结合DNA之间转换时所经历的结构转变。研究发现，在此转换过程中有许多自由度发生变化，并且如果仅对其中部分自由度施加偏置，该转换在模拟中不会可逆地发生。我们还解释了为什么这个问题对生物学家如此重要，以及对其的模拟将如何对理解DNA调控产生变革性影响。
英文摘要：
There are many problems in biochemistry that are difficult to study experimentally. Simulation methods are appealing due to direct availability of atomic coordinates as a function of time. However, direct molecular simulations are challenged by the size of systems and the time scales needed to describe relevant motions. In theory, enhanced sampling algorithms can help to overcome some of the limitations of molecular simulations. Here, we discuss a problem in biochemistry that offers a significant challenge for enhanced sampling methods and that could, therefore, serve as a benchmark for comparing approaches that use machine learning to find suitable collective variables. More in particular, we study the transitions LacI undergoes upon moving between being non-specifically and specifically bound to DNA. It is found that many degrees of freedom change during this transition and that the transition does not occur reversibly in simulations if only a subset of these degrees of freedom are biased. We also explain why this problem is so important to biologists and the transformative impact that a simulation of it would have on the understanding of DNA regulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
