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Understanding molecular recognition by kinetic network models constructed from molecular dynamics simulations

机译:通过由分子动力学模拟构建的动力学网络模型了解分子识别

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Molecular recognition, the process by which biological macromolecules selectively bind, plays an important role in many biological processes. Molecular simulations hold great potential to reveal the chemical details of molecular recognition and to complement experiments. However, it is challenging to reconstruct the binding process for two-body systems like protein-ligand complexes because the system's dynamics occurs on significantly different timescales due to several physical processes involved, such as diffusion, local interactions and conformational changes. In this chapter, we review some recent progress on applying Markov state models (MSMs) to two-body systems. Emphasis is placed on the value of projecting dynamics onto collective reaction coordinates and treating the ligand dynamics with different resolution models depending on the proximity of the protein and ligand. We also discuss some future directions on constructing MSMs to investigate molecular recognition processes.
机译:分子识别是生物大分子选择性结合的过程,在许多生物学过程中都起着重要作用。分子模拟具有揭示分子识别的化学细节和补充实验的巨大潜力。但是,重建像蛋白质-配体复合物这样的两体系统的结合过程具有挑战性,因为由于涉及多个物理过程(例如扩散,局部相互作用和构象变化),系统的动力学发生在明显不同的时间尺度上。在本章中,我们回顾了将马尔可夫状态模型(MSM)应用于两体系统的最新进展。重点放在将动力学投影到集体反应坐标上,并根据蛋白质和配体的接近程度,用不同的分辨率模型处理配体动力学的价值。我们还讨论了构建MSM来研究分子识别过程的一些未来方向。

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