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通过分子动力学模拟来理解游离态和蛋白结合态下糖胺聚糖的相互作用。

Molecular dynamics simulations to understand glycosaminoglycan interactions in the free- and protein-bound states.

机构信息

Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA; Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA 23219, USA.

Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, VA 23298, USA; Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA 23219, USA.

出版信息

Curr Opin Struct Biol. 2022 Jun;74:102356. doi: 10.1016/j.sbi.2022.102356. Epub 2022 Mar 17.

Abstract

Natural glycosaminoglycans (GAGs) are informational molecules with astounding structural diversity. Understanding the behavior of GAGs in the free and protein-bound states is critical for harnessing this diversity. Molecular dynamics (MD) offers atomistic insight into principles governing GAG recognition by proteins. Here, we discuss how MD can be used to understand local and global properties of GAGs in free solution, including torsions, puckering, hydrogen bonding, flexibility, and energetics. We discuss MD studies on GAG-protein complexes, which help elucidate the strength of interacting residues, role of water, energetics, and so on. The MD results accumulated so far suggest that GAG recognition of proteins is a continuum from the highly selective on one end to the fully non-selective on the other with intermediate levels of selectivity, including moderately selective and plastic. The advancements in MD technology, such as coarse-grained MD, coupled with really long simulations will help understand macroscale molecular movements in the future.

摘要

天然糖胺聚糖 (GAGs) 是具有惊人结构多样性的信息分子。了解 GAG 在游离态和与蛋白结合态下的行为对于利用这种多样性至关重要。分子动力学 (MD) 可以提供原子水平的 insight,从而了解 GAG 被蛋白识别的原理。在这里,我们讨论了如何使用 MD 来理解游离溶液中 GAG 的局部和整体性质,包括扭转、构象、氢键、柔韧性和能量学。我们还讨论了关于 GAG-蛋白复合物的 MD 研究,这些研究有助于阐明相互作用残基的强度、水的作用、能量学等。到目前为止积累的 MD 结果表明,GAG 对蛋白的识别是一个连续的过程,一端是高度选择性的,另一端是完全非选择性的,中间存在选择性的不同水平,包括中度选择性和可塑性。MD 技术的进步,如粗粒化 MD,以及与真正长的模拟相结合,将有助于未来理解宏观分子运动。

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