论文标题
分层链生长和模拟的无序蛋白质的结构集合
Structural ensembles of disordered proteins from hierarchical chain growth and simulation
论文作者
论文摘要
无序蛋白质和核酸在细胞功能和疾病中起关键作用。在这里，我们回顾了柔性生物分子构象动力学的计算探索的最新进展。我们专注于用原子分子动力学模拟构建的碎片库的分层链生长（HCG）。 HCG以统计上可再现的方式将链片段结合到全长原子详细的生物分子结构的合奏中。输入片段结构通常是从分子动力学模拟中收集的，但也可能来自结构数据库。实验数据可以在链条组装过程中和之后集成。在神经变性蛋白上的应用$α$ -Synuclein，Tau和TDP-43（包括作为冷凝物）的应用说明了HCG的使用。最后，我们强调了与基于AI的结构建模的新兴联系。
Disordered proteins and nucleic acids play key roles in cellular function and disease. Here we review recent advances in the computational exploration of the conformational dynamics of flexible biomolecules. We focus on hierarchical chain growth (HCG) from fragment libraries built with atomistic molecular dynamics simulations. HCG combines chain fragments in a statistically reproducible manner into ensembles of full-length atomically detailed biomolecular structures. The input fragment structures are typically collected from molecular dynamics simulations, but could also come from structural databases. Experimental data can be integrated during and after chain assembly. Applications to the neurodegeneration-linked proteins $α$-synuclein, tau, and TDP-43, including as condensate, illustrate the use of HCG. We conclude by highlighting the emerging connections to AI-based structural modeling.
