One bead per residue can describe all-atom protein structures.
One bead per residue can describe all-atom protein structures.
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DOI：
10.1016/j.str.2023.10.013
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发表时间：
2023-11
期刊：
影响因子：
5.7
通讯作者：
Lim Heo;M. Feig
中科院分区：
文献类型：
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作者：
Lim Heo;M. Feig
原子分辨率是高分辨率生物分子结构的标准，但实验结构数据往往是在较低的分辨率。粗粒度模型也被广泛用于计算研究，以达到生物相关的空间和时间尺度。这项研究探讨了使用先进的机器学习网络从简化的表示重建原子模型。主要发现是每个氨基酸残基的单个珠粒允许以最小的信息损失构建准确且立体化学上真实的全原子结构。这表明，蛋白质的低分辨率表示在与编码已知结构知识的机器学习框架相结合时可能足以满足许多应用。实际应用包括从实验或计算粗粒度模型中快速添加原子细节到低分辨率结构。在多尺度框架内的快速，确定性的全原子重建的应用程序进一步证明了从接近实验结构的冷冻EM密度生成准确的模型的快速协议。
Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.