Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Structure. 2024 Jan 4;32(1):97-111.e6. doi: 10.1016/j.str.2023.10.013. Epub 2023 Nov 23.
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.
原子分辨率是高分辨率生物分子结构的标准，但实验结构数据通常分辨率较低。在计算研究中，粗粒模型也被广泛用于达到生物相关的时空尺度。本研究探索了使用先进的机器学习网络从简化表示中重建原子模型。主要发现是，每个氨基酸残基使用一个珠子就可以构建准确且立体化学真实的全原子结构，而信息损失最小。这表明，当与从已知结构中编码知识的机器学习框架结合使用时，蛋白质的较低分辨率表示可能足以满足许多应用。实际应用包括将原子细节快速添加到实验或计算粗粒模型的低分辨率结构中。通过快速协议从接近实验结构的冷冻电镜密度生成准确模型，进一步展示了在多尺度框架中快速、确定性全原子重建的应用。
