Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA.
Protein Sci. 2023 Nov;32(11):e4792. doi: 10.1002/pro.4792.
Advances in computational tools for atomic model building are leading to accurate models of large molecular assemblies seen in electron microscopy, often at challenging resolutions of 3-4 Å. We describe new methods in the UCSF ChimeraX molecular modeling package that take advantage of machine-learning structure predictions, provide likelihood-based fitting in maps, and compute per-residue scores to identify modeling errors. Additional model-building tools assist analysis of mutations, post-translational modifications, and interactions with ligands. We present the latest ChimeraX model-building capabilities, including several community-developed extensions. ChimeraX is available free of charge for noncommercial use at https://www.rbvi.ucsf.edu/chimerax.
计算工具的进步正在推动原子模型构建，这使得我们能够在电子显微镜下观察到大型分子组装体的精确模型，通常分辨率在 3-4Å 左右。我们描述了 UCSF ChimeraX 分子建模包中的新方法，这些方法利用了机器学习结构预测，提供了基于可能性的映射拟合，并计算了每个残基的得分，以识别建模错误。其他模型构建工具有助于分析突变、翻译后修饰以及与配体的相互作用。我们介绍了最新的 ChimeraX 模型构建功能，包括几个社区开发的扩展。ChimeraX 可在 https://www.rbvi.ucsf.edu/chimerax 免费用于非商业用途。
