Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting
Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting
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DOI：
10.1021/acs.jcim.3c00350
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发表时间：
2023-09-04
影响因子：
5.6
通讯作者：
Singharoy，Abhishek
中科院分区：
文献类型：
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作者：
Sarkar，Daipayan;Lee，Hyungro;Singharoy，Abhishek
低温电子显微镜(CRYO-EM)的最新进展使得能够模拟作为细胞机械的重要组成部分的大分子复合体。从低温电磁实验得到的密度图通常与人工、知识驱动或人工智能驱动和物理指导的计算方法相结合，以建立、拟合和优化分子结构。除了单一的静态结构确定方案外，用一组有助于平均观测的模型来解释实验数据正变得越来越常见。因此，有必要在根据密度图精炼蛋白质结构的同时，决定动态蛋白质结构集合的质量。我们在生物分子的分子动力学柔性拟合(MDFF)中引入了这样一种自适应决策方案。使用自由基计算机工具，在高性能计算环境中研究了新的自由基增强MDFF实现(R-MDFF)，以求精两个原型蛋白质系统，腺苷酸激酶和一氧化碳脱氢酶。对于这些测试用例，在R-MDFF中使用灵活拟合和自适应决策的多个副本将与密度的总体相关性提高了40%，相对于暴力MDFF的改进。在较高的2-3äMAP分辨率时，改进尤其显著。更重要的是，系综模型捕捉到了生物相关分子动力学的关键特征，这些特征是单一模型解释无法获得的。最后，该管道适用于规模不断扩大的系统，这一点通过对黑猩猩腺病毒衣壳蛋白的整体提纯得到了证明。决策的开销仍然很低，并且对计算环境具有健壮性。该软件在GitHub上公开提供，并包括在不同计算环境(从基于Linux的本地工作站到高性能计算环境)上安装R-MDFF的简短用户指南。
Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge-driven or artificial intelligence-driven and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary-structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models that contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly while refining them against the density maps. We introduce such an adaptive decision-making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two prototypical protein systems, adenylate kinase and carbon monoxide dehydrogenase. For these test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2–3 Å map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that are inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from the chimpanzee adenovirus. The overhead for decision making remains low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install R-MDFF on different computing environments, from local Linux-based workstations to high-performance computing environments.