Kidder Katherine M, Noid W G
Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.
J Chem Phys. 2024 Oct 7;161(13). doi: 10.1063/5.0220989.
Low-resolution coarse-grained (CG) models provide significant computational and conceptual advantages for simulating soft materials. However, the properties of CG models depend quite sensitively upon the mapping, M, that maps each atomic configuration, r, to a CG configuration, R. In particular, M determines how the configurational information of the atomic model is partitioned between the mapped ensemble of CG configurations and the lost ensemble of atomic configurations that map to each R. In this work, we investigate how the mapping partitions the atomic configuration space into CG and intra-site components. We demonstrate that the corresponding coordinate transformation introduces a nontrivial Jacobian factor. This Jacobian factor defines a labeling entropy that corresponds to the uncertainty in the atoms that are associated with each CG site. Consequently, the labeling entropy effectively transfers configurational information from the lost ensemble into the mapped ensemble. Moreover, our analysis highlights the possibility of resonant mappings that separate the atomic potential into CG and intra-site contributions. We numerically illustrate these considerations with a Gaussian network model for the equilibrium fluctuations of actin. We demonstrate that the spectral quality, Q, provides a simple metric for identifying high quality representations for actin. Conversely, we find that neither maximizing nor minimizing the information content of the mapped ensemble results in high quality representations. However, if one accounts for the labeling uncertainty, Q(M) correlates quite well with the adjusted configurational information loss, Îmap(M), that results from the mapping.
低分辨率粗粒化（CG）模型在模拟软材料方面具有显著的计算和概念优势。然而，CG模型的性质非常敏感地依赖于映射M，它将每个原子构型r映射到一个CG构型R。特别地，M决定了原子模型的构型信息如何在CG构型的映射系综和映射到每个R的原子构型的丢失系综之间进行分配。在这项工作中，我们研究了映射如何将原子构型空间划分为CG和位点内分量。我们证明相应的坐标变换引入了一个非平凡的雅可比因子。这个雅可比因子定义了一个标记熵，它对应于与每个CG位点相关联的原子的不确定性。因此，标记熵有效地将构型信息从丢失系综转移到映射系综。此外，我们的分析突出了共振映射的可能性，这种映射将原子势分离为CG和位点内贡献。我们用一个用于肌动蛋白平衡涨落的高斯网络模型对这些考虑进行了数值说明。我们证明光谱质量Q提供了一种简单的度量方法来识别肌动蛋白的高质量表示。相反，我们发现最大化或最小化映射系综的信息含量都不会产生高质量的表示。然而，如果考虑标记不确定性，Q(M)与由映射导致的调整后的构型信息损失Îmap(M)有很好的相关性。
