EXCOGITO：一个借助低分辨率表示研究生物分子的可扩展粗粒化工具箱
EXCOGITO, an extensible coarse-graining toolbox for the investigation of biomolecules by means of low-resolution representation
AI总结：
本文介绍开源可扩展粗粒化工具箱EXCOGITO，其以映射熵为核心，可处理全原子轨迹、优化映射并关联低分辨率表示与分子系统特征。
摘要：
自下而上的粗粒化（CG）模型已被证明对于补充、有时甚至取代软物质系统和生物大分子的全原子表示至关重要。低分辨率模型的开发始于减少所采用的自由度，即在系统的高分辨率描述与其简化对应物之间定义一种映射。即使没有明确的CG模型参数化和模拟，以更简单的术语观察原子论系统也能提供信息：映射熵利用了这一思想，它是对粗粒化过程中固有信息损失的一种度量。映射熵位于可扩展粗粒化工具箱（即EXCOGITO）的核心，该工具箱被开发用于对分子系统执行以映射性质为核心的多种操作和分析。EXCOGITO可以处理全原子轨迹以计算映射熵，识别使其最小化的映射，并在低分辨率表示与系统的几何、结构和能量特征之间建立定量关系。本文介绍并展示了这款依据开源许可证免费提供的软件，以向潜在用户介绍其功能和用法。该文于2024年6月11日发表在《J. Chem. Inf. Model.》上。DOI：https://doi.org/10.1021/acs.jcim.4c00490
英文摘要：
Bottom-up coarse-grained (CG) models proved to be essential to complement and sometimes even replace all-atom representations of soft matter systems and biological macromolecules. The development of low-resolution models takes the moves from the reduction of the degrees of freedom employed, that is, the definition of a mapping between a system's high-resolution description and its simplified counterpart. Even in the absence of an explicit parametrisation and simulation of a CG model, the observation of the atomistic system in simpler terms can be informative: this idea is leveraged by the mapping entropy, a measure of the information loss inherent to the process of coarsening. Mapping entropy lies at the heart of the extensible coarse-graining toolbox, or EXCOGITO, developed to perform a number of operations and analyses on molecular systems pivoting around the properties of mappings. EXCOGITO can process an all-atom trajectory to compute the mapping entropy, identify the mapping that minimizes it, and establish quantitative relations between a low-resolution representation and the geometrical, structural, and energetic features of the system. Here, the software, which is available free of charge under an open-source licence, is presented and showcased to introduce potential users to its capabilities and usage. Published on the J. Chem. Inf. Model. on June 11, 2024. DOI: https://doi.org/10.1021/acs.jcim.4c00490
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
