Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus.
Departamento de Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica, Facultad de Ciencias, IMEYMAT, Campus Universitario Río San Pedro s/n., Puerto Real, Cádiz 11510, Spain.
J Chem Inf Model. 2024 Mar 25;64(6):1853-1867. doi: 10.1021/acs.jcim.3c01870. Epub 2024 Mar 1.
Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator ("one-to-one" problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones ("one-to-many" problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.
复杂分子体系（如大分子）的多尺度建模包括将来自分子精细和粗粒表示的信息结合起来的方法，以在广泛的时空尺度上捕获材料性质。能够在不同分辨率级别之间交换信息对于有效传递此信息至关重要。在粗粒（CG）分子构型中重新引入原子自由度的逆问题特别具有挑战性，因为从数学角度来看，它是一个不适定问题；从原子到 CG 描述的正向映射通常通过确定性算子定义（“一对一”问题），而从 CG 到原子模型的反向映射则指的是从许多可能的构型中创建一个代表构型（“一对多”问题）。迄今为止提出的大多数反向映射方法在准确性、效率和通用性之间取得平衡。对于具有不同类型异构体的大分子体系（即具有相同分子公式和键合原子序列但原子在空间中的三维构型不同的分子）来说，这一点尤其重要。在这里，我们引入了一种通用的深度学习方法，用于反向映射具有手性中心的多组分 CG 大分子，该方法经过训练可以学习聚合物构型在原子水平上与它们相应的 CG 描述之间的结构相关性。该方法旨在简单灵活，同时提供分辨率转换的通用解决方案。此外，该方法旨在尊重分子的结构特征，例如局部堆积，从而捕获材料的物理性质。作为说明性示例，我们将模型应用于线性聚（乳酸）（PLA）在熔体中，这是最受欢迎的可生物降解聚合物之一。该框架在一系列模型系统上进行了测试，这些模型系统从 PLA 的均聚物立体异构体开始，到具有随机放置手性中心的共聚物。结果证明了新方法的效率和效果。
