Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA.
Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany.
J Chem Phys. 2021 Apr 28;154(16):164113. doi: 10.1063/5.0041022.
The use of coarse-grained (CG) models is a popular approach to study complex biomolecular systems. By reducing the number of degrees of freedom, a CG model can explore long time- and length-scales inaccessible to computational models at higher resolution. If a CG model is designed by formally integrating out some of the system's degrees of freedom, one expects multi-body interactions to emerge in the effective CG model's energy function. In practice, it has been shown that the inclusion of multi-body terms indeed improves the accuracy of a CG model. However, no general approach has been proposed to systematically construct a CG effective energy that includes arbitrary orders of multi-body terms. In this work, we propose a neural network based approach to address this point and construct a CG model as a multi-body expansion. By applying this approach to a small protein, we evaluate the relative importance of the different multi-body terms in the definition of an accurate model. We observe a slow convergence in the multi-body expansion, where up to five-body interactions are needed to reproduce the free energy of an atomistic model.
使用粗粒化（CG）模型是研究复杂生物分子系统的一种流行方法。通过减少自由度的数量，CG 模型可以探索在更高分辨率下计算模型无法访问的长时间和长尺度。如果 CG 模型是通过正式整合系统的一些自由度来设计的，那么可以预期多体相互作用将出现在有效 CG 模型的能量函数中。在实践中，已经表明包含多体项确实可以提高 CG 模型的准确性。然而，还没有提出一种通用的方法来系统地构建包含任意阶多体项的 CG 有效能量。在这项工作中，我们提出了一种基于神经网络的方法来解决这一问题，并将 CG 模型构建为多体展开。通过将这种方法应用于一个小蛋白，我们评估了在定义准确模型时不同多体项的相对重要性。我们观察到多体展开的收敛速度较慢，需要五体相互作用才能再现原子模型的自由能。
