论文标题
利用机器学习来通过虚拟颗粒极大地扩大自下而上的粗粒模型的范围和准确性
Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models Through Virtual Particles
论文作者
论文摘要
用原子参考数据（即“自下而上” CG模型）进行参数化的粗粒（CG）模型已被证明可用于研究生物分子和其他软物质。但是，高度准确，低分辨率CG生物分子模型的构建仍然具有挑战性。我们在这项工作中证明了如何在相对熵最小化（REM）作为潜在变量的背景下将虚拟颗粒，无原子对应的CG位点合并到CG模型中。提出的方法是，变异衍生物相对熵最小化（VD-REM）可以通过机器学习帮助通过梯度下降算法优化虚拟粒子相互作用。 We apply this methodology to the challenging case of a solvent-free CG model of a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipid bilayer and demonstrate that introduction of virtual particles captures solvent-mediated behavior and higher-order correlations which REM alone cannot capture in a more standard CG model based only on the mapping of collections of atoms to the CG sites.
Coarse-grained (CG) models parameterized using atomistic reference data, i.e., 'bottom up' CG models, have proven useful in the study of biomolecules and other soft matter. However, the construction of highly accurate, low resolution CG models of biomolecules remains challenging. We demonstrate in this work how virtual particles, CG sites with no atomistic correspondence, can be incorporated into CG models within the context of relative entropy minimization (REM) as latent variables. The methodology presented, variational derivative relative entropy minimization (VD-REM), enables optimization of virtual particle interactions through a gradient descent algorithm aided by machine learning. We apply this methodology to the challenging case of a solvent-free CG model of a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipid bilayer and demonstrate that introduction of virtual particles captures solvent-mediated behavior and higher-order correlations which REM alone cannot capture in a more standard CG model based only on the mapping of collections of atoms to the CG sites.
