利用机器学习通过虚拟粒子极大地扩展自底向上粗粒度模型的范围和精度
Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models Through Virtual Particles
摘要：
使用原子参考数据参数化的粗粒度（CG）模型，即，“自下而上”的CG模型，已被证明在生物分子和其他软物质的研究中是有用的。然而，构建生物分子的高精度、低分辨率CG模型仍然具有挑战性。在这项工作中，我们演示了如何虚拟粒子，CG网站没有原子的对应关系，可以纳入CG模型的背景下，相对熵最小化（REM）作为潜变量。所提出的方法，变分导数相对熵最小化（VD-REM），使虚拟粒子的相互作用，通过梯度下降算法的机器学习辅助优化。我们将这种方法应用于具有挑战性的情况下，无溶剂的CG模型的1，2-二油酰基-sn-甘油-3-磷酸胆碱（DOPC）脂质双层，并证明引入虚拟粒子捕获溶剂介导的行为和高阶相关性，REM单独不能捕获在一个更标准的CG模型的基础上，仅基于原子的集合的映射到CG网站。
英文摘要：
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.
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