Inference of Calmodulin’s Ca2+-Dependent Free Energy Landscapes via Gaussian Mixture Model Validation,Journal of Chemical Theory and Computation

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Inference of Calmodulin’s Ca2+-Dependent Free Energy Landscapes via Gaussian Mixture Model Validation
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2017-12-06 00:00:00 , DOI: 10.1021/acs.jctc.7b00346
Annie M. Westerlund ₁ , Tyler J. Harpole ₁ , Christian Blau ₂ , Lucie Delemotte ₁

Affiliation

A free energy landscape estimation method based on the well-known Gaussian mixture model (GMM) is used to compare the efficiencies of thermally enhanced sampling methods with respect to regular molecular dynamics. The simulations are carried out on two binding states of calmodulin, and the free energy estimation method is compared with other estimators using a toy model. We show that GMM with cross-validation provides a robust estimate that is not subject to overfitting. The continuous nature of Gaussians provides better estimates on sparse data than canonical histogramming. We find that diffusion properties determine the sampling method effectiveness, such that diffusion-dominated apo calmodulin is most efficiently sampled by regular molecular dynamics, while holo calmodulin, with its rugged free energy landscape, is better sampled by enhanced sampling methods.

中文翻译：

通过高斯混合模型验证推断钙调蛋白的Ca ²⁺依赖性自由能态

使用基于众所周知的高斯混合模型（GMM）的自由能态势估计方法来比较热增强采样方法相对于常规分子动力学的效率。对钙调蛋白的两个结合状态进行了模拟，并使用玩具模型将自由能估计方法与其他估计方法进行了比较。我们表明，具有交叉验证的GMM提供了一个可靠的估计，该估计不会过度拟合。与典型的直方图相比，高斯函数的连续性提供了对稀疏数据更好的估计。我们发现，扩散特性决定了采样方法的有效性，因此，通过规则的分子动力学可以最有效地采样以扩散为主的载脂蛋白钙调蛋白，而具有坚固的自由能态的全钙调蛋白，

更新日期：2017-12-06

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