The Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas 66047, United States.
J Chem Theory Comput. 2022 Mar 8;18(3):1423-1436. doi: 10.1021/acs.jctc.1c01055. Epub 2022 Feb 24.
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A receptor (AAR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the AAR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.
我们引入了一种高斯加速分子动力学（GaMD）、深度学习（DL）和自由能剖析工作流程（GLOW），以预测生物分子的分子决定因素并绘制自由能景观。首先对感兴趣的生物分子进行全原子 GaMD 增强采样模拟。然后从 GaMD 模拟帧计算结构接触图，并将其转换为图像，使用卷积神经网络构建 DL 模型。重要的结构接触进一步从结构接触梯度的注意力图的 DL 模型中确定，这使我们能够识别系统反应坐标。最后，通过 GaMD 模拟的能量重新加权计算所选反应坐标的自由能分布。我们还成功地使用腺苷 A 受体（AAR）作为模型系统，展示了 GLOW 对 G 蛋白偶联受体的激活和变构调节的特征描述。GLOW 的发现与 AAR 的先前实验和计算研究高度一致，同时也为受体功能提供了进一步的机制见解。总之，GLOW 为绘制生物分子的自由能景观提供了一种系统的方法。GLOW 工作流程及其用户手册可在 http://miaolab.org/GLOW 下载。
