Machine learning in molecular simulations of biomolecules

Guan Xing-Yue; Huang Heng-Yan; Peng Hua-Qi; Liu Yan-Hang; Li Wen-Fei; Wang Wei

doi:10.7498/aps.72.20231624

Abstract

Molecular simulation has already become a powerful tool for studying life principles at a molecular level. The past 50-year researches show that molecular simulation has been able to quantitatively characterize the kinetic and thermodynamic properties of complex molecular processes, such as protein folding and conformational changes. In recent years, the application of machine learning algorithms represented by deep learning has further promoted the development of molecular simulation. This work reviews machine learning methods in biomolecular simulation, focusing on the important progress made by machine learning algorithms in improving the accuracy of molecular force fields, the efficiency of molecular simulation conformation sampling, and also the processing of high-dimensional simulation data. The future researches to further overcome the bottleneck of accuracy and efficiency of molecular simulation, expand the scope of molecular simulation, and realize the integration of computational simulation and experimental based on machine learning technique is prospected.

Keywords:

Authors and contacts

1.
School of Physics, Nanjing University, Nanjing 210093, China
2.
Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China

Corresponding author: Li Wen-Fei, wfli@nju.edu.cn ; Wang Wei, wangwei@nju.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 11974173).

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References

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Cited By

图 1 每年结合生物分子模拟与机器学习的文献数目随年份的变化, 数据来源于Scopus

Figure 1. Number of publications with the key words “molecular simulations” and “machine learning” published per year as a function of years. Data were taken from Scopus.

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图 2 神经网络用于生物分子构象能量面及力场的拟合

Figure 2. Schematic diagram for representing the biomolecular force field by a neural network.

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图 3 基于粗粒化结构的蛋白残基溶剂可及性表面积(SASA)计算. 左图: 蛋白分子(protein G, PDB code:1pgb)的全原子结构图与粗粒化结构图; 右图: 使用DeepCGSA由粗粒化结构计算得到的SASA与参考值的对比. 其中参考值使用Shrake-Rupley算法由全原子结构计算得到^[77]. DeepCGSA能够基于粗粒化结构给出接近参考值的SASA计算结果

Figure 3. SASA estimation based on coarse-grained protein structure. Left: All-atom structure and coarse-grained structure of protein G (PDB code: 1 pgb). Right: Correlation plot between the SASA values from DeepCGSA based on one-bead coarse-grained structure and the reference values by Shrake-Rupley algorithm based on all-atom structure. The DeepCGSA can well reproduce the SASA values based on coarse-grained structure.

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图 4 用PCA (左)、t-SNE (中)和UMAP(右)对蛋白分子Protein G的基于粗粒化分子动力学的模拟轨迹^[99] 降维效果对比. 蓝色到红色对应表征蛋白折叠程度的Q值; Q = 1 (红色)为完全折叠结构, Q = 0 (蓝色)为完全解折叠结构

Figure 4. Projection of the sampled snapshots of the coarse-grained molecular dynamics simulations for protein G ^[99] along the reaction coordinates constructed by PCA (left), t-SNE (middle), and UMAP (right), respectively. t-SNE and UMAP perform better than PCA in distinguishing the folded and unfolded structures. Colors from blue to red represent the structures with increasing folding extent: blue, fully unfolded; red, fully folded.

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图 5 不同生成模型的网络架构. 从左至右分别对应变分自编码器、生成对抗网络与标准化流. 即便目标同为生成符合某种分布的数据, 三种网络使用了不同的架构与方法. 变分自编码器将数据降维至低维空间后, 在低维空间采样并再次变换至高维空间; 生成对抗网络则通过生成器与分类器之间的互相对抗而使生成器生成的结果符合目标分布; 标准化流则是在目标分布与简单易采样的分布 (如高斯分布) 之间建立直接且可逆的映射

Figure 5. Network architecture of different generative models: Variational autoencoder (VAE, left), generative adversarial network (GAN, middle), and normalizing flow (NF, right). Three networks have different architectures. VAE first reduces data to a low-dimensional space, samples in the low-dimensional space, and then transforms back to a high-dimensional space. GAN generates target distribution by combining a generator and the discriminator. Normalizing flow model establishes a direct and reversible mapping between the target distribution and a simple and easy-to-sample distribution (such as Gaussian distribution).

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