Machine learning for <i>in silico</i> protein research

Zhang Jia-Hui

doi:10.7498/aps.73.20231618

Abstract

In silico protein calculation has been an important research subject for a long time, while its recent combination with machine learning promotes the development greatly in related areas. This review focuses on four major fields of the in silico protein research that combines with machine learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depend on the parameters of force field, which is necessary for obtaining accurate results. Machine learning can help researchers to obtain more accurate force field parameters. In molecular dynamics simulation, machine learning can also help to perform the free energy calculation in relatively low cost. Structure prediction is generally used to predict the structure given a protein sequence. Structure prediction is of high complexity and data volume, which is exactly what machine learning is good at. By the help of machine learning, scientists have gained great achievements in three-dimensional structure prediction of proteins. On the other hand, the predicting of protein properties based on its known information is also important to study protein. More challenging, however, is molecule design. Though marching learning has made breakthroughs in drug-like small molecule design and protein design in recent years, there is still plenty of room for exploration. This review focuses on summarizing the above four fields andlooks forward to the application of marching learning to the in silico protein research.

Keywords:

Authors and contacts

Zhang Jia-Hui

School of Life Sciences, University of Science and Technology of China, Hefei 230027, China

Corresponding author: Zhang Jia-Hui, jhzhang@ustc.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 22177107).

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Cited By

图 1 量子力学与机器学习间的相似性. 从左到右, 从上到下的图片分别是: Chignolin蛋白质在(a)无水环境和(b)有水环境下的情况, 使用SchNet模型得到的(c)可视化电荷密度和(d)局部化学势, (e)氢原子的波函数以及(f)Müller-Brown势能. 图片引自文献[42] (版权属于美国化学会)

Figure 1. Similarity between quantum mechanics and machine learning. Images from left to right from top to bottom: Chignolin protein (a) without and (b) with the water environment, (c) visualized total charge densities and (d) local chemical potentials obtained using the SchNet model, (e) wave functions for hydrogen atom and (f) Müller-Brown potential. Reprinted with permission from Ref. [42] (Copyright 2021 American Chemical Society).

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图 2 AlphaFold2的结构图

Figure 2. Architecture of AlphaFold2.

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图 3 AlphaFold-Multimer的多序列比对构建方法

Figure 3. Construction of MSA used in AlphaFold-Multimer.

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图 4 ProteinMPNN模型核心思想示意图

Figure 4. Main idea of ProteinMPNN.

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图 5 蛋白质结构序列协同设计的一种机器学习模型示意图

Figure 5. Illustration of a machine learning model of protein structure-sequence co-design.

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map

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