蛋白质计算中的机器学习

张嘉晖

doi:10.7498/aps.73.20231618

摘要

蛋白质计算一直以来都是科学领域中的重要课题, 而近年来其与机器学习的结合, 更是极大地推进了相关学科的发展. 本综述主要讨论了机器学习在四个重要的蛋白质计算领域内的研究进展, 这四个领域包括：分子动力学模拟、结构预测、性质预测和分子设计. 分子动力学模拟依赖于力场参数, 准确的力场参数是分子动力学模拟的必需品, 而机器学习可以帮助研究者得到更加准确的力场参数. 在分子动力学模拟中, 机器学习也可以从复杂的体系中以较小的代价计算出所需求解的自由能. 结构预测一般是给定蛋白质序列预测其结构. 结构预测复杂度高、数据量大, 而这恰恰是机器学习所擅长的. 在机器学习的协助下, 近年来科研人员已经在单个蛋白质三维结构预测上取得了不错的成果. 性质预测则是指通过给定的已知蛋白质信息, 推断其可能拥有的性质, 这对于蛋白质的研究也是至关重要的. 更具挑战性的是分子设计, 虽然近年来机器学习在蛋白质设计上取得突破, 但这一领域还有很大空间值得探索. 本综述将针对以上四点分别展开论述, 并对蛋白质计算中的机器学习研究进行展望.

关键词:

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:

作者及机构信息

张嘉晖

中国科学技术大学生命科学学院, 合肥　230027

通信作者: 张嘉晖, jhzhang@ustc.edu.cn

基金项目: 国家自然科学基金(批准号: 22177107)资助的课题.

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).

文章全文 : translate this paragraph

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施引文献

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)

Fig. 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).

下载: 全尺寸图片幻灯片

图 2 AlphaFold2的结构图

Fig. 2. Architecture of AlphaFold2.

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图 3 AlphaFold-Multimer的多序列比对构建方法

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

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图 4 ProteinMPNN模型核心思想示意图

Fig. 4. Main idea of ProteinMPNN.

下载: 全尺寸图片幻灯片

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

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

下载: 全尺寸图片幻灯片

map

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