深度学习在蛋白质结构预测中的应用及启示

王天尧; 李剑锋

doi:10.11777/j.issn1000-3304.2021.21401

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深度学习在蛋白质结构预测中的应用及启示

综述 | 更新时间：2024-10-08

- 深度学习在蛋白质结构预测中的应用及启示
- Application of Deep Learning in Protein Structure Prediction and Its Inspirations
- 高分子学报 2022年53卷第6期页码：581-591
- 作者机构：
  
  复旦大学高分子科学系聚合物分子工程国家重点实验室上海 200433
- 作者简介：
  
  [ "李剑锋，男，1980年生. 1999~2010年于复旦大学高分子科学系获得学士、硕士、博士学位；2007~2009年在加拿大McMaster大学公派出国留学生；2012~2013年复旦大学高分子系讲师，2013~2019年复旦大学高分子系副教授. 2019年至今，复旦大学高分子系教授. 主要从事高分子缠结理论、机器学习在高分子物理中的应用、非平衡热力学方法、大脑理论模型构建等方面研究." ]
- 基金信息：
  
  国家自然科学基金(基金号 ┣21973018);21534002┫
- DOI：10.11777/j.issn1000-3304.2021.21401
  中图分类号：
- 纸质出版日期：2022-06-20，
  
  网络出版日期：2022-03-17，
  
  收稿日期：2021-12-28，
  
  录用日期：2022-01-28
- 稿件说明：
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王天尧,李剑锋.深度学习在蛋白质结构预测中的应用及启示[J].高分子学报,2022,53(06):581-591.

Wang Tian-yao,Li Jian-feng.Application of Deep Learning in Protein Structure Prediction and Its Inspirations[J].ACTA POLYMERICA SINICA,2022,53(06):581-591.
王天尧,李剑锋.深度学习在蛋白质结构预测中的应用及启示[J].高分子学报,2022,53(06):581-591. DOI： 10.11777/j.issn1000-3304.2021.21401.

Wang Tian-yao,Li Jian-feng.Application of Deep Learning in Protein Structure Prediction and Its Inspirations[J].ACTA POLYMERICA SINICA,2022,53(06):581-591. DOI： 10.11777/j.issn1000-3304.2021.21401.

摘要

蛋白质结构预测通常指借助计算机计算模拟方法从氨基酸序列推断其三维空间结构. 而空间结构决定其生理功能，故结构预测问题尤为重要. 基于单纯物理学的预测仅能应对较短蛋白质且精度不高. 而基于数据驱动和生物信息学的方法近十多年备受重视. 本文主要回顾近十多年来深度学习在蛋白质预测领域的应用，重点介绍Deepmind团队的AlphaFold方法，此方法预测在单域蛋白质达到了中低分辨率实验精度，一定程度上解决了困扰人们五十多年的蛋白质结构预测难题.

Abstract

The goal of protein structure prediction is to determine

usually based on computer simulations or calculations

the three dimensional structure from a given amino acid sequence. Protein structure prediction is important since the 3D protein structure will further determine its biological functions. Nevertheless

traditional prediction method based on physics can only effectively deal with short proteins with the low accuracy. In the past decade

data-driven methods and methods based on genetic knowledge have become popular. This review covers several important developments about the deep-learning methods on protein prediction in the past ten years. Considering the education background of readers of the journal

we will first present a self-consistent but concise introduction about the prerequisite concepts and methods

related with genetic information and basis of deep learning

to understand the deep-learning methods for the protein structure prediction. The prerequisite concepts and methods include position-specific scoring matrix (PSSM)

multiple sequence alignment (MSA)

contact map

distogram

protein data bank (PDB)

critical assessment of protein structure prediction (CASP)

template modelling score (TM score)

universal approximation theorem and several important types of neural network closely related with protein structure prediction. Then

we compared the two most popular methods employed in the data-driven protein structure prediction

template-based method and template-free method. As for the deep-learning methods

the AlphaFold method from Deepmind will be specially discussed

which has achieved the prediction accuracy comparable to median or low experimental accuracy that even rendered some people to think that it has resolved the protein structure prediction problem to some extent. Nevertheless

all the above methods are too "overwhelming" and not friendly for beginners. Therefore

this review also introduced a simplest structure prediction problem

HP protein prediction problem

together with the corresponding deep-learning solution

strongly-correlated neural network

to novices in this area.

关键词

蛋白质折叠深度学习神经网络结构预测

Keywords

Protein foldingDeep learningNeural networkStructural prediction

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