Artificial intelligence-assisted protein engineering

doi:10.12211/2096-8280.2021-032

Abstract

Abstract:

Protein engineering is one of the important research fields of synthetic biology. However, de novo design of protein functions based on rational design is still challenging, because of the limited understanding on biological fundamentals such as protein folding and the natural evolution mechanism of enzymes. Directed evolution is capable of optimizing protein functions effectively by mimicking the principle of natural evolution in the laboratory without relying on structure and mechanism information. However, directed evolution is highly dependent on high-throughput screening methods, which also limits its applications on proteins which lack high-throughput screening methods. In recent years, artificial intelligence has been developed very rapidly for integrating into multidisciplinary fields. In synthetic biology, artificial intelligence-assisted protein engineering has become an efficient strategy for protein engineering besides rational design and directed evolution, which has shown unique advantages in predicting the structure, function, solubility of proteins and enzymes. Artificial intelligence models can learn the internal properties and relationships from given sequence-function data sets to make predictions on properties for virtual sequences. In this article, we review the application of artificial intelligence-assisted protein engineering. With the basic and process of the strategy introduced, three key points that affect the performance of the predictive model are analyzed: data, molecular descriptors and artificial intelligence algorithms. In order to provide useful tools for researchers who want to take advantage of this strategy, we summarize the main public database, diverse toolkits and web servers of the common molecular descriptors and artificial intelligence algorithms. We also comment on the functions, applications and websites of several artificial intelligence-assisted protein engineering platforms, through which a complete prediction task including protein sequences representation, feature analysis, model construction and output can be completed easily. Finally, we analyze some challenges that need to be solved in the artificial intelligence-assisted protein engineering, such as the lack of high-quality data, deviation in data sets and lacking of the universal models. However, with the development of automated gene annotations, ultra-high-throughput screening technologies and artificial intelligence algorithms, sufficient high-quality data and appropriate algorithms will be developed, which can enhance the performance of artificial intelligence-assisted protein engineering and thus facilitate the development of synthetic biology techniques.

Key words: protein engineering, synthetic biology, artificial intelligence, predictive model, database, molecular descriptor

摘要：

蛋白质工程是合成生物学领域的重要研究方向之一。但目前人类对于蛋白质折叠、酶天然进化机制等基础生物学问题的理解仍很有限，因此基于理性设计方法进行蛋白质的功能从头设计（de novo design）仍然是一个难题。定向进化（directed evolution）通过在实验室模拟自然进化的原理，可以在不依赖结构和机制信息的基础上对蛋白质的功能进行有效优化。但是定向进化高度依赖高通量筛选方法，也限制了其对缺少高通量筛选方法的蛋白质进行改造的能力。近年来，人工智能辅助的蛋白质工程逐渐发展成为一种高效的蛋白质分子设计新策略，在蛋白质的结构预测、功能预测、溶解度预测和指导智能文库设计等多个方面显现出独特的优势，成为理性设计和定向进化之后的又一次技术发展的浪潮。本文综述了近年来人工智能辅助的蛋白质工程的应用进展，对其中的代表性工作进行了重点阐述。在简单介绍了人工智能蛋白质工程策略的原理和流程之后，对数据、分子描述符和人工智能算法等三个影响预测模型性能的关键点进行了分析，总结了该策略中的主要数据库、分子描述符和算法的主流工具包及平台，介绍了它们的功能、用途和网址。我们还对人工智能策略目前仍面临的不足进行了探讨，如高质量数据不足、实验数据存在偏差、缺少通用模型等。随着自动基因功能注释技术、超高通量筛选技术和人工智能算法的不断发展，将会给人工智能辅助的蛋白质工程提供足够的高质量数据和更准确的算法，从而不断提升人工智能辅助的蛋白质工程预测准确度，为合成生物学研究提供更大的助力。

关键词: 蛋白质工程, 合成生物学, 人工智能, 预测模型, 数据库, 分子描述符

CLC Number:

Q816

Jiahao BIAN, Guangyu YANG. Artificial intelligence-assisted protein engineering[J]. Synthetic Biology Journal, 2022, 3(3): 429-444.

卞佳豪, 杨广宇. 人工智能辅助的蛋白质工程[J]. 合成生物学, 2022, 3(3): 429-444.

Figures/Tables 10

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名称	发表日期	分子描述符	程序/算法	软件/工具包地址
PRIAM	2003年 11月15日	序列比对构建的同源模块	PSI-BLAST序列比对程序	http://genopole.toulouse.inra.fr/bioinfo/priam/
CatFam	2008年 07月17日	序列比对和分层聚类	ClustalW和PSI-BLAST 序列比对程序	http://www.bhsai.org/downloads/catfam.tar.gz
EFICAz2.5	2012年 08月24日	序列比对和分层聚类	支持向量机(SVM)和分类树(classification trees)	http://cssb.biology.gatech.edu/EFICAz2.5
SVM-Prot	2016年 08月15日	多种分子描述符（多种氨基酸残基特性描述符和整体描述符）	支持向量机(SVM)， K最近邻(KNN)和概率神经网络(sPNN)	http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi
COFACTOR	2017年 05月02日	来自BioLiP文库的结构信息	基于TM分数的蛋白质结构比对算法	http://zhanglab.ccmb.med.umich.edu/COFACTOR/。
DEEPre	2017年 10月23日	序列独热编码，位置特异性得分矩阵，溶剂可及性，二级结构独热编码和功能结构域	卷积神经网络(CNN) 和循环神经网络(RNN)	http://www.cbrc.kaust.edu.sa/DEEPre
DETECT v2	2018年 05月02日	酶EC编号的正负密度分布图	贝叶斯框架(Bayesian framework)	https://github.com/ParkinsonLab/DETECT-v2
ECPred	2018年 09月21日	三种基于子序列，序列相似性和氨基酸物理化学特征的分子描述符：SPMap，BLAST-kNN和Pepstats-SVM	二进制分类算法	https://ecpred.kansil.org/
DeepEC	2019年 06月20日	独热编码	卷积神经网络(CNN)	https://bitbucket.org/kaistsystemsbiology/deepec

名称	发表日期	分子描述符	程序/算法	软件/工具包地址
PRIAM	2003年 11月15日	序列比对构建的同源模块	PSI-BLAST序列比对程序	http://genopole.toulouse.inra.fr/bioinfo/priam/
CatFam	2008年 07月17日	序列比对和分层聚类	ClustalW和PSI-BLAST 序列比对程序	http://www.bhsai.org/downloads/catfam.tar.gz
EFICAz2.5	2012年 08月24日	序列比对和分层聚类	支持向量机(SVM)和分类树(classification trees)	http://cssb.biology.gatech.edu/EFICAz2.5
SVM-Prot	2016年 08月15日	多种分子描述符（多种氨基酸残基特性描述符和整体描述符）	支持向量机(SVM)， K最近邻(KNN)和概率神经网络(sPNN)	http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi
COFACTOR	2017年 05月02日	来自BioLiP文库的结构信息	基于TM分数的蛋白质结构比对算法	http://zhanglab.ccmb.med.umich.edu/COFACTOR/。
DEEPre	2017年 10月23日	序列独热编码，位置特异性得分矩阵，溶剂可及性，二级结构独热编码和功能结构域	卷积神经网络(CNN) 和循环神经网络(RNN)	http://www.cbrc.kaust.edu.sa/DEEPre
DETECT v2	2018年 05月02日	酶EC编号的正负密度分布图	贝叶斯框架(Bayesian framework)	https://github.com/ParkinsonLab/DETECT-v2
ECPred	2018年 09月21日	三种基于子序列，序列相似性和氨基酸物理化学特征的分子描述符：SPMap，BLAST-kNN和Pepstats-SVM	二进制分类算法	https://ecpred.kansil.org/
DeepEC	2019年 06月20日	独热编码	卷积神经网络(CNN)	https://bitbucket.org/kaistsystemsbiology/deepec

名称	类型	数目/大小	参考文献	特点
UniProtKB	蛋白质序列、功能信息、研究论文索引的蛋白质数据库	UniProtKB/Swiss-Prot包括560 000多条手动注释的蛋白序列，UniProtKB/TrEMBL则包括了2亿多条自动注释的蛋白序列	[68]	已有学者利用该数据库提供的大量蛋白质序列信息，利用自然语言处理技术成功构建预测模型^[66-67,69]
Protein Data Bank	生物大分子三维结构的数据库	145 000多个来源于X射线单晶衍射、核磁共振、电子衍射等实验手段确定的蛋白质、DNA、RNA等生物大分子结构	[70]	该数据库为蛋白质结构预测模型的构建提供了大量的初始数据
ProThermDB	蛋白质信息、结构信息、实验条件、文献信息和实验热力学数据库	32 000多条数据	[71]	突变体数据中突变类型包括野生型、单点突变和多点突变
FireProtDB	蛋白质稳定性数据的数据库	242个蛋白质的6715个变体数据	[72]	手动管理，仅包含单点突变体蛋白质数据
SoluProtMut DB	突变体蛋白质溶解度数据库	917条突变数据	[73-78]	手动管理，数据已经针对机器学习应用进行了整理
ProtaBank	蛋白质工程数据的数据库	700多种蛋白质的1 800 000多个突变体	[79]	手动输入，不仅仅储存各种类型的突变信息，还提供完整的序列信息

名称	类型	数目/大小	参考文献	特点
UniProtKB	蛋白质序列、功能信息、研究论文索引的蛋白质数据库	UniProtKB/Swiss-Prot包括560 000多条手动注释的蛋白序列，UniProtKB/TrEMBL则包括了2亿多条自动注释的蛋白序列	[68]	已有学者利用该数据库提供的大量蛋白质序列信息，利用自然语言处理技术成功构建预测模型^[66-67,69]
Protein Data Bank	生物大分子三维结构的数据库	145 000多个来源于X射线单晶衍射、核磁共振、电子衍射等实验手段确定的蛋白质、DNA、RNA等生物大分子结构	[70]	该数据库为蛋白质结构预测模型的构建提供了大量的初始数据
ProThermDB	蛋白质信息、结构信息、实验条件、文献信息和实验热力学数据库	32 000多条数据	[71]	突变体数据中突变类型包括野生型、单点突变和多点突变
FireProtDB	蛋白质稳定性数据的数据库	242个蛋白质的6715个变体数据	[72]	手动管理，仅包含单点突变体蛋白质数据
SoluProtMut DB	突变体蛋白质溶解度数据库	917条突变数据	[73-78]	手动管理，数据已经针对机器学习应用进行了整理
ProtaBank	蛋白质工程数据的数据库	700多种蛋白质的1 800 000多个突变体	[79]	手动输入，不仅仅储存各种类型的突变信息，还提供完整的序列信息

名称	主要功能	类型	参考文献
PseAAC	从蛋白质序列生成氨基酸的疏水性、亲水性、侧链质量、α-COOH的pK值、α-NH₃₊的pK值以及25 °C时的pI值6种特征	网页平台	[80]
PROFEAT	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，包含11种不同类型分子描述符	网页平台	[81]
propy	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，包含13种不同类型分子描述符	Python工具包	[82]
PseAAC-General	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，包含13种基于PseAAC的分子描述符	Linux/Windows软件	[83]
protr/ProtrWeb	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，包含22种分子描述符	R工具包/网页平台	[84]
Rcpi	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，包含10种分子描述符	R/Bioconductor工具包	[85]
Pse-in-One	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，包含8种分子描述符	网页平台	[86]
ProFET	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，不支持PSSM矩阵和GO注释等非基于序列的特征	Python工具包	[87]
PseKRAAC	从蛋白质序列生成多种基于PseAAC的特征，并且利用氨基酸簇的概念，降低了特征向量的维度	网页平台	[88]
POSSUM	从蛋白质序列生成21种基于PSSM矩阵的特征	网页平台	[89]
iFeature	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征，包含18种分子描述符，并且提供12种常用的特征聚类，选择和降维算法	Python工具包/ 网页平台	[90]
BioSeq-Analysis2.0	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征；提供多种人工智能算法构建预测模型；提供特征选择算法和模型验证方法	Windows/Linux/ Unix软件/网页平台	[91]
iLearn	从蛋白质序列生成多种氨基酸分子的结构和物理化学特征；提供多种人工智能算法构建预测模型；提供特征选择算法和模型验证方法	Python工具包/ 网页平台	[92]
SOLart	支持从特征提取、预测模型构建到性能评估的完整流程。但是用户不能获得特征信息，不能选择算法和评估方式	网页平台	[93]