Synthetic Biology Journal ›› 2022, Vol. 3 ›› Issue (3): 429-444.DOI: 10.12211/2096-8280.2021-032
• Invited Review • Previous Articles Next Articles
Jiahao BIAN, Guangyu YANG
Received:
2021-03-16
Revised:
2021-05-24
Online:
2022-07-13
Published:
2022-06-30
Contact:
Guangyu YANG
卞佳豪, 杨广宇
通讯作者:
杨广宇
作者简介:
基金资助:
CLC Number:
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.
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URL: https://synbioj.cip.com.cn/EN/10.12211/2096-8280.2021-032
Fig. 1 Schematic diagram for rational design, directed evolution and artificial intelligence-assisted protein engineering(Rational design relies on sequence and structural information to design mutant libraries accurately. However, it is difficult for being applied to proteins lacking structural and functional information. In the directed evolution strategy, multiple rounds of mutation and screening experiments are performed on target genes, which are not limited by structural and functional information, but high-throughput screening methods are required. Artificial intelligence-assisted protein engineering requires a large amount of sequence-function data, which can be derived from experiments, calculations, and databases. Through the predictive model, the sequence space of protein mutants can be explored more effectively)
Fig. 2 Workflow for predicting the GT1 glycosyltransferase model (GT-Predict)[19](The function-based algorithmic learning approach, GT-Predict, uses a diverse training set of enzymes, electrophiles, and nucleophiles to create a physicochemical and local-sequence-based classifier for predicting the novel transformations and functional annotation of GT group-transfer enzymes.)
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 |
Tab. 1 Forecast tools for EC numbers
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 |
Fig. 3 Workflow for machine learning-guided channelrhodopsin engineering[26][102 ChR proteins characterized in the recombinant library, together with 61 variants reported in the literature, constitute the training set of the classification model (1). Then the trained classification model was used to predict whether 12000 uncharacterized ChR sequence variants are functional, and three regression models (2) were trained, one for each of the ChR photocurrent properties of interest: photocurrent strength, off-kinetics and wavelength sensitivity of the photocurrents.]
Fig. 4 Schematic diagram of the supervised learning process[27]Step (a): Preparing data. The data from experiments, calculations or databases are usually converted to a format that the computer can recognize and split into the training and test parts. Step(b): Constructing a predictive model. Using the training set to train different algorithms to find decision boundaries, such as random forests, neural networks and support vector machines, so as to build predictive models. Step (c): Validating the model. An appropriate evaluation method should be selected for tasks with classification or regression.
Fig. 5 Schematic diagram for k-fold cross-validation(The training data is further subsplit into k subsets, and the training workflow is repeated k times with each of the k subsets holding for evaluation and the remaining k-1 subsets used for training)
Fig. 6 Schematic diagram for one-hot encoding(A certain position of the L amino acids in the N protein mutant sequence contains S different amino acids. The one-hot encoding represents all S amino acids as an S-dimensional vector including S-1 zeros and one 1. The position of 1 indicates the type of amino acid at that position.)
Fig. 7 Workflow for the UniRep model[67][In the training part, 24 million amino acid sequences are used to train the UniRep model. Then the trained model is used to predict the next amino acid (minimizing the cross-entropy loss), so as to learn how to correctly represent the amino acid. In the application part, by extracting and assessing the numerical vector associated with the amino acid, the trained model is used to generate a single fixed-length vector representing the input sequence. Next, these vectors can be used to train top models, which can be applied to various sequence-function prediction tasks.]
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 | [ | 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[ |
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 | [ | 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 | [ | 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 | [ | 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 | [ | 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 | [ | 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 |
Tab. 2 Commonly used database
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 | [ | 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[ |
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 | [ | 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 | [ | 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 | [ | 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 | [ | 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 | [ | 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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
POSSUM | 从蛋白质序列生成21种基于PSSM矩阵的特征 | 网页平台 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
Tab. 3 Feature generation tools based on protein sequences
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
POSSUM | 从蛋白质序列生成21种基于PSSM矩阵的特征 | 网页平台 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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