合成生物学 ›› 2022, Vol. 3 ›› Issue (3): 429-444.doi: 10.12211/2096-8280.2021-032
卞佳豪, 杨广宇
收稿日期:
2021-03-16
修回日期:
2021-05-24
出版日期:
2022-06-30
发布日期:
2022-07-13
通讯作者:
杨广宇
作者简介:
基金资助:
Jiahao BIAN, Guangyu YANG
Received:
2021-03-16
Revised:
2021-05-24
Online:
2022-06-30
Published:
2022-07-13
Contact:
Guangyu YANG
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。
中图分类号:
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, doi: 10.12211/2096-8280.2021-032.
Jiahao BIAN, Guangyu YANG. Artificial intelligence-assisted protein engineering[J]. Synthetic Biology Journal, 2022, 3(3): 429-444, doi: 10.12211/2096-8280.2021-032.
表1
EC编号预测工具汇总表
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 |
表2
常见数据库汇总表
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 |
表3
基于蛋白质序列的特征生成工具汇总表
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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