生物分子序列的人工智能设计

doi:10.12211/2096-8280.2020-074

摘要/Abstract

摘要：

合成生物学研究本着师法自然、改造自然及超越自然的理念，其核心是通过人工方式将基因元件优化改造和重新组合，以得到满足需要的人工生物系统。获取性能优异的生物元件是构建和控制人工生物系统的基础。近年来，人工生物分子在代谢工程和基因治疗等领域有着广泛应用。如何在广袤的分子序列空间中高效地搜索与设计具有特定生物功能的分子序列，是合成生物学所面临的重要科学问题。伴随着人工智能技术的快速发展，智能算法在复杂生物特征的挖掘与生物分子的设计中表现出巨大潜力。本文从利用深度学习技术发掘的复杂特征规律为指导，智能化地探索新药物分子、核酸序列和蛋白质序列空间的角度出发，重点分析了深度生成式模型在不同人工生物序列设计中的应用特点。在此基础上，结合小分子化合物、核酸和蛋白质等生物分子设计的应用案例，总结分析了针对人工生物分子序列设计的定向寻优策略。为了对智能算法设计的分子进行评估，系统分析了不同领域中不同角度序列设计评估方案的特点，展望了人工生物序列智能设计的发展，需要充分考虑生物系统具有多层次间调控高度耦合的复杂特性，从系统角度对不同层次的生物序列进行优化设计，从而推动人工生物系统的智能适配与优化。

关键词: 合成生物学, 智能设计, 生物元件设计, 深度学习, 智能优化

Abstract:

Based on the concept of learning from nature， transforming and transcending nature， the core of synthetic biology is to optimize， reconstruct and recombine genetic elements in order to build synthetic biological systems that meet our needs. Obtaining desirable biological components is the basis for building and controlling synthetic biological systems. Recently， synthetic biomolecules have been widely used in areas such as metabolic engineering and gene therapy. How to search for biomolecular sequences with specific biological functions from the vast sequence library is a challenge for synthetic biology. With the rapid development of artificial intelligence， intelligent algorithms have shown great potentials in mining complex biological characteristics and designing biomolecules. In this review， the applications of deep generative models for the design of different artificial biological sequences are analyzed from the perspective of exploring new drug molecules， nucleic acid fragment sequences and protein sequence spaces under the guidance of complex feature rules discovered by deep learning technology. Furthermore， combined with the application cases in the design of small molecular compounds， nucleic acids and proteins， the directed optimization strategies for designing artificial biomolecules are summarized and analyzed. In order to evaluate the model-designed molecular sequences， this review systematically analyzes the schemes for sequence design evaluation from different perspectives in applications. As an important information writing carrier of synthetic life systems， how the artificial biological sequence interacts with the complex multi-level regulation in the cell is still an important issue to be studied. In the future， the intelligent design of artificial biological sequence needs to consider the characteristics of biological systems with multi-level regulation that is often coupling. Through the design of biological sequences at different levels， different regulation in natural biological systems should be elucidated at different levels properly for an overall intelligent adaptation and optimization of biological sequences and cell chassis environments.

Key words: synthetic biology, intelligent design, biological element design, deep learning, intelligent optimization

中图分类号:

王也, 王昊晨, 晏明皓, 胡冠华, 汪小我. 生物分子序列的人工智能设计[J]. 合成生物学, 2021, 2(1): 1-14.

Ye WANG, Haochen WANG, Minghao YAN, Guanhua HU, Xiaowo WANG. Design of biomolecular sequences by artificial intelligence[J]. Synthetic Biology Journal, 2021, 2(1): 1-14.

图/表 5

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深度生成式模型	生物序列	数据形式	模型名称	寻优算法	相关文献
生成对抗网络	核酸	碱基序列独热编码	WGAN	性能得分梯度寻优	[62]
	蛋白质	氨基酸距离矩阵	DCGAN	基于Rosetta的采样	[46]
	小分子药物	分子图矩阵编码	MolGAN	强化学习	[59]
变分/对抗自编码器	小分子药物	原子团的连接树编码	Junction Tree VAE	贝叶斯优化	[63]
		SMILES独热编码	ChemVAE	性能得分梯度寻优	[43]
		邻接矩阵与属性向量等组成的概率图	GraphVAE	条件生成	[64]
循环神经网络	小分子药物	SMILES独热编码	ChemTS	蒙特卡洛树搜索	[27]
	小分子药物	SMILES独热编码	LSTM	迁移学习	[30, 57, 65]
	蛋白质	氨基酸独热编码	LSTM	迁移学习	[35]

深度生成式模型	生物序列	数据形式	模型名称	寻优算法	相关文献
生成对抗网络	核酸	碱基序列独热编码	WGAN	性能得分梯度寻优	[62]
	蛋白质	氨基酸距离矩阵	DCGAN	基于Rosetta的采样	[46]
	小分子药物	分子图矩阵编码	MolGAN	强化学习	[59]
变分/对抗自编码器	小分子药物	原子团的连接树编码	Junction Tree VAE	贝叶斯优化	[63]
		SMILES独热编码	ChemVAE	性能得分梯度寻优	[43]
		邻接矩阵与属性向量等组成的概率图	GraphVAE	条件生成	[64]
循环神经网络	小分子药物	SMILES独热编码	ChemTS	蒙特卡洛树搜索	[27]
	小分子药物	SMILES独热编码	LSTM	迁移学习	[30, 57, 65]
	蛋白质	氨基酸独热编码	LSTM	迁移学习	[35]

评估指标类型	评估指标	小分子药物	蛋白质序列	核酸序列
分布拟合评估	合理性	SMILES/分子图的合理性	Rosetta仿真结果	连续碱基数目
	多样性	不重复小分子比例	不重复的蛋白质比例	设计的序列之间的相似性
	新颖性	新药比例	新蛋白比例	与天然序列的相似性
	分布拟合度	Frechet ChemNet Distance	经验性适应度分布得分	与天然序列的K-mer相关性
	物理化学约束符合度	物理化学性质的KL散度	统计能量函数	GC含量
定向优化评估	重要结构特征	化学结构特征	与已知重要功能团的相似性	功能性的Motif或重要间隔序列长度
	测试集重设计比例	药物分子重设计比例	未报道	未报道
	自定义优化功能得分	药物溶解度等性能得分	蛋白靶向位点的功能得分	调控强度等功能得分

评估指标类型	评估指标	小分子药物	蛋白质序列	核酸序列
分布拟合评估	合理性	SMILES/分子图的合理性	Rosetta仿真结果	连续碱基数目
	多样性	不重复小分子比例	不重复的蛋白质比例	设计的序列之间的相似性
	新颖性	新药比例	新蛋白比例	与天然序列的相似性
	分布拟合度	Frechet ChemNet Distance	经验性适应度分布得分	与天然序列的K-mer相关性
	物理化学约束符合度	物理化学性质的KL散度	统计能量函数	GC含量
定向优化评估	重要结构特征	化学结构特征	与已知重要功能团的相似性	功能性的Motif或重要间隔序列长度
	测试集重设计比例	药物分子重设计比例	未报道	未报道
	自定义优化功能得分	药物溶解度等性能得分	蛋白靶向位点的功能得分	调控强度等功能得分

生物序列	数量级	智能算法	优势	挑战
小分子药物序列	1.5×10^{6 [99]}	常用RNN、AAE、VAE、GAN结合强化学习和迁移学习进行药物序列设计	数据与数据库积累丰富；评估体系较为成熟	合成相对困难，需考虑与筛选易于合成的分子序列
	1.8×10^{6 [74]}
	1500^[29]
蛋白质序列	约100 000 ^[35]	常用RNN、GAN、ANN结合蛋白设计的Rosetta软件和迁移学习进行蛋白序列设计^[100]	模拟预测软件如Rosetta在领域内标准化程度高；蛋白设计可应用场景广阔	三维空间结构、折叠构象的搜索与预测准确性仍有限
核酸序列	与具体物种基因组大小以及核酸序列对应的功能相关	利用GAN结合专家知识、预测器等对核酸序列进行设计	核酸序列相对易于合成，设计灵活度高，合成周期较短	特定功能的核酸序列数据集规模小；调控元件等序列在基因组缺乏精确定义