Artificial Intelligence Transforms Protein Engineering： From Structural Analysis to Synthetic Biology through Algorithmic Advancements

doi:10.13560/j.cnki.biotech.bull.1985.2025-0300

Abstract

Abstract:

The intricate relationship between protein function and its three-dimensional structure has long been a fundamental guiding principle in life sciences research. While scientists have dedicated substantial effort to deciphering protein structures, the exponential growth in sequence data fueled by rapid advances in protein sequencing technologies has significantly outpaced progress in structural studies. Over the past decade, the burgeoning field of artificial intelligence (AI), underpinned by core algorithms such as deep learning and neural networks, has emerged as a transformative force in protein engineering, offering new avenues to address this disparity. Leveraging AI, next-generation methods for protein structure prediction and design have achieved remarkable breakthroughs. These advanced algorithm-based tools have dramatically enhanced both the accuracy and speed of protein structure modeling. They are not only accelerating progress in structural biology and drug discovery but also providing crucial foundations for protein synthesis. Furthermore, AI is catalyzing a paradigm shift in protein research, moving beyond ‘structure determination’ towards ‘inverse design’. By constructing multidimensional models that elucidate sequence-structure-function relationships, researchers can now reverse-engineer protein sequences with desired structural characteristics based on specific functional requirements. This capability for precise protein sequence design is paving new pathways for biosynthetic applications.This review focuses on the pivotal role of AI in protein engineering. Firstly, it outlines the current challenges in the protein engineering and the bottlenecks in traditional protein structure determination methods. Then it introduces the development of AI-based structure prediction tools, followed by an analysis of their application in protein synthesis. Finally, it then explores the algorithm-driven revolution facilitating the transition from structure determination to de novo protein synthesis, and discusses potential future directions, aiming to provide a reference framework for ongoing research in this field.

Key words: artificial intelligence, protein engineering, prediction of protein structure, protein design, biosynthesis

CAI Ru-feng, YANG Yu-xuan, YU Ji-zheng, LI Jia-nan. Artificial Intelligence Transforms Protein Engineering： From Structural Analysis to Synthetic Biology through Algorithmic Advancements[J]. Biotechnology Bulletin, 2025, 41(8): 1-10.

Figures/Tables 5

Table 1 Comparison of performance across AlphaFold model series

模型

Model

发布时间

Release time

CASP参赛版本

CASP entry version

GDT_TS中位数

GDT_TS med-number

覆盖UniProt比例

Coverage ratio of UniProt

关键技术革新

Key technological innovation

Table 2 Technical comparison between RoseTTAFold and AlphaFold2

特征 Characteristic	AlphaFold2	RoseTTAFold
计算资源	128 TPU v3 （4 d/蛋白）	4 GPU （8 h/蛋白）
核心架构	Evoformer+结构模块	三轨Transformer
动态结构预测	单构象输出	支持构象系综生成
膜蛋白预测精度	TM-score 0.72	TM-score 0.81
开源程度	部分开源	全代码公开

Fig. 1 Three-dimensional structures of β2-AR obtained by different methodsA: X-ray method, with PDB ID 4G8R; B: NMR method, with PDB ID 6KR8; C: cryo-EM method, with PDB ID 8GGI; D: AlphaFold prediction result, with the number AF-P07550

Fig. 2 Overview diagram of computational design methodA: Design of helical proteins containing ACE2 helix. B:Large-scale head-to-tail design of small helical scaffolds followed by RIF docking to identify shape and chemical complementary binding patterns

Fig. 3 Evo basic model overview diagram

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