关键词: 统计物理, 人工智能, 蛋白质结构, 蛋白质动力学, 结构生物信息学, AlphaFold数据库

Abstract:

Structural bioinformatics focuses on the computational study of three-dimensional biomolecular structures and their functions, with protein structures as its core research object. Traditional research in this field relied on protein structure databases of experimentally determined proteins but was constrained by the high cost and low-throughput nature of experimental methods. The revolution in protein structure prediction driven by deep learning, particularly AlphaFold2’s breakthrough, has fundamentally transformed the field’s data landscape by achieving atomic-level prediction accuracy from amino acid sequences alone. The deep integration of statistical physics with big data analysis methodologies has enabled researchers to overcome limitations of traditional case-by-case studies, systematically revealing universal principles of protein design from massive datasets. The accumulation of extensive protein structure data provides a crucial foundation for quantifying long-range correlations in protein dynamics and their evolutionary correspondence, revealing universal principles rooted in the interplay between sequence variability, structural constraint, and functional optimization. These principles not only offer a unified framework for understanding protein structure, dynamics, function, and evolution but also serve as the basis for predictive models and de novo protein design in engineering applications. Building upon this foundation, statistical analyses based on the AlphaFold Database highlight the crucial role of data-driven methods in uncovering universal statistical laws and dimensionality reduction principles in protein evolution across increasing organismal complexity, offering fresh perspectives on the fundamental constraints and convergent patterns driving molecular evolution. Recognizing that protein functions often depends on transitions between multiple conformational states, precise prediction of protein dynamics has become a core research direction. These advances are propelling protein engineering into an era of precise rational design where researchers can predict and manipulate conformational change pathways to regulate enzyme activity, optimize ligand specificity, and design allosteric responses with unprecedented precision. The research paradigm combining statistical physics and artificial intelligence continues to drive innovation in protein science, enhancing high-throughput screening and rational design efficiency to accelerate translation from basic discoveries to practical applications. As computational capabilities advance and AI models evolve, the field progresses from single protein design toward complex biological system construction, opening new frontiers in synthetic biology, precision medicine, and other applications.

Key words: statistical physics, artificial intelligence, protein structure, protein dynamics, structural bioinformatics, AlphaFold database