人工智能与物理原理融合驱动的蛋白计算模拟技术

Artificial intelligence-enhanced physics-based computational modeling technologies for proteins

摘要:

计算模拟驱动的生物元件、代谢网络乃至细胞系统的机理解析、定向改造和按需设计，可为解决不同层次的生物学问题提供新的技术方案，已成为生物制造的核心研究内容。在蛋白元件的计算模拟方面，基于物理原理的传统方法利用计算机软件和数学模型来模拟生物体系中蛋白行使功能的物理和化学过程，是理解复杂生物体系和指导实验设计的有力工具。随着生物系统模拟尺度的不断扩大，传统计算模拟技术面临计算精度和计算速度难以平衡的困境。近年来，生物数据量呈现爆炸式增长，使得构建高性能人工智能(artificial intelligence, AI)模型成为可能，为蛋白计算模拟带来了新思路和新契机，AI和物理原理融合驱动的蛋白计算模拟技术应运而生。本文对基于物理原理的传统蛋白计算模拟方法及其应用进行了详细介绍，并对融合AI和物理原理的最新计算模拟技术进行了梳理和讨论，进而提出在AI模型中结合严谨的化学知识和既定的物理原理，可有效提升数据处理和模式识别能力，从而提高计算效率和预测的准确性，使其具有更强的解释能力、通用性和稳健性。目前，AI与物理原理融合驱动的计算模拟技术已在生物催化领域展现出巨大的潜力和价值。本文聚焦于主流和先进的蛋白计算模拟技术，通过对这些技术的系统性回顾和前瞻性分析，梳理了蛋白质计算模拟技术的发展脉络，以推动其在酶催化机制解析、蛋白从头设计与理性改造等领域的应用，助力生物制造的发展。

关键词:人工智能;计算模拟;计算生物学;分子对接;分子动力学模拟;量子化学计算

Abstract:

Computational modeling is an invaluable tool for mechanism analysis, directed engineering, and rational design of biological parts, metabolic networks, and even cellular systems. It can provide new technological solutions to address biological challenges at different levels and has become a central focus of research in biomanufacturing. In the computational modeling of proteins, which are the key parts in biological systems, the traditional physics-based methods (computer software and mathematical model) have been widely used to study the physical and chemical processes in the functioning of proteins, and have thus been recognized as a powerful tool for understanding complex biological systems and guiding experimental designs. As the scale of computational modeling continues to expand, traditional modeling techniques face difficulties in balancing computational accuracy and speed. In recent years, the explosive growth of biological data has made it possible to construct high-performance artificial intelligence (AI) models, which brings new opportunities to the computational modeling of proteins, and the AI-enhanced physics-based computational modeling technologies have emerged. This combined strategy not only incorporates the chemical knowledge and established physical principles but also is powerful in data processing and pattern recognition, which greatly improves the computational efficiency and prediction accuracy, as well as possesses stronger interpretation ability, transferability, and robustness. The AI-enhanced physics-based computational modeling technologies have already shown great potential and value in biocatalysis, paving a new way for the future development of biomanufacturing.

Key words:artificial intelligence;computational modeling;computational biology;molecular docking;molecular dynamics simulation;quantum chemistry calculation