从头设计蛋白质——从新结构到可编程功能。
De novo protein design-From new structures to programmable functions.
机构信息
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
出版信息
Cell. 2024 Feb 1;187(3):526-544. doi: 10.1016/j.cell.2023.12.028.
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.
现在，基于人工智能的方法可以使用大型序列和结构数据集“编写”具有新形状和分子功能的蛋白质，而无需从自然界中发现的蛋白质开始。在这篇观点文章中，我将讨论基于物理建模方法和人工智能的从头设计蛋白质领域的现状。可以相当高的实验成功率来设计新的蛋白质折叠和更高阶的组装体，并且越来越能够解决需要对蛋白质构象进行可调控制和精确形状互补以进行分子识别的难题。新兴的方法从一开始就将工程原理——可调性、可控性和模块化——纳入设计过程。激动人心的前沿领域在于使用从头设计的蛋白质来解构细胞功能，反之，从基础构建合成细胞信号。随着方法的改进，还有许多挑战尚未解决。
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