Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
Curr Med Chem. 2024;31(20):2841-2854. doi: 10.2174/0929867330666230602143700.
Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.
蛋白质在调节与人类生命相关的多种生物过程中发挥着关键作用。随着需求的增加,功能蛋白在这个巨大的序列空间中是稀缺的。因此,蛋白质设计已成为包括医学、食品、能源、材料等在内的各个领域的重要任务。定向进化最近取得了重大成就。通过定向进化技术对蛋白质进行分子修饰,显著推动了酶工程、代谢工程、医学等领域的发展。然而,仅从大量合成序列中识别理想序列是不可能的。因此,计算方法,包括数据驱动的机器学习和基于物理的分子建模,已被引入蛋白质工程,以产生更多功能的蛋白质。本文重点介绍了计算蛋白质设计的最新进展,强调了不同方法的适用性及其局限性。