Casadevall Guillem, Duran Cristina, Osuna Sílvia
Institut de Química Computacional i Catàlisi (IQCC) and Departament de Química, Universitat de Girona, Maria Aurèlia Capmany 69, 17003 Girona, Spain.
ICREA, Passeig Lluís Companys 23, 08010 Barcelona, Spain.
JACS Au. 2023 Jun 6;3(6):1554-1562. doi: 10.1021/jacsau.3c00188. eCollection 2023 Jun 26.
The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structural biology and protein design fields. The 3D structure indeed reveals key information on the arrangement of the catalytic machinery of enzymes and which structural elements gate the active site pocket. However, comprehending enzymatic activity requires a detailed knowledge of the chemical steps involved along the catalytic cycle and the exploration of the multiple thermally accessible conformations that enzymes adopt when in solution. In this Perspective, some of the recent studies showing the potential of AF2 in elucidating the conformational landscape of enzymes are provided. Selected examples of the key developments of AF2-based and DL methods for protein design are discussed, as well as a few enzyme design cases. These studies show the potential of AF2 and DL for allowing the routine computational design of efficient enzymes.
近期，AlphaFold2（AF2）及其他深度学习（DL）工具在准确预测蛋白质和酶的折叠三维（3D）结构方面取得的成功，彻底改变了结构生物学和蛋白质设计领域。3D结构确实揭示了有关酶催化机制排列以及哪些结构元件构成活性位点口袋的关键信息。然而，要理解酶活性，需要详细了解催化循环中涉及的化学步骤，以及探索酶在溶液中所采取的多种热可及构象。在此观点中，提供了一些近期研究，展示了AF2在阐明酶的构象景观方面的潜力。讨论了基于AF2和DL的蛋白质设计方法的关键进展的选定示例，以及一些酶设计案例。这些研究表明，AF2和DL有潜力实现高效酶的常规计算设计。
