Rudden Lucas S P, Hijazi Mahdi, Barth Patrick
Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Front Mol Biosci. 2022 Aug 10;9:928534. doi: 10.3389/fmolb.2022.928534. eCollection 2022.
Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.
在深度学习方法成功应用于蛋白质结构预测之后，越来越多的设计方法试图利用生成模型来设计功能优于天然蛋白质或具有新颖结构和功能的蛋白质。蛋白质固有的灵活性，从侧链运动到更大规模的构象重排，对设计方法构成了挑战，理想的方法必须在蛋白质功能能力的背景下考虑其空间和时间演变。在这篇综述中，我们在讨论基于深度学习的前沿设计方法如何适应灵活性以及该领域未来可能的发展方向之前，先重点介绍现有的蛋白质设计方法。
