Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
Nat Commun. 2024 Jul 25;15(1):6273. doi: 10.1038/s41467-024-50571-y.
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. To validate the method, we show that it can produce highly thermostable, catalytically active enzymes with high success rates. This concept is anticipated to improve the versatility of protein design pipelines for crafting desired functions.
蛋白质设计和工程正在以前所未有的速度发展，利用深度学习的进步。然而，目前的模型不能在设计过程中原生地考虑非蛋白质实体。在这里，我们引入了一种仅基于原子坐标和元素名称的几何变换的深度学习方法，该方法可以从具有不同分子环境约束的骨架中预测蛋白质序列。为了验证该方法，我们表明它可以以高成功率产生高度热稳定、催化活性的酶。预计这一概念将提高蛋白质设计管道的多功能性，以实现所需的功能。
