The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.

深度学习领域的快速进展对蛋白质设计产生了重大影响。深度学习方法最近在蛋白质结构预测方面取得了突破，为数百万种蛋白质提供了高质量模型。除了用于生成建模和序列分析的新颖架构之外，它们在过去几年中通过提高识别新蛋白质序列和结构的准确性和能力，显着改变了蛋白质设计领域。深度神经网络现在可以学习和提取蛋白质结构的基本特征，预测它们如何与其他生物分子相互作用，并有潜力创造新的有效治疗疾病的药物。随着它们在蛋白质设计中的适用性迅速增长，我们回顾了深度学习方法的最新发展和技术，并提供了它们生成新型功能蛋白质的性能示例。