Proteins are sequences of amino acids that serve as the basic building blocks of living organisms. Despite rapidly growing databases documenting structural and functional information for various protein sequences, our understanding of proteins remains limited because of the large possible sequence space and the complex inter- and intra-molecular forces. Deep learning, which is characterized by its ability to learn relevant features directly from large datasets, has demonstrated remarkable performance in fields such as computer vision and natural language processing. It has also been increasingly applied in recent years to the data-rich domain of protein sequences with great success, most notably with Alphafold2's breakout performance in the protein structure prediction. The performance improvements achieved by deep learning unlocks new possibilities in the field of protein bioinformatics, including protein design, one of the most difficult but useful tasks. In this paper, we broadly categorize problems in protein bioinformatics into three main categories: 1) structural prediction, 2) functional prediction, and 3) protein design, and review the progress achieved from using deep learning methodologies in each of them. We expand on the main challenges of the protein design problem and highlight how advances in structural and functional prediction have directly contributed to design tasks. Finally, we conclude by identifying important topics and future research directions.

本研究针对蛋白质生物信息学中的多项挑战，尤其是蛋白质设计领域存在的知识空白，提供了深度学习方法的综述。论文提出了通过深度学习提高结构预测和功能预测的成果，这些进展为蛋白质设计任务提供了新机遇。研究表明，深度学习的应用可以显著推动蛋白质设计的效率和精确性，为未来相关研究方向奠定基础。

深度学习方法在蛋白质生物信息学中的调查及其对蛋白质设计的影响