标题： 蛋白质生物信息学中深度学习方法的综述及其对蛋白质设计的影响 显示英文标题

标题： A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design

摘要： 蛋白质是氨基酸的序列，作为生物体的基本构建块。 尽管记录各种蛋白质序列的结构和功能信息的数据库迅速增长，但由于可能的序列空间庞大以及分子间和分子内力的复杂性，我们对蛋白质的理解仍然有限。 深度学习以其能够直接从大数据集中学习相关特征的能力为特点，在计算机视觉和自然语言处理等领域表现出色。 近年来，它也被越来越多地应用于蛋白质序列这一数据丰富的领域，并取得了巨大成功，最著名的是AlphaFold2在蛋白质结构预测中的突破性表现。 深度学习取得的性能提升为蛋白质生物信息学领域带来了新的可能性，包括蛋白质设计，这是最具挑战性但最有用的任务之一。 在本文中，我们将蛋白质生物信息学中的问题大致分为三个主要类别：1）结构预测，2）功能预测，3）蛋白质设计，并回顾了在每个领域中使用深度学习方法所取得的进展。 我们进一步探讨了蛋白质设计问题的主要挑战，并强调了结构和功能预测的进展如何直接促进了设计任务。 最后，我们通过识别重要主题和未来研究方向来总结。 显示英文摘要

摘要： 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.