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arXiv 2501.01477q-bio.BMcs.AI
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蛋白质生物信息学中的深度学习方法及其对蛋白质设计影响的综述

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

Hong Kong University of Science and Technology（香港科技大学）

Weihang Dai

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PhD Qualifying Exam (2021)
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AI总结：

本文综述了深度学习在蛋白质结构预测、功能预测和蛋白质设计三大任务中的进展，重点分析了结构预测与功能预测对蛋白质设计的推动作用，并展望了未来研究方向。

摘要：

蛋白质是氨基酸序列，是生物体的基本构建模块。尽管记录各种蛋白质序列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巨大以及分子内和分子间作用力复杂，我们对蛋白质的理解仍然有限。深度学习的特点是能够直接从大型数据集中学习相关特征，已在计算机视觉和自然语言处理等领域展现出卓越性能。近年来，它也被越来越多地应用于数据丰富的蛋白质序列领域，并取得了巨大成功，最引人注目的是AlphaFold2在蛋白质结构预测中的突破性表现。深度学习带来的性能提升为蛋白质生物信息学领域开启了新的可能性，其中包括蛋白质设计这一最困难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大类：1）结构预测，2）功能预测，3）蛋白质设计，并回顾了在每一类中使用深度学习方法所取得的进展。我们进一步阐述了蛋白质设计问题的主要挑战，并强调了结构和功能预测方面的进展如何直接促进了设计任务。最后，我们通过指出重要主题和未来研究方向进行总结。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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