论文标题
蛋白质结构建模和设计中的深度学习
Deep Learning in Protein Structural Modeling and Design
论文作者
论文摘要
深度学习正在催化一场由大数据，可访问工具包和强大的计算资源推动的科学革命，从而影响了包括蛋白质结构建模在内的许多领域。蛋白质结构建模，例如预测氨基酸序列和进化信息的结构，将蛋白质设计为理想的功能，或预测蛋白质的特性或行为，对于分子水平的理解和设计生物系统至关重要。在这篇综述中，我们总结了应用深度学习技术来解决蛋白质结构建模和设计问题的最新进展。我们使用深度学习技术进行蛋白质结构建模的技术解剖，并讨论必须解决的进步和挑战。我们主张结构的核心重要性，按照“序列 - > struction->函数”范式。这篇综述旨在帮助计算生物学家熟悉蛋白质建模中应用的深度学习方法，并计算机科学家对可能受益于深度学习技术受益的生物学上有意义的问题的看法。
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling, and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence -> structure -> function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
