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.

该综述总结了近年来将深度学习技术应用于蛋白质结构建模及设计问题的最新进展，讨论了已经出现和需解决的挑战，针对该领域提出了许多有益的见解。