The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.

中文翻译：

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蛋白质序列到结构学习：这是端到端的革命吗？

深度学习的潜力已经在蛋白质结构预测界得到了一段时间的认可，并在 CASP13 之后成为不争的事实。在 CASP14 中，深度学习将该领域提升到了意想不到的水平，达到了接近实验的准确性。这一成功来自其他机器学习领域的进步，以及专门设计用于处理蛋白质序列和结构及其抽象的方法。新出现的方法包括 (i) 几何学习，即学习图形、三维 (3D) Voronoi 镶嵌和点云等表示；(ii) 利用注意力的预训练蛋白质语言模型；(iii) 保持 3D 空间对称性的等变架构；(iv) 使用大型元基因组数据库；(v) 蛋白质表示的组合；(vi) 最终真正的端到端架构，即从序列开始并返回 3D 结构的可微模型。在这里，我们对过去 2 年开发并在 CASP14 中广泛使用的新型深度学习方法进行概述和我们的看法。