Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years saw dramatic advances driven by the increasing “neuralization” of structure prediction pipelines, whereby computations previously based on energy models and sampling procedures are replaced by neural networks. The extraction of physical contacts from the evolutionary record; the distillation of sequence–structure patterns from known structures; the incorporation of templates from homologs in the Protein Databank; and the refinement of coarsely predicted structures into finely resolved ones have all been reformulated using neural networks. Cumulatively, this transformation has resulted in algorithms that can now predict single protein domains with a median accuracy of 2.1 Å, setting the stage for a foundational reconfiguration of the role of biomolecular modeling within the life sciences.

由于该问题的重要性及其独特的定义明确的物理和计算基础，从序列预测蛋白质结构已被深入研究了数十年。虽然历史上的进步有起有伏，但在过去的两年里，结构预测管道的“神经化”程度不断提高，从而使以前基于能量模型和采样程序的计算被神经网络取代，从而取得了巨大的进步。从进化记录中提取物理接触；从已知结构中提取序列结构模式；在蛋白质数据库中加入来自同源物的模板；将粗略预测的结构细化为精细解析的结构都已使用神经网络进行了重新表述。累积起来，