Protein function is fundamentally reliant on inter-molecular interactions that underpin the ability of proteins to form complexes driving biological processes in living cells. Increasingly, such interactions are recognised as being formed between proteins that exist on a broad spectrum of dynamic conformational states and levels of intrinsic disorder. Additionally, the sizes of the structures formed can range from simple binary complexes to large dynamic biomolecular condensates measuring 100 nm or more. Understanding the parameters that govern such interactions, how they form, how they lead to function and what happens when they take place in unintended manners and lead to disease, represent some of the core questions for molecular biosciences. In light of recent advances made in solving the protein folding problem by machine learning methods, we discuss here the challenges and opportunities brought by these new data-driven approaches for the next frontiers of biomolecular science.

蛋白质功能从根本上依赖于分子间相互作用，而分子间相互作用支持蛋白质形成复合物的能力，从而驱动活细胞中的生物过程。越来越多地，这种相互作用被认为是在广泛的动态构象状态和内在紊乱水平上存在的蛋白质之间形成的。此外，所形成结构的大小范围可以从简单的二元复合物到测量 100 nm 或更大的大型动态生物分子缩合物。了解控制这种相互作用的参数、它们如何形成、它们如何导致功能以及当它们以非预期的方式发生并导致疾病时会发生什么，代表了分子生物科学的一些核心问题。鉴于最近在通过机器学习方法解决蛋白质折叠问题方面取得的进展，