Accurate predictions of the three-dimensional structures of proteins from their amino acid sequences have come of age. AlphaFold, a deep learning-based approach to protein structure prediction, shows remarkable success in independent assessments of prediction accuracy. A significant epoch in structural bioinformatics was the structural annotation of over 98% of protein sequences in the human proteome. Interestingly, many predictions feature regions of very low confidence, and these regions largely overlap with intrinsically disordered regions (IDRs). That over 30% of regions within the proteome are disordered is congruent with estimates that have been made over the past two decades, as intense efforts have been undertaken to generalize the structure-function paradigm to include the importance of conformational heterogeneity and dynamics. With structural annotations from AlphaFold in hand, there is the temptation to draw inferences regarding the “structures” of IDRs and their interactomes. Here, we offer a cautionary note regarding the misinterpretations that might ensue and highlight efforts that provide concrete understanding of sequence-ensemble-function relationships of IDRs. This perspective is intended to emphasize the importance of IDRs in sequence-function relationships (SERs) and to highlight how one might go about extracting quantitative SERs to make sense of how IDRs function.

从蛋白质的氨基酸序列准确预测蛋白质的三维结构已经成熟。AlphaFold 是一种基于深度学习的蛋白质结构预测方法，在预测准确性的独立评估方面取得了显着的成功。结构生物信息学的一个重要时代是人类蛋白质组中超过 98% 的蛋白质序列的结构注释。有趣的是，许多预测具有非常低置信度的区域，并且这些区域与内在无序区域 (IDR) 大部分重叠。蛋白质组中超过 30% 的区域是无序的，这与过去二十年所做的估计是一致的，因为已经付出了大量努力来推广结构 - 功能范式，以包括构象异质性和动力学的重要性。有了 AlphaFold 的结构注释，就有可能对 IDR 的“结构”及其相互作用组进行推断。在这里，我们提供了一个关于可能随之而来的误解的警告，并强调了提供对 IDR 的序列-集合-功能关系的具体理解的努力。这一观点旨在强调 IDR 在序列-功能关系 (SER) 中的重要性，并强调人们如何提取定量 SER 以了解 IDR 的功能。我们提供了关于可能发生的误解的警告，并强调了提供对 IDR 的序列-集合-功能关系的具体理解的努力。这一观点旨在强调 IDR 在序列-功能关系 (SER) 中的重要性，并强调人们如何提取定量 SER 以了解 IDR 的功能。我们提供了关于可能发生的误解的警告，并强调了提供对 IDR 的序列-集合-功能关系的具体理解的努力。这一观点旨在强调 IDR 在序列-功能关系 (SER) 中的重要性，并强调人们如何提取定量 SER 以了解 IDR 的功能。