The sequence–structure–function paradigm of proteins has been revolutionized by the discovery of intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs). In contrast to traditional ordered proteins, IDPs/IDRs are unstructured under physiological conditions. The absence of well‐defined three‐dimensional structures in the free state of IDPs/IDRs is fundamental to their function. Folding upon binding is an important mode of molecular recognition for IDPs/IDRs. While great efforts have been devoted to investigating the complex structures and binding kinetics and affinities, our knowledge on the binding mechanisms of IDPs/IDRs remains very limited. Here, we review recent advances on the binding mechanisms of IDPs/IDRs. The structures and kinetic parameters of IDPs/IDRs can vary greatly, and the binding mechanisms can be highly dependent on the structural properties of IDPs/IDRs. IDPs/IDRs can employ various combinations of conformational selection and induced fit in a binding process, which can be templated by the target and/or encoded by the IDP/IDR. Further studies should provide deeper insights into the molecular recognition of IDPs/IDRs and enable the rational design of IDP/IDR binding mechanisms in the future.

Preventing tau aggregation is a potential therapeutic strategy in Alzheimer’s disease and other tauopathies. Recently, liquid–liquid phase separation has been found to facilitate the formation of pathogenic tau conformations and fibrillar aggregates, although many aspects of the conformational transitions of tau during the phase transition process remain unknown. Here, we demonstrate that the tau aggregation inhibitor methylene blue promotes tau liquid–liquid phase separation and accelerates the liquid-to-gel transition of tau droplets independent of the redox activity of methylene blue. We further show that methylene blue inhibits the conversion of tau droplets into fibrils and reduces the cytotoxicity of tau aggregates. Although gelation slows down the mobility of tau and tubulin, it does not impair microtubule assembly within tau droplets. These findings suggest that methylene blue inhibits tau amyloid fibrillization and accelerates tau droplet gelation via distinct mechanisms, thus providing insights into the activity of tau aggregation inhibitors in the context of phase transition.

Electroencephalography (EEG) is widely used for mental stress classification, but effective feature extraction and transfer across subjects remain challenging due to its variability. In this paper, a novel deep neural network combining convolutional neural network (CNN) and adversarial theory, named symmetric deep convolutional adversarial network (SDCAN), is proposed for stress classification based on EEG. The adversarial inference is introduced to automatically capture invariant and discriminative features from raw EEG, which aims to improve the classification accuracy and generalization ability across subjects. Experiments were conducted with 22 human subjects, where each participant’s stress was induced by the Trier Social Stress Test paradigm while EEG was collected. Stress states were then calibrated into four or five stages according to the changing trend of salivary cortisol concentration. The results show that the proposed network achieves improved accuracies of 87.62% and 81.45% on the classification of four and five stages, respectively, compared to conventional CNN methods. Euclidean space data alignment approach (EA) was applied and the improved generalization ability of EA-SDCAN across subjects was also validated via the leave-one-subject-out-cross-validation, with the accuracies of four and five stages being 60.52% and 48.17%, respectively. These findings indicate that the proposed SDCAN network is more feasible and effective for classifying the stages of mental stress based on EEG compared with other conventional methods.