Intelligent stimulus-response (S/R) systems are the basis of natural process and machine control, which are intensively explored in biomimetic design and analytical/biological applications. However, nonmonotonic multi-S/R systems are still rarely studied so far. In this work, a rational design strategy is proposed to achieve such a unique S/R system by integrating opposite luminescence behaviors in one molecule. When solvent polarity increases, many heterocyclic or carbonyl-containing compounds often become more emissive due to the suppression of the proximity effect, whereas molecules with donor-acceptor (D-A) structures tend to be less emissive because of the twisted intramolecular charge transfer. Meanwhile, protonation on D/A moieties will weaken/strengthen the D-A interaction to result in blue/redshifted emissions. By combining a protonatable heterocyclic acceptor and a protonatable donor together in one molecule, nonmonotonic brightness responses to polarity stimuli and nonmonotonic color responses to pH stimuli can be achieved. The design strategy is successfully verified by a simple molecule named 4-(dimethylamino)styryl)quinoxalin-2(1H)-one (ASQ). ASQ exhibits nonmonotonic responses to polarity and pH stimuli, and aggregation-induced emission (AIE) with a nonmonotonic AIE curve. Meanwhile, ASQ can be adjusted to emit white light in an acidic environment, and it shows multivalent functionalities including albumin protein sensing, ratiometric pH sensing, and amine gas sensing.

Molecular dynamics (MD) simulation has become a powerful tool to investigate the structurefunction relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets containing millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape (geometric clustering) and kinetics (kinetic clustering). In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, agglomerative algorithms (single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage), center-based algorithms (K-Means, K-Medoids, K-Centers, and APM), density-based algorithms (neighbor-based, DBSCAN, density-peaks, and Robust-DB), and spectral-based algorithms (PCCA and PCCA+). In particular, differences between geometric and kinetic clustering metrics will be discussed along with the performances of different clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algorithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.