Cross-view contrastive representation learning approach to predicting DTIs via integrating multi-source information.

Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive learning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.

Assay of 1-hydroxypyrene via aggregation-induced quenching of the fluorescence of protamine-modified gold nanoclusters and 9-hydroxyphenanthrene-based sensitization.

This work describes a method for the determination of 1-hydroxypyrene (OH-Py) via aggregation-induced quenching of the emission of protamine-coated gold nanoclusters using 9-hydroxyphenanthrene (OH-Phe) as a sensitizer to boost the emission efficiency of nanoprobe. Under optimum conditions, the drop in fluorescence intensity at excitation/emission wavelengths of 300/596 nm is proportional to the concentrations of OH-Py in the range from 1.0 to 65 nM. The relative standard deviations are 4.2, 2.4 and 1.9% (for n = 11) at concentration levels of 8.0, 32 and 48 nM of OH-Py, respectively. The detection limit is 0.3 nM which is much lower than that of some previously reported methods. The recoveries from urine samples spiked with OH-Py ranged between 94.4 and 98.8%. Graphical abstract 1-Hydroxypyrene (OH-Py) can trigger the aggregation of protamine-gold nanoclusters (PRT-AuNCs), resulting in the emission quenching of PRT-AuNCs. 9-Hydroxyphenanthrene (OH-Phe) can boost the emission efficiency of nanoprobe. Thereby, a highly sensitive assay of OH-Py was established.

Aggregation-induced photoluminescence enhancement of protamine-templated gold nanoclusters for 1-hydroxypyrene detection using 9-hydroxyphenanthrene as a sensitizer.

In this study, we confirmed that protamine-templated gold nanoclusters (PRT-AuNCs) exhibit aggregation-induced emission properties (AIE-PRT-AuNCs). 1-Hydroxypyrene (1-OHPy) further induced the aggregation of AIE-PRT-AuNCs via hydrogen bonding and electrostatic and hydrophobic interactions, resulting in the aggregation-induced photoluminescence enhancement of AIE-PRT-AuNCs. 9-Hydroxyphenanthrene was able to decrease the background signal, thus increasing the sensitivity of the method. Based on these findings, a cost-effective, highly sensitive and selective strategy was proposed for the quantitative detection of 1-OHPy. This method displayed a wide linear range of 0.924 - 74.1 nmol/L with a low detection limit of 0.277 nmol/L, showing great potential for the monitoring of 1-OHPy in human urine. This strategy may provide a theoretical basis for future studies of the AIE properties of metal nanoclusters and their applications in the field of chemical and biological sensing.