NRhFluors: Quantitative Revealing the Interaction between Protein Homeostasis and Mitochondria Dysfunction via Fluorescence Lifetime Imaging

Degenerative diseases are closely related to the changes of protein conformation beyond the steady state. The development of feasible tools for quantitative detection of changes in the cellular environment is crucial for investigating the process of protein conformational variations. Here, we have developed a near-infrared AIE probe based on the rhodamine fluorophore, which exhibits dual responses of fluorescence intensity and lifetime to local viscosity changes. Notably, computational analysis reveals that NRhFluors fluorescence activation is due to inhibition of the RACI mechanism in viscous environment. In the chemical regulation of rhodamine fluorophores, we found that variations of electron density distribution can effectively regulate CI states and achieve fluorescence sensitivity of NRhFluors. In addition, combined with the AggTag method, the lifetime of probe A9-Halo exhibits a positive correlation with viscosity changes. This analytical capacity allows us to quantitatively monitor protein conformational changes using fluorescence lifetime imaging (FLIM) and demonstrate that mitochondrial dysfunction leads to reduced protein expression in HEK293 cells. In summary, this work developed a set of near-infrared AIE probes activated by the RACI mechanism, which can quantitatively detect cell viscosity and protein aggregation formation, providing a versatile tool for exploring disease-related biological processes and therapeutic approaches.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

CellSTAR: a comprehensive resource for single-cell transcriptomic annotation

Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.