ABSTRACT
Huntington's disease (HD) is a rare single-gene neurodegenerative disease, which can only be treated symptomatically. Currently, there are no approved drugs for HD on the market. Studies have found that MAPK11 can serve as a potential therapeutic target for HD. Regrettably, no MAPK11 small molecule inhibitors have been approved at present. This paper presents three series of compounds that were designed and synthesized based on the structure of skepinone-L, a known MAPK14 inhibitor. Among the synthesized compounds, 13a and 13b, with IC50 values of 6.40 nM and 4.20 nM, respectively, displayed the best inhibitory activities against MAPK11. Furthermore, the structure-activity relationship (SAR) is discussed in detail, which is constructive in optimizing the MAPK11 inhibitors for better activity and effect against HD.
Subject(s)
Drug Design , Mitogen-Activated Protein Kinase 11/antagonists & inhibitors , Mitogen-Activated Protein Kinase 11/chemistry , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Kinase Inhibitors/chemistry , Protein Kinase Inhibitors/pharmacology , Animals , Binding Sites , Chemistry Techniques, Synthetic , Humans , Molecular Conformation , Molecular Structure , Protein Binding , Protein Kinase Inhibitors/chemical synthesis , Structure-Activity RelationshipABSTRACT
Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.