Stereospecific Synthesis and Biological Evaluation of KRN7000 Analogues with Thio-modifications at the Acyl Moiety.

α-Galactosylceramide (KRN7000 or α-GalCer) analogues terminated with phenyl (Ph) groups at the acyl moiety possess more potency than KRN7000 to activate invariant natural killer T (iNKT) cells for inducing a T helper 1 (Th1)-biased immune response. However, biological activities of phenyl glycolipids with thio-modifications at the acyl moiety remain unknown, and facile approaches for highly stereoselective synthesis of KRN7000 and its analogues are rather scarce. Herein, we exploited 4,6-di-O-tert-butylsilylene (DTBS)-directed stereospecific galactosylation to efficiently synthesize various α-GalCer analogues bearing thioamide, terminal thiophenyl and dual modifications at the acyl moiety. Biological evaluations suggest that a new analogue S34 featuring a terminal Ph-S-Ph-F group exhibits a more superior Th1-biased immune response in mice. Molecular docking analysis revealed that the introduction of a sulfur atom influences vital hydrogen bonding interactions between glycolipids and the cluster of differentiation 1d (CDld), thus adjusting the stability of the glycolipid-CDld complex.

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.

Design, Synthesis, and Bioevaluation of Transcriptional Enhanced Assocciated Domain (TEAD) PROTAC Degraders.

Dysregulation of the Hippo pathway has been observed in various cancers. The transcription factor TEAD, together with its coactivators YAP/TAZ, plays a crucial role in regulating the transcriptional output of the Hippo pathway. Recently, extensive research has focused on small molecule inhibitors targeting TEAD, but studies on TEAD degraders are comparatively rare. In this study, we designed and synthesized a series of TEAD PROTACs by connecting a pan-TEAD inhibitor with the CRBN ligand thalidomide. A representative compound, 27, exhibited potent antiproliferative activity against NF2-deficient NCI-H226 cells. It dose-dependently induced TEAD degradation dependent on CRBN and proteasome system and decreased key YAP target genes CYR61 and CTGF expressions in NCI-H226 cells. Further degradation selectivity studies revealed that 27 exhibited more potent activity against TEAD2 compared to those of the other three family members in Flag-TEADs transfected 293T cells. Therefore, 27 may serve as a valuable tool for advancing biological studies related to TEAD2.

An overview of recent advances and challenges in predicting compound-protein interaction (CPI).

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.