G protein-coupled receptors (GPCRs) are membrane-embedded proteins responsible for signal transduction; these receptors are, therefore, among the most important pharmaceutical drug targets. In the absence of X-ray structures, there have been numerous attempts to model the three-dimensional (3D) structure of GPCRs. In this review, the current status of GPCR modeling is evaluated, highlighting recent progress made in rhodopsin-based homology modeling and de novo modeling technology. Assessment of recent rhodopsin-based homology modeling studies indicates that, despite significant progress, these models do not yield hit rates that are sufficiently high for in silico screening (10 to 40% when screening for known binders). In contrast, the PREDICT modeling algorithm, which is independent of the rhodopsin structure, has now been fully validated in the context of drug discovery. PREDICT models are successfully used for drug discovery, yielding excellent hit rates (85 to 100% when screening for known binders), leading to the discovery of nanomolar-range new chemical entities for a variety of GPCR targets. Thus, 3D models of GPCRs should now allow the use of productive structure-based approaches for drug discovery.
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Biochem Biophys Res Commun
January 2025
Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia. Electronic address:
Objective And Significance: Transforming growth factor-beta (TGF-β) plays a pivotal role in breast development by modulating tissue composition during the developmental phase. The TGFβ type II receptor (TGFβ RII) is implicated in breast cancer and represents a valuable therapeutic target. Due to the off-target side effects of many existing TGFβI/TGFβ RII inhibitors, a more targeted approach to drug discovery is necessary.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Predicting protein-protein interactions (PPIs) is crucial for advancing drug discovery. Despite the proposal of numerous advanced computational methods, these approaches often suffer from poor usability for biologists and lack generalization. In this study, we designed a deep learning model based on a coattention mechanism that was capable of both PPI and site prediction and used this model as the foundation for PPI-CoAttNet, a user-friendly, multifunctional web server for PPI prediction.
View Article and Find Full Text PDFAnal Chem
January 2025
Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
Single-cell proteomics (SCP) detected based on different technologies always involves batch-specific variations because of differences in sample processing and other potential biases. How to integrate SCP data effectively has become a great challenge. Integration of SCP data not only requires the conservation of true biological variances, but also realizes the removal of unwanted batch effects.
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January 2025
Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom.
This Perspective summarizes successful fragment-to-lead (F2L) studies that were published in 2023 and is the ninth installment in an annual series. A tabulated summary of the relevant articles published in 2023 is provided (17 entries from 16 articles), and a comparison of the target classes, screening methods, and overall fragment or lead property trends for 2023 examples and for the combined entries over the years 2015-2023 is discussed. In addition, we identify several trends and innovations in the 2023 literature that promise to further increase the success of fragment-based drug discovery (FBDD), particularly in the areas of NMR and virtual screening, fragment library design, and fragment linking.
View Article and Find Full Text PDFJ Agric Food Chem
January 2025
Ph.D. Program in Clinical Drug Development of Herbal Medicine, College of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan.
Based on molecular networking-guided isolation, 15 previously undescribed hydrogenated phenanthrene glycosides, including eight hexahydro-phenanthrenone glycosides, four tetrahydro-phenanthrenone glycosides, one dihydro-phenanthrenol glycoside, two dimers, and two known dihydrophenanthrene glycosides, were isolated from W.T.Wang, a popular regional edible vegetable at the northwest region of Vietnam.
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