A major challenge in structure-based virtual screening (VS) involves the treatment of explicit water molecules during docking in order to improve the enrichment of active compounds over decoys. Here we have investigated this in the context of the adenosine A2A receptor, where water molecules have previously been shown to be important for achieving high enrichment rates with docking, and where the positions of some binding site waters are known from a high-resolution crystal structure. The effect of these waters (both their presence and orientations) on VS enrichment was assessed using a carefully curated set of 299 high affinity A2A antagonists and 17,337 decoys. We show that including certain crystal waters greatly improves VS enrichment and that optimization of water hydrogen positions is needed in order to achieve the best results. We also show that waters derived from a molecular dynamics simulation - without any knowledge of crystallographic waters - can improve enrichments to a similar degree as the crystallographic waters, which makes this strategy applicable to structures without experimental knowledge of water positions. Finally, we used decision trees to select an ensemble of structures with different water molecule positions and orientations that outperforms any single structure with water molecules. The approach presented here is validated against independent test sets of A2A receptor antagonists and decoys from the literature. In general, this water optimization strategy could be applied to any target with waters-mediated protein-ligand interactions.
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http://dx.doi.org/10.1021/ci5000455 | DOI Listing |
Clin Neuropharmacol
January 2025
MedStar Georgetown University Hospital, Washington, DC.
Introduction: Adjunctive therapies to treat OFF episodes resulting from long-term levodopa treatment in Parkinson disease (PD) are hampered by safety and tolerability issues. Istradefylline offers an alternative mechanism (adenosine A2A receptor antagonist) and therefore potentially improved tolerability.
Methods: A systematic review of PD adjuncts published in 2011 was updated to include randomized controlled trials published from January 1, 2010-April 15, 2019.
iScience
January 2025
Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compounds that interact with multiple therapeutic target proteins. The molecular structure generation is performed by a fragment-based approach using a genetic algorithm with chemical substructures and a deep learning approach using reinforcement learning with stochastic policy gradients in the framework of generative adversarial networks.
View Article and Find Full Text PDFNeurochem Res
January 2025
Department of Biochemistry and Molecular Biology, Federal University of Santa Maria (UFSM), Santa Maria, RS, Brazil.
Purinergic signaling plays a major role in aging and neurodegenerative diseases, which are associated with memory decline. Blackcurrant (BC), an anthocyanin-rich berry, is renowned for its antioxidant and neuroprotective activities. However, evidence on the effects of BC on purinergic signaling is lacking.
View Article and Find Full Text PDFActa Pharmacol Sin
January 2025
Department of Pharmacology, School of Basic Medical Sciences, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Joint International Research Laboratory of Sleep, Fudan University, Shanghai, 200032, China.
Adenosine A receptor (AR) plays a pivotal role in the regulation of sleep-wake behaviors. We previously reported an AR selective antagonist compound 38 with an IC value of 29.0 nM.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
CEITEC─Central European Institute of Technology, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic.
All-atom molecular dynamics simulations are powerful tools for studying cell membranes and their interactions with proteins and other molecules. However, these processes occur on time scales determined by the diffusion rate of phospholipids, which are challenging to achieve in all-atom models. Here, we present a new all-atom model that accelerates lipid diffusion by splitting phospholipid molecules into head and tail groups.
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