Xi Chen, Xinle Yang, Roufen Chen, Lei Xu, Xiaowu Dong, Zhen Cai
{"title":"通过虚拟筛选和分子动力学模拟发现潜在的GPRC5D抑制剂。","authors":"Xi Chen, Xinle Yang, Roufen Chen, Lei Xu, Xiaowu Dong, Zhen Cai","doi":"10.1002/open.202500360","DOIUrl":null,"url":null,"abstract":"<p><p>G protein-coupled receptor family C, group 5, member D (GPRC5D), a member of the G protein-coupled receptor (GPCR) family, has recently emerged as a promising target for immunotherapy in hematologic malignancies, particularly multiple myeloma. However, no systematic virtual screening studies have been conducted to identify small-molecule inhibitors targeting GPRC5D. To address this gap, a multistep computational screening strategy is developed that integrates Protein-Ligand Affinity prediction NETwork (PLANET), a GPU-accelerated version of AutoDock Vina (Vina-GPU), molecular mechanics/generalized born surface area (MM/GBSA), and an online tool for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) property prediction (admetSAR 3.0), complemented by molecular dynamics (MD) simulations and absolute binding free energy (ABFE). From an initial library of 8,617 compounds, four candidates (compounds 1, 2, 7, and 8) are prioritized. Among them, compound 2 shows relatively strong binding affinity (MM/GBSA ΔG = -79.8 kcal mol<sup>-1</sup>, ABFE = -9.0 kcal mol<sup>-1</sup>) and high drug-likeness (quantitative estimate of drug-likeness = 0.670). MD simulations confirm its stable salt bridge interactions with key residues ASP238 and ASP239. This study proposes a systematic virtual screening workflow to facilitate the discovery of GPRC5D-targeted therapeutics.</p>","PeriodicalId":9831,"journal":{"name":"ChemistryOpen","volume":" ","pages":"e202500360"},"PeriodicalIF":3.1000,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Discovery of Potential GPRC5D Inhibitors through Virtual Screening and Molecular Dynamics Simulations.\",\"authors\":\"Xi Chen, Xinle Yang, Roufen Chen, Lei Xu, Xiaowu Dong, Zhen Cai\",\"doi\":\"10.1002/open.202500360\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>G protein-coupled receptor family C, group 5, member D (GPRC5D), a member of the G protein-coupled receptor (GPCR) family, has recently emerged as a promising target for immunotherapy in hematologic malignancies, particularly multiple myeloma. However, no systematic virtual screening studies have been conducted to identify small-molecule inhibitors targeting GPRC5D. To address this gap, a multistep computational screening strategy is developed that integrates Protein-Ligand Affinity prediction NETwork (PLANET), a GPU-accelerated version of AutoDock Vina (Vina-GPU), molecular mechanics/generalized born surface area (MM/GBSA), and an online tool for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) property prediction (admetSAR 3.0), complemented by molecular dynamics (MD) simulations and absolute binding free energy (ABFE). From an initial library of 8,617 compounds, four candidates (compounds 1, 2, 7, and 8) are prioritized. Among them, compound 2 shows relatively strong binding affinity (MM/GBSA ΔG = -79.8 kcal mol<sup>-1</sup>, ABFE = -9.0 kcal mol<sup>-1</sup>) and high drug-likeness (quantitative estimate of drug-likeness = 0.670). MD simulations confirm its stable salt bridge interactions with key residues ASP238 and ASP239. This study proposes a systematic virtual screening workflow to facilitate the discovery of GPRC5D-targeted therapeutics.</p>\",\"PeriodicalId\":9831,\"journal\":{\"name\":\"ChemistryOpen\",\"volume\":\" \",\"pages\":\"e202500360\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ChemistryOpen\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1002/open.202500360\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ChemistryOpen","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1002/open.202500360","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Discovery of Potential GPRC5D Inhibitors through Virtual Screening and Molecular Dynamics Simulations.
G protein-coupled receptor family C, group 5, member D (GPRC5D), a member of the G protein-coupled receptor (GPCR) family, has recently emerged as a promising target for immunotherapy in hematologic malignancies, particularly multiple myeloma. However, no systematic virtual screening studies have been conducted to identify small-molecule inhibitors targeting GPRC5D. To address this gap, a multistep computational screening strategy is developed that integrates Protein-Ligand Affinity prediction NETwork (PLANET), a GPU-accelerated version of AutoDock Vina (Vina-GPU), molecular mechanics/generalized born surface area (MM/GBSA), and an online tool for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) property prediction (admetSAR 3.0), complemented by molecular dynamics (MD) simulations and absolute binding free energy (ABFE). From an initial library of 8,617 compounds, four candidates (compounds 1, 2, 7, and 8) are prioritized. Among them, compound 2 shows relatively strong binding affinity (MM/GBSA ΔG = -79.8 kcal mol-1, ABFE = -9.0 kcal mol-1) and high drug-likeness (quantitative estimate of drug-likeness = 0.670). MD simulations confirm its stable salt bridge interactions with key residues ASP238 and ASP239. This study proposes a systematic virtual screening workflow to facilitate the discovery of GPRC5D-targeted therapeutics.
期刊介绍:
ChemistryOpen is a multidisciplinary, gold-road open-access, international forum for the publication of outstanding Reviews, Full Papers, and Communications from all areas of chemistry and related fields. It is co-owned by 16 continental European Chemical Societies, who have banded together in the alliance called ChemPubSoc Europe for the purpose of publishing high-quality journals in the field of chemistry and its border disciplines. As some of the governments of the countries represented in ChemPubSoc Europe have strongly recommended that the research conducted with their funding is freely accessible for all readers (Open Access), ChemPubSoc Europe was concerned that no journal for which the ethical standards were monitored by a chemical society was available for such papers. ChemistryOpen fills this gap.