{"title":"Chalcones reloaded: an integration of network pharmacology and molecular docking for type 2 diabetes therapy.","authors":"Sarvesh Sabarathinam, Nila Ganamurali","doi":"10.1080/07391102.2023.2252085","DOIUrl":null,"url":null,"abstract":"<p><p>Chalcones have various biological effects, from immune boosting to anti-cancer and anti-diabetic. Structurally modified chalcones (SMC) are clinically relevant for diabetes and cardiometabolic complications. From the original research articles, a structurally proven and biologically outstanding 14 structurally modified chalcones were screened and inducted in this study. This study evaluated the effects of SMC towards diabetes <i>via</i> network pharmacology analysis. The network data shows compounds S2, S3, S5, S9 &S12 suit the diabetes target. Especially Compounds S5 and S9 have a higher binding affinity towards the targets of TNF, PI3K, MAPK1 and AKT1 active sites. Compound S9 [(E)-3-(4-(1H-imidazol-1-yl)phenyl)-1-(4-(2,4-difluorobenz-yloxy)phenyl)prop-2-en-1-one] have identified with stronger binding affinities towards the active sites of MAPK3 (PDB:4QTB) -10.5(Kcal/mol). To provide a more effective mechanism for demonstrating protein-ligand interaction, one of the molecular docking complex (ERK2 kinase-S5) was subjected to a molecular dynamic at 300K for 100 ns. In term of structural stability, structure compactness, residual flexibility and hydrogen bond interaction of the complex was evaluated Integrating network pharmacology, <i>in silico</i> virtual screening, and molecular docking analysis shows that structurally modified compounds are effective and may help identify lead compounds towards glycemic control.Communicated by Ramaswamy H. Sarma.</p>","PeriodicalId":15272,"journal":{"name":"Journal of Biomolecular Structure & Dynamics","volume":" ","pages":"9505-9517"},"PeriodicalIF":2.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomolecular Structure & Dynamics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1080/07391102.2023.2252085","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/29 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Chalcones have various biological effects, from immune boosting to anti-cancer and anti-diabetic. Structurally modified chalcones (SMC) are clinically relevant for diabetes and cardiometabolic complications. From the original research articles, a structurally proven and biologically outstanding 14 structurally modified chalcones were screened and inducted in this study. This study evaluated the effects of SMC towards diabetes via network pharmacology analysis. The network data shows compounds S2, S3, S5, S9 &S12 suit the diabetes target. Especially Compounds S5 and S9 have a higher binding affinity towards the targets of TNF, PI3K, MAPK1 and AKT1 active sites. Compound S9 [(E)-3-(4-(1H-imidazol-1-yl)phenyl)-1-(4-(2,4-difluorobenz-yloxy)phenyl)prop-2-en-1-one] have identified with stronger binding affinities towards the active sites of MAPK3 (PDB:4QTB) -10.5(Kcal/mol). To provide a more effective mechanism for demonstrating protein-ligand interaction, one of the molecular docking complex (ERK2 kinase-S5) was subjected to a molecular dynamic at 300K for 100 ns. In term of structural stability, structure compactness, residual flexibility and hydrogen bond interaction of the complex was evaluated Integrating network pharmacology, in silico virtual screening, and molecular docking analysis shows that structurally modified compounds are effective and may help identify lead compounds towards glycemic control.Communicated by Ramaswamy H. Sarma.
期刊介绍:
The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.