Comparison of potential inhibitors and targeting fat mass and obesity-associated protein causing diabesity through docking and molecular dynamics strategies.

Genome-wide association studies (GWAS) have identified an association between polymorphisms in the FTO gene and obesity. The FTO: rs9939609, an intronic variant, is considered a risk allele for developing diabesity in homozygous and heterozygous forms. This study aimed to investigate the molecular structure of the available inhibitors specific to the FTO mutations along with the rs9939609 variant. We identified the best-suited inhibitor molecules for each mutant type containing the rs9939609 risk allele. Missense mutations unique to obesity and containing the risk allele of rs9939609 were retrieved from dbSNP for this study. Further stability testing for the mutations were carried out using DynaMut and iStable tools. Three mutations (G187A, M223V, and I492V) were highly destabilizing the FTO structure. These three mutants and native FTO were docked with each of the nine-inhibitor molecules collected from literature studies with the help of PyRx and AutoDock. Further structural behavior of the mutants and native FTO were identified with molecular dynamics simulations and MM-PBSA analyses, along with the 19complex inhibitor compound. We found the compound 19complex exhibited better binding interactions and is the top candidate inhibitor for the M223V and I492V mutants. This study provided insights into the structural changes caused due to mutations in FTO, and the binding mechanism of the inhibitor molecules. It could aid in developing antiobesity drugs for treating patients with mutations and risk alleles predisposing to obesity.

Involvement of Essential Signaling Cascades and Analysis of Gene Networks in Diabesity.

(1) Aims: Diabesity, defined as diabetes occurring in the context of obesity, is a serious health problem that is associated with an increased risk of premature heart attack, stroke, and death. To date, a key challenge has been to understand the molecular pathways that play significant roles in diabesity. In this study, we aimed to investigate the genetic links between diabetes and obesity in diabetic individuals and highlight the role(s) of shared genes in individuals with diabesity. (2) Methods: The interactions between the genes were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) tool after the compilation of obesity genes associated with type 1 diabetes (T1D), type 2 diabetes (T2D), and maturity-onset diabetes of the young (MODY). Cytoscape plugins were utilized for enrichment analysis. (3) Results: We identified 546 obesity genes that are associated with T1D, T2D, and MODY. The network backbone of the identified genes comprised 514 nodes and 4126 edges with an estimated clustering coefficient of 0.242. The Molecular Complex Detection (MCODE) generated three clusters with a score of 33.61, 16.788, and 6.783, each. The highest-scoring nodes of the clusters were AGT, FGB, and LDLR genes. The genes from cluster 1 were enriched in FOXO-mediated transcription of oxidative stress, renin secretion, and regulation of lipolysis in adipocytes. The cluster 2 genes enriched in Src homology 2 domain-containing (SHC)-related events triggered by IGF1R, regulation of lipolysis in adipocytes, and GRB2: SOS produce a link to mitogen-activated protein kinase (MAPK) signaling for integrins. The cluster 3 genes ere enriched in IGF1R signaling cascade and insulin signaling pathway. (4) Conclusion: This study presents a platform to discover potential targets for diabesity treatment and helps in understanding the molecular mechanism.