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

Abstract

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.

An integrated investigation of structural and pathway alteration caused by PIK3CA and TP53 mutations identified in cfDNA of metastatic breast cancer

“…In clinical trials, PI3Kα and PIK3δ inhibitors, as well as AKT inhibitors, are being utilized to treat ER‐positive BC patients with PIK3CA mutations. The comparison of protein‐ligand interaction using molecular docking and dynamics studies was previously reported in several proteins responsible for cancer and genetic diseases 99,101 . Extrinsic events such as hypoxia, oxidative stress, and acidosis, as well as intrinsic factors such as MYC amplification, PIK3CA, and TP53 mutations (Supporting Information: Figure S4), all lead to various metabolic reprogramming patterns in metastatic breast tumors.…”

“…The comparison of protein-ligand interaction using molecular docking and dynamics studies was previously reported in several proteins responsible for cancer and genetic diseases. 99,101 Extrinsic events such as hypoxia, oxidative stress, and acidosis, as well as intrinsic factors such as MYC amplification, PIK3CA, and TP53 mutations (Supporting Information: Figure S4), all lead to various metabolic reprogramming patterns in metastatic breast tumors. The functions of PIK3CA and TP53 in cancer and treatment resistance have been the subject of research; however, their interactions and potential involvement in promoting cancer hallmarks have received much less attention.…”

Identification of potential inhibitors, conformational dynamics, and mechanistic insights into mutant Kirsten rat sarcoma virus (G13D) driven cancers

“…The remaining hit molecules showed an average (~1–3) H‐bonds (Figure 6B). As the protein functions through cumulative atomic motions, the PCA 10,49–51 study was employed to understand the movements of the Cα atoms structurally for the five complexes. Based on the cumulative motion of the complexes, the first and last principal components were employed to construct the PCA graph.…”

Identifying Potent Fat Mass and Obesity-Associated Protein Inhibitors Using Deep Learning-Based Hybrid Procedures

“…Discovering potent inhibitors with a restricted number of studies would pose a challenge [82]. According to the literature, molecular modeling and molecular dynamics simulations are highly effective in reducing the number of potential FTO inhibitors while still being computationally efficient [83]. Drug virtual screening has a lengthy and extensive history, encompassing the development of various related techniques like docking, Quantitative Structure Activity Relationship (QSAR), pharmacophore, and structure-based ligand similarity [84].…”