Molecular docking, pharmacokinetic prediction and molecular dynamics simulations of tankyrase inhibitor compounds with the protein glucokinase, induced in the development of diabetes.

GCK is a protein that plays a crucial role in the sensing and regulation of glucose homeostasis, which associates it with disorders of carbohydrate metabolism and the development of several pathologies, including gestational diabetes. This makes GCK an important therapeutic target that has aroused the interest of researchers to discover GKA that are simultaneously effective in the long term and free of side effects. TNKS is a protein that interacts directly with GCK; recent studies have shown that it inhibits GCK action, which affects glucose detection and insulin secretion. This justifies our choice of TNKS inhibitors as ligands to test their effects on the GCK-TNKS complex. For this purpose, we investigated the interaction of the GCK-TNKS complex with 13 compounds (TNKS inhibitors and their analogues) using the molecular docking approach as a first step, after which the compounds that generated the best affinity scores were evaluated for drug similarity and pharmacokinetic properties. Subsequently, we selected the six compounds that generated high affinity and that were in accordance with the parameters of the drug rules as well as pharmacokinetic properties to ensure a molecular dynamics study. The results allowed us to favor the two compounds (XAV939 and IWR-1), knowing that even the tested compounds (TNKS 22, (2215914) and (46824343)) produced good results that can also be exploited. These results are therefore interesting and encouraging, and they can be exploited experimentally to discover a treatment for diabetes, including gestational diabetes.Communicated by Ramaswamy H. Sarma.

Computational analysis of missense variants of human MC4R and childhood obesity.

Industrialized and developing nations face severe public health problems related to childhood obesity. Previous studies revealed that the melanocortin-4 receptor gene (MC4R) is the most prevalent monogenic cause of severe early obesity. Due to its influence on food intake and energy expenditure via neuronal melanocortin-4 receptor pathways, MC4R is recognized as a regulator of energy homeostasis. This study used a variety of computational systems to analyze 273 missense variations of MC4R in silico. Several tools, including PolyPhen, PROVEAN, SIFT, SNAP2, MutPred2, PROVEAN, SNP&GO and Mu-Pro, I-Mutant, PhD-SNP, SAAFEC-SEQ I-Mutant, and ConSurf, were used to make predictions of 13 extremely confident nsSNPs that are harmful and disease-causing (E308k, P299L, D298H, C271F, C271R, P260L, T246N, G243R, C196Y, W174C, Y157S, D126Y, and D90G). The results of our study suggest that these MC4R nsSNPs may disrupt normal protein function, leading to an increased risk of childhood obesity. These results highlight the potential use of these nsSNPs as biomarkers to predict susceptibility to obesity and as targets for personalized interventions.