In silico identification of structure requirement for novel thiazole and oxazole derivatives as potent fructose 1,6-bisphosphatase inhibitors

Intermediate-aided allostery mechanism for α-glucosidase by Xanthene-11v as an inhibitor using residue interaction network analysis

Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

Fructose 1,6-bisphosphatase: getting the message across

Fructose 1,6-bisphosphatase (FBPase) is a key enzyme in gluconeogenesis. It is a potential drug target in the treatment of type II diabetes. The protein is also associated with a rare inherited metabolic disease and some cancer cells lack FBPase activity which promotes glycolysis facilitating the Warburg effect. Thus, there is interest in both inhibiting the enzyme (for diabetes treatment) and restoring its activity (in relevant cancers). The mammalian enzyme is tetrameric, competitively inhibited by Fructose 2,6-bisphosphate and negatively allosterically regulated by AMP. This allosteric regulation requires information transmission between the AMP binding site and the active site of the enzyme. A recent paper by Topaz et al. (Bioscience Reports (2019) 39, pii:BSR20180960) has added additional detail to our understanding of this information transmission process. Two residues in the AMP binding site (Lys¹¹² and Tyr¹¹³) were shown to be involved in initiating the message between the two sites. This tyrosine residue has recently be shown to be important with protein’s interaction with the antidiabetic drug metformin. A variant designed to increase metal ion affinity (M248D) resulted in a five-fold increase in enzymatic activity. Interestingly alterations of two residues at the subunit interfaces (Tyr¹⁶⁴ and Met¹⁷⁷) resulted in increased responsiveness to AMP. Overall, these findings may have implications in the design of novel FBPase inhibitors or activators.

Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method

Fructose-1,6-bisphosphatase (FBPase) is an enzyme important for regulation of gluconeogenesis, which is a major process in the liver responsible for glucose production. Inhibition of FBPase enzyme causing blockage of the gluconeogenesis process represents a newer scheme in the progress of anti-diabetic drugs. The current research describes the development of hybrid optimal descriptors-based quantitative structure-activity relationship (QSAR) models intended for a set of 62 FBPase inhibitors with the Monte Carlo method. The molecular structures were expressed by the simplified molecular input line entry system (SMILES) notation. Three splits were prepared by random division of the molecules into training set, calibration set and validation set. Statistical parameters obtained from QSAR modelling were good for various designed splits. The best QSAR model showed the following parameters: the values of r² for calibration set and validation set of the best model were 0.6837 and 0.8623 and of Q² were 0.6114 and 0.8036, respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoter of the endpoint. Further, these structural attributes were used in designing of new FBPase inhibitors and a molecular docking study was completed for the determination of interactions of the designed molecules with the enzyme.

The ex vivo and in vivo biological performances of graphene oxide and the impact of surfactant on graphene oxide's biocompatibility

Graphene oxide (GO) displays promising properties for biomedical applications including drug delivery and cancer therapeutics. However, GO exposure also raises safety concerns such as potential side effects on health. Here, the biological effects of GO suspended in phosphate buffered saline (PBS) with or without 1% nonionic surfactant Tween 80 were investigated. Based on the ex vivo experiments, Tween 80 significantly affected the interaction between GO and peripheral blood from mice. GO suspension in PBS tended to provoke the aggregation of diluted blood cells, which could be prevented by the addition of Tween 80. After intravenous administration, GO suspension with or without 1% Tween 80 was quickly eliminated by the mononuclear phagocyte system. Nevertheless, GO suspension without Tween 80 showed greater accumulation in lungs than that containing 1% Tween 80. In contrast, less GO was found in livers for GO suspension compared to Tween 80 assisted GO suspension. Organs including hearts, livers, lungs, spleens, kidneys, brains, and testes did not reveal histological alterations. The indexes of peripheral blood showed no change upon GO exposure. Our results together demonstrated that Tween 80 could greatly alter GO's biological performance and determine the pattern of its biodistribution in mice.

Toward the prediction of FBPase inhibitory activity using chemoinformatic methods

Currently, Chemoinformatic methods are used to perform the prediction for FBPase inhibitory activity. A genetic algorithm-random forest coupled method (GA-RF) was proposed to predict fructose 1,6-bisphosphatase (FBPase) inhibitors to treat type 2 diabetes mellitus using the Mold² molecular descriptors. A data set of 126 oxazole and thiazole analogs was used to derive the GA-RF model, yielding the significant non-cross-validated correlation coefficient r²_ncv and cross-validated r²_cv values of 0.96 and 0.67 for the training set, respectively. The statistically significant model was validated by a test set of 64 compounds, producing the prediction correlation coefficient r²_pred of 0.90. More importantly, the building GA-RF model also passed through various criteria suggested by Tropsha and Roy with r²_o and r²_m values of 0.90 and 0.83, respectively. In order to compare with the GA-RF model, a pure RF model developed based on the full descriptors was performed as well for the same data set. The resulting GA-RF model with significantly internal and external prediction capacities is beneficial to the prediction of potential oxazole and thiazole series of FBPase inhibitors prior to chemical synthesis in drug discovery programs.