Prediction of matrix metal proteinases-12 inhibitors by machine learning approaches

Employing comparative QSAR techniques for the recognition of dibenzofuran and dibenzothiophene derivatives toward MMP-12 inhibition

Among various matrix metalloproteinases (MMPs), MMP-12 is one of the potential targets for cancer and other diseases. However, none of the MMP-12 inhibitors has passed the clinical trials to date. Therefore, designing potential MMP-12 inhibitors as new drug molecules can provide effective therapeutic strategies for several diseases. In this study, a series of dibenzofuran and dibenzothiophene derivatives were subjected to different 2D and 3D-QSAR techniques to point out the crucial structural contributions highly influential toward the MMP-12 inhibitory activity. These techniques identified some structural attributes of these compounds that are responsible for influencing their MMP-12 inhibition. The carboxylic group may enhance proper binding with catalytic Zn2+ ion at the MMP-12 active site. Again, the i-propyl sulfonamido carboxylic acid function contributed positively toward MMP-12 inhibition. Moreover, the dibenzofuran moiety conferred stable binding at the S1' pocket for higher MMP-12 inhibition. The steric and hydrophobic groups were found favourable near the furan ring substituted at the dibenzofuran moiety. Besides these ligand-based approaches, molecular docking and molecular dynamic (MD) simulation studies not only elucidated the importance of several aspects of these MMP-12 inhibitors while disclosing the significance of the finding of these QSAR studies and their influences toward MMP-12 inhibition. The MD simulation study also revealed stable and compact binding between such compounds at the MMP-12 active site. Therefore, the findings of these validated ligand-based and structure-based molecular modeling studies can aid the development of selective and potent lead molecules that can be used for the treatment of MMP-12-associated diseases.Communicated by Ramaswamy H. Sarma.

Genetic deletion of MMP12 ameliorates cardiometabolic disease by improving insulin sensitivity, systemic inflammation, and atherosclerotic features in mice

BACKGROUND

Matrix metalloproteinase 12 (MMP12) is a macrophage-secreted protein that is massively upregulated as a pro-inflammatory factor in metabolic and vascular tissues of mice and humans suffering from cardiometabolic diseases (CMDs). However, the molecular mechanisms explaining the contributions of MMP12 to CMDs are still unclear.

METHODS

We investigated the impact of MMP12 deficiency on CMDs in a mouse model that mimics human disease by simultaneously developing adipose tissue inflammation, insulin resistance, and atherosclerosis. To this end, we generated and characterized low-density lipoprotein receptor (Ldlr)/Mmp12-double knockout (DKO) mice fed a high-fat sucrose- and cholesterol-enriched diet for 16-20 weeks.

RESULTS

DKO mice showed lower cholesterol and plasma glucose concentrations and improved insulin sensitivity compared with LdlrKO mice. Untargeted proteomic analyses of epididymal white adipose tissue revealed that inflammation- and fibrosis-related pathways were downregulated in DKO mice. In addition, genetic deletion of MMP12 led to alterations in immune cell composition and a reduction in plasma monocyte chemoattractant protein-1 in peripheral blood which indicated decreased low-grade systemic inflammation. Aortic en face analyses and staining of aortic valve sections demonstrated reduced atherosclerotic plaque size and collagen content, which was paralleled by an improved relaxation pattern and endothelial function of the aortic rings and more elastic aortic sections in DKO compared to LdlrKO mice. Shotgun proteomics revealed upregulation of anti-inflammatory and atheroprotective markers in the aortas of DKO mice, further supporting our data. In humans, MMP12 serum concentrations were only weakly associated with clinical and laboratory indicators of CMDs.

CONCLUSION

We conclude that the genetic deletion of MMP12 ameliorates obesity-induced low-grade inflammation, white adipose tissue dysfunction, biomechanical properties of the aorta, and the development of atherosclerosis. Therefore, therapeutic strategies targeting MMP12 may represent a promising approach to combat CMDs.

Selective Inhibitors of Medium-Size S1' Pocket Matrix Metalloproteinases: A Stepping Stone of Future Drug Discovery

Among various matrix metalloproteinases (MMPs), MMPs having medium-size S1' pockets are established as promising biomolecular targets for executing crucial roles in cancer, cardiovascular diseases, and neurodegenerative diseases. However, no such MMP inhibitors (MMPIs) are available to date as drug candidates despite a lot of continuous research work for more than three decades. Due to a high degree of structural resemblance among these MMPs, designing selective MMPIs is quite challenging. However, the variability and uniqueness of the S1' pockets of these MMPs make them promising targets for designing selective MMPIs. In this perspective, the overall structural aspects of medium-size S1' pocket MMPs including the unique binding patterns of enzyme-inhibitor interactions have been discussed in detail to acquire knowledge regarding selective inhibitor designing. This overall knowledge will surely be a curtain raiser for the designing of selective MMPIs as drug candidates in the future.

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

Molecular insights into a mechanism of resveratrol action using hybrid computational docking/CoMFA and machine learning approach

A phytoalexin, Resveratrol remains a legendary anticancer drug candidate in the archives of scientific literature. Although earlier wet-lab experiments rendering its multiple biological targets, for example, epidermal growth factors, Pro-apoptotic protein p53, sirtuins, and first apoptosis signal (Fas) receptor, Mouse double minute 2 (MDM2) ubiquitin-protein ligase, Estrogen receptor, Quinone reductase, etc. However, notwithstanding some notable successes, identification of an appropriate Resveratrol target(s) has remained a major challenge using physical methods, and hereby limiting its translation into an effective therapeutic(s). Thus, computational insights are much needed to establish proof-of-concept towards potential Resveratrol target(s) with minimum error rate, narrow down the search space, and to assess a more accurate Resveratrol signaling pathway/mechanism at the starting point. Herein, a brute-force technique combining computational receptor-, ligand-based virtual screening, and classification-based machine learning, reveals the precise mechanism of Resveratrol action. Overall, MDM2 ubiquitin-protein ligase (4OGN.pdb) and co-crystallized quinone reductases 2 (4QOH.pdb) were found two suitable drug targets in the case of Resveratrol derivatives. Indeed, carotenoid cleaving oxygenase together with later twos gave gigantic momentum in guiding the rational drug design of Resveratrol derivatives. These molecular modeling insights would be useful for Resveratrol lead optimization into a more precise science.Communicated by Ramaswamy H. Sarma.

Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors

In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for predicting whether an unknown molecule is an inhibitor of human topoisomerase I (Top1) protein. All these models have achieved satisfactory results, with total prediction accuracies from 89.70% to 97.12%. Through comparative analysis, it can be found that the RF model has the best forecasting effect. The parameters were further optimized to generate the best-performing RF model. At the same time, features selection was implemented to choose properties most relevant to the inhibition of Top1 from 189 molecular descriptors through a special RF procedure. Subsequently, a ligand-based virtual screening was performed from the Maybridge database by the optimal RF model and 596 hits were picked out. Then, 67 molecules with relative probability scores over 0.7 were selected based on the screening results. Next, the 67 molecules above were docked to Top1 using AutoDock Vina. Finally, six top-ranked molecules with binding energies less than −10.0 kcal/mol were screened out and a common backbone, which is entirely different from that of existing Top1 inhibitors reported in the literature, was found.