A combined molecular modeling study on gelatinases and their potent inhibitors

Matrix metalloproteinases induce extracellular matrix degradation through various pathways to alleviate hepatic fibrosis

Liver fibrosis is the common consequence of various chronic liver injuries and is mainly characterized by the imbalance between the production and degradation of extracellular matrix, which leads to the accumulation of interstitial collagen and other matrix components. Matrix metalloproteinases (MMPs) and their specific inhibitors, that is, tissue inhibitors of metalloproteinases (TIMPs), play a crucial role in collagen synthesis and lysis. Previous in vivo and in vitro studies of our laboratory found repressing extracellular matrix (ECM) accumulation by restoring the balance between MMPs and TIMPs can alleviate liver fibrosis. We conducted a review of articles published in PubMed and Science Direct in the last decade until February 1, 2023, which were searched for using these words "MMPs/TIMPs" and "Hepatic Fibrosis." Through a literature review, this article reviews the experimental studies of liver fibrosis based on MMPs/TIMPs, summarizes the components that may exert an anti-liver fibrosis effect by affecting the expression or activity of MMPs/TIMPs, and attempts to clarify the mechanism of MMPs/TIMPs in regulating collagen homeostasis, so as to provide support for the development of anti-liver fibrosis drugs. We found the MMP-TIMP-ECM interaction can result in better understanding of the pathogenesis and progression of hepatic fibrosis from a different angle, and targeting this interaction may be a promising therapeutic strategy for hepatic fibrosis. Additionally, we summarized and analyzed the drugs that have been found to reduce liver fibrosis by changing the ratio of MMPs/TIMPs, including medicine natural products.

Profiling the Structural determinants of pyrrolidine derivative as gelatinases (MMP-2 and MMP-9) inhibitors using in silico approaches

Quantitative structure activity relationship (QSAR) studies on pyrrolidine derivatives have been established using CoMFA, CoMSIA, and Hologram QSAR analysis to estimate the values (pIC₅₀) of gelatinase inhibitors. When the CoMFA cross-validation value, Q², was 0.625, the training set coefficient of determination, R² was 0.981. In CoMSIA, Q² was 0.749 and R² was 0.988. In the HQSAR, Q² was 0.84 and R² was 0.946. Visualization of these models was performed by contour maps showing favorable and unfavorable regions for activity, while visualization of HQSAR model was performed by a colored atomic contribution graph. Based on the results obtained of external validation, the CoMSIA model was statistically more significant and robust and was selected as the best model to predict new, more active inhibitors. To study the modes of interactions of the predicted compounds in the active site of MMP-2 and MMP-9, a simulation of molecular docking was realized. A combined study of MD simulations and calculation of free binding energy, were also carried out to validate the results obtained on the best predicted and most active compound in dataset and the compound NNGH as control compound. The results confirm the molecular docking results and indicate that the predicted ligands were stable in the binding site of MMP-2 and MMP-9.

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.

Insight into the structural requirement of aryl sulphonamide based gelatinases (MMP-2 and MMP-9) inhibitors - Part I: 2D-QSAR, 3D-QSAR topomer CoMFA and Naïve Bayes studies - First report of 3D-QSAR Topomer CoMFA analysis for MMP-9 inhibitors and jointly inhibitors of gelatinases together

Gelatinases [gelatinase A - matrix metalloproteinase-2 (MMP-2), gelatinase B - matrix metalloproteinase-9 (MMP-9)] play key roles in many disease conditions including cancer. Despite some research work on gelatinases inhibitors both jointly and individually had been reported, challenges still exist in achieving potency as well as selectivity. Here in part I of a series of work, we have reported the structural requirement of some arylsulfonamides. In particular, regression-based 2D-QSARs, topomer CoMFA (comparative molecular field analysis) and Bayesian classification models were constructed to refine structural features for attaining better gelatinase inhibitory activity. The 2D-QSAR models exhibited good statistical significance. The descriptors nsssN, SHBint6, SHBint7, PubchemFP629 were directly correlated with the MMP-2 binding affinities whereas nsssN, SHBint10 and AATS2i were directly proportional to MMP-9 binding affinities. The topomer CoMFA results indicated that the steric and electrostatic fields play key roles in gelatinase inhibition. The established Naïve Bayes prediction models were evaluated by fivefold cross validation and an external test set. Furthermore, important molecular descriptors related to MMP-2 and MMP-9 binding affinities and some active/inactive fragments were identified. Thus, these observations may be helpful for further work of aryl sulphonamide based gelatinase inhibitors in future.

Nuclear localization of matrix metalloproteinases

Matrix metalloproteinases (MMPs) were originally identified as matrixin proteases that act in the extracellular matrix. Recent works have uncovered nontraditional roles for MMPs in the extracellular space as well as in the cytosol and nucleus. There is strong evidence that subspecialized and compartmentalized matrixins participate in many physiological and pathological cellular processes, in which they can act as both degradative and regulatory proteases. In this review, we discuss the transcriptional and translational control of matrixin expression, their regulation of intracellular sorting, and the structural basis of activation and inhibition. In particular, we highlight the emerging roles of various matrixin forms in diseases. The activity of matrix metalloproteinases is regulated at several levels, including enzyme activation, inhibition, complex formation and compartmentalization. Most MMPs are secreted and have their function in the extracellular environment. MMPs are also found inside cells, both in the nucleus, cytosol and organelles. The role of intracellular located MMPs is still poorly understood, although recent studies have unraveled some of their functions. The localization, activation and activity of MMPs are regulated by their interactions with other proteins, proteoglycan core proteins and / or their glycosaminoglycan chains, as well as other molecules. Complexes formed between MMPs and various molecules may also include interactions with noncatalytic sites. Such exosites are regions involved in substrate processing, localized outside the active site, and are potential binding sites of specific MMP inhibitors. Knowledge about regulation of MMP activity is essential for understanding various physiological processes and pathogenesis of diseases, as well as for the development of new MMP targeting drugs.

Identifying factors controlling protein release from combinatorial biomaterial libraries via hybrid data mining methods

Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO- nonconjugated bonds, the number of aromatic rings, and the number of -CH₂- bonds.