Non-genetic MMP-2 insufficiency is a relatively unexplored condition which could be induced by pathological overexpression of endogenous MMP-2 inhibitors such as TIMPs and/or the acute phase reactant alpha-2-macroglobulin. Here, we investigate the hypothesis that human fibrinogen (FBG) – an acute phase reactant – inhibits human MMP-2. Following an unexpected observation where sera from human donors including arthritis patients with increased levels of serum FBG exhibited reduced binding of serum proMMP-2 to gelatin, we found that human FBG (0 to 3.6 mg/mL i.e., 0 to 10.6 μM) concentration-dependently inhibited human proMMP-2 and MMP2 from binding to gelatin. Moreover, at normal physiological concentrations, FBG (5.29–11.8 μM) concentration-dependently inhibited (40–70% inhibition) the cleavage of fluorescein-conjugated gelatin by MMP-2, but not MMP-9. Indicative of a mixed-type (combination of competitive and non-competitive) inhibition mechanism, FBG reduced the Vmax (24.9 ± 0.7 min−1 to 17.7 ± 0.9 min−1, P < 0.05) and increased the Michaelis-Menten constant KM (204 ± 6 nM to 478 ± 50 nM, P < 0.05) for the reaction of MMP-2 cleavage of fluorescein-conjugated gelatin. In silico analyses and studies of FBG neutralization with anti-FBG antibodies implicated the domains D and E of FBG in the inhibition of MMP-2. In conclusion, FBG is a natural selective MMP-2 inhibitor, whose pathological elevation could lead to MMP-2 insufficiency in humans.

Dexamethasone may reduce mortality in COVID-19 patients. Whether dexamethasone or endogenous glucocorticoids, such as cortisol, biochemically interact with SARS-CoV-2 spike 1 protein (S1), or its cellular receptor ACE2, is unknown. Using molecular dynamics (MD) simulations and binding energy calculations, we identified 162 druggable pockets in various conformational states of S1 and all possible binding pockets for cortisol and dexamethasone. Through biochemical binding studies, we confirmed that cortisol and dexamethasone bind to S1. Limited proteolysis and mass spectrometry analyses validated several MD identified binding pockets for cortisol and dexamethasone on S1. Interaction assays indicated that cortisol and dexamethasone separately and cooperatively disrupt S1 interaction with ACE2, through direct binding to S1, without affecting ACE2 catalytic activity. Cortisol disrupted the binding of the mutant S1 Beta variant (E484K, K417N, N501Y) to ACE2. Delta and Omicron variants are mutated in or near identified cortisol-binding pockets in S1, which may affect cortisol binding to them. In the presence of cortisol, we find increased inhibition of S1 binding to ACE2 by an anti-SARS-CoV-2 S1 human chimeric monoclonal antibody against the receptor binding domain. Whether glucocorticoid/S1 direct interaction is an innate defence mechanism that may have contributed to mild or asymptomatic SARS-CoV-2 infection deserves further investigation.

Matrix metalloproteinases (MMPs) cleave extracellular matrix proteins, growth factors, cytokines, and receptors to influence organ development, architecture, function, and the systemic and cell-specific responses to diseases and pharmacological drugs. Conversely, many diseases (such as atherosclerosis, arthritis, bacterial infections (tuberculosis), viral infections (COVID-19), and cancer), cholesterol-lowering drugs (such as statins), and tetracycline-class antibiotics (such as doxycycline) alter MMP activity through transcriptional, translational, and post-translational mechanisms. In this review, we summarize evidence that the aforementioned diseases and drugs exert significant epigenetic pressure on genes encoding MMPs, tissue inhibitors of MMPs, and factors that transcriptionally regulate the expression of MMPs. Our understanding of human pathologies associated with alterations in the proteolytic activity of MMPs must consider that these pathologies and their medicinal treatments may impose epigenetic pressure on the expression of MMP genes. Whether the epigenetic mechanisms affecting the activity of MMPs can be therapeutically targeted warrants further research.

A member of the matrix metalloproteinase family, matrix metalloproteinase-2 (MMP-2, gelatinase A), has been extensively studied for its role in both normal physiology and pathological processes. Whereas most research efforts in recent years have investigated the pathologies associated with MMP-2 overactivity, the pathological mechanisms elicited by MMP-2 underactivity are less well understood. Here, we distinguish between 2 states and describe their causes: (i) MMP-2 deficiency (complete loss of MMP-2 activity) and (ii) MMP-2 insufficiency (defined as MMP-2 activity below baseline levels). Further, we review the biology of MMP-2, summarizing the current literature on MMP-2 underactivity in both mice and humans, and describe research being conducted by our lab towards improving our understanding of the pathological mechanisms elicited by MMP-2 deficiency/insufficiency. We think that this research could stimulate the discovery of new therapeutic approaches for managing pathologies associated with MMP-2 underactivity. Moreover, similar concepts could apply to other members of the matrix metalloproteinase family.

Determining the interacting proteins in multiprotein complexes can be technically challenging. An emerging biochemical approach to this end is based on the ‘thermal proximity co-aggregation’ (TPCA) phenomenon. Accordingly, when two or more proteins interact to form a complex, they tend to co-aggregate when subjected to heat-induced denaturation and thus exhibit similar melting curves. Here, we explore the potential of leveraging TPCA for determining protein interactions. We demonstrate that dissimilarity measure-based information retrieval applied to melting curves tends to rank a protein-of-interest’s interactors higher than its non-interactors, as shown in the context of pull-down assay results. Consequently, such rankings can reduce the number of confirmatory biochemical experiments needed to find bona fide protein–protein interactions. In general, rankings based on dissimilarity measures generated through metric learning further reduce the required number of experiments compared to those based on standard dissimilarity measures such as Euclidean distance. When a protein mixture’s melting curves are obtained in two conditions, we propose a scoring function that uses melting curve data to inform how likely a protein pair is to interact in one condition but not another. We show that ranking protein pairs by their scores is an effective approach for determining condition-specific protein–protein interactions. By contrast, clustering melting curve data generally does not inform about the interacting proteins in multiprotein complexes. In conclusion, we report improved methods for dissimilarity measure-based computation of melting curves data that can greatly enhance the determination of interacting proteins in multiprotein complexes.