Collecting and assessing human lactate dehydrogenase-A conformations for structure-based virtual screening

Development of human lactate dehydrogenase a inhibitors: high-throughput screening, molecular dynamics simulation and enzyme activity assay

Lactate dehydrogenase A (LDHA) is highly expressed in many tumor cells and promotes the conversion of pyruvate to lactic acid in the glucose pathway, providing energy and synthetic precursors for rapid proliferation of tumor cells. Therefore, inhibition of LDHA has become a widely concerned tumor treatment strategy. However, the research and development of highly efficient and low toxic LDHA small molecule inhibitors still faces challenges. To discover potential inhibitors against LDHA, virtual screening based on molecular docking techniques was performed from Specs database of more than 260,000 compounds and Chemdiv-smart database of more than 1,000 compounds. Through molecular dynamics (MD) simulation studies, we identified 12 potential LDHA inhibitors, all of which can stably bind to human LDHA protein and form multiple interactions with its active central residues. In order to verify the inhibitory activities of these compounds, we established an enzyme activity assay system and measured their inhibitory effects on recombinant human LDHA. The results showed that Compound 6 could inhibit the catalytic effect of LDHA on pyruvate in a dose-dependent manner with an EC50 value of 14.54 ± 0.83 µM. Further in vitro experiments showed that Compound 6 could significantly inhibit the proliferation of various tumor cell lines such as pancreatic cancer cells and lung cancer cells, reduce intracellular lactic acid content and increase intracellular reactive oxygen species (ROS) level. In summary, through virtual screening and in vitro validation, we found that Compound 6 is a small molecule inhibitor for LDHA, providing a good lead compound for the research and development of LDHA related targeted anti-tumor drugs.

Recent Developments in Methicillin-Resistant Staphylococcus aureus (MRSA) Treatment: A Review

Staphylococcus aureus (S. aureus) is a Gram-positive bacterium that may cause life-threatening diseases and some minor infections in living organisms. However, it shows notorious effects when it becomes resistant to antibiotics. Strain variants of bacteria, viruses, fungi, and parasites that have become resistant to existing multiple antimicrobials are termed as superbugs. Methicillin is a semisynthetic antibiotic drug that was used to inhibit staphylococci pathogens. The S. aureus resistant to methicillin is known as methicillin-resistant Staphylococcus aureus (MRSA), which became a superbug due to its defiant activity against the antibiotics and medications most commonly used to treat major and minor infections. Successful MRSA infection management involves rapid identification of the infected site, culture and susceptibility tests, evidence-based treatment, and appropriate preventive protocols. This review describes the clinical management of MRSA pathogenesis, recent developments in rapid diagnosis, and antimicrobial treatment choices for MRSA.

Magnetic nanoparticles-based lactate dehydrogenase microreactor as a drug discovery tool for rapid screening inhibitors from natural products

Lactate dehydrogenase (LDH), catalyzing the conversion of pyruvate to lactate during glycolysis, is overexpressed in cancer cells. LDH inhibitors are a promising approach for the treatment of cancer. But up till now, there is limited method for rapid screening of LDH inhibitors. Herein, the use of LDH functionalized magnetic nanoparticles as a drug discovery tool for the selective enrichment of LDH potential inhibitors from natural products was firstly reported in this study. Firstly, LDH was immobilized onto the surface of amino-modified magnetic nanoparticles via covalent binding. In order to obtain the maximum enzyme activity, the immobilization conditions including pH, time and LDH concentration were optimized. The amount of LDH immobilized on MNPs was about 49 μg enzyme/mg carrier under the optimized conditions. Subsequently, the ligand fishing assay was performed to validate the specificity and selectivity of immobilized LDH using a model mixture, which consisted of galloflavin, chlorogenic acid and verbascoside. Finally, the immobilized LDH approach combined with ultra-high performance liquid chromatography-tandem mass spectrometry technique (UHPLC-MS/MS) was applied to screen potential LDH inhibitors from two anthraquinone-rich natural products (Rhubarb and Polygonum cuspidatum). Nine and six compounds were identified from Rhubarb and Polygonum cuspidatum extracts respectively, of which three compounds were common to both. Our results have proven that LDH functionalized magnetic nanoparticles have a significant prospect for drug discovery from complex matrices.

Lactate Dehydrogenases as Metabolic Links between Tumor and Stroma in the Tumor Microenvironment

Cancer is a metabolic disease in which abnormally proliferating cancer cells rewire metabolic pathways in the tumor microenvironment (TME). Molecular reprogramming in the TME helps cancer cells to fulfill elevated metabolic demands for bioenergetics and cellular biosynthesis. One of the ways through which cancer cell achieve this is by regulating the expression of metabolic enzymes. Lactate dehydrogenase (LDH) is the primary metabolic enzyme that converts pyruvate to lactate and vice versa. LDH also plays a significant role in regulating nutrient exchange between tumor and stroma. Thus, targeting human lactate dehydrogenase for treating advanced carcinomas may be of benefit. LDHA and LDHB, two isoenzymes of LDH, participate in tumor stroma metabolic interaction and exchange of metabolic fuel and thus could serve as potential anticancer drug targets. This article reviews recent research discussing the roles of lactate dehydrogenase in cancer metabolism. As molecular regulation of LDHA and LDHB in different cancer remains obscure, we also review signaling pathways regulating LDHA and LDHB expression. We highlight on the role of small molecule inhibitors in targeting LDH activity and we emphasize the development of safer and more effective LDH inhibitors. We trust that this review will also generate interest in designing combination therapies based on LDH inhibition, with LDHA being targeted in tumors and LDHB in stromal cells for better treatment outcome.

Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles

Molecular docking can account for receptor flexibility by combining the docking score over multiple rigid receptor conformations, such as snapshots from a molecular dynamics simulation. Here, we evaluate a number of common snapshot selection strategies using a quality metric from stratified sampling, the efficiency of stratification, which compares the variance of a selection strategy to simple random sampling. We also extend the metric to estimators of exponential averages (which involve an exponential transformation, averaging, and inverse transformation) and minima. For docking sets of over 500 ligands to four different proteins of varying flexibility, we observe that, for estimating ensemble averages and exponential averages, many clustering algorithms have similar performance trends: for a few snapshots (less than 25), medoids are the most efficient, while, for a larger number, optimal (the allocation that minimizes the variance) and proportional (to the size of each cluster) allocation become more efficient. Proportional allocation appears to be the most consistently efficient for estimating minima.

Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.

Lactate dehydrogenase inhibition: exploring possible applications beyond cancer treatment

Lactate dehydrogenase (LDH) inhibition is considered a worthwhile attempt in the development of innovative anticancer strategies. Unfortunately, in spite of the involvement of several research institutions and pharma-companies, the discovery of LDH inhibitors with drug-like properties seems a hardly resolvable challenge. While awaiting new advancements, in the present review we will examine other pathologic conditions characterized by increased glycolysis and LDH activity, which could potentially benefit from LDH inhibition. The rationale for targeting LDH activity in these contexts is the same justifying the LDH-based approach in anticancer therapy: because of the enzyme position at the end of glycolytic pathway, LDH inhibitors are not expected to hinder glucose metabolism of normal cells. Moreover, we will summarize the latest contributions in the discovery of enzyme inhibitors and try to glance over the reasons underlying the complexity of this research.

Screening of novel inhibitors targeting lactate dehydrogenase A via four molecular docking strategies and dynamics simulations

Lactate dehydrogenase A (LDHA) is a metabolic enzyme which catalyzes the interconversion of lactate and pyruvate in the glycolysis pathway, thus playing key roles in aerobic glycolysis. The inhibition of LDHA by small molecules has become an attractive strategy for anticancer therapy in recent years. However, very few LDHA inhibitors have been reported, even though a great deal of effort has directed into identifying LDHA inhibitors using structure-based approaches. Therefore, high-throughput and high-accuracy screening approaches are still urgently needed in order to target LDHA effectively. In the present work, after establishing that our docking strategies performed well using test datasets, we screened 32791 Specs products for their docking scores with the substrate-binding pocket and, separately, the cofactor-binding pocket of LDHA. We subsequently identified 76 hits (i.e., ligands that show low docking scores) for the cofactor-binding pocket and 27 hits for the substrate-binding pocket. Two representative compounds, ZINC20036549 and ZINC19369718, were then chosen for further MD simulation analysis, and we found that these compounds maintained their inhibitory activity during the MD simulations. Meanwhile, we found that ZINC19369718 interacts with a novel binding site close to the active site, and that this interaction may inhibit the catalytic activity of LDHA. Together, these results offer not only a new paradigm for identifying Specs drug-like products for novel therapeutic use but they also provide further opportunity to adopt LDHA inhibition as a strategy for cancer therapy.

Protein Flexibility in Drug Discovery: From Theory to Computation

Nowadays it is widely accepted that the mechanisms of biomolecular recognition are strongly coupled to the intrinsic dynamic of proteins. In past years, this evidence has prompted the development of theoretical models of recognition able to describe ligand binding assisted by protein conformational changes. On a different perspective, the need to take into account protein flexibility in structure-based drug discovery has stimulated the development of several and extremely diversified computational methods. Herein, on the basis of a parallel between the major recognition models and the simulation strategies used to account for protein flexibility in ligand binding, we sort out and describe the most innovative and promising implementations for structure-based drug discovery.