Targeting Caspase 8: Using Structural and Ligand-Based Approaches to Identify Potential Leads for the Treatment of Multi-Neurodegenerative Diseases

Computational strategies to identify new drug candidates against neuroinflammation

The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches

Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Synthesis, biological evaluation, and molecular docking study of some new rohitukine analogs as protein tyrosine phosphatase 1B inhibitors

Rohitukine (RH) was extracted from the stem bark of Dysoxylum binectariferum Hook. It was derivatized to different arylsulphanmides by treating with the corresponding aryl sulphonyl chlorides. These derivatives were tested in-vitro on protein tyrosine phosphatase 1B (PTP1B) inhibition. Among these the active compounds K2, K3, K5, and K8 significantly inhibited the PTP1B by 51.3%, 65.6%, 71.9%, and 55.9% respectively at 10 µg/ml, the results were also supported by in-silico docking experiments. The most potent compound K5 was analyzed for antidiabetic and antidyslipidemic activity in vivo. It showed a marked reduction in blood glucose level (random and fasting) and serum insulin level in db/db mice. It improved glucose intolerance as ascertained by the oral glucose tolerance test (OGTT). These NCEs (New Chemical Entities) also lowered cholesterol and triglyceride profiles while improved high-density lipoprotein cholesterol in db/db mice. The K5 was further evaluated for antiadipogenic activity on MDI (Methylisobutylxanthine, dexamethasone, and insulin)-induced adipogenesis. where it significantly inhibited MDI-induced adipogenesis in 3 T3-L1 preadipocytes, at 10 µM and 20 µM concentration. These results were compared with the parent compound RH which inhibited 35% and 45% lipid accumulation while the RH analog K5 inhibited the lipid accumulation by 41% and 51% at 10 and 20 µM concentration, respectively. These results well corroborated with in-silico studies.

Understanding Calcium-Dependent Conformational Changes in S100A1 Protein: A Combination of Molecular Dynamics and Gene Expression Study in Skeletal Muscle

The S100A1 protein, involved in various physiological activities through the binding of calcium ions (Ca²⁺), participates in several protein-protein interaction (PPI) events after Ca²⁺-dependent activation. The present work investigates Ca²⁺-dependent conformational changes in the helix-EF hand-helix using the molecular dynamics (MD) simulation approach that facilitates the understanding of Ca²⁺-dependent structural and dynamic distinctions between the apo and holo forms of the protein. Furthermore, the process of ion binding by inserting Ca²⁺ into the bulk of the apo structure was simulated by molecular dynamics. Expectations of the simulation were demonstrated using cluster analysis and a variety of structural metrics, such as interhelical angle estimation, solvent accessible surface area, hydrogen bond analysis, and contact analysis. Ca²⁺ triggered a rise in the interhelical angles of S100A1 on the binding site and solvent accessible surface area. Significant configurational regulations were observed in the holo protein. The findings would contribute to understanding the molecular basis of the association of Ca²⁺ with the S100A1 protein, which may be an appropriate study to understand the Ca²⁺-mediated conformational changes in the protein target. In addition, we investigated the expression profile of S100A1 in myoblast differentiation and muscle regeneration. These data showed that S100A1 is expressed in skeletal muscles. However, the expression decreases with time during the process of myoblast differentiation.

Pharmacophore-based approaches in the rational repurposing technique for FDA approved drugs targeting SARS-CoV-2 Mpro

Novel coronavirus (CoV) is the primary etiological virus responsible for the pandemic that started in Wuhan in 2019–2020. This viral disease is extremely prevalent and has spread around the world. Preventive steps are restricted social contact and isolation of the sick individual to avoid person-to-person transmission. There is currently no cure available for the disease and the search for novel medications or successful therapeutics is intensive, time-consuming, and laborious. An effective approach in managing this pandemic is to develop therapeutically active drugs by repurposing or repositioning existing drugs or active molecules. In this work, we developed a feature-based pharmacophore model using reported compounds that inhibit SARS-CoV-2. This model was validated and used to screen the library of 565 FDA-approved drugs against the viral main protease (M^pro), resulting in 66 drugs interacting with M^pro with higher binding scores in docking experiments than drugs previously reported for the target diseases. The study identified drugs from many important classes, viz. D₂ receptor antagonist, HMG-CoA inhibitors, HIV reverse transcriptase and protease inhibitors, anticancer agents and folate inhibitors, which can potentially interact with and inhibit the SARS-CoV-2 M^pro. This validated approach may help in finding the urgently needed drugs for the SARS-CoV-2 pandemic with infinitesimal chances of failure.

Novel coronavirus (CoV) is the primary etiological virus responsible for the pandemic that started in Wuhan in 2019–2020.