Molecular Docking Simulations with ArgusLab

Chroman-Schiff base derivatives as potential Anti-Tubercular Agents: In silico studies, Synthesis, and Biological evaluation

Tuberculosis (TB) continues to pose a significant public health challenge worldwide. Hydrazide-containing compounds have demonstrated considerable potential as anti- tubercular agents. In this study, we designed, synthesized, and evaluated a series of chroman- Schiff base derivatives, integrating a chroman scaffold with substituted phenyl moieties, as potential therapeutic candidates against TB. In silico studies were conducted to assess the binding interactions of the synthesized derivatives, specifically their R- and S-isomers, with the tuberculosis target protein InhA (PDB ID: 1ZID). Molecular docking revealed that two R-isomer derivatives, SM-5A and SM-6A, exhibited superior binding affinities (-10.6 kcal/mol) compared to the reference ligand INH-NADH (-10.3 kcal/mol) and the natural substrate NADH (-7.5 kcal/mol). Molecular dynamics simulations confirmed the long-term stability of these compound-protein complexes over a 100 ns trajectory, further substantiating their potential as stable inhibitors. The structures of the synthesized derivatives were validated using spectroscopic techniques, including FTIR, 13C NMR, 1H NMR, and HR-MS. Biological evaluation via in vitro anti-tubercular assays against Mycobacterium tuberculosis H37Rv (using the Microplate Alamar Blue Assay) demonstrated that several RRR-isomers displayed notable activity. Among them, SM-2 and SM-5 showed the most potent effects, with minimum inhibitory concentrations (MIC) of 32 µg/mL, comparable to standard anti-tubercular drugs such as isoniazid, ethambutol, and rifampicin. These findings highlight the chroman-schiff base scaffold as a promising foundation for the development of novel anti-tubercular agents. The integration of computational and experimental approaches in this study underscores the potential of these derivatives for further optimization and development into effective anti-tubercular therapeutics.

In-silico assessment of novel peptidomimetics inhibitor targeting STAT3 and STAT4 N-terminal domain dimerization: A comprehensive study using molecular docking, molecular dynamics simulation, and binding free energy analysis

Dysregulation in Janus kinase-Signal Transducer and Activation of Transcription (JAK-STAT) pathway is closely linked to various cancer types. The N-terminal domain (NTD) of STAT proteins, upon dimerization, assumes a multifaceted role with remarkable adaptability in mediating interactions between proteins. Consequently, the strategic targeting of the N-terminal domain of STATs has emerged as a promising tactic for disrupting dimerization and impeding the translocation of STAT proteins. In this study, we have deployed an integrated in-silico methodology to rationally design Peptidomimetic foldamers as inhibitors of the N-terminal domains of STAT3 and STAT4, with the objective of disrupting protein dimerization. Consequently, we have judiciously designed a series of peptidomimetics that encompass β3-amino acids, bearing side chains that mimic the residues within interface II of the dimeric structures of the NTDs. Employing molecular docking techniques; we have assessed the binding affinity of these designed peptidomimetics toward both the NTDs. Furthermore, we have conducted an evaluation of the stability and conformational alterations within the docked complexes over an extensive Molecular Dynamics, subsequently computing the binding free energy utilizing MM/PBSA calculations. Our findings unequivocally demonstrate that the peptidomimetic foldamers we have devised (Peptide-A, Peptide-B, and Peptide-C) exhibit a propensity to bind to and impede the dimerization process of the NTDs of both STAT3 and STAT4. These outcomes serve to underscore the potential of these meticulously designed peptidomimetics as potential candidates meriting further exploration in the realm of cancer prevention and management.

Updating the Pharmacological Effects of α-Mangostin Compound and Unraveling Its Mechanism of Action: A Computational Study Review

α-Mangostin, initially identified in 1855, is a xanthone derivative compound predominantly located in the pericarp of the mangosteen fruit (Garcinia mangostana L). This compound is known for its beneficial properties as an antioxidant and anti-inflammatory agent, still holding promise for potential benefits in other related pathologies. In the investigative process, computational studies have proven highly valuable in providing evidence and initial screening before progressing to preclinical and clinical studies. This review aims to present the pharmacological findings and mechanisms of action of α-mangostin based on computational studies. The compilation of this review is founded on the analysis of relevant articles obtained from PubMed, Scopus, and ScienceDirect databases. The study commences with an elucidation of the physicochemical characteristics, drug-likeness, pharmacokinetics, and toxicity profile of α-mangostin, which demonstrates that α-mangostin complies with the Lipinski's Rule of Five, exhibits favorable profiles of absorption, distribution, metabolism, and excretion, and presents low toxicity. Subsequent investigations have revealed that computational studies employing various software tools including ArgusLab, AutoDock, AutoDock Vina, Glide, HEX, and MOE, have been pivotal to comprehend the pharmacology of α-mangostin. Beyond its well established roles as an antioxidant and anti-inflammatory agent, α-mangostin is now recognized for its pharmacological effects in Alzheimer's disease, diabetes, cancer, chronic kidney disease, chronic periodontitis, infectious diseases, and rheumatoid arthritis. Moreover, α-mangostin is projected to have applications in pain management and as a potent mosquito larvicide. All of these findings are based on the attainment of adequate binding affinity to specific target receptors associated with each respective pathological condition. Consequently, it is anticipated that these findings will serve as a foundation for future scientific endeavours, encompassing both in vitro and in vivo studies, as well as clinical investigations, to better understand the pharmacological effects of α-mangostin.

Green-synthesized silver nanoparticles from Zingiber officinale extract: antioxidant potential, biocompatibility, anti-LOX properties, and in silico analysis

INTRODUCTION

Zingiber officinale extract has emerged as a compelling candidate for green synthesis of nanoparticles, offering diverse applications across medicine, cosmetics, and nutrition. This study delves into the investigation of in vitro toxicity and explores the biomedical utility of green-synthesized silver nanoparticles derived from ginger extract (GE-AgNPs).

METHODS

We employed established protocols to evaluate in vitro aspects such as antioxidant capacity, anti-inflammatory potential, and biocompatibility of GE-AgNPs. Additionally, molecular docking was employed to assess their anti-lipoxygenase (anti-LOX) activity.

RESULTS

Our findings highlight that the extraction of ginger extract at a pH of 6, utilizing a cosolvent blend of ethanol and ethyl acetate in a 1:1 ratio, yields heightened antioxidant capacity attributed to its rich phenolic and flavonoid content. In the context of silver nanoparticle synthesis, pH 6 extraction yields the highest quantity of nanoparticles, characterized by an average size of 32.64 ± 1.65 nm. Of particular significance, GE-AgNPs (at pH 6) demonstrated remarkable efficacy in scavenging free radicals, as evidenced by an IC50 value of 6.83 ± 0.47 µg/mL. The results from the anti-LOX experiment indicate that GE-AgNPs, at a concentration of 10 µg/mL, can inhibit LOX activity by 25%, outperforming ginger extract which inhibits LOX by 17-18%. Notably, clionasterol exhibited higher binding energy and enhanced stability (-8.9 kcal/mol) compared to nordihydroguaiaretic acid. Furthermore, a cell viability study confirmed the safety of GE-AgNPs at a concentration of 17.52 ± 7.00 µg/mL against the L929 cell line.

CONCLUSION

These comprehensive findings underscore the significant biomedical advantages of GE-AgNPs and emphasize their potential incorporation into cosmetic products at a maximum concentration of 10 µg/mL.

Electron Spin-Dependent Electrocatalysis for the Oxygen Reduction Reaction in a Chiro-Self-Assembled Iron Phthalocyanine Device

The chiral-induced spin selectivity effect (CISS) is a breakthrough phenomenon that has revolutionized the field of electrocatalysis. We report the first study on the electron spin-dependent electrocatalysis for the oxygen reduction reaction, ORR, using iron phthalocyanine, FePc, a well-known molecular catalyst for this reaction. The FePc complex belongs to the non-precious catalysts group, whose active site, FeN4, emulates catalytic centers of biocatalysts such as Cytochrome c. This study presents an experimental platform involving FePc self-assembled to a gold electrode surface using chiral peptides (L and D enantiomers), i.e., chiro-self-assembled FePc systems (CSAFePc). The chiral peptides behave as spin filters axial ligands of the FePc. One of the main findings is that the peptides' handedness and length in CSAFePc can optimize the kinetics and thermodynamic factors governing ORR. Moreover, the D-enantiomer promotes the highest electrocatalytic activity of FePc for ORR, shifting the onset potential up to 1.01 V vs. RHE in an alkaline medium, a potential close to the reversible potential of the O2 /H2 O couple. Therefore, this work has exciting implications for developing highly efficient and bioinspired catalysts, considering that, in biological organisms, biocatalysts that promote O2 reduction to water comprise L-enantiomers.

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery

BACKGROUND

The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

OBJECTIVE

Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity.

METHODS

We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space.

RESULTS

The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces.

CONCLUSION

The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

Effects of Scutellaria strigillosa Hemsl. extract on HepG2 cell proliferation and apoptosis through binding to aspartate β-hydroxylase

This study aims to examine the impacts of Scutellaria strigillosa Hemsl. (SSH) on the proliferation, apoptosis of human hepatoma cell HepG2 and screen the bioactive components. We found that SSH extract inhibited HepG2 proliferation, arrested cell division prior to S phase. Additionally, SSH extract exposure induced apoptosis, and increased the proportions of late apoptotic cells. Specifically, we focus on the inhibitory effect of SSH extract on aspartate β-hydroxylase, a key therapeutic target of hepatocellular carcinoma closely related with the proliferation and apoptosis of HepG2. We found SSH extract with notable inhibitory activity against aspartate β-hydroxylase, elucidated the main bioactive constituents by HPLC-Q-TOF/MS and Molecular docking analysis. In conclusion, these results provided the antiproliferative and proapoptotic effects of SSH on HepG2 cell, elucidated the main bioactive constituents based on aspartate β-hydroxylase inhibition. These data revealed the potential value of SSH and its bioactive components for the prevention and treatment of liver cancer for the first time.

Multitargeted Molecular Docking and Dynamic Simulation Studies of Bioactive Compounds from Rosmarinus officinalis against Alzheimer's Disease

Alzheimer's disease (AD) has been associated with the hallmark features of cholinergic dysfunction, amyloid beta (Aβ) aggregation and impaired synaptic transmission, which makes the associated proteins, such as β-site amyloid precursor protein cleaving enzyme 1 (BACE I), acetylcholine esterase (AChE) and synapsin I, II and III, major targets for therapeutic intervention. The present study investigated the therapeutic potential of three major phytochemicals of Rosmarinus officinalis, ursolic acid (UA), rosmarinic acid (RA) and carnosic acid (CA), based on their binding affinity with AD-associated proteins. Detailed docking studies were conducted using AutoDock vina followed by molecular dynamic (MD) simulations using Amber 20. The docking analysis of the selected molecules showed the binding energies of their interaction with the target proteins, while MD simulations comprising root mean square deviation (RMSD), root mean square fluctuation (RMSF) and molecular mechanics/generalized born surface area (MM/GBSA) binding free energy calculations were carried out to check the stability of bound complexes. The drug likeness and the pharmacokinetic properties of the selected molecules were also checked through the Lipinski filter and ADMETSAR analysis. All these bioactive compounds demonstrated strong binding affinity with AChE, BACE1 and synapsin I, II and III. The results showed UA and RA to be potential inhibitors of AChE and BACE1, exhibiting binding energies comparable to those of donepezil, used as a positive control. The drug likeness and pharmacokinetic properties of these compounds also demonstrated drug-like characteristics, indicating the need for further in vitro and in vivo investigations to ascertain their therapeutic potential for AD.

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts have been made and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein-ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and exhibited the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors.

Naturally occurring anthraquinones as potential inhibitors of SARS-CoV-2 main protease: an integrated computational study

The novel coronavirus disease (COVID-19) has spread throughout the globe, affecting millions of people. The World Health Organization (WHO) has declared this infectious disease a pandemic. At present, several clinical trials are going on to identify possible drugs for treating this infection. SARS-CoV-2 M^pro is one of the most critical drug targets for the blockage of viral replication. The aim of this study was to identify potential natural anthraquinones that could bind to the active site of SARS-CoV-2 main protease and stop the viral replication. Blind molecular docking studies of 13 anthraquinones and one control drug (Boceprevir) with SARS-CoV-2 M^pro were carried out using the SwissDOCK server, and alterporriol-Q that showed the highest binding affinity towards M^pro were subjected to molecular dynamics simulation studies. This study indicated that several antiviral anthraquinones could prove to be effective inhibitors for SARS-CoV-2 M^pro of COVID-19 as they bind near the active site having the catalytic dyad, HIS41 and CYS145 through non-covalent forces. The anthraquinones showed less inhibitory potential as compared to the FDA-approved drug, boceprevir. Among the anthraquinones studied, alterporriol-Q was found to be the most potent inhibitor of SARS-CoV-2 M^pro. Further, MD simulation studies for M^pro- alterporriol-Q system suggested that alterporriol-Q does not alter the structure of M^pro to a significant extent. Considering the impact of COVID-19, identification of alternate compounds like alterporriol-Q that could inhibit the viral infection will help in accelerating the process of drug discovery.

Emerging Need of Today: Significant Utilization of Various Databases and Softwares in Drug Design and Development

In drug discovery, in silico methods have become a very important part of the process. These approaches impact the entire development process by discovering and identifying new target proteins as well as designing potential ligands with a significant reduction of time and cost. Furthermore, in silico approaches are also preferred because of reduction in the experimental use of animals as; in vivo testing for safer drug design and repositioning of known drugs. Novel software-based discovery and development such as direct/indirect drug design, molecular modelling, docking, screening, drug-receptor interaction, and molecular simulation studies are very important tools for the predictions of ligand-target interaction pattern, pharmacodynamics as well as pharmacokinetic properties of ligands. On the other part, the computational approaches can be numerous, requiring interdisciplinary studies and the application of advanced computer technology to design effective and commercially feasible drugs. This review mainly focuses on the various databases and software used in drug design and development to speed up the process.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Structure-Function Relationship Study of a Secretory Amoebic Phosphatase: A Computational-Experimental Approach

Phosphatases are hydrolytic enzymes that cleave the phosphoester bond of numerous substrates containing phosphorylated residues. The typical classification divides them into acid or alkaline depending on the pH at which they have optimal activity. The histidine phosphatase (HP) superfamily is a large group of functionally diverse enzymes characterized by having an active-site His residue that becomes phosphorylated during catalysis. HP enzymes are relevant biomolecules due to their current and potential application in medicine and biotechnology. Entamoeba histolytica, the causative agent of human amoebiasis, contains a gene (EHI_146950) that encodes a putative secretory acid phosphatase (EhHAPp49), exhibiting sequence similarity to histidine acid phosphatase (HAP)/phytase enzymes, i.e., branch-2 of HP superfamily. To assess whether it has the potential as a biocatalyst in removing phosphate groups from natural substrates, we studied the EhHAPp49 structural and functional features using a computational-experimental approach. Although the combined outcome of computational analyses confirmed its structural similarity with HP branch-2 proteins, the experimental results showed that the recombinant enzyme (rEhHAPp49) has negligible HAP/phytase activity. Nonetheless, results from supplementary activity evaluations revealed that rEhHAPp49 exhibits Mg²⁺-dependent alkaline pyrophosphatase activity. To our knowledge, this study represents the first computational-experimental characterization of EhHAPp49, which offers further insights into the structure-function relationship and the basis for future research.

Structural Analysis of the Regulatory GAF Domains of cGMP Phosphodiesterase Elucidates the Allosteric Communication Pathway

Regulation of photoreceptor phosphodiesterase (PDE6) activity is responsible for the speed, sensitivity, and recovery of the photoresponse during visual signaling in vertebrate photoreceptor cells. It is hypothesized that physiological differences in the light responsiveness of rods and cones may result in part from differences in the structure and regulation of the distinct isoforms of rod and cone PDE6. Although rod and cone PDE6 catalytic subunits share a similar domain organization consisting of tandem GAF domains (GAFa and GAFb) and a catalytic domain, cone PDE6 is a homodimer whereas rod PDE6 consists of two homologous catalytic subunits. Here we provide the x-ray crystal structure of cone GAFab regulatory domain solved at 3.3 Å resolution, in conjunction with chemical cross-linking and mass spectrometric analysis of conformational changes to GAFab induced upon binding of cGMP and the PDE6 inhibitory γ-subunit (Pγ). Ligand-induced changes in cross-linked residues implicate multiple conformational changes in the GAFa and GAFb domains in forming an allosteric communication network. Molecular dynamics simulations of cone GAFab revealed differences in conformational dynamics of the two subunits forming the homodimer and allosteric perturbations on cGMP binding. Cross-linking of Pγ to GAFab in conjunction with solution NMR spectroscopy of isotopically labeled Pγ identified the central polycationic region of Pγ interacting with the GAFb domain. These results provide a mechanistic basis for developing allosteric activators of PDE6 with therapeutic implications for halting the progression of several retinal degenerative diseases.