Molecular Dynamics Study of Conformational Changes of Tankyrase 2 Binding Subsites upon Ligand Binding

Deconstruction of Dual-Site Tankyrase Inhibitors Provides Insights into Binding Energetics and Suggests Critical Hotspots for Ligand Optimization

Designing inhibitors is a complex task that requires a deep understanding of protein-ligand interactions and their dynamics. Ligands often interact with multiple binding subsites, with noncovalent interactions affecting binding affinity. Conformational changes and plasticity of both, the ligand and the protein influence binding energetics. We investigated the tankyrase ADP-ribosyltransferase as a promising drug target regulating many cellular pathways. Despite the existence of diverse tankyrase inhibitors, their binding energetics and contributions of flexible cryptic subpockets to binding affinity remain elusive. To examine these aspects, we deconstructed inhibitors to key fragments, dissected their energetic contribution to the affinity, and determined their binding mode by X-ray crystallography. Varying ligand efficiencies of the deconstructed, pocket-binding fragments revealed the cryptic nature of subpockets. These insights enabled us to redesign inhibitors with novel linkers, the observed key area for optimization, increasing the potency in enzymatic and cell-based assays by 7.5-fold and 6.2-fold compared to the parent ligand.

Mapping the distribution and affinities of ligand interaction sites on human serum albumin

Ligands in many instances interact with a protein at multiple sites with a range of affinities. In this study, ligand-protein interaction sites on human serum albumin (HSA) are mapped using the site-identification by ligand competitive saturation (SILCS)-Biologics approach in conjunction with hydrogen-deuterium exchange (HDX)-mass spectrometry (MS) experiments. Ligands studied include known HSA binders, ibuprofen and ketoprofen, and compounds arginine, alanine, sucrose, and trehalose, excipients used in therapeutic formulations of protein-based drugs. In addition, the impact of excipient binding to HSA on its stability is investigated through temperature-ramp stability studies monitoring solution viscosity. For the studied ligands, interactions that correspond to known drug-binding sites (DSs) are identified. These include previously identified ibuprofen and ketoprofen interaction sites as well as additional sites and, in the case of the excipients, the ligands are shown to also bind at previously unidentified interaction sites, termed excipient sites (ESs) with 20 or more sites identified for the studied compounds. HDX-MS titrations were used to determine dissociation constants for a subset of the interaction sites for ibuprofen, ketoprofen, arginine, and sucrose, which exhibited Kd values in the low micromolar to millimolar range in satisfactory agreement with SILCS-Biologics predicted affinities, validating the computational approach to identify both high- and low-affinity interaction sites. The stability studies indicate the excipients offer protection at low excipient/protein ratios up to 66 with destabilization occurring at ratios above 132 with the exception of sucrose at the t0 time point, indicating that the more favorable affinities of sucrose seen in the SILCS-Biologics and HDX-MS analyses contribute to protein stabilization. These results indicate that ligands can bind to large numbers of interaction sites on proteins, with those interactions having implications for the development of formulations for therapeutic proteins.

Direct Determination of Ratios of All Conformations and Their Lifetimes for Small Flexible Molecules from Molecular Dynamics Simulations: 1,3-Propanediol in an Aqueous Environment

For the first time in the course of molecular dynamics modeling of a liquid, the conformations of each of the small flexible molecules present in the system were fixed at short (1 ps) time intervals. This allowed the establishment of the ratios between various individual conformations and their families and determination of the average lifetimes of both individual conformations and families. As an example, data are presented for modeling boxes with different numbers of molecules (800, 2700, and 6400) for an aqueous solution with 1 mol. % 1,3-propanediol at 298.15 K and 1 atm. The results of the conformational analysis turned out to be very close for systems with different numbers of molecules and with different choices of initial conformations. For the systems under investigation, the tTTg conformation, which does not have intramolecular hydrogen bond, predominated (37-39%), and the total fractions of all conformations in the TT family were 74-76%. Only 0.4-0.5% of 1,3-propanediol molecules had conformations with the possible formation of intramolecular hydrogen bond, although the most stable conformers of free 1,3-propanediol molecules exhibit such a bond. The average lifetimes of each individual conformation did not exceed 7 ps in simulated systems, while the maximum lifetimes reached 60 ps. The average lifetimes of the main chain vary from ~110 ps in TT family to ~12 ps in GG' family, in which the conformations tend to have intramolecular hydrogen bonds. It was found that calculations for an individual 1,3-propanediol molecule at the MP2/aug-cc-pVDZ or MP2/aug-cc-pVTZ theoretical levels lead to 22 conformers both in vacuum and by using the PCM model for implicit aqueous solvation (at the MP2/aug-cc-pVDZ level) and that such solvation reduces the energy difference between the conformers.

Structure based exploration of potential lead molecules against the extracellular cysteine protease (EcpA) of Staphylococcus epidermidis: a therapeutic halt

Nosocomial infection caused by Staphylococcus epidermidis is one of the most widely spread diseases affecting the world's population. No strategies have been developed to overcome this infection and inhibit its spread in immunocompromised patients or patients with indwelling medical devices. EcpA is an extracellular cysteine protease protein involved in biofilm formation on medical devices. Thus, blocking this mechanism may be viable for developing a drug against S. epidermidis. The current research aimed to find new, potent inhibitors that could stop the S. epidermidis EcpA protein from functioning. This study attempted to identify the most promising drug candidates using structure-based virtual screening (SBVS) from libraries of natural ligands. The top-scored molecules were shortlisted based on their IC50 values and pharmacophore properties and further validated through density functional theory (DFT) studies. We found five inhibitors using virtual screening, and the results indicate that these drugs had the highest energy binding potential towards the EcpA targets when compared to the reference molecule E-64, a known cysteine protease inhibitor. In order to evaluate the binding conformational stability of protein-ligand complexes, molecular dynamics (MD) simulations were performed in triplicate for 100 ns, revealing the significant stability of anticipated molecules at the docked site. Furthermore, principal component analysis and binding free energy calculations were performed to understand the dynamics and stability of the complexes. The current study indicated that these compounds looked to be suitable novel inhibitors of the EcpA protein and pave the path for further discovery of novel inhibitors of EcpA.Communicated by Ramaswamy H. Sarma.

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

Structural and functional effects of the L84S mutant in the SARS-COV-2 ORF8 dimer based on microsecond molecular dynamics study

The L84S mutation has been observed frequently in the ORF8 protein of SARS-CoV-2, which is an accessory protein involved in various important functions such as virus propagation, pathogenesis, and evading the immune response. However, the specific effects of this mutation on the dimeric structure of ORF8 and its impacts on interactions with host components and immune responses are not well understood. In this study, we performed one microsecond molecular dynamics (MD) simulation and analyzed the dimeric behavior of the L84S and L84A mutants in comparison to the native protein. The MD simulations revealed that both mutations caused changes in the conformation of the ORF8 dimer, influenced protein folding mechanisms, and affected the overall structural stability. In particular, the 73YIDI76 motif has found to be significantly affected by the L84S mutation, leading to structural flexibility in the region connecting the C-terminal β4 and β5 strands. This flexibility might be responsible for virus immune modulation.  The free energy landscape (FEL) and principle component analysis (PCA) have also supported our investigation. Overall, the L84S and L84A mutations affect the ORF8 dimeric interfaces by reducing the frequency of protein-protein interacting residues (Arg52, Lys53, Arg98, Ile104, Arg115, Val117, Asp119, Phe120, and Ile121) in the ORF8 dimer.  Our findings provide detail insights for further research in designing structure-based therapeutics against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Phytochemical Analysis and Evaluation of Antioxidant and Antimicrobial Properties of Essential Oils and Seed Extracts of Anethum graveolens from Southern Morocco: In Vitro and In Silico Approach for a Natural Alternative to Synthetic Preservatives

Anethum graveolens is an aromatic plant traditionally used as an antispasmodic and carminative. The objective of this study is to analyze the chemical composition of the essential oils and extracts obtained from seeds gathered in Errachidia, southern Morocco. Additionally, the antioxidant and antimicrobial properties of these oils and extracts will be evaluated. GC-MS analysis of the EO isolated by hydrodistillation revealed that its main compounds were E-anethole (38.13%), estragole (29.32%), fenchone (17.21%), and α-pinene (7.37%). The phenolic components were extracted using the methods of decoction and Soxhlet. The assay of the phenolic compounds showed that A. graveolens seeds contained considerable amounts of polyphenols, flavonoids, and condensed tannins, with variable levels depending on the extract analyzed. HPLC/UV-ESI-MS analyses performed on the decoction revealed a structural diversity of the molecules present in this extract, the most important of which were umbelliferone (12.35%), 3-hydroxyflavone (11.23%), rosmanol (8.95%), biotin (8.36%), emmotin H (4.91%), and coumarin (4.21%). The antioxidant activity, as determined by three techniques (DPPH•, FRAP, and CAT), demonstrated that the essential oils (EOs) and extracts had a potent capacity to counteract detrimental free radicals, control the generation of reactive oxygen species, and mitigate oxidative damages. The antimicrobial activity of the Eos and extracts was carried out in a liquid medium against five strains (E. cloacae, K. pneumoniae, E. coli, S. aureus, and S. epidermidis) and four candidiasis (C. albicans, C. dubliniensis, C. tropicalis, and C. parapsilosis) and Aspergillus niger. The results showed the effectiveness of the EOs compared to the aqueous, ethanolic, and decoction extracts against most of the microorganisms tested. In addition, the ethanolic extract showed antifungal activity that was distinguished from that of the other extracts. The antimicrobial efficacy of the essential oils under study can primarily be attributed to the synergistic interactions among its three principal constituents (E-anethole, estragole, and fenchone). Furthermore, molecular docking and molecular dynamics simulation results reveal significant interactions and stability between the selected bioactive compounds and different target proteins involved in antimicrobial and antioxidant activities. Compounds like 3-hydroxyflavone, emmotin H, trans-caftaric acid, methyl rosmarinate, 1-caffeoyl-beta-D-glucose, and kaempferol exhibited better binding energies with the explored proteins, indicating their potential as antimicrobial and antioxidant agents. Finally, our findings emphasize the significance of A. graveolens seeds as a promising reservoir of advantageous health compounds that can serve as organic substitutes for the presently employed synthetic preservatives.

Unveiling the Mechanisms of EGCG-p53 Interactions through Molecular Dynamics Simulations

Green tea consumption is associated with protective and preventive effects against various types of cancer. These effects are attributed to polyphenols, particularly epigallocatechin-3-gallate (EGCG). EGCG acts by directly inhibiting tumor suppressor protein p53. The binding mechanism by which EGCG inhibits p53 activity is associated with residues Trp23-Lys24 and Pro47-Thr55 within the p53 N-terminal domain (NTD). However, the structural and thermodynamic aspects of the interaction between EGCG and p53 are poorly understood. Therefore, based on crystallographic data, we combine docking, molecular dynamics (MD) simulations, and molecular mechanics generalized Born surface area approaches to explore the intricacies of the EGCG-p53 binding mechanism. A triplicate microsecond MD simulation for each system is initially performed to capture diverse p53 NTD conformations. From the start, the most populated cluster of the second run (R2-1) stands out due to a unique opening between Trp23 and Trp53. During MD simulations, this conformation allows EGCG to sustain a high level of stability and affinity while interacting with both regions of interest and deepening the binding pocket. Structural analysis emphasizes the significance of pyrogallol motifs in EGCG binding. Therefore, the conformational shift in this gap is pivotal, enabling EGCG to impede p53 interactions and manifest its anticancer properties. These findings enhance the present comprehension of the anticancer properties of green tea polyphenols and pave the way for future therapeutic developments.

Uncovering allostery and regulation in SORCIN through molecular dynamics simulations

Soluble resistance-related calcium-binding protein or Sorcin is an allosteric, calcium-binding Penta-EF hand (PEF) family protein implicated in multi-drug resistant cancers. Sorcin is known to bind chemotherapeutic molecules such as Doxorubicin. This study uses in-silico molecular dynamics simulations to explore the dynamics and allosteric behavior of Sorcin in the context of Ca2+ uptake and Doxorubicin binding. The results show that Ca2+ binding induces large, but reversible conformational changes in the Sorcin structure which manifest as rigid body reorientations that preserve the local secondary structure. A reciprocal allosteric handshake centered around the EF5 hand is found to be key in Sorcin dimer formation and stabilization. Binding of Doxorubicin results in rearrangement of allosteric communities which disrupts long-range allosteric information transfer from the N-terminal domain to the middle lobe. However, this binding does not result in secondary structure destabilization. Sorcin does not appear to have a distinct Ca2+ activated mode of Doxorubicin binding.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening and molecular dynamics studies to explore potential natural inhibitors against 3C protease of foot-and-mouth disease virus

Foot-and-mouth disease (FMD) is a highly infectious animal disease caused by foot-and-mouth disease virus (FMDV) and primarily infects cloven-hoofed animals such as cattle, sheep, goats, and pigs. It has become a significant health concern in global livestock industries because of diverse serotypes, high mutation rates, and contagious nature. There is no specific antiviral treatment available for FMD. Hence, based on the importance of 3C protease in FMDV viral replication and pathogenesis, we have employed a structure-based virtual screening method by targeting 3C protease with a natural compounds dataset (n = 69,040) from the InterBioScreen database. Virtual screening results identified five potential compounds, STOCK1N-62634, STOCK1N-96109, STOCK1N-94672, STOCK1N-89819, and STOCK1N-80570, with a binding affinity of -9.576 kcal/mol, -8.1 kcal/mol, -7.744 kcal/mol, -7.647 kcal/mol, and - 7.778 kcal/mol, respectively. The compounds were further validated through physiochemical properties and density functional theory (DFT). Subsequently, the comparative 300-ns MD simulation of all five complexes exhibited overall structural stability from various MD analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), H-bonds, principal component analysis (PCA), and free energy landscape (FEL). Furthermore, MM-PBSA calculation suggests that all five compounds, particularly STOCK1N-62634, STOCK1N-96109, and STOCK1N-94672, can be considered as potential inhibitors because of their strong binding affinity toward 3C protease. Thus, we hope that these identified compounds can be studied extensively to develop natural therapeutics for the better management of FMD.

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

Applications of Molecular Dynamics Simulations in Drug Discovery

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Enzyme ChE, cholinergic therapy and molecular docking: Significant considerations and future perspectives

Enzyme Che plays an essential role in cholinergic and non-cholinergic functions. It is present in the fertilized/unfertilized eggs and sperm of different species. Inclusion criteria for data collection from electronic databases NCBI and Google Scholar are enzyme AChE/BChE, cholinergic therapy, genomic organization and gene transcription, enzyme structure, biogenesis, transport, processing and localization, molecular signaling and biological function, polymorphism and influencing factors. Enzyme Che acts as a signaling receptor during hematopoiesis, protein adhesion, amyloid fiber formation, neurite outgrowth, bone development, and maturation, explaining the activity out of synaptic neurotransmission. Polymorphism in the Che genes correlates to various diseases and diverse drug responses. In particular, change accompanies cancer, neurodegenerative, and cardiovascular disease. Literature knowledge indicates the importance of Che inhibitors that influence biochemical and molecular pathways in disease treatment, genomic organization, gene transcription, structure, biogenesis, transport, processing, and localization of Che enzyme. Enzyme Che polymorphism changes indicate the possibility of efficient and new inhibitor drug target mechanisms in diverse research areas.

Discovery of Nonretinoid Inhibitors of CRBP1: Structural and Dynamic Insights for Ligand-Binding Mechanisms

The dysregulation of retinoid metabolism has been linked to prevalent ocular diseases including age-related macular degeneration and Stargardt disease. Modulating retinoid metabolism through pharmacological approaches holds promise for the treatment of these eye diseases. Cellular retinol-binding protein 1 (CRBP1) is the primary transporter of all-trans-retinol (atROL) in the eye, and its inhibition has recently been shown to protect mouse retinas from light-induced retinal damage. In this report, we employed high-throughput screening to identify new chemical scaffolds for competitive, nonretinoid inhibitors of CRBP1. To understand the mechanisms of interaction between CRBP1 and these inhibitors, we solved high-resolution X-ray crystal structures of the protein in complex with six selected compounds. By combining protein crystallography with hydrogen/deuterium exchange mass spectrometry, we quantified the conformational changes in CRBP1 caused by different inhibitors and correlated their magnitude with apparent binding affinities. Furthermore, using molecular dynamic simulations, we provided evidence for the functional significance of the "closed" conformation of CRBP1 in retaining ligands within the binding pocket. Collectively, our study outlines the molecular foundations for understanding the mechanism of high-affinity interactions between small molecules and CRBPs, offering a framework for the rational design of improved inhibitors for this class of lipid-binding proteins.

Exploring bixin from Bixa orellana L. seeds: quantification and in silico insights into its anti-cancer potential

Bixin, the key pigment of Bixa orellana L., is an apo-carotenoid found in the seed arils. The present study aimed to quantitatively determine the bixin content of seeds and explore its anti-cancer activity through in silico studies. The bixin content from the seeds of the local genotype, TNMTP8, quantified by RP-HPLC was 4.58 mg per gram. The prediction of pharmacological activity suggested that bixin may serve as a BRAF, MMP9, TNF expression inhibitors, and TP53 expression enhancer. According to molecular docking analysis, bixin interacted with eight different skin cancer targets and had the lowest binding energy compared to the standard drug, 5-fluorouracil. The binding score between bixin and the targets ranged from -4.7 to -8.7 kcal/mol. The targets BRAF and SIRT3 interacted well with bixin, with binding energies as low as -8.3 and -8.7 kcal/mol, respectively. Hence, the dynamic behavior of these two docked complexes throughout a 500 ns trajectory run was investigated further. The Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) values, and total contacts as a function of time recorded during scrutiny suggest that both complexes were stable. This was validated by post-molecular dynamics analysis using Molecular Mechanics Generalized Born Surface Area (MM-GBSA). Principal component analysis (PCA) was used to analyze the significant differences in motion exhibited by BRAF-Bixin and SIRT3-Bixin. The results showed that bixin is a promising source for potential treatment interventions in skin cancer therapies.Communicated by Ramaswamy H. Sarma.

A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules

Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many kinases remains only partly understood. In this study, we describe a computational approach to unlocking qualitative and quantitative kinome-wide binding measurements for structure-based machine learning. Our study has three components: (i) a Kinase Inhibitor Complex (KinCo) data set comprising in silico predicted kinase structures paired with experimental binding constants, (ii) a machine learning loss function that integrates qualitative and quantitative data for model training, and (iii) a structure-based machine learning model trained on KinCo. We show that our approach outperforms methods trained on crystal structures alone in predicting binary and quantitative kinase-compound interaction affinities; relative to structure-free methods, our approach also captures known kinase biochemistry and more successfully generalizes to distant kinase sequences and compound scaffolds.

In Silico Discovery of Small-Molecule Inhibitors Targeting SARS-CoV-2 Main Protease

The COVID-19 pandemic has caused severe health threat globally, and novel SARS-Cov-2 inhibitors are urgently needed for antiviral treatment. The main protease (M^pro) of the virus is one of the most effective and conserved targets for anti-SARS-CoV-2 drug development. In this study, we utilized a molecular docking-based virtual screening approach against the conserved catalytic site to identify small-molecule inhibitors of SARS-CoV-2 M^pro. Further biological evaluation helped us identify two compounds, AF-399/40713777 and AI-942/42301830, with moderate inhibitory activity. Besides that, the in silico data, including molecular dynamics (MD) simulation, binding free energy calculations, and AMDET profiles, suggested that these two hits could serve as the starting point for the future development of COVID-19 intervention treatments.

New molecular insights for 4H-1,2,4-triazole derivatives as inhibitors of tankyrase and Wnt-signaling antagonist: a molecular dynamics simulation study

Tankyrase (TNKS) enzymes remained central biotargets to treat Wnt-driven colorectal cancers. The success of Olaparib posited the druggability of PARP family enzymes depending on their role in tumor proliferation. In this work, an MD-simulation-based comparative assessment of the protein-ligand interactions using the best-docked poses of three selected compounds (two of the designed and previously synthesized molecules obtained through molecular docking and one reported TNKS inhibitor) was performed for a 500 ns period. The PDB:ID-7KKP and 3U9H were selected for TNKS1 and TNKS2, respectively. The Molecular Mechanics Generalized Born Surface Area (MM-GBSA) based binding energy data exhibited stronger binding of compound-15 (average values of -102.92 and -104.32 kcal/mol for TNKS1 and TNKS2, respectively) as compared to compound-22 (average values of -82.99 and -85.68 kcal/mol for TNKS1 and TNKS2, respectively) and the reported compound-32 (average values of -81.89 and -74.43 kcal/mol for TNKS1 and TNKS2, respectively). Compound-15 and compound-22 exhibited comparable or superior binding to both receptors forming stable complexes when compared to that of compound-32 upon examining their MD trajectories. The key contributors were hydrophobic stacking and optimum hydrogen bonding allowing these molecules to occupy the adenosine pocket by interfacing D-loop residues. The results of bond distance analysis, radius of gyration, root mean square deviation, root mean square fluctuation, snapshots at different time intervals, LUMO-HUMO energy differences, electrostatic potential calculations, and binding free energy suggested better binding efficiency for compound-15 to TNKS enzymes. The computed physicochemical and ADMET properties of compound-15 were encouraging and could be explored further for drug development.Communicated by Ramaswamy H. Sarma.

Study of the Molecular Dynamics Stability in the Inhibitory Interaction of Tenofovir Disoproxil Fumarate against CTLA-4 in Chronic Hepatitis B Patients

Background

Tenofovir disoproxil fumarate (TDF) is a first-line nucleotide analog (NA) drug for hepatitis B therapy. Long-term NA therapy increases peripheral T cell levels to enhance antiviral response, while CTLA-4 inhibits the activation.

Objective

This study analyzed the interaction between TDF and CTLA-4 through molecular docking.

Methods

Target protein and ligand data mining were performed, and proteins were prepared by removing water molecules in the Discovery Studio 2019 software. The energy minimization was performed on ligands using Pyrx v.0.9.8 software. Protein-ligand docking was performed using Autodock Vina integrated with Pyrx v.09.8. Meanwhile, the docking of proteins was accomplished using the Haddock server. The BioVia Discovery Studio 2019 software visualized the interaction between the compound and the docked protein. Molecular dynamics simulations were carried out using the YASARA Dynamic program developed by Biosciences GmbH.

Results

TDF ligand has good and stable inhibitory activity against the CTLA-4/B7-1 and CTLA4/B7-2 complexes. TDF docking has been shown to initiate conformational changes, indicating the ligand's inhibitory activity. The significant conformational changes based on superimposition results were shown by the CTLA-4/TDF/B7-2 and CTLA-4/B7-1/TDF complexes. TDF in all ligands undergoes bonding and displacement of binding sites.

Conclusion

Treatment with TDF was predicted to have inhibitory activity against CTLA-4, especially in its complex form with B7-1 and B7-2.

Design of ionic liquids containing glucose and choline as drug carriers, finding the link between QM and MD studies

Designing drug delivery systems for therapeutic compounds whose receptors are located in the cytosol of cells is challenging as a bilayer cell membrane is negatively charged. The newly designed drug delivery systems should assist the mentioned drugs in passing the membrane barriers and achieving their targets. This study concentrated on developing novel ionic liquids (ILs) that interact effectively with cell membranes. These ILs are based on glucose-containing choline and are expected to be non-toxic. The binding energies of the known pharmaceutically active ionic liquids were calculated at the B3LYP/6-311++G(d,p) level in the gas phase and compared with those of our newly designed carbohydrate-based ionic liquids. Subsequently, we employed MD simulations to obtain information about the interactions of these known and designed ILs with the cell membrane. In our approach, we adopted QM and MD studies and illustrated that there could be a link between the QM and MD results.

Fludarabine, a Potential DNA-Dependent RNA Polymerase Inhibitor, as a Prospective Drug against Monkeypox Virus: A Computational Approach

Monkeypox is a zoonotic contagious disease that has recently re-emerged in different countries worldwide. Due to the lack of an effective treatment that eliminates the virus, there is an urgent need to find effective drugs to stop the spread of the multi-country outbreak. The current study aimed to use computational methods to quickly identify potentially effective drugs against the Monkeypox virus (MPXV). Three MPXV proteins were targeted in this study due to their essential role in viral replication (a DNA-Dependent RNA Polymerase subunit (A6R)), a protein involved in cell entry (D8L), and a protein catalyzing the envelopment of intracellular mature virus particles (F13L). We virtually screened a library of 1615 FDA-approved compounds, utilizing different in-silico approaches including computational modeling, molecular docking, molecular dynamic (MD) simulation, and MM-GBSA. The compound Fludarabine was found to have the best docking score (−7.53 kcal/mol) in relation to the MPXV A6R protein. Additionally, Fludarabine showed in-silico activity on the D8L and F13L proteins. During the whole period of the 100 ns MD simulation, the complex of A6R and Fludarabine exhibited the best stability. This stability was reflected in a good score of MM-GBSA, with an average value of −44.62 kcal/mole in a range between −53.26 and −35.49 and a low value of standard deviation (3.76). Furthermore, Fludarabine blocked efficiently the Asn175 residue which has an important role in the attachment of the virus to a host cell. The results of this study recommend more in vitro studies on this compound, as a starting point to develop a novel treatment against MPXV.