Molecular Dynamics Free Energy Research Articles

Screening of Optimal Phytoconstituents through in silico Docking, Toxicity, Pharmacokinetic, and Molecular Dynamics Approach for Fighting against Polycystic Ovarian Syndrome.

Polycystic ovarian syndrome (PCOS) is a hormonal disorder caused by excessive secretion of male sex hormones in females. Herbal remedies for PCOS are lightning up as they bypass the adverse effects and are profoundly safe on prolonged usage. The present study included a selection of 34 herbs pursuing biological effects on the uterus, and their major chemical constituents were subjected to a series of in silico techniques using different software. The proteins contributing majorly to the hormonal functions like Human cytochrome P450 CYP17A1 (3RUK), Progesterone (1E3K), and estrogen receptor (1X7R) were selected for the study. Molecular docking studies were performed using AutoDock 1.5.7. The pharmacokinetic properties were predicted using the SwissADME online tool, while toxicity parameters were assessed with OSIRIS toxicity explorer and pkCSM. Molecular dynamics simulations and free energy calculations were performed using the Schrödinger suite. Constituents with a basic steroidal nucleus demonstrated high binding energy values. An analysis of all the in silico techniques showed that Sarsasapogenin from Asparagus racemosus exhibited strong binding energies of -10.88 kcal/mol, -10.51 kcal/mol, and -9.79 kcal/mol with the selected specific proteins. In molecular dynamics simulations, Sarsasapogenin displayed ideal stability, with RMSD fluctuations below 3 Å and RMSF slightly higher than the corresponding peak of apoprotein. Additionally, it showed a favorable druglikeness profile and non-toxic effects across all screened parameters. From the list of the selected constituents, sarsasapogenin was found to be ideal, and further research on it for targeting PCOS is expected to yield promising results.

Development of Promising CDK5 Inhibitors Using Structure‐Based Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Approach

AbstractCancer is highlighted as one of the deadliest diseases globally, with CDK5 identified as a key enzyme in cancer progression. Despite its potential as a therapeutic target, developing CDK5 inhibitors has been challenging. We used multicomplex‐based pharmacophore modeling on CDK5 complexes, identifying hydrophobic groups, hydrogen bond donors, and acceptors as crucial inhibition features. Validated models were used for the virtual screening of drug‐like natural product databases. Thereafter, the screened candidates were selected to study their binding pattern and binding efficiency in the enzyme. Four molecules were shortlisted and analyzed for electrostatic potential (ESP) energy maps. Molecular dynamic simulations and free energy calculations on the docked complexes revealed stable behavior for all, with three (CNP0299652, CNP0362830, and CNP0009633) showing higher Poisson Boltzmann surface area continuum solvation (MM‐PBSA) binding scores than the reference. These candidates demonstrated drug‐like characteristics, crucial amino acid interactions, favorable electron potentials in ESP plots, stable dynamicigher free energy, highlighting their potential as CDK5 inhibitors.

Modulation of ABCG2 Transporter Activity by Ko143 Derivatives.

ABCG2 is a multidrug transporter that protects tissues from xenobiotics, affects drug pharmacokinetics, and contributes to multidrug resistance of cancer cells. Here, we present tetracyclic fumitremorgin C analog Ko143 derivatives, evaluate their in vitro modulation of purified ABCG2, and report four high-resolution cryo-EM structures and computational analyses to elucidate their interactions with ABCG2. We found that Ko143 derivatives that are based on a ring-opened scaffold no longer inhibit ABCG2-mediated transport activity. In contrast, closed-ring, tetracyclic analogs were highly potent inhibitors. Strikingly, the least potent of these compounds, MZ82, bound deeper into the central ABCG2 cavity than the other inhibitors and it led to partial closure of the transmembrane domains and increased flexibility of the nucleotide-binding domains. Minor structural modifications can thus convert a potent inhibitor into a compound that induces conformational changes in ABCG2 similar to those observed during binding of a substrate. Molecular dynamics simulations and free energy binding calculations further supported the correlation between reduced potency and distinct binding pose of the compounds. We introduce the highly potent inhibitor AZ99 that may exhibit improved in vivo stability.

Ab initio molecular dynamics simulation reveals the influence of entropy effect on Co@BEA zeolite-catalyzed dehydrogenation of ethane

The C–H bond activation in alkane dehydrogenation reactions is a key step in determining the reaction rate. To understand the impact of entropy, we performed ab initio static and molecular dynamics free energy simulations of ethane dehydrogenation over Co@BEA zeolite at different temperatures. AIMD simulations showed that a sharp decrease in free energy barrier as temperature increased. Our analysis of the temperature dependence of activation free energies uncovered an unusual entropic effect accompanying the reaction. The unique spatial structures around the Co active site at different temperatures influenced both the extent of charge transfer in the transition state and the arrangement of 3d orbital energy levels. We provided explanations consistent with the principles of thermodynamics and statistical physics. The insights gained at the atomic level have offered a fresh interpretation of the intricate long-range interplay between local chemical reactions and extensive chemical environments.

Deciphering the multi-scale mechanism of herbal phytoconstituents in targeting breast cancer: a computational pharmacological perspective

Breast Cancer (BC) is the most common cause of cancer-associated deaths in females worldwide. Despite advancements in BC treatment driven by extensive characterization of its molecular hallmarks, challenges such as drug resistance, tumor relapse, and metastasis persist. Therefore, there is an urgent need for alternative treatment approaches with multi-modal efficacy to overcome these hurdles. In this context, natural bioactives are increasingly recognized for their pivotal role as anti-cancer compounds. This study focuses on predicting molecular targets for key herbal phytoconstituents—gallic acid, piperine, quercetin, resveratrol, and beta-sitosterol—present in the polyherbal formulation, Krishnadi Churna. Using an in-silico network pharmacology model, key genes were identified and docked against these marker compounds and controls. Mammary carcinoma emerged as the most significant phenotype of the putative targets. Analysis of an online database revealed that out of 135 predicted targets, 134 were mutated in breast cancer patients. Notably, ESR1, CYP19A1, and EGFR were identified as key genes which are known to regulate the BC progression. Docking studies demonstrated that the herbal phytoconstituents had similar or better docking scores than positive controls for these key genes, with convincing protein-ligand interactions confirmed by molecular dynamics simulations, MM/GBSA and free energy landscape (FEL) analysis. Overall, this study highlights the predictive potential of herbal phytoconstituents in targeting BC genes, suggesting their promise as a basis for developing new therapeutic formulations for BC.

In Silico Analysis of Vitamin D Interactions with Aging Proteins: Docking, Molecular Dynamics, and Solvation Free Energy Studies

Aging is a natural process that is also influenced by some factors like the food someone eats, lifestyle decisions, and impacts on general health. Despite the recognized role of nutrition in modulating the molecular and cellular mechanisms underlying aging, there is a lack of comprehensive exploration into potential interventions that can effectively mitigate these effects. In this study, we investigated the potential anti-aging properties of vitamin D by examining its interactions with key molecular targets involved in aging-related pathways. By using molecular docking and dynamics techniques, we evaluate the interactions and stability of vitamins D2 and D3 with key proteins involved in aging pathways, such as SIRT1, mTOR, AMPK, Klotho, AhR, and MAPK. Our results reveal promising binding affinities between vitamin D and SIRT1 forms, with energy values of −48.33 kJ/mol and −45.94 kJ/mol for vitamins D2 and D3, respectively, in aqueous environments. Moreover, molecular dynamics simulations revealed that the vitamin D3–SIRT1 complex exhibited greater stability compared with the vitamin D2–SIRT1 complex. The study calculated the solvation free energy to compare the solubility of vitamins D2 and D3 in water and various organic solvents. Despite their strong interactions with water, both vitamins exhibited low solubility, primarily due to the high energy cost associated with cavity formation in the aqueous environment. Compared with other solvents, water demonstrated particularly low solubility for both vitamins. This suggested that vitamins D2 and D3 preferred binding to aging receptors over dissolving in bulk aqueous environments, supporting their strong therapeutic interactions with these receptors. These findings shed light on the molecular mechanisms underlying vitamin D’s potential anti-aging effects and lay the groundwork for developing nutraceuticals targeting aging and associated diseases. Understanding these mechanisms holds promise for future interventions aimed at promoting healthy aging and enhancing overall well-being.

Comparative Assessment of Water Models in Protein-Glycan Interaction: Insights from Alchemical Free Energy Calculations and Molecular Dynamics Simulations.

Accurate computational simulations of protein-glycan dynamics are crucial for a comprehensive understanding of critical biological mechanisms, including host-pathogen interactions, immune system defenses, and intercellular communication. The accuracy of these simulations, including molecular dynamics (MD) simulation and alchemical free energy calculations, critically relies on the appropriate parameters, including the water model, because of the extensive hydrogen bonding with glycan hydroxyl groups. However, a systematic evaluation of water models' accuracy in simulating protein-glycan interaction at the molecular level is still lacking. In this study, we used full atomistic MD simulations and alchemical absolute binding free energy (ABFE) calculations to investigate the performance of five distinct water models in six protein-glycan complex systems. We evaluated water models' impact on structural dynamics and binding affinity through over 5.8 μs of simulation time per system. Our results reveal that most protein-glycan complexes are stable in the overall structural dynamics regardless of the water model used, while some show obvious fluctuations with specific water models. More importantly, we discover that the stability of the binding motif's conformation is dependent on the water model chosen when its residues form weak hydrogen bonds with the glycan. The water model also influences the conformational stability of the glycan in its bound state according to density functional theory (DFT) calculations. Using alchemical ABFE calculations, we find that the OPC water model exhibits exceptional consistency with experimental binding affinity data, whereas commonly used models such as TIP3P are less accurate. The findings demonstrate how different water models affect protein-glycan interactions and the accuracy of binding affinity calculations, which is crucial in developing therapeutic strategies targeting these interactions.

Structure-Based Discovery of Phytocompounds from Azadirachta indica as Potential Inhibitors of Thioredoxin Glutathione Reductase in Schistosoma mansoni.

Schistosomiasis, a parasitic disease caused by Schistosoma species such as S. haematobium, S. mansoni, and S. japonicum, poses a significant global health burden. The thioredoxin glutathione reductase (TGR) enzyme, crucial for maintaining the parasite's redox balance and preventing oxidative stress, has been identified as a promising target for anti-schistosomal drug development. This study aims to identify potential TGR inhibitors from Azadirachta indica phytochemicals using molecular modeling approaches. We screened 60 compounds derived from A. indica bark and leaves through molecular docking to assess their binding affinity, followed by the evaluation of binding-free energies for the most promising candidates. Drug-likeness and pharmacokinetic properties were assessed, and molecular dynamics simulations were conducted to explore the conformational stability of the protein-ligand complexes. Our findings revealed that several A. indica compounds exhibited significantly lower docking scores (up to -9.669 kcal/mol) compared to the standard drug praziquantel (-4.349 kcal/mol). Notably, Isorhamnetin, Isomargolonone, Nimbaflavone, Quercetin, and Nimbionol demonstrated strong interactions with TGR, although Isorhamnetin showed potential mutagenicity. Further binding free energy calculations and molecular dynamics simulations confirmed the stability of Isomargolonone, Nimbionol, and Quercetin as potential TGR inhibitors. In conclusion, these findings suggest that Isomargolonone, Nimbionol, and Quercetin warrant further experimental validation as promising candidates for anti-schistosomal therapy.

From Bulk to Binding: Decoding the Entry of PET into Hydrolase Binding Pockets.

Plastic-degrading enzymes facilitate the biocatalytic recycling of poly(ethylene terephthalate) (PET), a significant synthetic polymer, and substantial progress has been made in utilizing PET hydrolases for industrial applications. To fully exploit the potential of these enzymes, a deeper mechanistic understanding followed by targeted protein engineering is essential. Through advanced molecular dynamics simulations and free energy analysis methods, we elucidated the complete pathway from the initial binding of two PET hydrolases-the thermophilic leaf-branch compost cutinase (LCC) and polyester hydrolase 1 (PES-H1)-to an amorphous PET substrate, ultimately leading to a PET chain entering the active site in a hydrolyzable conformation. Our findings indicate that initial PET binding is nonspecific and driven by polar and hydrophobic interactions. We demonstrate that the subsequent entry of PET into the active site can occur via one of three key pathways, identifying barriers related to both PET-PET and PET-enzyme interactions, as well as specific residues highlighted through in silico and in vitro mutagenesis. These insights not only enhance our understanding of the mechanisms underlying PET degradation and facilitate the development of targeted enzyme enhancement strategies but also provide a novel framework applicable to enzyme studies across various disciplines.

Screening of DNMT3A inhibitors from phytochemicals using molecular docking and molecular dynamics simulation for their anti-cancer potential

ABSTRACT There is a consensus that epigenomic changes play a significant role in carcinogenesis. The effect of DNA methylation and its increased modulations on gene expression during carcinogenesis is significant for diagnostic and therapeutic purposes. Potential phytochemicals as anticancer approach modulators of epigenetic pathways are the focus of this investigation. Molecular docking studies were conducted to foretell how phytochemicals will interact with DNMT3A. As part of our effort to identify novel anti-cancer treatments, we examined a wide variety of phytochemicals from many chemical classes, including cannabinoids, carotenoids, chalcones, fatty acids, lignans, polysaccharides, saponins, steroids, and tannins. The docking scores for Dihydromyricetin are -10.207 kcal/mol, -9.313 kcal/mol for S-Adenosyl-L-methionine (SAM), and -8.757 kcal/mol for Zeylenol were determined to be superior than to phytochemical inhibitors against DNMT3A in the virtual screening method. Docking scores, hydrogen bond interactions, ADME characteristics, and DFT. Molecular Dynamics Simulation and free energy calculations demonstrated that these compounds bind stably to DNMT3A, potentially inhibiting its activity. Network Pharmacology suggested Dihydromyricetin's specific anticancer potential against cervical cancer. These findings provide insights into protein-Ligand interactions with Dnmt3A and highlights the need for In-vitro validation.

Using a dual immunoinformatics and bioinformatics approach to design a novel and effective multi-epitope vaccine against human torovirus disease

Human Torovirus (HToV), a member of the Coronaviridae family, causes severe enteric diseases with no specific medication available. To develop novel preventative measures, we employed immunoinformatics techniques to design a multi-epitope-based subunit vaccine (HToV-MEV) triggering diverse immune responses. We selected non-allergenic, non-toxic, and antigenic epitopes from structural polyproteins, joined them with suitable linkers, and added an adjuvant 50S ribosomal L7/L12 peptide. The vaccine's solubility score of 0.903678 and physiochemical properties were found effective. Molecular dynamics simulations and free energy calculations revealed strong binding affinity for Toll-like receptor 3 (TLR-3), with a lowest energy score of −303.88, indicating high affinity. In-silico cloning and codon optimization showed significant production potential in E. coli. Immune simulations predicted a human immunological response. Our results are promising, but subsequent in vivo research is recommended. The HToV-MEV vaccine design demonstrates potential for preventing HToV-related diseases, and further investigation is warranted to explore its therapeutic applications.

Unveiling therapeutic biomarkers and druggable targets in ALS: An integrative microarray analysis, molecular docking, and structural dynamic studies

Amyotrophic lateral sclerosis (ALS), commonly known as Lou Gehrig's disease, is a debilitating neurodegenerative disorder characterized by the progressive degeneration of nerve cells in the brain and spinal cord. Despite extensive research, its precise etiology remains elusive, and early diagnosis is challenging due to the absence of specific tests. This study aimed to identify potential blood-based biomarkers for early ALS detection and monitoring using datasets from whole blood samples (GSE112680) and oligodendrocytes, astrocytes, and fibroblasts (GSE87385) obtained from the NCBI-GEO repository. Through bioinformatics analysis, including protein-protein interactions and molecular pathway analyses, we identified differentially expressed genes (DEGs) associated with ALS. Notably, ALS2, ADH7, ALDH8A1, ALDH3B1, ABHD2, ABHD17B, ABHD12, ABHD13, PGAM2, AURKB, ANAPC11, VAPA, UNC45B, and TNNT2 emerged as top-ranked DEGs, implicated in drug metabolism, protein depalmytilation, and the AKT/mTOR signaling pathways. Among these, AurKB established as a potential therapeutic biomarker with relevance to various neurological conditions. Consequently, AurKB was selected for identifying potential therapeutic molecules and utilized for in silico structural characterization studies. Exploration of the IMPATT database led to the discovery of a lead compound similar to Fostamatinib, currently used for AurKB. Initial molecular docking and MMGBSA-based binding energy analysis were followed by molecular dynamics simulation (MDS) and free energy landscape (FEL) analysis to validate the ligand's binding efficacy and understand dynamic processes within the biological system. The identified potential biomarkers and lead molecule provide novel insights into the correlation between blood cell transcripts and ALS pathology, paving the way for blood-based diagnostic tools for early ALS detection and ongoing disease monitoring.

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations.

Active learning (AL) is a specific instance of sequential experimental design and uses machine learning to intelligently choose the next data point or batch of molecular structures to be evaluated. In this sense, it closely mimics the iterative design-make-test-analysis cycle of laboratory experiments to find optimized compounds for a given design task. Here, we describe an AL protocol which combines generative molecular AI, using REINVENT, and physics-based absolute binding free energy molecular dynamics simulation, using ESMACS, to discover new ligands for two different target proteins, 3CLpro and TNKS2. We have deployed our generative active learning (GAL) protocol on Frontier, the world's only exa-scale machine. We show that the protocol can find higher-scoring molecules compared to the baseline, a surrogate ML docking model for 3CLpro and compounds with experimentally determined binding affinities for TNKS2. The ligands found are also chemically diverse and occupy a different chemical space than the baseline. We vary the batch sizes that are put forward for free energy assessment in each GAL cycle to assess the impact on their efficiency on the GAL protocol and recommend their optimal values in different scenarios. Overall, we demonstrate a powerful capability of the combination of physics-based and AI methods which yields effective chemical space sampling at an unprecedented scale and is of immediate and direct relevance to modern, data-driven drug discovery.

In silico analysis of aptamer-RNA conjugate interactions with human transferrin receptor

The human transmembrane protein Transferrin Receptor-1 is regarded as a promising target for the systemic delivery of therapeutic agents, particularly of nucleic acid therapeutics, such as short double stranded RNAs. This ubiquitous receptor is involved in cellular iron uptake, keeping intracellular homeostasis. It is overexpressed in multiple cancer cell types and is internalized via clathrin-mediated endocytosis. In previous studies, a human transferrin receptor-1 RNA aptamer, identified as TR14 ST1–3, was shown to be capable of effectively internalizing into cells in culture and to deliver small, double stranded RNAs in vitro and in vivo, via systemic administration.To understand, at the molecular level, the aptamer binding to the receptor and the impact of conjugation with the therapeutic RNA, a multi-level in silico protocol was employed, including protein-aptamer docking, molecular dynamics simulations and free energy calculations. The competition for the binding pocket, between the aptamer and the natural ligand human Transferrin, was also evaluated.The results show that the aptamer binds to the same region as Transferrin, with residues from the helical domain showing a critical role. Moreover, the conjugation to the therapeutic RNA, was shown not to affect aptamer binding. Overall, this study provides an atomic-level understanding of aptamer association to human Transferrin Receptor-1 and of its conjugation with a short model-therapeutic RNA, providing also important clues for futures studies aiming to deliver other oligonucleotide-based therapeutics via Transferrin Receptor.

Fluorine Domains Induced Ultrahigh Nitrogen Solubility in Ionic Liquids.

Fluorinated ionic liquids (ILs) are well-known as electrolytes in the nitrogen (N2) electroreduction reaction due to their exceptional gas solubility. However, the influence of fluorinated functional group on N2 solvation and solubility enhancement remains unclear. Massive molecular dynamics simulations and free energy perturbation methods are conducted to investigate the N2 solubility in 11 traditional and 9 fluorinated ILs. It shows that the fluorinated IL of 1-Ethyl-3-methylimidazolium tris(pentafluoroethyl) trifluorophosphate ([Emim]FAP) exhibits ultrahigh solubility, 4.844 × 10-3, approximately 118 times higher than that of traditional IL 1-Ethyl-3-methylimidazolium nitrate ([Emim]NO3). Moreover, fluorinated ILs with more than 10 C-F bonds possess higher N2 solubility than others and show an exothermic nature during solvation. As the C-F bonds number in ILs decreases, the N2 solubility decreases significantly and displays the opposite endothermic behavior. To understand the ultrahigh N2 solubility in fluorinated ILs, we propose a concept of fluorine densification energy (FDE), referring to the average strength of interaction between atoms per unit volume in ILs with fluorine domains, demonstrating a linear relationship with C-F bonds. Physically, lower FDE results in lower N2-anion pair dissociation energy and higher free volume, finally enhancing the N2 solubility. Consequently, medium to long alkyl fluorine tails within a polar environment defines a distinct fluorine domain, emphasizing FDE's role in enhancing N2 solubility. Overall, these quantitative results will not only deepen the understanding of N2 solvation in ILs but may also shed light on the rational design of IL-based high-performance N2 capture and conversion technologies.

Unveiling Therapeutic Avenues for Crohn's Disease Management: Exploring Inhibitors for Adherent-Invasive Escherichia coli Propanediol Dehydratase.

Inflammatory Bowel Disease (IBD) encompasses a group of chronic disorders distinguished by inflammation of the gastrointestinal tract. Among these, Crohn's Disease (CD) stands out as a complex and impactful condition due to challenges for both diagnosis and management, making it a cynosure of research. In CD, there is the predominance of proinflammatory bacteria, including the Adherentinvasive Escherichia coli (AIEC) with virulence-associated metabolic enzyme Propanediol Dehydratase (pduC), which has been identified as a therapeutic target for the management of CD. Herein, molecular modeling techniques, including molecular docking, Molecular Mechanics with Generalized Born and Surface Area (MMGBSA), drug-likeness, and pharmacokinetics profiling, were utilized to probe the potentials of eighty antibacterial compounds to serve as inhibitors of pduC. The results of this study led to the identification of five compounds with promising potentials; the results of the molecular docking simulation revealed the compounds as possessing better binding affinities for the target compared to the standard drug (sulfasalazine), while Lipinski's rule of five-based assessment of their drug-likeness properties revealed them as potential oral drugs. MMGBSA free energy calculation and Molecular Dynamics (MD) simulation of the complexes formed a sequel to molecular docking, revealing the compounds as stable binders in the active site of the protein. Ultimately, the results of this study have revealed five compounds to possess the potential to serve as inhibitors of pduC of AIEC. However, experimental studies are still needed to validate the findings of this study.

Advanced sampling simulations of coupled folding and binding of phage P22 N-peptide to boxB RNA

Protein-RNA interactions are crucially important for numerous cellular processes and often involve coupled folding and binding of peptide segments upon association. The Nut-utilization site (N)-protein of bacteriophages contains an N-terminal arginine-rich motif that undergoes such a folding transition upon binding to the boxB RNA hairpin loop target structure. Molecular dynamics free energy simulations were used to calculate the absolute binding free energy of the N-peptide of bacteriophage P22 in complex with the boxB RNA hairpin motif at different salt concentrations and using two different water force field models. We obtained good agreement with experiment also at different salt concentrations for the TIP4P-D water model that has a stabilizing effect on unfolded protein structures. It allowed us to estimate the free energy contribution resulting from restricting the molecules’ spatial and conformational freedom upon binding, which makes a large opposing contribution to binding. In a second set of umbrella sampling simulations to dissociate/associate the complex along a separation coordinate, we analyzed the onset of preorientation of the N-peptide and onset of structure formation relative to the RNA and its dependence on the salt concentration. Peptide orientation and conformational transitions are significantly coupled to the first contact formation between peptide and RNA. The initial contacts are mostly formed between peptide residues and the boxB hairpin loop nucleotides. A complete transition to an α-helical bound peptide conformation occurs only at a late stage of the binding process a few angstroms before the complexed state has been reached. However, the N-peptide orients also at distances beyond the contact distance such that the sizable positive charge points toward the RNA’s center-of-mass. Our result may have important implications for understanding protein- and peptide-RNA complex formation frequently involving coupled folding and association processes.

Computational discovery of tripeptide inhibitors targeting monkeypox virus A42R profilin-like protein

Monkeypox is an infectious disease caused by the monkeypox virus (MPXV), a member of the Orthopoxvirus genus closely related to smallpox. The structure of the A42R profilin-like protein is the first and only available structure among MPXV proteins. Biochemical studies of A42R were conducted in the 1990s and later work also analyzed the protein's function in viral replication in cells. This study aims to screen tripeptides for their potential inhibition of the A42R profilin-like protein using computational methods, with implications for MPXV therapy. A total of 8000 tripeptides underwent molecular docking simulations, resulting in the identification of 20 compounds exhibiting strong binding affinity to A42R. To validate the docking results, molecular dynamics simulations and free energy perturbation calculations were performed. These analyses revealed two tripeptides with sequences TRP-THR-TRP and TRP-TRP-TRP, which displayed robust binding affinity to A42R. Markedly, electrostatic interactions predominated over van der Waals interactions in the binding process between tripeptides and A42R. Three A42R residues, namely Glu9, Ser12, and Arg38, appear to be pivotal in mediating the interaction between A42R and the tripeptide ligands. Notably, tripeptides containing two or three tryptophan residues demonstrate a pronounced binding affinity, with the tripeptide comprising three tryptophan amino acids showing the highest level of affinity. These findings offer valuable insights for the selection of compounds sharing a similar structure and possessing a high affinity for A42R, potentially capable of inhibiting its enzyme activity. The study highlights a structural advantage and paves the way for the development of targeted therapies against MPXV infections.

Crystal structures of coronaviral main proteases in complex with the non-covalent inhibitor X77

Main proteases (Mpros) are a class of conserved cysteine hydrolases among coronaviruses and play a crucial role in viral replication. Therefore, Mpros are ideal targets for the development of pan-coronavirus drugs. X77, previously developed against SARS-CoV Mpro, was repurposed as a non-covalent tight binder inhibitor against SARS-CoV-2 Mpro during COVID-19 pandemic. Many novel inhibitors with favorable efficacy have been discovered using X77 as a reference, suggesting that X77 could be a valuable scaffold for drug design. However, the broad-spectrum performance of X77 and underlying mechanism remain less understood. Here, we reported the crystal structures of Mpros from SARS-CoV-2, SARS-CoV, and MERS-CoV, and several Mpro mutants from SARS-CoV-2 variants bound to X77. A detailed analysis of these structures revealed key structural determinants essential for interaction and elucidated the binding modes of X77 with different coronaviral Mpros. The potencies of X77 against these investigated Mpros were further evaluated through molecular dynamic simulation and binding free energy calculation. These data provide molecular insights into broad-spectrum inhibition against coronaviral Mpros by X77 and the similarities and differences of X77 when bound to various Mpros, which will promote X77-based design of novel antivirals with broad-spectrum efficacy against different coronaviruses and SARS-CoV-2 variants.

Computational investigation of nanoemulsion-released tocopherol for treating cardiovascular diseases: Insights from molecular dynamic simulation and deep learning prediction

This research investigated the potential of tocopherol released from nanoemulsion as a therapeutic agent for cardiovascular diseases (CVD) through comprehensive in-silico site-specific molecular docking analysis and AI- driven Molecular Dynamic Simulation, between tocopherol and seven CVD-related proteins (Angiotensin Converting Enzyme (ACE) (1O8a), Human Angiotensin Receptor (4YAY), P38 Mitogen-Activated Protein Kinase (MAPK) (4DLI), HMG-CoA Reductase (1HW9), β-1-Adrenergic Receptor (2YCW), Human C-reactive Protein (1BO9), and Cyclooxygenase-2 (1CX2)). Our findings revealed significant interactions. α-Tocopherol exhibited strong binding affinities, particularly with the 2YCW protein, evidenced by a Vina score of -11.9 ± 0.86 kcal/mol and an inhibition constant of 1.4 ± 0.12 µM. Extensive hydrophobic interactions and significant hydrogen bonds were identified as critical for the stability of these complexes. Furthermore, Molecular Dynamics (MD) simulations, RMSD, Rg, RMSF, and Free Energy Landscape (FEL) profiles, and a deep convolutional neural network (CNN) used for predicting interaction sites from protein sequences, illustrated that the protein-ligand complexes attained dynamic configuration stability with RMSD values converging around 0.3 nm, underscoring the robustness of tocopherol-protein interactions. The pathway analysis highlights the possibility of tocopherol's involvement in interacting with several key biological pathways, Dilated Cardiomyopathy, Arrhythmogenic Right Ventricular Cardiomyopathy, Hypertrophic Cardiomyopathy, and Apoptosis.This study revealed significant insights into the interactions of Nanoemulsion-released tocopherol with key proteins involved in the development of cardiovascular diseases, aimed at improving cardiovascular health. However, the findings of the study require meticulous experimental study and clinical trials to provide accurate results and validate tocopherol's therapeutic efficacy.