Molecular Modeling Simulations Research Articles

Computational supported experimental insights in adsorption of Congo Red using ZnO/doped ZnO in aqueous solution

The rapid growth of industrialisation has led to the discharge of harmful effluents into water bodies, severely disrupting the balance of ecosystems. The detection and removal of dyes from wastewater remains a significant challenge. In this study, we report the synthesis of zinc oxide (ZnO) and its doped derivatives through a facile chemical method, followed by comprehensive characterisation and analysis to assess their potential in various applications. The structural properties of the synthesised materials were confirmed by X-ray diffraction (XRD), which verified their crystalline nature and phase purity. Morphological analysis using field emission scanning electron microscopy (FE-SEM) coupled with energy dispersive X-ray analysis (EDAX) revealed well-defined nanostructures and uniform elemental distribution. The adsorption ability of the synthesized adsorbents was investigated through electrochemical methods, cyclic voltammetry and Tafel plots, which demonstrated enhanced conductivity and charge transfer characteristics. Additionally, theoretical studies through molecular modelling and molecular dynamic simulations were employed to elucidate the interactions between Congo red molecules and the surface of the synthesized compounds. Adsorption experiments were conducted to evaluate the efficacy of ZnO and its doped variants as adsorbents. To investigate any structural or functional changes after dye adsorption FTIR and XRD were compared, provided a deeper insight into the adsorption mechanism. This multi-faceted approach highlights the potential of ZnO-based materials for effective wastewater treatment and other environmental applications.

Research on the microscopic mechanism and model applicability differences of CH4 and CO2 adsorption based on molecular dynamics

To verify the accuracy and rationality of the molecular simulation model, the microscopic mechanism of gas adsorption was analyzed and clarified from a microscopic perspective. The effect of molecular models created by direct construction and supercell expansion on the adsorption capacities of CH4 and CO2 has been analyzed. Eight coal-based molecular configurations were created to simulate the isothermal adsorption process of CH4 and CO2 using various coal-based molecular configurations. Using physical experiments, the validity of different construction methods for coal-based molecular structures was verified. The direct construction method was chosen to simulate the effect of various coal-based molecular numbers on the adsorption capacity of CH4 and CO2. The results show that the adsorption capacity of CO2 on coal is greater than that of CH4. The isothermal adsorption curves of CH4 and CO2 in coal-based molecular systems constructed using different methods reveal that the adsorption of methane on coal molecules adheres to the Langmuir monolayer adsorption theory. The adsorption sites for methane increase with the rise in free volume. The structure directly constructed and the supercell expansion directly determines the amount of methane adsorbed by each coal based molecule. The adsorption constants of CH4 and CO2 on the supercell extended structure are more volatile than those on the directly constructed structure, and the structure constructed directly for different coal based molecules is better than the supercell extended structure. The simulation using the direct construction method shows that as the number of coal-based molecules in the molecular configuration increases, The isothermal adsorption quantities of CH4 and CO2 increase linearly. The molecular configuration is not affected by the number of anthracite molecules. Molecular configuration is not affected by the number of anthracite molecules. The rationality of model construction has no obvious relationship with the number of coal based molecules.

Predictive immunoinformatics reveal promising safety and anti-onchocerciasis protective immune response profiles to vaccine candidates (Ov-RAL-2 and Ov-103) in anticipation of phase I clinical trials.

Onchocerciasis (river blindness) is a debilitating tropical disease that causes significant eye and skin damage, afflicting millions worldwide. As global efforts shift from disease management to elimination, vaccines have become crucial supplementary tools. The Onchocerciasis Vaccine for Africa (TOVA) Initiative was established in 2015, to advance at least one vaccine candidate initially targeting onchocerciasis in infants and children below 5 years of age, through Phase I human trials by 2025. Notably, Ov-RAL-2 and Ov-103 antigens have shown great promise during pre-clinical development, however, the overall success rate of vaccine candidates during clinical development remains relatively low due to certain adverse effects and immunogenic limitations. This study, thus, aimed at predicting the safety and immunogenicity of Ov-RAL-2 and Ov-103 potential onchocerciasis vaccine candidates prior to clinical trials. Advanced molecular simulation models and analytical immunoinformatics algorithms were applied to predict potential adverse side effects and efficacy of these antigens in humans. The analyses revealed that both Ov-RAL-2 and Ov-103 demonstrate favourable safety profiles as toxicogenic and allergenic epitopes were found to be absent within each antigen. Also, both antigens were predicted to harbour substantial numbers of a wide range of distinct epitopes (antibodies, cytokines, and T- Cell epitopes) associated with protective immunity against onchocerciasis. In agreement, virtual vaccination simulation forecasted heightened, but sustained levels of primary and secondary protective immune responses to both vaccine candidates over time. Ov-103 was predicted to be non-camouflageable, as it lacked epitopes identical to protein sequences in the human proteome. Indeed, both antigens were able to bind with high affinity and activate the innate immune TLR4 receptor, implying efficient immune recognition. These findings suggest that Ov-RAL-2 and Ov-103 can induce sufficient protective responses through diverse humoral and cellular mechanisms. Overall, our study provides additional layer of evidence for advancing the clinical development of both vaccine candidates against onchocerciasis.

Multilayer Adsorption Characteristics of CO2 in Organic Nanopores with Different Pore Sizes: Molecular Simulation and Ono-Kondo Lattice Model.

Shale contains numerous organic micropores with significant potential for CO2 storage. To precisely evaluate the CO2 storage potential of shale reservoirs, it is essential to accurately quantify the adsorption of CO2 within these pores. This study used Grand Canonical Monte Carlo (GCMC) molecular simulations to analyze the CO2 adsorption behavior in organic micropores of varying sizes. The study clarified the number and width of the CO2 adsorption layers in micropores of various sizes and proposed a method for segmenting the multilayer adsorption structure. Additionally, the classic Ono-Kondo lattice (OK) model was extended to characterize pore-filling adsorption, incorporating solid-gas and gas-gas interactions. Accurate characterization of CO2 multilayer adsorption and precise calculation of CO2 absolute adsorption in micropores were achieved. Results indicate that CO2 exhibits pore-filling adsorption behavior in organic micropores, forming a multilayer adsorption structure governed by the pore size. Following symmetry principles, the adsorption layer structure in organic micropores can be simplified to a maximum of three layers. When only one adsorption layer forms, its width equals the gas-accessible pore size. For two or more layers, the width of the original layer stabilizes as additional layers form. The stable adsorption layer widths, from nearest to farthest from the pore wall, are 0.33, 0.45, and 0.39 nm. The improved OK model accurately describes CO2 excess and absolute adsorption isotherms across different pore sizes and calculates the CO2 density in each adsorption layer, showing high consistency with GCMC simulation results. These findings highlight the importance of understanding the CO2 multilayer adsorption structure for accurately estimating CO2 adsorption in organic micropores.

Computational Studies in Dermo-cosmetics: In silico Discovery of Therapeutic Agents Targeting a Variety of Proteins for Skin Diseases.

Healthy skin is essential for balanced health. Currently, skin diseases are considered a major global health issue, impacting individuals of all ages. Skin conditions can vary broadly, ranging from common issues like acne and eczema to more serious diseases such as psoriasis, melanoma, and other types of skin cancer. In recent years, computational methods have appeared as powerful tools for explaining the lurking mechanisms of skin diseases and the advancement of the discovery regarding updated therapeutics. This review spotlights the notable researches that have been performed in using computational approaches such as virtual screening, molecular modelling, and molecular dynamics simulations to discover potential treatments for dermatological conditions such as eczema, psoriasis, acne vulgaris, skin cancer, and tyrosinase-related disorders. Moreover, using in silico methods, researchers have explored the molecular interactions between cosmetic actives and skin targets, providing insights into the binding affinities, stability, and efficacy of these compounds. This computational exploration allows the identification of potential off-target effects and toxicity profiles, ensuring that only the most promising candidates proceed to clinical testing. In addition, the use of molecular dynamics simulations helps to understand conformational changes and interaction dynamics over time, further refining the selection of effective cosmetic actives. Overall, the integration of computational chemistry into dermo-cosmetic research has immense potential to accelerate the discovery and development of innovative treatments to improve skin health and address dermatological concerns.

Strategic lead compound design and development utilizing computer-aided drug discovery (CADD) to address herbicide-resistant Phalaris minor in wheat fields.

Wheat (Triticum aestivum) is a vital cereal crop and a staple food source worldwide. However, wheat grain productivity has significantly declined as a consequence of infestations by Phalaris minor. Traditional weed control methods have proven inadequate owing to the physiological similarities between P. minor and wheat during early growth stages. Consequently, farmers have turned to herbicides, targeting acetyl-CoA carboxylase (ACCase), acetolactate synthase (ALS) and photosystem II (PSII). Isoproturon targeting PSII was introduced in mid-1970s, to manage P. minor infestations. Despite their effectiveness, the repetitive use of these herbicides has led to the development of herbicide-resistant P. minor biotypes, posing a significant challenge to wheat productivity. To address this issue, there is a pressing need for innovative weed management strategies and the discovery of novel herbicide molecules. The integration of computer-aided drug discovery (CADD) techniques has emerged as a promising approach in herbicide research, that facilitates the identification of herbicide targets and enables the screening of large chemical libraries for potential herbicide-like molecules. By employing techniques such as homology modelling, molecular docking, molecular dynamics simulation and pharmacophore modelling, CADD has become a rapid and cost-effective medium to accelerate the herbicide discovery process significantly. This approach not only reduces the dependency on traditional experimental methods, but also enhances the precision and efficacy of herbicide development. This article underscores the critical role of bioinformatics and CADD in developing next-generation herbicides, offering new hope for sustainable weed management and improved wheat cultivation practices. © 2024 Society of Chemical Industry.

Development of a novel papain gel formulation: Exploring different concentrations for smear-layer deproteinization and enhanced dentin bonding

BackgroundThe self-etch adhesive system modifies but does not completely remove the smear layer, leading to the weakening of the bond strength due to the formation of a hybridized layer. Smear-layer deproteinization with papain enzyme partially removes the smear layer, and increases the bond strength with self-etch adhesive. The aim was to develop a deproteinizing agent with a high papain enzyme concentration to enhance dentin bonding with self-etch adhesives. MethodsPapain enzyme gel formulations (15 and 30 IU/g) were prepared and tested for physical stability, viscosity, pH, homogeneity, and organoleptic properties. Moreover, 64 teeth were used to test the deproteinization efficiency of the formed gel. Fourier transform infrared was used to calculate the ratio of organic to inorganic components of smear-layer after deproteinization with 15 and 30 IU/g papain gel and a 6 IU/g commercial papain gel. Moreover, tensile bond strength was measured after deproteinization and dentin bonding with self-etching adhesive for the same groups. A molecular modeling simulation was also performed to evaluate the protein-protein binding interaction, predict the conformational/orientation patterns, and estimate the binding energies of papain with collagen target protein. ResultsBoth 15 and 30 IU/g gels exhibited similar viscosity, pH, homogeneity, and organoleptic properties. However, after 60 s, the 15 IU/g gel was solid, while the 30 IU/g gel was half-solid. All tested groups decreased the amide:phosphate ratio and increased tensile bond strength. Binding complexes between papain and three deposited collagen-1 structures formed strong binding energies with high negative values and residue-wise binding patterns. ConclusionsThe production of the papain enzyme gel with a concentration of 15 IU/g was successful. In addition, it demonstrated promising results when used as a smear-layer deproteinization agent. Clinical significanceEnzymatic smear-layer deproteinization may improve dentin adhesion, and high concertation papain enzyme gels may improve dentin adhesion with the use of self-etch adhesive

Towards identification of therapeutics against multi-infections and cancers causing Propionibacterium acnes: Molecular modeling and dynamics simulation investigation

Skin conditions related to Propionibacterium acnes are a noteworthy health concern affecting millions globally. However, recent studies suggest that P. acne is also linked to other serious health conditions such as various kinds of cancer such as prostate cancer, breast cancer, and gastric cancer. The P. acnes sialidase enzyme plays an important role in the pathogenesis of this infection by supporting bacterial invasion and adhesion to the host tissues. In the current study, Medicinal Fungi Secondary Metabolites and Therapeutics (MeFSAT) consisting of 1830 compounds were put through comprehensive computational analysis to identify the strongest inhibitors that target the primary pathogenic enzyme that triggers the dermal infection. Upon analyzing docking studies, it is anticipated that most of these compounds are strongly attached to the specific protein vital for bacterial infection based on their binding score (threshold < −7 kcal/mol). The top 3 compounds were selected as MSID000006, MSID000010, and MSID000012 as strong inhibitors based on their binding score. Furthermore, every compound showed an excellent ADMET profile and adhered to Lipinski’s rule. Next, Density Functional Theory (DFT) analysis was carried out to analyze compounds’ optimized structures and electrostatic interactions with high accuracy. Subsequently, molecular dynamics simulations (MDS) were carried out to evaluate compound stability. The particular compounds showed better stability under a dynamic environment. Furthermore, principal component analysis (PCA) and hydrogen bond analysis was carried out to understand the complexity of vast datasets and the stability of the compounds respectively. Moreover, in salt bridge analysis, Asp-Arg were the most observed interactions, suggesting better electrostatic interaction and good stability. Secondary structure analysis was conducted for better comprehension of protein structure. MMPBSA/GBSA analysis was performed to calculate the free energies of simulation trajectories as MSID000006 favorable binding energy as compared to the MSID000010 and MSID000012. WaterSwap validated the MMPBSA findings by calculating the binding free energies of the compounds upon swapping the water molecule with the ligand. In the end, entropy energy was estimated to figure out the disorder in the process. The entropy results showed that MSID00006 has greater stability as compared to MSID000010 due to lesser disorder in the process. Our findings revealed the compounds MSID000006, MSID000010, and MSID000012 could play favorable roles in the management of P. acnes disease by showing their potential as strong inhibitors.

Coarse-grained molecular dynamic model and wettability simulation of graphite materials

Although progress has been made in high-performance computing, there are still limitations on temporal and spatial scales of molecular dynamic calculations. A major issue in molecular dynamic (MD) simulations is the computational cost, and coarse-grained methods can save computational costs and accelerate calculations by reducing the degrees of freedom in the system. This method takes a selected group of representative atoms in the atomic microstructure as a bead and uses the proportional relationship of atomic scale atomic potentials in MD simulation to define the interaction between beads and solve the motion equation of beads. This article proposed a coarse-grained potential for graphite based on the modification of Airebo potential, with an n3: 1 mapping, and established a corresponding n3: 1 coarse-grained model, where one bead represents the number of n3 atoms. The results indicated that the coarse-grained model well reflected the basic thermal and mechanical properties of graphite. In addition, this article also proposed a coarsening method for the Lennard–Jones (L–J) potential function parameters and established a coarse-grained wetting model with n = 2. The results indicate that the coarse-grained wetting model can effectively predict the wetting performance of copper droplets on graphite in only 60% of the time using the all-atom model. The coarsening potential function and model proposed in this article are also applicable to graphite with rough surfaces. It can be anticipated that coarse-grained molecular dynamics methods will have more applications in the future, as they can handle large-scale calculations more quickly.

Synthesis and Molecular Dynamic Simulation of Novel Cationic and Non-cationic Pyrimidine Derivatives as Potential G-quadruplex-ligands.

Drug resistance has been a problem in cancer chemotherapy, which often causes shortterm effectiveness. Further, the literature indicates that telomere G-quadruplex could be a promising anti-cancer target. We synthesized and characterized two new pyrimidine derivatives as ligands for G-quadruplex DNA. The interaction of novel non-cationic and cationic pyrimidine derivatives (3a, b) with G-quadruplex DNA (1k8p and 3qsc) was explored by circular dichroism (CD) and ultraviolet-visible spectroscopy and polyacrylamide gel electrophoresis (PAGE) methods. The antiproliferative activity of desired compounds was evaluated by the MTT assay. Apoptosis induction was assessed by Propidium iodide (P.I.) staining and flow cytometry. Computational molecular modeling (CMM) and molecular dynamics simulation (MD) were studied on the complexes of 1k8p and 3qsc with the compounds. The van der Waals, electrostatic, polar solvation, solventaccessible surface area (SASA), and binding energies were calculated and analyzed. The experimental results confirmed that both compounds 3a and 3b interacted with 1k8p and 3qsc and exerted cytotoxic and proapoptotic effects on cancer cells. The number of hydrogen bonds and the RMSD values increased in the presence of the ligands, indicating stronger binding and suggesting increased structural dynamics. The electrostatic contribution to binding energy was higher for the cationic pyrimidine 3b, indicating more negative binding energies. Both experimental and MD results confirmed that 3b was more prone to form a complex with DNA G-quadruplex (1k8p and 3qsc), inhibit cell growth, and induce apoptosis, compared to the non-cationic pyrimidine 3a.

Analysis of viscoelastic rheological properties and storage stability of solvent cold patching asphalt

Solvent cold patching asphalt (CPA) is commonly mixed with aggregate for rapid pavement pothole repairing in cold and wet seasons, as its ordinary temperature fluidity and gradually increased viscosity. Currently, the evaluation criteria and expectations for the CPA are often related to the modified asphalt without considering the metastable viscoelastic properties of CPA required to ensure the preparation and storage effectiveness. This paper selects diesel oil, kerosene, aromatics solvent oil and isomeric hexadecane four solvents to prepare CPAs, and investigates the viscoelastic properties and storage stability of CPAs with considering the storage time, temperature and solvents incorporation proportion based on penetration test, viscosity test and dynamic shear rheological test. The experimental results show that the optimal dosage of the four diluents is 20%, among which IH-CPA has better storage stability at 30℃, and its 30d viscosity change is the smallest, which is 19.2%. Moreover, the interaction mechanism between solvent and asphalt is explored through molecular simulation model. The research outcome shows that findings from this research can contribute to a better understanding of the metastable structure of CPA, and furnish the high-performance CPA production and storage strategies.

Synthesis, QTAIM, anticancer activity analysis of pyrrole-imidazole/benzimidazole derivatives and investigation of their reactivity properties using DFT calculations and molecular docking

As part of our ongoing investigation into pyrrole derivatives, we here present the synthesis, spectroscopic characterization, QTAIM, reactivity, anticancer activity, and comparative experimental and computational analysis of pyrrole-imidazole ((3l), (3p)) and benzimidazole derivatives ((3h), (3i), (3j), (3k), (3m), (3n), (3o)). All synthesized compounds (3h-3p) have been well characterized by spectroscopic techniques (FT-IR, 1H and 13C NMR, Mass spectrometry). The experimental 1H and 13C NMR chemical shift values of synthesized pyrrole-imidazole (3l, 3p) and benzimidazole (3h-3o) derivatives have been well corroborated with computed chemical shifts. The vibrational analysis for four derivatives of pyrrole-benzimidazole (3m), (3n), (3o), and pyrrole-imidazole (3p) has the suitability for dimer formation in solid state by intermolecular heteronuclear hydrogen bonding (N–H···O) and the interaction energy of dimers are calculated to be 10.88, 11.70, 9.89 and 10.72 kcal/mol, using DFT. After basis-set superposition error (BSSE) correction, the interaction energies of dimers were found to be 11.12, 12.11, 10.13, and 11.52 kcal/mol. Estimated intermolecular H-bond in (3m-3n) compounds were found to be -13.38, -12.86, -11.02, and -11.28 kJ/mol using QTAIM. All the synthetic pyrrole-imidazole (3l, 3p) and benzimidazole (3h-3o) derivatives exhibit strong antileukemia activity in vitro against the proliferative cell line L1210 leukemia cells. A dataset of investigated compounds was subjected for molecular modelling simulation against three distinct proteins (SYK, PI3K, and BTK), which was found to be highly implicated in the favourable interaction between the compounds and their target proteins in every instance. The synthesized pyrrole-imidazole (3l, 3p) and benzimidazole (3h-3o) derivatives shown favourable interactions with their target proteins, with high activity and binding free energy values falling between 15.22 and 13.43 and -7.4 and -9.8 kcal/mol, respectively. The structure of pyrrole-imidazole (3l, 3p) and benzimidazole (3h-3o) derivatives is thoroughly examined, and several parameters are proposed here to improve their anticancer activity.

New Benzylidene‐Triazole Tethered Coumarin Molecular Hybrids: Synthesis, SAR, Molecular Modelling Simulations Targeting Tubulin α/β Dimer and its Antimicrobial Evaluation

AbstractA series of new 7‐{3‐[3‐(Benzylidene‐amino)‐[1,2,4]triazole‐1‐yl]‐propoxy}‐4‐methyl‐chromen‐2‐one molecular hybrids 7(a–k) were synthesized using molecular hybridization approach. The synthesized new benzylidene‐triazole tethered coumarin molecular hybrids were characterized by spectroscopic studies and elemental analysis. The coumarin molecular hybrids have been explored for their antifungal efficacy in terms of tubulin inhibitors using molecular modelling studies against tubulin alphabeta hetero dimer protein. The results clearly demonstrated strong binding affinity of coumarin molecular hybrids with indole substituted and 2,4‐dihydroxy substituted as antagonists with respective binding scores of −10.5 and −10.7 kcal/mol towards tubulin alphabeta hetero dimer protein which was higher in comparison to standards griseofulvin (−8.2 kcal/mol) and fluconazole (−8.1 kcal/mol). In addition, broth dilution method was used to conduct in‐vitro antifungal studies against three fungal strains namely Candida albicans, Aspergillus niger and Aspergillus fumigatus. The results obtained clearly illustrate the antifungal activity of indole and 2,4‐dihydroxy substituted compounds which are consistent with docking simulations. Out of all the synthesized molecular hybrids, indole and 2,4‐dihydroxy substituted compounds depict outstanding antifungal activity with Minimum Inhibitory Concentration (MIC) value of 31.25 μg/ml and 62.5 μg/ml towards C.albicans and with MIC value of 31.25 for both these molecules towards A.niger in each case which is quite higher than griseofulvin and fluconazole respectively. Present work clearly demonstrates the potential efficacy of synthesized indole and 2,4‐dihydroxy substituted coumarin molecular hybrids to be considered as lead compounds for the development of new agents against resistant strains for the treatment of fungal infection.

Clinical and structural insights into the rare but oncogenic HER2-activating missense mutations in non-small cell lung cancer: a retrospective ATLAS cohort study

BackgroundUnlike human epidermal growth factor receptor 2 (HER2) amplification or exon 20 insertions, missense mutations in the extracellular domain (ECD), transmembrane domain (TMD), and intracellular domain (ICD) of the HER2 protein have been implicated as oncogenic in non-small cell lung cancer (NSCLC). However, their molecular subtypes, structural disparities, and clinical responses to current medical treatments, particularly HER2-targeted tyrosine kinase inhibitors (TKIs), remain unclear in NSCLC and warrant investigation.MethodsA real-world observational ATLAS study was conducted to gather and analyze therapeutic outcomes of chemotherapy or TKIs for heterogeneous HER2 missense mutations in NSCLC. Computational models of typical ECD, TMD, and ICD mutations were utilized to explore their structural variances.ResultsWe screened 37 eligible patients with HER2-activating missense mutations, of which 35 patients who had received chemotherapy or HER2-targeted TKIs as first-line therapy were available for response assessment. The median progression-free survival (PFS) for chemotherapy was 4.43 months (95% confidence interval [CI], 3.77–5.10), with an objective response rate (ORR) of 26.1% (6/23) and a disease control rate (DCR) of 17/23 (73.9%). The administration of afatinib, dacomitinib, and pyrotinib, HER2-targeted TKIs, achieved a median PFS of 4.65 months, with an ORR of 33.3% (4/12) and a DCR of 83.3% (10/12). Molecular modeling and computational simulations of ECD, TMD, and ICD mutations revealed their distinct structural characteristics.ConclusionIn comparison to chemotherapy, HER2-targeted TKIs demonstrated similar activity and PFS benefits for HER2-activating missense mutations in NSCLC.

Design and Characterization of Neutral Linker-Based Bis-Intercalator via Computer Simulations: Balancing DNA Binding and Cellular Uptake.

Bis-intercalators refer to a class of chemical compounds known for their unique ability to simultaneously intercalate, or insert, into DNA at two distinct sites. These molecules typically feature two intercalating moieties connected by a linker, allowing them to engage with DNA base pairs at multiple locations. The bis-intercalation phenomenon plays a significant role in altering the DNA structure, affecting its stability, and potentially influencing various cellular processes. These compounds have gained considerable attention in medicinal chemistry and biochemistry due to their potential applications in cancer therapy, where they may interfere with DNA replication and transcription, leading to anticancer effects. Traditionally, these molecules often possess a high positive charge to enhance their affinity for the negatively charged DNA. However, due to a high positive charge, their cellular uptake is compromised, along with their enhanced potential off-target effects. In this study, we utilized bis-intercalator TOTO and replaced the charged linker segment (propane-1,3-diammonium) with a neutral peroxodisulphuric acid linker. Using molecular modeling and computer simulations (500 ns, 3 replicas), we investigated the potential of the designed molecule as a bis-intercalator and compared the properties with the control bis-intercalator bound to DNA. We observed that the designed bis-intercalator exhibited improved DNA binding (as assessed through MM-PBSA and Delphi methods) and membrane translocation permeability. With an overall reduced charge, significantly less off-target binding of the designed molecule is also anticipated. Consequently, bis-intercalators based on peroxodisulphuric linkers can potentially target DNA effectively, and their role in the future design of bis-intercalators is foreseen.

Computational discovery of AKT serine/threonine kinase 1 inhibitors through shape screening for rheumatoid arthritis intervention.

Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

Design, synthesis, biological evaluation, and molecular modeling simulations of new phthalazine-1,4-dione derivatives as anti-Alzheimer's agents.

The development of targeted phthalazine-1,4-dione acetylcholinesterase (AChE) inhibitors for treating Alzheimer's disease involved the synthesis of 32 compounds via a multistage process. Various analytical techniques confirmed the compounds' identities. Thirteen compounds were found to inhibit AChE by more than 50% without affecting butyrylcholinesterase (BChE). Among these, three compounds, 8m, 8n, and 8p, exhibited extraordinary activity similar to donepezil, a reference AChE inhibitor. During enzyme kinetic studies, compound 8n, displaying the highest AChE inhibitory activity, underwent evaluation at three concentrations (2 × IC50, IC50, and IC50/2). Lineweaver-Burk plots indicated mixed inhibition activity for compound 8n against AChE, suggesting a combination of competitive and noncompetitive characteristics. Additionally, effective derivatives 8m, 8n, and 8p exhibited high blood-brain barrier (BBB) permeability in in vitro parallel artificial membrane permeability assay tests. Molecular docking studies revealed that these compounds bind to the enzyme's active site residues in a position similar to donepezil. Molecular dynamic simulations confirmed the stability of the protein-ligand system, and the chemical reactivity characteristics of the compounds were investigated using density functional theory. The compounds' wide energy gaps suggest stability and therapeutic potential. This research represents a significant step toward finding a potential cure for Alzheimer's disease. However, further research and testing are required to determine the compounds' safety and efficacy. The unique structure of phthalazine derivatives makes them suitable for various biological activities, and these compounds show promise for developing effective drugs for treating Alzheimer's disease. Overall, the development of these targeted compounds is a crucial advancement in the search for an effective treatment for Alzheimer's disease.

Mechanism of PARP1 Elongation Reaction Revealed by Molecular Modeling

Poly(ADP-ribose) polymerase 1 (PARP1) plays a major role in the DNA damage repair and transcriptional regulation, and is targeted by a number of clinical inhibitors. Despite this, catalytic mechanism of PARP1 remains largely underexplored because of the complex substrate/product structure. Using molecular modeling and metadynamics simulations we have described in detail elongation of poly(ADP-ribose) chain in the PARP1 active site. It was shown that elongation reaction proceeds via the SN1-like mechanism involving formation of the intermediate furanosyl oxocarbenium ion. Intriguingly, nucleophilic 2′A-OH group of the acceptor substrate can be activated by the general base Glu988 not directly but through the proton relay system including the adjacent 3′A-OH group.

Unveiling the adsorption mechanisms of macrolides by mesoporous carbons through molecular dynamics simulation and multilinear regression modelling

Mesoporous carbon materials (MCs) are known for their high specific surface area and porous structure, rendering them promising candidates for efficient antibiotic adsorption. However, the influence of antibiotic properties and the porous structure of MCs on adsorption remains unclear. This study investigated the adsorption behaviors of ten macrolides (MLs) onto four MCs with porous structures, aiming to understanding the key factors governing their adsorption. The results revealed that the adsorption rate constant (k2) and adsorption constant of Freundlich isotherm (KF) were influenced by the McGowan molar volume of MLs. Molecular dynamics simulations uncovered that a distance of approximately 4 Å between adsorbed MLs from the material surface, suggesting hydrophobic interactions play a crucial role. Moreover, ML diffusion was more favorable on the surface of 10 nm pore compared to 2 nm pores, highlighting the impact of pore size on diffusion. Additionally, the diffusion coefficient of MLs was mainly determined by logKow, emphasizing the significance of hydrophobic interactions in diffusion. Multilinear regression modelling further supports hydrophobic interactions as the main adsorption mechanism for MLs. Overall, this study provides valuable insights into the complex adsorption mechanisms of MLs on MCs, informing the development of more effective antibiotic removal strategies.

Computational Analysis of Albaflavenone Interaction with SlMAPK1 for Drought Resistance in Tomato.

As global agricultural challenges intensify, particularly drought stress, the exploration of innovative strategies for crop resilience has become crucial. This study focuses on the role of the microbial endophyte metabolite Albaflavenone in enhancing drought resistance in tomato (Solanum lycopersicum L.) through the activation of the SlMAPK1 protein in the MAPK pathway. To computationally analyze the interaction between Albaflavenone and SlMAPK1 and to elucidate the potential enhancement of drought tolerance in tomato plants through this interaction. We utilized molecular docking, homology modeling, and molecular dynamics simulations to investigate the binding affinities and interaction dynamics between SlMAPK1 and Albaflavenone. Functional network analysis was employed to examine protein-protein interactions within the MAPK pathway, while the MM-GBSA method was used to calculate binding free energies. Our computational analyses revealed that Albaflavenone exhibited a high binding affinity to SlMAPK1 with a binding energy of - 8.9kcal/mol. Molecular dynamics simulations showed this interaction significantly stabilized SlMAPK1, suggesting enhanced activity. Specifically, the root mean square deviation (RMSD) of the Albaflavenone-SlMAPK1 complex stabilized at around 3.1Å, while the root mean square fluctuations (RMSF) indicated consistent amino acid conformations. Additionally, the radius of gyration (Rg) analysis demonstrated minimal variance, suggesting a compact and stable protein-ligand complex. The significant binding affinity between Albaflavenone and SlMAPK1 highlights the potential of leveraging plant-microbe interactions in developing sustainable agricultural practices. These findings also demonstrate the effectiveness of computational methods in dissecting complex biological interactions, contributing to a deeper understanding of plant resilience strategies against environmental stresses.