Molecular Modeling Research Articles

Exploring biological activities of novel Schiff bases derived from amlodipine and in silico molecular modeling studies

Exploring biological activities of novel Schiff bases derived from amlodipine and <i>in silico</i> molecular modeling studies

SAR Analysis of Novel Coxsackie virus A9 Capsid Binders.

Enterovirus infections are common in humans, yet there are no approved antiviral treatments. In this study we concentrated on inhibition of one of the Enterovirus B (EV-B), namely Coxsackievirus A9 (CVA9), using a combination of medicinal chemistry, virus inhibition assays, structure determination from cryogenic electron microscopy and molecular modeling, to determine the structure activity relationships for a promising class of novel N-phenylbenzylamines. Of the new 29 compounds synthesized, 10 had half maximal effective concentration (EC50) values between 0.64-10.46 μM, and of these, 7 had 50% cytotoxicity concentration (CC50) values higher than 200 μM. In addition, this new series of compounds showed promising physicochemical properties and act through capsid stabilization, preventing capsid expansion and subsequent release of the genome.

Development of new pyrazoles as class I HDAC inhibitors: Synthesis, molecular modeling, and biological characterization in leukemia cells.

Class I histone deacetylases (HDACs) are considered promising targets in current cancer research. To obtain subtype-selective and potent HDAC inhibitors, we used the aminobenzamide scaffold as the zinc-binding group and prepared new derivatives with a pyrazole ring as the linking group. The synthesized compounds were analyzed in vitro using an enzymatic assay against HDAC1, -2, and -3. Compounds 12b, 15b, and 15i were found to be potent HDAC1 inhibitors, also in comparison to the reference compounds entinostat and tacedinaline, with IC50 values of 0.93, 0.22, and 0.68 μM, respectively. The best compounds were measured for their cellular effect and target engagement in acute myeloid leukemia (AML) cells. In addition, we studied the interaction of the compounds with HDAC subtypes using docking and molecular dynamic simulations. In summary, we have developed a new chemotype of HDAC1 inhibitors that can be used for further structure-based optimization.

Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes

BackgroundThe study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors.MethodThree single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained.ResultsThis study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup’s expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures.DiscussionThis study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer.

Molecular Dynamics Simulation Model of Alkali Metal Reduction of Gaseous Halides and Reaction Mechanism Analysis

Molecular Dynamics Simulation Model of Alkali Metal Reduction of Gaseous Halides and Reaction Mechanism Analysis

Mesoscale explorer: Visual exploration of large-scale molecular models.

The advent of cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), coupled with computational modeling, has enabled the creation of integrative 3D models of viruses, bacteria, and cellular organelles. These models, composed of thousands of macromolecules and billions of atoms, have historically posed significant challenges for manipulation and visualization without specialized molecular graphics tools and hardware. With the recent advancements in GPU rendering power and web browser capabilities, it is now feasible to render interactively large molecular scenes directly on the web. In this work, we introduce Mesoscale Explorer, a web application built using the Mol* framework, dedicated to the visualization of large-scale molecular models ranging from viruses to cell organelles. Mesoscale Explorer provides unprecedented access and insight into the molecular fabric of life, enhancing perception, streamlining exploration, and simplifying visualization of diverse data types, showcasing the intricate details of these models with unparalleled clarity.

Квантовые вычисления в медицинской физике: особенности алгоритмов и их перспективы

Background: Quantum computing represents a paradigm leap in computational capabilities, with potential applications across various scientific disciplines. The complicated computations required for imaging, radiation therapy, and molecular modeling in medical physics give novel prospects for quantum computing. Objective: This article investigates the role of quantum computing in medical physics research, with an emphasis on its potential to improve computational efficiency and accuracy and develop novel treatment approaches. Methods: A thorough literature analysis was undertaken to investigate recent advances in quantum computing and their implications in medical physics. Case studies were reviewed to demonstrate practical applications and possible advantages. Theoretical models were tested to forecast future developments. Statistical data from quantum computing implementations were gathered from many sources, particularly on improving processing speed and accuracy in medical physics applications. Results: The findings show that quantum computing can significantly improve the processing speed of complex simulations and imaging techniques, with some quantum algorithms outperforming classical algorithms by up to 100 times. In radiation therapy planning, quantum computing has exhibited a 25% boost in dose distribution calculation precision. Quantum algorithms have lowered calculation times in molecular modeling by about 40%, potentially boosting drug development and customized therapy by 30%. Conclusion: Quantum computing has the potential to alter medical physics by increasing computational capacity, which could lead to advancements in diagnostics, treatment planning, and medication development. The statistical results corroborate the promise of quantum computing in these domains, emphasizing the importance of ongoing interdisciplinary collaboration and research to fully achieve these benefits and address present hurdles in incorporating quantum computing into clinical practice.

Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal–Chelator Complexes

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal–chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug’s efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal–chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal–chelator complexes, with a direct impact for rational drug design of this important class of drugs.

A Transient Iron Carbide Generated by Cyaphide Cleavage.

Despite their potential relevance as molecular models for industrial and biological catalysis, well-defined mononuclear iron carbide complexes are unknown, in part due to the limited number of appropriate C1 synthons. Here, we show the ability of the cyaphide anion (C≡P-) to serve as a C1 source. The high spin (S = 2) cyaphide complex PhB(tBuIm)3Fe-C≡P (PhB(tBuIm)3- = phenyl(tris(3-tert-butylimidazol-2-ylidene)borate) is readily accessed using the new cyaphide transfer reagent [Mg(DippNacNac)(CP)]2 (DippNacNac = CH{C(CH3)N(Dipp)}2 and Dipp = 2,6-di(iso-propyl)phenyl). Phosphorus atom abstraction is effected by the three-coordinate Mo(III) complex Mo(NtBuAr)3 (Ar = 3,5-Me2C6H3), which produces the known phosphide (tBuArN)3Mo≡P along with a transient iron carbide complex PhB(tBuIm)3Fe≡C. Electronic structure calculations reveal that PhB(tBuIm)3Fe≡C adopts a doublet ground state with nonzero spin density on the carbide ligand. While isolation of this complex is thwarted by rapid dimerization to afford the corresponding diiron ethynediyl complex, the carbide can be intercepted by styrene to provide an iron alkylidene.

How Membrane Flexibility Impacts Permeation and Separation of Gas through Nanoporous Graphenes.

In recent years, extensive research has used molecular dynamics simulations to investigate gas separation through nanoporous graphene (NPG) membranes. However, most studies have considered graphene membranes as rigid, overlooking the impact of their inherent flexibility. This study systematically quantifies the effect of graphene flexibility on gas permeation by comparing the diffusion of various gases through flexible and rigid single-layer NPG models. The results demonstrate that flexibility notably increases permeance, particularly for gases with larger molecular diameters/pore size ratios, by allowing gas molecules greater mobility within the pore. Interestingly, the effect of flexibility boils down to the expansion of the average pore size, and the detail of the membrane's vibrational dynamics is of little importance in quantifying permeance. Our work shows that accounting for flexibility in molecular models improves the alignment of simulation results with experimental data, emphasizing the importance of considering membrane flexibility in predictive models of NPG membrane performance.

Exploring protein structural ensembles: Integration of sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling methods.

Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure-function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.

Enhanced Recognition Memory through Dual Modulation of Brain Carbonic Anhydrases and Cholinesterases.

This study introduces a novel multitargeting strategy that combines carbonic anhydrase (CA) activators and cholinesterase (ChE) inhibitors to enhance cognitive functions. A series of tacrine-based derivatives with amine/amino acid moieties were synthesized and evaluated for their dual activity on brain CA isoforms and ChEs (AChE and BChE). Several derivatives, notably compounds 26, 30, 34, and 40, demonstrated potent CA activation, particularly of hCA II and VII, and strong ChE inhibition with subnanomolar to low nanomolar IC50 values. In vivo studies using a mouse model of social recognition memory showed that these derivatives significantly improved memory consolidation at doses 10-100 times lower than the reference compounds (either alone or in combination). Molecular modeling and ADMET predictions elucidated the compound binding modes and confirmed favorable pharmacokinetic and safety profiles. The findings suggest that dual modulation of CA and ChE activities is a promising strategy for treating cognitive deficits associated with neurodegenerative and psychiatric disorders.

Molecular architecture of the assembly of Bacillus spore coat protein GerQ revealed by cryo-EM

Protein filaments are ubiquitous in nature and have diverse biological functions. Cryo-electron microscopy (cryo-EM) enables the determination of atomic structures, even from native samples, and is capable of identifying previously unknown filament species through high-resolution cryo-EM maps. In this study, we determine the structure of an unreported filament species from a cryo-EM dataset collected from Bacillus amyloiquefaciens biofilms. These filaments are composed of GerQ, a spore coat protein known to be involved in Bacillus spore germination. GerQ assembles into a structurally stable architecture consisting of rings containing nine subunits, which stacks to form filaments. Molecular dockings and model predictions suggest that this nine-subunit structure is suitable for binding CwlJ, a protein recruited by GerQ and essential for Ca2+-DPA induced spore germination. While the assembly state of GerQ within the spores and the direct interaction between GerQ and CwlJ have yet to be validated through further experiments, our findings provide valuable insights into the self-assembly of GerQ and enhance our understanding of its role in spore germination.

Investigation of effect of complexing agent and processing method on solubility of Efonidipine

Nanosponges are versatile carriers due to their exceptional capability to improve the solubility of poorly water-soluble drugs, taste masking and enhancement of stability. This study investigates potential of nanosponges, prepared by crosslinking β-cyclodextrin with diphenyl carbonate in 1:4 ratio for enhancing solubility of Efonidipine. Nanosponges were characterized in terms of particle size. Phase solubility studies and solution state interaction studies were employed to understand the nature and strength of drug: nanosponge complexation. Complexes were prepared by kneading method and spray drying. Saturation solubility, in vitro dissolution studies and molecular modeling studies were performed. Complexes were characterized by differential scanning calorimetry (DSC), infrared spectroscopy, X ray powder diffraction (XRPD) and scanning electron microscopy. Phase solubility studies revealed 1:2 complexation between drug and carriers. Saturation solubility studies showed significant solubility increase with both beta CD and nanosponges. The increase was higher in case of spray dried complexes in all media. An 18 fold increase was observed in case of spray dried complexes in distilled water. DSC and PXRD confirmed amorphization of drug in the complexes. Thus solubility enhancement of EFD could be attributed to complexation and amorphization. Molecular modeling studies revealed the mode of entrapment of efonidipine in the carriers. In conclusion the versatility of nanosponges in encapsulating both hydrophilic and hydrophobic drug molecules holds immense promise for personalized medicine and targeted therapy, ultimately leading to improved patient outcomes. Moreover, the study introduces the efficacy of spray drying as a scalable and practical approach to maximize the solubility-enhancing benefits of nanosponges. This research highlights the potential of nanosponges to overcome a fundamental challenge in pharmaceuticals, opening new avenues for drug delivery and therapeutic advancement. Keywords: Nanosponges, β -cyclodextrin, inclusion complexes, solubility enhancement, molecular modelling

Uncovering naringin’s anticancer mechanisms in glioblastoma via molecular docking and network pharmacology approaches

Naringin, a flavonoid, exhibits diverse therapeutic properties and has been proven to exert cytotoxic effects on cancer cells. Nevertheless, the precise mechanism of naringin maintaining its cytotoxic effect on glioblastoma (GBM) remains unknown. Thus, the current study aimed to establish a plausible cellular mechanism for Naringin’s inhibition of GBM. We employed various system biology techniques to forecast the primary targets, including gene ontology and cluster analysis, KEGG enrichment pathway estimation, molecular docking, MD (molecular dynamic) simulation and MMPBSA analysis. Glioblastoma target sequences were obtained via DisGeNet and Therapeutic Target Prediction, aligned with naringin targets, and analyzed for gene enrichment and ontology. Gene enrichment analysis identified the top ten hub genes. Further, molecular docking was conducted on all identified targets. For molecular dynamics modelling, we selected the two complexes that exhibited the most docking affinity and the two most prominent genes of the hub identified through analysis of the enrichment of genes. The PARP1 and ALB1 signalling pathways were found to be the main regulated routes. Naringin exhibited the highest binding potential of − 12.90 kcal/mol with PARP1 (4ZZZ), followed by ABL1 (2ABL), with naringin showing a − 8.4 kcal/mol binding score, as determined by molecular docking. The molecular dynamic approach and MM-PBSA investigation along with PCA study revealed that the complex of Naringin, with 4ZZZ (PARP1) and, 2ABL (ABL1), are highly stable compared to that of imatinib and talazoparib. Analyses of the signalling pathway suggested that naringin may have anticancer effects against GBM by influencing the protein PARP and ALB1 levels. Cytotoxicity assay was performed on two different glioblastoma cell lines C6 and U87MG cells. Naringin demonstrates a higher cytotoxic potency against U87MG human glioblastoma cells compared to C6 rat glioma cells.

Structure- and Cation-Dependent Mechanism of the Interaction of Tricyclic Antidepressants with NMDA Receptor According to Molecular Modeling Data

Structure- and Cation-Dependent Mechanism of the Interaction of Tricyclic Antidepressants with NMDA Receptor According to Molecular Modeling Data

Mei5-Sae3 stabilizes Dmc1 nucleating clusters for efficient Dmc1 assembly on RPA-coated single-stranded DNA.

Interhomolog recombination in meiosis requires a meiosis-specific recombinase, Dmc1. In Saccharomyces cerevisiae, the Mei5-Sae3 complex facilitates the loading of Dmc1 onto the replication protein A (RPA)-coated single-stranded DNA (ssDNA) to form nucleoprotein filaments. In vivo, Dmc1 and Mei5-Sae3 are interdependent in their colocalization on the chromosomes. However, the mechanistic role of Mei5-Sae3 in mediating Dmc1 activity remains unclear. We used single-molecule fluorescence resonance energy transfer and colocalization single-molecule spectroscopy experiments to elucidate how Mei5-Sae3 stimulates Dmc1 assembly on ssDNA and RPA-coated ssDNA. We showed that Mei5-Sae3 stabilized Dmc1 nucleating clusters with two to threemolecules on naked DNA by preferentially reducing Dmc1 dissociation rates. Mei5-Sae3 also stimulated Dmc1 assembly on RPA-coated DNA. Using green fluorescent protein-labeled RPA, we showed the coexistence of an intermediate with Dmc1 and RPA on ssDNA before RPA dissociation. Moreover, the displacement efficiency of RPA depended on Dmc1 concentration, and its dependence was positively correlated with the stability of Dmc1 clusters on short ssDNA. These findings suggest a molecular model that Mei5-Sae3 mediates Dmc1 binding on RPA-coated ssDNA by stabilizing Dmc1 nucleating clusters, thus altering RPA dynamics on DNA to promote RPA dissociation.

Chemical Reaction Enhanced Graph Learning for Molecule Representation.

Molecular representation learning (MRL) models molecules with low-dimensional vectors to support biological and chemical applications. Current methods primarily rely on intrinsic molecular information to learn molecular representations, but they often overlook effectively integrating domain knowledge into MRL. In this paper, we develop a reaction-enhanced graph learning (RXGL) framework for MRL, utilizing chemical reactions as domain knowledge. RXGL introduces dual graph learning modules to model molecule representation. One module employs graph convolutions on molecular graphs to capture molecule structures. The other module constructs a reaction-aware graph from chemical reactions and designs a novel graph attention network on this graph to integrate reaction-level relations into molecular modeling. To refine molecule representations, we design a reaction-based relation learning task, which considers the relations between the reactant and product sides in reactions. In addition, we introduce a cross-view contrastive task to strengthen the cooperative associations between molecular and reaction-aware graph learning. Experiment results show that our RXGL achieves strong performance in various downstream tasks, including product prediction, reaction classification, and molecular property prediction. The code is publicly available at https://github.com/coder-ACAC/RLM. Supplementary data is available at Bioinformatics online.

Enhanced corrosion inhibition performance of 5-amino-3‑bromo-1-methylindazole on copper in sulfuric acid environments

The corrosion inhibitor 5-amino-3‑bromo-1-methylindazole (ABMI) with multiple substituents can effectively protect the copper substrate. According to the electrochemical experiment results, at 298 K, 3 mM ABMI may suppress copper corrosion by 92.1 %. Furthermore, going from 298 K to 308 K raises the temperature at which 3 mM ABMI may continue to suppress corrosion with an efficiency exceeding 90 %. The weight loss test, SEM, AFM, and XRD data all reveal that the presence of ABMI efficiently prevents the corrosion media from dissolving the copper substrate. The ΔGads0 value of ABMI is −32.97 kJ/mol, demonstrating that it works on the metal/solution interface through physical adsorption and chemisorption. The results of molecular dynamics modeling and quantum chemical computation show that ABMI adsorbs to the metal superficies in a nearly parallel way, minimizing the exposure area among the metal superficies with the medium with corrosive substances and providing the best possible protection for copper.

Discovery of novel dihydro-pyrimidine hybrids: insight into the design, synthesis, biological evaluation and absorption, distribution, metabolism and excretion studies.

Aim: By keeping in aspects, the pharmacological potential of heterocyclic compounds, pyrimidine-based compounds were designed, synthesized and evaluated for α-amylase inhibitory potential.Materials & methods: Five new series 1a-l, 2a-d, 3a-d, 4a-d and 5a-d of 1,2,3,4-tetrahydroprimidine-5-carboxylate derivatives were designed by de novo method by taking Alogliptin as reference compound. Here in we describe synthesis and characterization of compounds as potential α-amylase inhibitor.Results: Structure activity relationship (SAR), in vitro analysis and molecular modelling approaches generate compounds 1h, 1i, 1k and 4c as potential lead with good α-amylase inhibitory selection. However, compound 1k failed the criteria of optimization as drug lead by ADME studies while all other compounds showed optimum range for all in silico ADME parameters.Conclusion: Therefore, these compounds can serve as potential lead candidate in developing anti-diabetic therapy.