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.

Research on prediction of human oral bioavailability of drugs based on improved deep forest.

Human oral bioavailability is a crucial factor in drug discovery. In recent years, researchers have constructed a variety of different prediction models. However, given the limited size of human oral bioavailability data sets, the challenge of making accurate predictions with small sample sizes has become a critical issue in the field. The deep forest model, with its adaptively determinable number of cascade levels, can perform exceptionally well even on small-scale data. However, the original deep forest suffers unbalanced multi-grained scanning process and premature stopping of cascade forest training. In this paper, we propose a human oral bioavailability predict method based on an improved deep forest, called balanced multi-grained scanning mapping cascade forest (bgmc-forest). Firstly, the mordred descriptor method is selected to feature extraction, then enhanced features are obtained by the improved balanced multi-grained scanning, which solves the problem of missing features at both ends. And finally, the prediction results are obtained by feature mapping cascaded forests, which is based on principal component analysis and cascade forests, ensures the effectiveness of the cascade forest. The superiority of the model constructed in this paper is demonstrated through comparative experiments, while the effectiveness of the improved module is verified through ablation experiments. Finally the decision-making process of the model is explained by the shapley additive explanations interpretation algorithm.

Preparation of T8 and double-decker silsesquioxane-based Janus-type molecules: molecular modeling and DFT insights.

We present a methodology for the synthesis of inorganic-organic Janus-type molecules based on mono-T8 and difunctionalized double-decker silsesquioxanes (DDSQs) via hydrosilylation reactions, achieving exceptionally high yields and selectivities. The synthesized compounds were extensively characterized using various spectroscopic techniques, and their sizes and spatial arrangements were predicted through molecular modelling and density functional theory (DFT) calculations. Quantum chemical calculations were employed to examine the interactions among four molecules of the synthesized compounds. These computational results allowed us to determine the propensity for molecular aggregation, identify the functional groups involved in these interactions, and understand the changes in interatomic distances during aggregation. Understanding the aggregation behaviour of silsesquioxane molecules is crucial for tailoring their properties for specific applications, such as nanocomposites, surface coatings, drug delivery systems, and catalysts. Through a combination of experimental and computational approaches, this study provides valuable insights into the design and optimization of silsesquioxane-based Janus-type molecules for enhanced performance across various fields.

Synthesis and Biological Activity of New Hydrazones Based on N-Aminomorpholine.

The data on the synthesis of N-aminomorpholine hydrazones are presented. It is shown that the interaction of N-aminomorpholine with functionally substituted benzaldehydes and 4-pyridinaldehyde in isopropyl alcohol leads to the formation of corresponding hydrazones. The structure of the synthesized compounds was studied by 1H and 13C NMR spectroscopy methods, including the COSY (1H-1H), HMQC (1H-13C) and HMBC (1H-13C) methodologies. The values of chemical shifts, multiplicity, and integral intensity of 1H and 13C signals in one-dimensional NMR spectra were determined. The COSY (1H-1H), HMQC (1H-13C), and HMBC (1H-13C) results revealed homo- and heteronuclear interactions, confirming the structure of the studied compounds. The antiviral, cytotoxic, and antimicrobial activity of some synthesized hydrazones were investigated. It is shown that 2-((morpholinoimino)methyl)benzoic acid has a pronounced viral inhibitory property, comparable in its activity to commercial drugs Tamiflu and Remantadine. A docking study was performed using the influenza virus protein models (1930 Swine H1 Hemagglutinin and Neuraminidase of 1918 H1N1 strain). The potential binding sites that are complementary with 2-((morpholinoimino)methyl)benzoic acid were found.

Evidence for the O-H···O=C Resonance-Assisted Hydrogen Bond in Tropolones and Quantification of its σ- and π-Components Using Molecular Tailoring Approach.

For a series of tropolones, the nature of the intramolecular O-H×××O=C hydrogen bond closing the five-membered quasi-cycle was studied. Enhancement of conjugation in the hydrogen-bonded rotamer was revealed. Quantification of hydrogen bond energy in tropolones via the molecular tailoring approach yields values in the range from 15 to 20 kcal/mol suggesting that the intramolecular interaction in tropolones has nature of the resonance-assisted hydrogen bond. The total resonance-assisted hydrogen bond energy in the tropolones was divided into σ- and π-components. The magnitudes of total energy of resonance-assisted hydrogen bond in the substituted tropolones can be controlled by the electronic properties of the substituents at the tropone ring. In 3-, 4-, and 5-substituted tropolones, the resonance-assisted hydrogen bond energy is raised due to electron-donating substituents and lowered due to electron-withdrawing ones. The opposite trend is observed in 7-substituted tropolones. The size of the π-shares plays a crucial role in establishing the total energy of resonance-assisted hydrogen bond. The reason for the occurrence of a resonance-assisted hydrogen bond in the tropolones is the molecular backbone aromaticity, since, in accordance with the Hückel rule, 10 π-electrons are delocalized.

Advancements in Computational Approaches for Antidiabetic Drug Discovery: A Review.

Diabetes mellitus (DM) manifests as a complex and chronic metabolic disorder, posing a significant threat to global public health and contributing substantially to mortality rates. It is characterized by elevated blood glucose levels or hyperglycemia and requires effective preventive and therapeutic strategies. One promising approach involves targeting the inhibition of α- glucosidase and α-amylase, key enzymes responsible for carbohydrate hydrolysis. Inhibiting these enzymes proves beneficial in reducing postprandial glucose levels and mitigating postprandial hyperglycemia. However, existing antidiabetic medications are associated with undesirable side effects, highlighting the need to develop new molecules with increased efficacy and reduced side effects. Traditional methods for designing such molecules are often lengthy and costly. To address this, computer-based molecular modeling tools offer a promising approach to evaluate the antidiabetic activities of chemical compounds. This review aims to compile information on chemical compounds assessed for their anti-diabetic activities through molecular modeling, with a particular focus on the period from 2020 to 2023.

Biomolecular Interactions and Anticancer Mechanisms of Ru(II)-Arene Complexes of Cinnamaldehyde-Derived Thiosemicarbazone Ligands: Analysis Combining In Silico and In Vitro Approaches.

Our study focuses on synthesizing and exploring the potential of three N-(4) substituted thiosemicarbazones derived from cinnamic aldehyde, alongside their Ru(II)-(η6 -p-cymene)/(η6-benzene) complexes. The synthesized compounds were comprehensively characterized using a range of analytical techniques, including FT-IR, UV-visible spectroscopy, NMR (1H, 13C), and HRMS. We investigated their electronic and physicochemical properties via density functional theory (DFT). X-ray crystal structures validated structural differences identified by DFT. Molecular docking predicted promising bioactivities, supported by experimental observations. Notably, docking with EGFR suggested an inhibitory potential against this cancer-related protein. Spectroscopic titrations revealed significant DNA/BSA binding affinities, particularly with DNA intercalation and BSA hydrophobic interactions. RuPCAM displayed the strongest binding affinity with DNA (Kb = 6.23 × 107 M-1) and BSA (Kb = 9.75 × 105 M-1). Assessed the cytotoxicity of the complexes on cervical cancer cells (HeLa), and breast cancer cells (MCF-7 and MDA-MB 231), revealing remarkable potency. Additionally, selectivity was assessed by examining MCF-10a normal cell lines. The active complexes were found to trigger apoptosis, a vital cellular process crucial for evaluating their potential as anticancer agents utilizing staining assays and flow cytometry analysis. Intriguingly, complexation with Ru(II)-arene precursors significantly amplified the bioactivity of thiosemicarbazones, unveiling promising avenues toward the creation of powerful anticancer agents.

Comparative in silico analysis of CNS-active molecules targeting the blood-brain barrier choline transporter for Alzheimer's disease therapy.

This study investigated the blood‒brain barrier (BBB) permeability of the central nervous system (CNS)-active compounds donepezil (DON), methionine (MET), and memantine (MEM) by employing a comprehensive in silico approach. These compounds are of particular interest for Alzheimer's disease (AD) therapy. Rigid-flexible molecular docking simulations indicated favorable binding affinities of all the compounds with BBB-ChT, with DON exhibiting the highest binding affinity (ΔGbind = -10.26 kcal/mol), predominantly mediated by significant hydrophobic interactions. In silico kinetic profiling suggested the stability of the DON/BBB-ChT complex, with ligand release prompted by conformational changes. 3D molecular alignment corroborated a minor conformational shift for DON in its minimal binding energy pose. Predictions indicated that active transport mechanisms notably enhance the brain distribution of donepezil compared to that of MET and MEM. Additionally, DON and MEM exhibited low mutagenic probabilities, while MET was identified as highly mutagenic. Overall, these findings highlight the potential of donepezil for superior BBB penetration, primarily through active transport mechanisms, underscoring the need for further validation through in vitro and in vivo studies for effective AD treatment. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00245-w.

An automated calculation pipeline for differential pair interaction energies with molecular force fields using the Tinker Molecular Modeling Package.

An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.Scientific ContributionTo our knowledge, there is currently no open computational pipeline for differential pair interaction energies at all. This work aims to contribute an (at least academically available, open) approach based on molecular force fields that provides a robust and efficient computational scheme for their automated calculation for small to medium-sized (organic) molecular dimers. The usefulness of the proposed new calculation scheme is demonstrated for the generation of mesoscopic particles with their mutual repulsive interactions.

Using AlphaFold2 and Molecular Dynamics Simulation to Model Protein Recognition.

In this chapter, we predict the structure of the Arabidopsis receptor-homology-transmembrane-RING-H2 isoform 1 (RMR1) in complex with the C-terminal sorting determinant of cruciferin (CRU1) by AlphaFold2 using the ColabFold web interface and to perform molecular dynamics simulation to probe the dynamics of the predicted structures. Our results predict that the C-terminal carboxylate group of ctVSD of CRU1 is recognized by the conserved Arg89 of the cargo-binding loop of RMR1 and Arg468 of CRU1 by negative charge residues in the cargo-binding pocket of RMR1. The procedures described here are useful for modeling of other protein complexes.

Targeting PERK and GRP78 in colorectal cancer: Genetic insights and novel therapeutic approaches.

Colorectal cancer (CRC) ranks among the leading causes of cancer-related deaths worldwide. Enhancing CRC diagnosis and prognosis requires the development of improved biomarkers and therapeutic targets. Emerging evidence suggests that the unfolded protein response (UPR) plays a pivotal role in CRC progression, presenting new opportunities for diagnosis, treatment, and prevention. This study hypothesizes that genetic variants in endoplasmic reticulum (ER) stress response genes influence CRC susceptibility. We examined the frequencies of SNPs in PERK (rs13045) and GRP78/BiP (rs430397) within a South Iranian cohort. We mapped the cellular and molecular features of PERK and GRP78 genes in colorectal cancer, observing their differential expressions in tumor and metastatic tissues. We constructed co-expression and protein-protein interaction networks and performed gene set enrichment analysis, highlighting autophagy as a significant pathway through KEGG. Furthermore, the study included 64 CRC patients and 60 control subjects. DNA extraction and genotyping were conducted using high-resolution melting (HRM) analysis. Significant differences in PERK and GRP78 expressions were observed between CRC tissues and controls. Variations in PERK and GRP78 genotypes were significantly correlated with CRC risk. Utilizing a Multi-Target Directed Ligands approach, a dual PERK/GRP78 inhibitor was designed and subjected to molecular modeling studies. Docking experiments indicated high-affinity binding between the proposed inhibitor and both genes, PERK and GRP78, suggesting a novel therapy for CRC. These findings highlight the importance of understanding genetic backgrounds in different populations to assess CRC risk. Polymorphisms in UPR signaling pathway elements may serve as potential markers for predicting CRC susceptibility, paving the way for personalized therapeutic strategies.

Development of a novel multi-epitope vaccine for brucellosis prevention.

Brucellosis, a zoonotic disease caused by Brucella, presents a significant threat to both animal and human health. In animals, the disease can lead to infertility, miscarriage, and high fever, while in humans, symptoms may include recurrent fever, fatigue, sweating, hepatosplenomegaly, and joint and muscle pain following infection. Treatment often involves long-term antibiotic therapy, placing a substantial psychological and financial burden on patients. While vaccination is crucial for prevention, current animal vaccines have drawbacks such as residual virulence, and a safe and effective human vaccine is lacking. Hence, the development of a vaccine for brucellosis is imperative. In this study, we utilized bioinformatics methods to design a multi-epitope vaccine targeting Brucella. Targeting Heme transporter BhuA and polysaccharide export protein, we identified antigenic epitopes, including six cytotoxic T lymphocyte (CTL) dominant epitopes, six helper T lymphocyte (HTL) dominant epitopes, one conformation B cell dominant epitope, and three linear B cell dominant epitopes. By linking these epitopes with appropriate linkers and incorporating a Toll-like receptor (TLR) agonist (human beta-defensin-2) and an auxiliary peptide (Pan HLA-DR epitopes), we constructed the multi-epitope vaccine (MEV). The MEV demonstrated high antigenicity, non-toxicity, non-allergenicity, non-human homology, stability, and solubility. Molecular docking analysis and molecular dynamics simulations confirmed the interaction and stability of the MEV with receptors (MHCI, MHCII, TLR4). Codon optimization and in silico cloning validated the translation efficiency and successful expression of MEV in Escherichia coli. Immunological simulations further demonstrated the efficacy of MEV in inducing robust immune responses. In conclusion, our findings suggest that the engineered MEVs have the potential to stimulate both humoral and cellular immune responses, offering valuable insights for the future development of safe and efficient Brucella vaccines.

Computational Methods for the Discovery and Optimization of TAAR1 and TAAR5 Ligands.

G-protein-coupled receptors (GPCRs) represent a family of druggable targets when treating several diseases and continue to be a leading part of the drug discovery process. Trace amine-associated receptors (TAARs) are GPCRs involved in many physiological functions with TAAR1 having important roles within the central nervous system (CNS). By using homology modeling methods, the responsiveness of TAAR1 to endogenous and synthetic ligands has been explored. In addition, the discovery of different chemo-types as selective murine and/or human TAAR1 ligands has helped in the understanding of the species-specificity preferences. The availability of TAAR1-ligand complexes sheds light on how different ligands bind TAAR1. TAAR5 is considered an olfactory receptor but has specific involvement in some brain functions. In this case, the drug discovery effort has been limited. Here, we review the successful computational efforts developed in the search for novel TAAR1 and TAAR5 ligands. A specific focus on applying structure-based and/or ligand-based methods has been done. We also give a perspective of the experimental data available to guide the future drug design of new ligands, probing species-specificity preferences towards more selective ligands. Hints for applying repositioning approaches are also discussed.

In silico development of novel angiotensin-converting-enzyme-I inhibitors by Monte Carlo optimization based QSAR modeling, molecular docking studies and ADMET predictions.

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility.

miRVim: Three-dimensional miRNA Structure Database.

INTRODUCTION: MicroRNAs (miRNAs), a distinct category of non-coding RNAs, exert multifaceted regulatory functions in a variety of organisms, including humans, animals, and plants. The inventory of identified miRNAs stands at approximately 60,000 among all species, and 1,926 in Homo sapiens manifest miRNA expression. METHOD: Their theranostic role has been explored by researchers over the last few decades, positioning them as prominent therapeutic targets as our understanding of RNA targeting advances. However, the limited availability of experimentally determined miRNA structures has constrained drug discovery efforts relying on virtual screening or computational methods, including machine learning and artificial intelligence. RESULTS: To address this lacuna, miRVim has been developed, providing a repository of human miRNA structures derived from both two-dimensional (MXFold2, CentroidFold, and RNAFold) and three-dimensional (RNAComposer and 3dRNA) structure prediction algorithms, in addition to experimentally available structures from the RCSB PDB repository. miRVim contains 13,971 predicted secondary structures and 17,045 predicted three-dimensional structures, filling the gap of unavailability of miRNA structure data bank. This database aims to facilitate computational data analysis for drug discovery, opening new avenues for advancing technologies, such as machine learning-based predictions in the field of RNA biology. CONCLUSION: The publicly accessible structures provided by miRVim, available at https://mirna.in/miRVim, offer a valuable resource for the research community, advancing the field of miRNA-related computational analysis and drug discovery.

Data Mining and in Silico Analysis of Ethiopian Traditional Medicine: Unveiling the Therapeutic Potential of Rumex abyssinicus Jacq.

Multicomponent traditional medicine prescriptions are widely used in Ethiopia for disease treatment. However, inconsistencies across practitioners, cultures, and locations have hindered the development of reliable therapeutic medicines. Systematic analysis of traditional medicine data is crucial for identifying consistent and reliable medicinal materials. In this study, we compiled and analyzed a dataset of 505 prescriptions, encompassing 567 medicinal materials used for treating 106 diseases. Using association rule mining, we identified significant associations between diseases and medicinal materials. Notably, wound healing-the most frequently treated condition-was strongly associated with Rumex abyssinicus Jacq., showing a high support value. This association led to further in silico and network analysis of R. abyssinicus Jacq. compounds, revealing 756 therapeutic targets enriched in various KEGG pathways and biological processes. The Random-Walk with Restart (RWR) algorithm applied to the CODA PPI network identified these targets as linked to diseases such as cancer, inflammation, and metabolic, immune, respiratory, and neurological disorders. Many hub target genes from the PPI network were also directly associated with wound healing, supporting the traditional use of R. abyssinicus Jacq. for treating wounds. In conclusion, this study uncovers significant associations between diseases and medicinal materials in Ethiopian traditional medicine, emphasizing the therapeutic potential of R. abyssinicus Jacq. These findings provide a foundation for further research, including in vitro and in vivo studies, to explore and validate the efficacy of traditional and natural product-derived medicines.

Chemical analogue based drug design for cancer treatment targeting PI3K: integrating machine learning and molecular modeling.

Cancer is a generic term for a group of disorders defined by uncontrolled cell growth and the potential to invade or spread to other parts of the body. Gene and epigenetic alterations disrupt normal cellular control, leading to abnormal cell proliferation, resistance to cell death, blood vessel development, and metastasis (spread to other organs). One of the several routes that play an important role in the development and progression of cancer is the phosphoinositide 3-kinase (PI3K) signaling pathway. Moreover, the gene PIK3CG encodes the catalytic subunit gamma (p110γ) of phosphoinositide 3-kinase (PI3Kγ), a member of the PI3K family. Therefore, in this study, PIK3CG was targeted to inhibit cancer by identifying a novel inhibitor through computational methods. The study screened 1015 chemical fragments against PIK3CG using machine learning-based binding estimation and docking to select the potential compounds. Later, the analogues were generated from the selected hits, and 414 analogues were selected, which were further screened, and as most potential candidates, three compounds were obtained: (a) 84,332, 190,213, and 885,387. The protein-ligand complex's stability and flexibility were then investigated by dynamic modeling. The 100 ns simulation revealed that 885,387 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 885,387 demonstrated a superior binding free energy (ΔG = -18.80 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 885,387 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the PIK3CG target implicated in cancer.

Exploring protein functions from structural flexibility using CABS-flex modeling.

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Regulation of chromatin architecture by protein binding: insights from molecular modeling.

Histone and non-histone proteins play key roles in the activation and repression of genes. In addition to experimental studies of their regulation of gene expression, molecular modeling at the nucleosome, chromatin, and chromosome levels can contribute insights into the molecular mechanisms involved. In this review, we provide an overview for protein-bound chromatin modeling, and describe how our group has integrated protein binding into genome systems across the scales, from all-atom to coarse-grained models, using explicit to implicit descriptions. We describe the associated applications to protein binding effects and biological mechanisms of genome folding and gene regulation. We end by illustrating the application of machine learning tools like AlphaFold2 to proteins relevant to chromatin systems.

Conformational alterations and functional changes of pepsin induced by a novel food supplement tetrahydrocurcumin: Multispectral techniques and computer simulations.

Tetrahydrocurcumin (THC), as a novel food supplement, has generated significant interests for its potential impact on health and nutrition. Pepsin serves as the primary enzyme involved in the digestive mechanism. This research investigated the conformational and functional alterations of pepsin induced by THC using multispectral techniques and computer simulations. The results showed that THC enters the cavity of pepsin, in which hydrophobic forces play a major role. The binding constant is 1.044 × 104 M-1 at 310 K. The upregulation or downregulation effect of THC on pepsin activity depends on its concentration. Molecular docking outcomes indicated that THC was encapsulated by various amino acids and established H-bonds with Tyr189 and Ser294, revealing that hydrogen bonds also contribute to maintaining the stability of THC-pepsin complex. In addition, the altered activity of pepsin may be related to the interaction between THC and the amino acids at the active site (Asp32) according to energy contribution results. 3D fluorescence spectroscopy, CD spectra and molecular dynamic simulations show that THC causes conformational changes in pepsin. The existence of THC makes pepsin structure to be less dense, leading to the decrease of energy traps. This suggests that pepsin becomes conformationally more suitable to bind to THC.