Predicted Binding Energy Research Articles

Engineering inhibitory repeat domains of Pin-II type proteinase inhibitors indicate their high structural-functional tolerance to mutagenesis

Plant proteinase inhibitors (PIs) are critical in defending against biotic stress. Most PIs contain an inhibitory repeat domain (IRD), which serves as the functional component, displaying a high degree of sequence and structural conservation. In this study, we examined the structural and functional resilience of IRDs using a combination of computational modeling and experimental validation. We have taken an evolution-based approach to enhance the PIs effectiveness of two previously identified Capsicum annuum IRDs, IRD4 and IRD10. Through in silico site-saturation mutagenesis of IRD4 and IRD10, we identified key sites associated with enhanced PI activity for targeted mutagenesis. Binding energy predictions for a mutant IRD library, tested against target proteases, suggested that positions R11 and N32 in IRD4 and N32 and H33 in IRD10 were promising candidates for further modification to improve inhibitory potential. Subsequent experimental validation revealed that the mutant proteins IRD4_R11K and IRD4_N32S exhibited stronger chymotrypsin inhibition than the wild-type (WT) IRD4. Similarly, the mutants IRD10_N32S and IRD10_H33 N demonstrated improved trypsin inhibition relative to the WT IRD10. These findings indicate that engineered IRD variants can tolerate structural changes while maintaining or enhancing their inhibitory activity against target proteases. Overall, this study demonstrates the potential of engineering PIs to increase their structural and functional resilience, offering new opportunities for biotechnological applications.

Resistance to Acetyl Coenzyme A Carboxylase (ACCase) Inhibitor in Lolium multiflorum: Effect of Multiple Target-Site Mutations

Italian ryegrass (Lolium multiflorum Lam.) is a persistent weed species that poses significant management challenges in key agricultural crops such as wheat, corn, cotton, and soybean. This study investigated the prevalence of resistance to ACCase inhibitor herbicides, specifically diclofop and pinoxaden, among field-collected Italian ryegrass populations. The survey revealed widespread resistance to diclofop and emerging cross-resistance to pinoxaden. To elucidate the physiological mechanism of ACCase herbicide resistance, we investigated mutations in the carboxyl-transferase (CT) domain of the ACCase enzyme, a critical region for herbicide sensitivity. Using dCAPS assays and CT domain sequencing, several known resistance-conferring mutations were detected in diclofop survivors, including I1781L, W2027C, I2041N, D2078G, and C2088R. Additionally, other mutations such as L1701M, E1874A, N1878H, G1946E/Q, V1992D, and E2039D were identified. To understand the functional role of these mutations in herbicide resistance, homology modeling was performed using AutoDock Vina for selected mutation combinations. The computational analysis revealed that all mutations and their combinations resulted in reduced binding affinity with diclofop and pinoxaden compared to the wild-type ACCase CT domain. Computational binding energy predictions indicated that the G1946E mutation and the L1701M + I1781L + E1874A + N1878H combination exhibited the lowest affinities for diclofop and pinoxaden, respectively. This study provides valuable insights into the molecular basis of ACCase inhibitor resistance in Italian ryegrass. However, further research is needed to validate the functional significance of each new substitution and its combinations in conferring herbicide resistance.

3,5-disubstituted pyridines with potent activity against drug-resistant Mycobacterium tuberculosis clinical isolates.

Aim: We designed and synthesized a series of compounds with a 3,5-disubstituted pyridine moiety and evaluated them against Mycobacterium tuberculosis (Mtb) and drug-resistant Mtb clinical isolates.Methodology: A library of 3,5-disubstituted pyridine was synthesized. The compounds were screened for activity against M. tuberculosis H37Rv. The optimal substitutions needed for the activity were identified through structure-activity relationship (SAR) studies.Results: From the screening studies, compounds 24 and 26 were identified as potent members of this series with Minimum Inhibitory Concentration (MIC) of 1.56μg/ml against M. tuberculosis H37Rv. These compounds did not show any inhibition against a panel of ESKAPE pathogens at >50μg/ml indicating their selective killing of M. tuberculosis H37Rv. Importantly, compound 24 showed a selectivity index of 54.64 against CHO-K1 and 78.26 against VERO cell lines, while compound 26 showed a selectivity index of 108.5 against CHO-K1 and 63.2 against VERO cell lines, respectively. Compound 24 formed a stable complex with the target protein DprE1 with predicted binding energy -8.73kcal/mol and inhibited multidrug-resistant clinical isolate of M. tuberculosis at 6.25μg/ml.Conclusion: This study identified the 3,5-disubstituted pyridine derivative 24 with potent antituberculosis activity and can be taken forward to generate new preclinical candidate.

Prediction of binding energy using machine learning approach

The liquid drop model is an empirical hypothesis established on the idea that nuclei can be thought of as incompressible liquid droplets. The AME2020 dataset was used in this work to determine binding energy using a semi-empirical mass formula and compare it with binding energies predicted by a machine learning algorithm. Random forest regressor, MLPRegressor, and XGBoost models were employed. In terms of accuracy, root mean square error, and mean absolute error, machine learning models performed better than the semi-empirical mass formula. Compared to RFR, XGBoost, and SEMF, MLPRegressor performed better in predicting binding energies for lighter nuclei. Using estimated binding energies, nuclear masses were computed, and it was shown that all three models adequately predicted nuclear masses with minimal error. This finding highlights how machine learning can be applied to nuclear physics to predict various nuclei’s properties.

Data-Driven Design of Single-Atom Electrocatalysts with Intrinsic Descriptors for Carbon Dioxide Reduction Reaction

The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts (SACs) is crucial for catalyzing the CO2 reduction reaction (CO2RR). However, it remains a major challenge. While density-functional theory calculations serve as a powerful tool for catalyst screening, their time-consuming nature poses limitations. This paper presents a machine learning (ML) model based on easily accessible intrinsic descriptors to enable rapid, cost-effective, and high-throughput screening of efficient SACs in complex systems. Our ML model comprehensively captures the influences of interactions between 3 and 5d metal centers and 8 C, N-based coordination environments on CO2RR activity and selectivity. We reveal the electronic origin of the different activity trends observed in early and late transition metals during coordination with N atoms. The extreme gradient boosting regression model shows optimal performance in predicting binding energy and limiting potential for both HCOOH and CO production. We confirm that the product of the electronegativity and the valence electron number of metals, the radius of metals, and the average electronegativity of neighboring coordination atoms are the critical intrinsic factors determining CO2RR activity. Our developed ML models successfully predict several high-performance SACs beyond the existing database, demonstrating their potential applicability to other systems. This work provides insights into the low-cost and rational design of high-performance SACs.

Exploiting the Correlation between the 1s, 2s, and 2p Energies for the Prediction of Core-Level Binding Energies of Si, P, S, and Cl species.

The binding energies (BEs) of the 1s, 2s, and 2p core electrons of third-period elements (Si, P, S, Cl) were calculated using Hartree-Fock (HF) and B3LYP, BH&HLYP, and LC-BOP ΔSCF, and the shifted KS ΔSCF methods. Linear relationships between two BEs were derived and compared with the Auger parameter. The derived lines are essentially parallel, with only the intercepts differing. The difference in intercepts is due to the lack of electron correlation effects in HF and the self-interaction errors (SIEs) of the functional. The slope is the slope of the linear relationship between the chemical shifts. The straight lines between the 2s and 2p BEs also coincided with the Auger parameter lines, which have a slope of 1 by definition and an intercept being the difference between the 2s and 2p BEs. The shifted KS ΔSCF scheme compensates for SIEs, yielding equations that are approximately invariant. The calculated average gaps for the 2s and 2p BEs are 51.21 eV for Si, 57.48 eV for P, 63.85 eV for S, and 70.48 eV for Cl. The straight lines representing the relationships between the BEs of the 1s and 2s and 1s and 2p electrons are also parallel to each other in ΔSCF and converge into a single line in the shifted ΔSCF scheme. However, these lines are steeper than the Auger parameter line. The derived relationships can be used to predict unknown BEs, which we have applied to many molecules. The results are highly accurate, with mean absolute errors (MAEs) of less than 0.2 eV compared to experimental values.

Design, synthesis, molecular docking, dynamics simulations and antiviral activities of quinoline derivatives

The designed and synthesized quinoline derivatives were screened for their anti-RSV, anti-YFV and broad spectrum anti-viral activity. Initially, physicochemical and drug-like properties of all compounds were evaluated then molecular docking and molecular dynamics (MD) simulations were performed on the active site of RSV G (glycoproteins) and Mtase (methyltransferase) enzyme against RSV and YFV respectively. All compounds interacted well with amino acid residues Thr173 (2.85 Å), Ser123 (3.02 Å), Gln89 (3.00 Å), Tyr34 (3.62 Å), Val147 (3.08 Å), Leu46 (3.03 Å), Tyr34 (3.62 Å), Trp96 (2.70 Å) of RSV G protein and with amino acid residues Ile124 (2.94 Å), Phe126 (3.08 Å), Tyr89 (3.77 Å), Arg41 (2.91 Å), Arg37 (2.89 Å), Arg84 (2.97 Å) of the MTase protein, showing their nature as probable inhibitors of RSV and YFV. Antiviral screening revealed that compound 4 was an effective inhibitor against RSV, showing an SI value of 11.6 and an EC50 of 8.6 µg/mL (3.2 × 103 µM), while compound 6 was effective against YFV, with an SI value of 28.5 and an EC50 of 3.5 µg/mL (1.17 × 103 µM). Molecular dynamics parameters, such as RMSD, RMSF, and RGyr, were satisfactory for the 6BLH:4 complex, with a predicted binding energy of -5.64 kcal/mol, which was lower than that of the 6BLH: ribavirin complex (-4.13 kcal/mol). Similarly, the MD parameters for the 3EVA:6 complex were also highly satisfactory. Therefore, compounds 4 and 6 may serve as potential inhibitors against RSV and YFV, respectively.

In silico studies of bis-spiro- and Furano-Labdane diterpenoids from Rydingia persica Scheen (Otostegia persica) as α-glucosidase enzyme inhibitor

The inhibition potential of α-glucosidase enzyme by crude- dichloromethane, methanol, and ethanol -extracts of Rydingia persica were evaluated using colorimetric method. We have isolated four labdane diterpenoids: 15, 16- epoxy-3α, 7β, 9α -trihydroxylabdan-13- (16), 14-dien-6-one (1), 15, 16- epoxy-3α, 7α, 9α -trihydroxylabdan-13- (16), 14-dien-6-one (2), 9, 13, 15, 16-diepoxy- 3α, 7β, 15α (β)- trihydroxy-labdan- 6 one (3, 4) from the most potent enzyme inhibitor fraction; the ethyl acetate soluble part of ethanol extract of the aerial parts of R. persica. The structures of the compounds were elucidated by their 1H and13C NMR and ESIMS spectral data analyses. The enzyme inhibition potential of the compounds was evaluated against acetylcholine esterase (AChE) and α-glucosidase by simulation studies. The predicted binding energy of most diterpenes towards mouse AChE enzyme was low, while the binding energy of diterpenes towards α-glucosidase enzyme was moderate to potent.

In-silico study unveils potential phytocompounds in Andrographis paniculata against E6 protein of the high-risk HPV-16 subtype for cervical cancer therapy

Despite therapeutic advancements, cervical cancer caused by high-risk subtypes of the human papillomavirus (HPV) remains a leading cause of cancer-related deaths among women worldwide. This study aimed to discover potential drug candidates from the Asian medicinal plant Andrographis paniculata, demonstrating efficacy against the E6 protein of high-risk HPV-16 subtype through an in-silico computational approach. The 3D structures of 32 compounds (selected from 42) derived from A. paniculata, exhibiting higher binding affinity, were obtained from the PubChem database. These structures underwent subsequent analysis and screening based on criteria including binding energy, molecular docking, drug likeness and toxicity prediction using computational techniques. Considering the spectrometry, pharmacokinetic properties, docking results, drug likeliness, and toxicological effects, five compounds—stigmasterol, 1H-Indole-3-carboxylic acid, 5-methoxy-, methyl ester (AP7), andrographolide, apigenin and wogonin—were selected as the potential inhibitors against the E6 protein of HPV-16. We also performed 200 ns molecular dynamics simulations of the compounds to analyze their stability and interactions as protein–ligand complexes using imiquimod (CID-57469) as a control. Screened compounds showed favorable characteristics, including stable root mean square deviation values, minimal root mean square fluctuations and consistent radius of gyration values. Intermolecular interactions, such as hydrogen bonds and hydrophobic contacts, were sustained throughout the simulations. The compounds displayed potential affinity, as indicated by negative binding free energy values. Overall, findings of this study suggest that the selected compounds have the potential to act as inhibitors against the E6 protein of HPV-16, offering promising prospects for the treatment and management of CC.

Theoretical predictions of nuclear binding energy for the observed nuclei: the influence of coefficients and terms in a semi-empirical mass formula

The aim of this study is to calculate the nuclear binding energy for the 2457 nuclei which experimentally observed, as tabulated in the atomic mass evaluation AME2020, by using the semi-empirical mass formula based on the renowned liquid drop model (LDM). The Bethe-Weizsäcker (BW) mass formula-four terms (BWMF-4T) is extended to incorporate an additional six terms, resulting in a formula of ten terms. Our findings demonstrate commendable accuracy and reliability, as substantiated by the acceptable level of uncertainty observed when compared them to experimental data and existing models. The influence of coefficients and terms in the semi-empirical mass formula has been determined. All possible combinations of the terms are fitted in turn to the measured nuclear masses, and the outcomes are analyzed in order to reveal correlations and mutual influences between the various terms in all observed mass regions of the periodic table. The fitted surface energy and symmetry coefficients in the semi-empirical mass formula-ten terms (SEMF-10T) are remaining 26.4 and 33.3 MeV respectively. In contrast, coefficients fitted in the classic BW formula with and without one or two additional terms are around 17 and 23 MeV, respectively. The interplay between different terms in this mass formula is found to be significant and interesting.

Robust Prediction of Relative Binding Energies for Protein–Protein Complex Mutations Using Free Energy Perturbation Calculations

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein–protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how Protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

Computational investigation of Y. aloifolia variegate as anti-Human Immunodeficiency Virus (HIV) targeting HIV-1 protease: A multiscale in-silico exploration

IntroductionHuman Immunodeficiency Virus (HIV) is a global challenge for the health sector due to the absence of a definitive cure. More than 38 million people are affected by the disease worldwide, with approximately 1.5 million new cases reported annually. This situation has spurred continued efforts in searching for drug candidates. Meanwhile, rutin, luteolin, and quercetin are compounds known for efficacy in treating infectious diseases. The compounds have also been detected in Yucca aloifolia (絲蘭) variegate L. Therefore, this study aimed to conduct a computational investigation utilizing multiscale in silico exploration to assess the potential of Y. aloifolia as an anti-HIV agent targeting HIV-1 Protease (H1P). MethodsFor the in silico study, the three-dimensional structure of H1P (PDB: 5V4Y) was retrieved and prepared using YASARA Structure. The binding pockets were identified using Cavity Plus server, and ligands were obtained from Y. aloifolia leaves alcohol extract. Furthermore, molecular docking was conducted with YASARA Structure to predict binding energies, followed by QSAR analysis for activity prediction. Density Functional Theory (DFT) analysis was performed to assess stability and reactivity, while toxicity was evaluated using ProTox 3.0. Molecular dynamics (MD) simulation and Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) calculation were also conducted for further analysis. ResultsThe results showed that Ramachandran plot analysis indicated favorable residue distribution based on the evaluation of enzyme preparation quality. Cavity Plus identified potential binding sites, with cavity no.2 showing the highest druggability. Molecular docking showed rutin and isorhamnetin-3-O-rutinoside as top binders to H1P, with favorable binding energies. Moreover, post-docking analysis produced specific interactions between ligands and the receptor. PASS prediction indicated the potential of rutin and isorhamnetin-3-O-rutinoside (narcissin) as H1P inhibitors. DFT analysis assessed stability, showing comparable values for the investigated compounds. Toxicity analysis suggested both compounds to be non-toxic. Finally, MD simulation demonstrated the superior stability and binding affinity of rutin compared to isorhamnetin-3-O-rutinoside and the control drug, grl-09510. DiscussionRutin, hecogenin, and isorhamnetin-3-O-rutinoside from Y. aloifolia (絲蘭) leaves showed potential as H1P inhibitors through in silico study. Docking simulations indicated that rutin had the most favorable binding interactions, while MD simulation showed only the rutin-H1P complex to be stable, signifying the potential for further drug development. Rutin was identified as a promising lead for H1P inhibition due to its strong binding, stability, and predicted safety properties, underscoring the need for wet lab validation.

Combination of Chromatographic Analysis and Chemometric Methods with Bioactivity Evaluation of the Antibacterial Properties of Helichrysum italicum Essential Oil.

Helichrysum italicum (immortelle) essential oil is one of the most popular essential oils worldwide and it has many beneficial properties, including antimicrobial. However, in this plant, the chemical diversity of the essential oil is very pronounced. The aim of this work was to process the GC-MS results of four samples of H. italicum essential oil of Serbian origin by chemometric tools, and evaluate the antimicrobial activity in vitro and in silico. Overall, 47 compounds were identified, the most abundant were γ-curcumene, α-pinene, and ar-curcumene, followed by α-ylangene, neryl acetate, trans-caryophyllene, italicene, α-selinene, limonene, and italidiones. Although the four samples of H. italicum essential oil used in this study were obtained from different producers in Serbia, they belong to the type of essential oil rich in sesquiterpenes (γ-curcumene and ar-curcumene chemotype). In vitro antimicrobial potential showed that five were sensitive among ten strains of tested microorganisms: Staphylococcus aureus, Listeria monocytogenes, Bacillus cereus, Saccharomyces cerevisiae, and Candida albicans. Therefore, these microorganism models were used further for in silico molecular docking through the mechanism of ATP-ase inhibitory activity. Results showed that among all compounds from H. italicum essential oil, neryl acetate has the highest predicted binding energy. Artificial neural network modeling (ANN) showed that two major compounds γ-curcumene and α-pinene, as well as minor compounds such as trans-β-ocimene, terpinolene, terpinene-4-ol, isoitalicene, italicene, cis-α-bergamotene, trans-α-bergamotene, italidiones, trans-β-farnesene, γ-selinene, β-selinene, α-selinene, and guaiol are responsible for the antimicrobial activity of H. italicum essential oil. The results of this study indicate that H. italicum essential oil samples rich in γ-curcumene, α-pinene, and ar-curcumene cultivated in Serbia (Balkan) have antimicrobial potential both in vitro and in silico. In addition, according to ANN modeling, the proportion of neryl acetate and other compounds detected in these samples has the potential to exhibit antimicrobial activity.

In silico exploration of antinociceptive activity of 1,4-benzodiazepines: Molecular docking on α1 A-adrenoceptor, and phosphodiesterase 4

Recently, scientists have established that several benzodiazepines were found to enhance the activation of a cAMP response element pathway by α1A-adrenergic receptors, but this effect was attributed to off-target inhibition of phosphodiesterases 4. The study explores the pain-relief potential of 1,4-benzodiazepines using in silico methods, focusing on their interaction with α1A-adrenoceptors (α1-AR) and phosphodiesterase 4 (PDE4). AutoDock Vina-1.2.5 and Glide (Schrödinger Suite) (2023-2) were used to calculate the binding affinities and determine the features of their interactions by the molecular docking method; PlayMolecule software was used to perform molecular dynamics. Propoxazepam exhibits moderate free binding energy for α1A-adrenoceptors, as indicated by its average molecular mechanics/generalized Born surface area (MMGBSA) and Glide Score values. Compared to propoxazepam, 3-hydroxypropoxazepam has enhanced predicted affinity values for the alpha 1A adrenergic receptor, primarily due to the hydroxyl group, which facilitates the formation of additional hydrogen bonds. Propoxazepam, along with its metabolite 3-hydroxypropoxazepam, demonstrates promising interactions with PDE4A, characterized by notably low predicted free binding energy MMGBSA and strong binding affinity computed via AutoDock Vina. Among other ligands, propoxazepam demonstrates the lowest MMGBSA value with PDE4A (phosphodiesterase 4A). The best predicted binding scores of interaction with phosphodiesterase 4 is observed for propoxazepam with PDE4B (phosphodiesterase 4B) -10.3 kcal/mol, according to AutoDock Vina. Propoxazepam and its derivative 3-hydroxypropoxazepam interact with the active sites of PDE4B and PDE4D (phosphodiesterase 4 B) via a “hydrophobic clamp”, a typical binding mode for PDE inhibitors, which relies on crucial hydrophobic interactions. Binding of propoxazepam and its metabolite 3-hydroxypropoxazepa to PDE4B reduces the fluctuations of M-pocket residues and supports the conclusion that ligand binding stabilizes the protein structure of PDE4B. The MMGBSA method predicts that propoxazepam and 3-hydroxypropoxazepam have the most favourable predicted binding energies with PDE4D (2FMO). Since 1,4-benzodiazepines bind to phosphodiesterase 4 similarly to its inhibitors, this may support the hypothesis that benzodiazepines may affect α1-AR by inhibiting PDE4. The study of the binding mechanisms of 1,4-benzodiazepines with phosphodiesterase 4 and alpha-1A adrenoceptors helps to expand the understanding of the analgesic and anti-inflammatory effect of benzodiazepines associated with these proteins, which can be taken into account in the development of new analgesic and anti-inflammatory agents.

Screening of specific binding peptide for β-lactoglobulin using phage display technology

β-lactoglobulin (β-Lg) is a major food allergen, there is an urgent need to develop a rapid method for detecting β-Lg in order to avoid contact or ingestion by allergic patients. Peptide aptamers have high affinity, specificity, and stability, and have broad prospects in the field of rapid detection. Using β-Lg as the target, this study screened 11 peptides (P1–11) from a phage display library. Using molecular docking technology to predict binding energy and binding mode of proteins and peptides. Select the peptides with the best binding ability to β-Lg (P5, P7, P8) through ELISA. Combining them with whey protein, casein, and bovine serum protein, it was found that P7 has the best specificity for β-Lg, with an inhibition rate of 87.99%. Verified by molecular dynamics that P7 binds well with β-Lg. Therefore, this peptide can be used for the recognition of β-Lg, becoming a new recognition element for detecting β-Lg.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Human UDP-glucuronosyltransferase 1As catalyze aristolochic acid D O-glucuronidation to form a lesser nephrotoxic glucuronide

Ethnopharmacological relevanceAristolochic acids (AAs) are naturally occurring nitro phenanthrene carboxylic acids primarily found in plants of the Aristolochiaceae family. Aristolochic acid D (AAD) is a major constituent in the roots and rhizomes of the Chinese herb Xixin (the roots and rhizomes of Asarum heterotropoides F. Schmidt), which is a key material for preparing a suite of marketed Chinese medicines. Structurally, AAD is nearly identical to the nephrotoxic aristolochic acid I (AAI), with an additional phenolic group at the C-6 site. Although the nephrotoxicity and metabolic pathways of AAI have been well-investigated, the metabolic pathway(s) of AAD in humans and the influence of AAD metabolism on its nephrotoxicity has not been investigated yet. Aim of the studyTo identify the major metabolites of AAD in human tissues and to characterize AAD O-glucuronidation kinetics in different enzyme sources, as well as to explore the influence of AAD O-glucuronidation on its nephrotoxicity. Materials and methodsThe O-glucuronide of AAD was biosynthesized and its chemical structure was fully characterized by both 1H-NMR and 13C-NMR. Reaction phenotyping assays, chemical inhibition assays, and enzyme kinetics analyses were conducted to assess the crucial enzymes involved in AAD O-glucuronidation in humans. Docking simulations were performed to mimic the catalytic conformations of AAD in human UDP-glucuronosyltransferases (UGTs), while the predicted binding energies and distances between the deprotonated C-6 phenolic group of AAD and the glucuronyl moiety of UDPGA in each tested human UGT isoenzyme were measured. The mitochondrial membrane potentials (MMP) and reactive oxygen species (ROS) levels in HK-2 cells treated with either AAI, or AAD, or AAD O-glucuronide were tested, to elucidate the impact of O-glucuronidation on the nephrotoxicity of AAD. ResultsAAD could be rapidly metabolized in human liver and intestinal microsomes (HLM and HIM, respectively) to form a mono-glucuronide, which was purified and fully characterized as AAD-6-O-β-D-glucuronide (AADG) by NMR. UGT1A1 was the predominant enzyme responsible for AAD-6-O-glucuronidation, while UGT1A9 contributed to a lesser extent. AAD-6-O-glucuronidation in HLM, HIM, UGT1A1 and UGT1A9 followed Michaelis-Menten kinetics, with the Km values of 4.27 μM, 9.05 μM, 3.87 μM, and 7.00 μM, respectively. Docking simulations suggested that AAD was accessible to the catalytic cavity of UGT1A1 or UGT1A9 and formed catalytic conformations. Further investigations showed that both AAI and AAD could trigger the elevated intracellular ROS levels and induce mitochondrial dysfunction and in HK-2 cells, but AADG was hardly to trigger ROS accumulation and mitochondrial dysfunction. ConclusionCollectively, UGT1A-catalyzed AAD 6-O-glucuronidation represents a crucial detoxification pathway of this naturally occurring AAI analogs in humans, which is very different from that of AAI.

Predicting Natural Evolution in the RBD Region of the Spike Glycoprotein of SARS-CoV-2 by Machine Learning.

Machine learning (ML) is a key focus in predicting protein mutations and aiding directed evolution. Research on potential virus variants is crucial for vaccine development. In this study, the machine learning software PyPEF was employed to conduct mutation analysis within the receptor-binding domain (RBD) of the Spike glycoprotein of SARS-CoV-2. Over 48,960,000 variants were predicted. Eight prospective variants that could surface in the future underwent modeling and molecular dynamics simulations. The study forecasts that the latest variant, ISOY2P5O1, may potentially emerge around 17 November 2023, with an approximate window of uncertainty of ±22 days. The ISOY8P5O2 variant displayed an increased binding capacity in the dry assay, with a total predicted binding energy of -110.306 kcal/mol. This represents an 8.25% enhancement in total binding energy compared to the original SARS-CoV-2 strain discovered in Wuhan (-101.892 kcal/mol). Reverse research confirmed the structural significance of mutation sites using ML models, particularly in the context of protein folding. The study validated regression methods (SVR, RF, and PLS) with different data structures. This study investigates the effectiveness of the "ML-Guided Design Correctly Predicts Combinatorial Effects Strategy" compared to the "ML-Guided Design Correctly Predicts Natural Evolution Prediction Strategy". To enhance machine learning, we created a timestamping algorithm and two auxiliary programs using advanced techniques to rapidly process extensive data, surpassing batch sequencing capabilities. This study not only advances machine learning in guiding protein evolution but also holds potential for forecasting future viruses and vaccine development.

Computational analysis and experimental validation of a quinoline derivative for optoelectronic and pharmacological applications

This study presents a comprehensive analysis of a novel quinoline derivative, exploring its potential in optoelectronic and pharmacological applications through Density Functional Theory (DFT) simulations and experimental validations. The research meticulously investigates the electronic structure, identifying crucial properties such as the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) energy gaps, which suggest promising optoelectronic applications. Additionally, the study delves into the compound's pharmacological potential, evidenced by significant binding energy predictions, indicating a strong affinity towards biological targets. The derivative's molecular stability and electrophilicity, quantified through various DFT methods, alongside a detailed vibrational spectroscopy analysis using Fourier Transform Infrared (FT-IR) spectroscopy, further underscore its versatility and application potential. The findings not only highlight the quinoline derivative's multifaceted utility but also emphasize the critical role of methodological precision in theoretical and experimental studies of molecular properties. This work significantly contributes to the field by demonstrating the derivative's dual functionality, which could be harnessed for advancing optoelectronic devices and pharmaceuticals, thereby opening new avenues for future research and development.

In-silico studies and docking of n-substituted isoindoline-1, 3-dioine analogues as anti-proliferative agents

Recently,isoindoline-1,3-dione compounds based folpet, phosmet, captonand thalidomide were developed because they have a comparable degree of anti-proliferative efficacy owing to their diverse mechanisms such as HDAC inhibitors, tryptase inhibitors, inhibits the mode of tnf-α, and angiogenesis inhibitors. It was investigated how the phthalimide pharmacophore interacted with molecules such tnf-α, HDAC, VEGF, EGF, and Tyrosine Kinase Angiogenesis. In order to assess the inhibitory activity against enzyme assay, a series of phthalimidepharmacophores with various substituent’s (Schiff's base) at the N-phenyl ring were submitted to protein-ligand docking investigations using the lib-dock method in the current work. All of the compounds' chemical structures were designed using Cambridge software, ChemBioOffice Ultra 12.0, and their molecular characteristics were determined using the online molecular modelling tool Molinspiration. Utilizing the Discovery Studio Client version 4.1, ADMETlab 2.0, and Lazar 1.4.2 softwares, ADME, Toxicity, and Molecular Docking investigations using the lib-dock method were carried out to evaluate the binding mode and interactions of synthetic hits at the binding site of receptors. Docking studies demonstrated that these sorts of ligands interacted mostly with TNF-α, HDAC, VEGF, EGF, Tyrosine kinase, and angiogenesis reports, among others, by forming hydrogen bonds and interacting hydrophobically with the domain. Our docking results indicate that compounds A1, A10, A11, A22, A26, and A28 demonstrated the greatest binding affinity with the corresponding proteins based on the predicted binding energy. These computer simulations have shown that phthalimide compounds with N-phenyl rings replaced can effectively suppress enzymatic assay.