Energy Calculations Research Articles

Essential Considerations for Free Energy Calculations of RNA-Small Molecule Complexes: Lessons from the Theophylline-Binding RNA Aptamer.

Alchemical free energy calculations are widely used to predict the binding affinity of small molecule ligands to protein targets; however, the application of these methods to RNA targets has not been deeply explored. We systematically investigated how modeling decisions affect the performance of absolute binding free energy calculations for a relatively simple RNA model system: theophylline-binding RNA aptamer with theophylline and five analogs. The goal of this investigation was 2-fold: (1) understanding the performance levels we can expect from absolute free energy calculations for a simple RNA complex and (2) learning about practical modeling considerations that impact the success of RNA-binding predictions, which may be different from the best practices established for protein targets. We learned that magnesium ion (Mg2+) placement is a critical decision that impacts affinity predictions. When information regarding Mg2+ positions is lacking, implementing RNA backbone restraints is an alternative way of stabilizing the RNA structure that recapitulates prediction accuracy. Since mistakes in Mg2+ placement can be detrimental, omitting magnesium ions entirely and using RNA backbone restraints are attractive as a risk-mitigating approach. We found that predictions are sensitive to modeling experimental buffer conditions correctly, including salt type and ionic strength. We explored the effects of sampling in the alchemical protocol, choice of the ligand force field (GAFF2/OpenFF Sage), and water model (TIP3P/OPC) on predictions, which allowed us to give practical advice for the application of free energy methods to RNA targets. By capturing experimental buffer conditions and implementing RNA backbone restraints, we were able to compute binding affinities accurately (mean absolute error (MAE) = 2.2 kcal/mol, Pearson's correlation coefficient = 0.9, Kendall's τ = 0.7). We believe there is much to learn about how to apply free energy calculations for RNA targets and how to enhance their performance in prospective predictions. This study is an important first step for learning best practices and special considerations for RNA-ligand free energy calculations. Future studies will consider increasingly complicated ligands and diverse RNA systems and help the development of general protocols for therapeutically relevant RNA targets.

Weak Spots in Semiconductor Nanoplatelets: From Isolated Defects Toward Directed Nanoscale Assemblies.

The chemical engineering of nanostructures with atomic-scale precision is a fundamental scientific challenge. Cation exchange reactions in nanoplatelets (NPLs) offer an attractive platform for this precision chemistry, as it is relatively simple to carry out, extremely versatile, and allows the production of heterogeneous nanostructures that cannot be produced by any other means. A major hindrance has, however, been the lack of knowledge of the "weak spots" of the platelets where the ionic exchange reaction is initiated to optimally control the process toward directed nanoscale assemblies. Here, mercury selenide formation in cadmium selenide NPLs is investigated. The study of Cd(1-n)HgnSe NPLs using scanning transmission electron microscopy (STEM) pinpoints at the corners (vertices) and edges of the NPLs as the exchanged sites. Comprehensive first principles density functional calculations of Hg substitution energies in a hierarchy of models of four monolayers (ML) CdSe NPL stabilized with acetate ligands unambiguously underscore that the energetically preferred exchange positions are platelet corners - in line with the experimental findings. The results thus not only explain in detail how the cation exchange process in 2D CdSe NPLs is kicked-off, but also establish an arena for future studies in 2D nanomaterials reaching far beyond these reactions.

Beyond Fundamental Building Blocks: Plasticity in Structurally Complex Crystals.

Intermetallics, which encompass a wide range of compounds, often exhibit similar or closely related crystal structures, resulting in various intermetallic systems with structurally derivative phases. This study examines the hypothesis that deformation behavior can be transferred from fundamental building blocks to structurally related phases using the binary samarium-cobalt system. SmCo2 and SmCo5 are investigated as fundamental building blocks and compared them to the structurally related SmCo3 and Sm2Co17 phases. Nanoindentation and micropillar compression tests are performed to characterize the primary slip systems, complemented by generalized stacking fault energy (GSFE) calculations via atomic-scale modeling. The results show that while elastic properties of the structurally complex phases follow a rule of mixtures, their plastic deformation mechanisms are more intricate, influenced by the stacking and bonding nature within the crystal's building blocks. These findings underscore the importance of local bonding environments in predicting the mechanical behavior of structurally related intermetallics, providing crucial insights for the development of high-performance intermetallicmaterials.

Network Pharmacology and In Silico Elucidation of Phytochemicals Extracted from Ajwa Dates (Phoenix dactylifera L.) to Inhibit Akt and PI3K Causing Triple Negative Breast Cancer (TNBC).

About 10-15% of all breast cancers comprise triple-negative breast cancer (TNBC), defined as cancer cells that lack receptors for the ER, PR, and HER2 protein receptors. Due to the absence of these receptors, treating TNBC using conventional chemotherapy is challenging and, therefore, requires the discovery of novel chemotherapeutic agents derived from natural sources. The current work was intended to study the potential phytochemicals of Ajwa dates (Phoenix dactylifera L.) with the predicted potential targets (namely, Akt and PI3K) to determine possible TNBC inhibitors. We harnessed network pharmacology, molecular docking, drug-likeness studies, Molecular Dynamics (MD) simulation, and binding free energy (MM-GBSA) calculation to get phytochemicals with potential effects against TNBC. Firstly, molecular docking was performed on 125 phytochemicals against the Akt and PI3K proteins utilizing PyRx. Then, the phytochemicals with the highest binding affinity (≤ -8.1 kcal/mol) were examined for in silico drug-likeness and toxicity profiles. Finally, phytochemicals with optimal druglikeness and toxicity profiles were studied by Molecular Dynamics (MD) simulation and binding free energy (MM-GBSA) to identify compounds that can form stable complexes. The results of the network pharmacology revealed that the Akt and PI3K proteins are potential targets of TNBC for the phytochemicals of Phoenix dactylifera L. used in this study. The outcomes of molecular docking displayed that among 125 phytochemicals, 42 of them (with a binding affinity ≤ -8.1 kcal/mol) have potentially inhibiting effects on both proteins PI3K and Akt expressed in TNBC. Then, the results of in silico drug-likeness identified seven phytochemicals with optimal pharmacokinetic profiles. Furthermore, toxicity studies showed that three phytochemicals (namely, Chrysoeriol, Daidzein, and Glycitein) did not cause any toxicities. Finally, the Molecular Dynamics (MD) simulation studies and binding free energy (MM-GBSA) verified that Daidzein stayed within the binding cavities of both proteins (Akt and PI3K) by establishing a stable protein-ligand complex during simulation. Taken together, the current work emphasizes the potential effects of Daidzein from Phoenix dactylifera L. against TNBC, and it can be further studied to establish it as a standard chemotherapy for TNBC.

Potential VEGFR2 inhibitors for managing metastatic cervical cancer: insights from molecular dynamics and free energy landscape studies.

Metastatic cervical cancer, the advanced stage where the cancer spreads beyond the cervix to other parts of the body, poses significant treatment challenges and is associated with poor survival rates. Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), a critical angiogenic mediator, is upregulated in metastatic cervical cancer, driving the formation of new blood vessels that fuel tumor growth and spread, making it an attractive target for anti-angiogenic therapies aimed at halting metastasis. This study aims to determine the anti-angiogenic effects of natural compounds to identify new VEGFR2 inhibitors for managing metastatic cervical cancer. The potential effect of these compounds as VEGFR2 inhibitors at the structural level was assessed using various methods such as virtual screening, docking, MD simulations (1000ns), binding free energy calculations, and free energy landscape analysis. Four compounds, including IMPHY007574, IMPHY004129, IMPHY008783, and IMPHY004928, were found to be potential VEGFR2 inhibitors. Among the structures analyzed in the present work, IMPHY007574 revealed the highest binding stability with VEGFR2 and the most favorable interaction pattern, thus proving the possibility of its use as an effective anti-angiogenic compound. The other three compounds also demonstrated a reasonably good promise in VEGFR2 inhibition. These findings provide a foundation for developing novel therapeutic strategies for metastatic cervical cancer, potentially overcoming drug resistance and improving patient survival rates.

Intrinsic defects in α-Al2O3: structural and electronic properties

Abstract Due to their high dielectric constants and wide band gaps, Al 2 O 3 has become popular in semiconductor applications, but with high-density interface defect structures. Understanding the structural distortions of intrinsic defects and their impact on electronic properties, is of particular importance for the applications. Herein, we study the impacts of four intrinsic defects on the structural and electronic properties of α- Al 2 O 3 using first-principles calculations. The four intrinsic defects cause structural distortions at their respective defect sites and defect states within the band gap, with O i inducing the most significant distortions and exhibiting the most complex defect states accordingly. When the Fermi level is 3.05 eV under O-poor conditions, VO is the most favorable defects based on the formation energy calculations. Additionally, the intrinsic defects in α- Al 2 O 3 can effectively modulate band edges, and thus modify the leakage currents. In summary, our results provide significant theoretical insights for α- Al 2 O 3 -based semiconductor applications.

Computational revealing Infigratinib resistance to the FGFR2 N549H and N549K mutations

Fibroblast growth factor receptor 2 (FGFR2) is a receptor tyrosine kinase that is involved in many human cancers such as intrahepatic cholangiocarcinomas and hepatocellular carcinomas. The clinically acquired FGFR2 N549H/K mutations make the treatment of Infigratinib ineffective. Here, molecular docking, multiple-replica molecular dynamics (MD) simulations, and binding free energy calculations were used to reveal the mechanism of Infigratinib resistance to the FGFR2 N549H/K mutants. MD simulations indicated that both N549H/K mutations disrupt the local hydrogen bonds or salt bridges formed by His544, Asn549, Glu565, and Lys641. Binding free energy calculation results suggested that both the decrease of van der Waals interactions and electrostatic interaction are responsible for weakening the binding affinity of Infigratinib to the FGFR2 N549H/K mutants. Residue-based energy decomposition analysis further showed that the interactions of Infigratinib with key residues Leu487, Glu565, Ala567, Gly570, and Leu633 become significantly weaker in the N549H/K mutants compared to the wild-type kinase. We anticipate that our results will provide a useful guide to design potential inhibitors against the FGFR2 N549H/K mutants.

Adsorption and Diffusion of CH4, N2, and Their Mixture in MIL-101(Cr): A Molecular Simulation Study.

A comprehensive quantitative grasp of methane (CH4), nitrogen (N2), and their mixture's adsorption and diffusion in MIL-101(Cr) is crucial for wide and important applications, e.g., natural gas upgrading and coal-mine methane capturing. Previous studies often overlook the impact of gas molecular configuration and MIL-101 topology structure on adsorption, lacking quantitative assessment of primary and secondary adsorption sites. Additionally, understanding gas mixture adsorption mechanisms remains a research gap. To bridge this gap and to provide new knowledge, we utilized Monte Carlo and molecular dynamics simulations for computing essential MIL-101 properties, encompassing adsorption isotherms, density profiles, self-diffusion coefficients, radial distribution function (RDF), and CH4/N2 selectivity. Several novel and distinctive findings are revealed by the atomic-level analysis, including (1) the significance of C=C double bond of the benzene ring within MIL-101 for CH4 and N2 adsorption, with Cr and O atoms also exerting notable effects. (2) Density distribution analysis reveals CH4's preference for large and medium cages, while N2 is evenly distributed along pentagonal and triangular window edges and small tetrahedral cages. (3) Calculations of self-diffusion and diffusion activation energies suggest N2's higher mobility within MIL-101 compared to CH4. (4) In the binary mixture, the existence of CH4 can decrease the diffusion coefficient of N2. In summary, this investigation provides valuable microscopic insights into the adsorption and diffusion phenomena occurring in MIL-101, thereby contributing to a comprehensive understanding of its potential for applications, e.g., natural gas upgrading and selective capture of coal-mine methane.

Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent.

Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of r = 0.79 with the experimental values) is on par with what can be expected from the fixed charge explicit solvent models. Best agreement with the experiment is achieved when the implicit salt concentration is set equal or close to the experimental conditions.

Leveraging a Separation of States Method for Relative Binding Free Energy Calculations in Systems with Trapped Waters.

Methods for calculating the relative binding free energy (RBFE) between ligands to a target protein are gaining importance in the structure-based drug discovery domain, especially as methodological advances and automation improve accuracy and ease of use. In an RBFE calculation, the difference between the binding affinities of two ligands to a protein is calculated by transforming one ligand into another, in the protein-ligand complex, and in solvent. Alchemical binding free energy calculations are often used for such ligand transformations. Such calculations are not without challenges, however; for example, it can be challenging to handle interfacial waters when these play a crucial role in mediating protein-ligand binding. In some cases, the exchange of the interfacial waters with solvent water might be very infrequent in the course of typical molecular simulations, and such interfacial waters can be considered trapped on the simulation time scale. In these cases, RBFE calculation between two ligands, where one ligand binds with a trapped water while the other ligand displaces it, can result in inaccuracies if the surrounding water structure is not sampled adequately for both ligands. So far, a popular choice for treating the trapped waters in RBFE calculations is to combine free energy calculations with enhanced sampling methods that insert/delete waters in the binding site. Despite recent developments in the enhanced sampling methods, they can result in hysteresis in the RBFE estimate, depending on whether the simulations were started with or without the trapped waters. In this study, we introduce an alternative method, separation of states, to calculate the RBFE between ligand pairs where the ligands bind to the protein with different numbers/positions of trapped waters. The separation of states approach treats the sampling of the trapped waters separately from the free energy calculation of the ligand transformation. In our method, a trapped water in protein's binding site is decoupled from the system first, and the cavity created by its decoupling is stabilized. We then grow a larger ligand into this cavity- a ligand that is known to displace the trapped water. In this study, we show that our method results in precise and accurate estimates of RBFEs for ligand pairs involving the rearrangement of trapped water via RBFE calculations for five such ligand pairs. We have optimized our simulation protocol to be suited for large distributed computational resources and have automated our RBFE calculation workflow.

Repurposed drugs as PCSK9-LDLR disruptors for lipid lowering and cardiovascular disease therapeutics.

The PCSK9 protein binds to LDL receptors (LDLR), leading to their degradation and reduced expression on cell surfaces. This decreased the clearance of LDL cholesterol from the bloodstream, thereby increasing the risk of coronary artery diseases. Targeting the PCSK9-LDL receptor interaction is crucial for regulating LDL cholesterol levels and preventing cardiovascular disease. This study aims to screen low molecular weight inhibitors to disrupt the PCSK9-LDLR interaction. We employed a comprehensive approach combining high-throughput virtual screening of DrugBank database, followed by molecular docking studies using CDOCKER and flexible docking methods. The top four lead compounds were further validated through molecular dynamics (MD) simulations and binding free energy calculations using MM-PBSA. Finally, the in vitro assay confirmed that Benazepril and Quinapril exhibited the highest potency as PCSK9-LDLR disruptors among the top candidates. These lead compounds have the potential to be repurposed as lipid-lowering agents for the treatment of cardiovascular diseases, offering a promising therapeutic strategy.

Structural transitions of calcium carbonate by molecular dynamics simulation.

Calcium carbonate (CaCO3) plays a crucial role in the global carbon cycle, and its phase diagram is of significant scientific interest. We used molecular dynamics to investigate selected structural phase transitions of calcium carbonate. Using the Raiteri potential, we explored the structural transitions occurring at the constant pressure of 1bar, with temperatures ranging from 300 to 2500K, and at the constant temperature of 1600K, with pressures ranging from 0 to 13GPa. With increasing temperature, the transitions between calcite, CaCO3-IV, and CaCO3-V were characterized. In the calcite structure, the carbonate ions are ordered in a planar triangular arrangement, alternating with layers of calcium ions. As the temperature increases, the transition from calcite to CaCO3-IV occurs, leading to partial disordering of the carbonate ions. At higher temperatures, CaCO3-IV transforms into CaCO3-V. Through free energy analysis, we classified the latter transition as a continuous phase transition. At a temperature of 2000K, a "disordered CaCO3" structure appears, characterized by low order within the calcium and carbonate sublattices and the free rotation of the carbonate ions. With increasing pressure, two calcium carbonate transformations were observed. At P = 2GPa, the CaCO3-V phase undergoes a phase transition into CaCO3-IV, demonstrating that the model can describe the transition between these two phases as pressure- and temperature-driven. At P = 4.25GPa, CaCO3-IV undergoes a phase transition into the CaCO3-Vb phase. This transition is classified as first-order based on free energy calculations.

Identification of Novel Drug Molecules Against NS3-Like Helicase Enzyme of Alongshan Virus.

Alongshan virus (ALSV) is a novel tick-borne virus associated with human diseases. The ALSV is a segmented flavivirus from the family Flaviviridae. It is currently considered as tick-borne arbovirus. There is a high incidence of fever and headache among patients with ALSV infection, and some patients also present with fatigue, coma, depression, nausea, myalgia/arthralgia, and skin rashes. Neither a licensed vaccine nor a drug is currently available to treat ALSV. The development of new, practical, and innovative therapeutic approaches is needed to overcome the emergence of the pathogen. Research on drugs remains a complex, time-consuming, and expensive. The field of drug development has undergone a revolution due to the use ofcomputational approaches, which provide several benefits that speed up and improve the process of developing novel drugs. The goal of this study is to identify novel drug-like molecules against NS3-like helicase enzyme of Alongshan virus. Using molecular docking, the binding potential of the top three ligands to the specified targetwas determined. Molecular dynamic simulations wereused to identifythe stabilities of the best-dockedconformations followed by energy calculations and ADMET analysis. Three potential and promising compounds were identified by performing structure-based virtual screening of non-structural protein 3 (NS3) like helicase of Alongshan virus. The best-docked complexes identified through virtual screening were BDC-23169381, BDB-26412846, BDB-2641954. All these compounds had good pharmacokinetics characteristics and were identified as drug like.

Integrative Machine Learning, Virtual Screening, and Molecular Modeling for BacA-Targeted Anti-Biofilm Drug Discovery Against Staphylococcal Infections

The rise in antibiotic-resistant Staphylococcal infections necessitates innovative approaches to identify new therapeutic agents. This study investigates the application of machine learning models to identify potential phytochemical inhibitors against BacA, a target related to Staphylococcal infections. Active compounds were retrieved from BindingDB while the decoy was generated from DUDE. The RDKit was utilized for feature engineering. Machine learning models such as k-nearest neighbors (KNN), the support vector machine (SVM), random forest (RF), and naive Bayes (NB) were trained on an initial dataset consisting of 226 active chemicals and 2550 inert compounds. Accompanied by an MCC of 0.93 and an accuracy of 96%, the RF performed better. Utilizing the RF model, a library of 9000 phytochemicals was screened, identifying 300 potentially active compounds, of which 192 exhibited drug-like properties and were further analyzed through molecular docking studies. Molecular docking results identified Ergotamine, Withanolide E, and DOPPA as top inhibitors of the BacA protein, accompanied by interaction affinities of −8.8, −8.1, and −7.9 kcal/mol, respectively. Molecular dynamics (MD) was applied for 100 ns to these top hits to evaluate their stability and dynamic behavior. RMSD, RMSF, SASA, and Rg analyses showed that all complexes remained stable throughout the simulation period. Binding energy calculations using MMGBSA analysis revealed that the BacA_Withanolide E complex exhibited the most favorable binding energy profile with significant van der Waals interactions and a substantial reduction in gas-phase energy. It also revealed that van der Waals interactions contributed significantly to the binding stability of Withanolide E, while electrostatic interactions played a secondary role. The integration of machine learning models with molecular docking and MD simulations proved effective in identifying promising phytochemical inhibitors, with Withanolide E emerging as a potent candidate. These findings provide a pathway for developing new antibacterial agents against Staphylococcal infections, pending further experimental validation and optimization.

Protocols for Metallo- and Serine-β-Lactamase Free Energy Predictions: Insights from Cross-Class Inhibitors.

While relative binding free energy (RBFE) calculations using alchemical methods are routinely carried out for many pharmaceutically relevant protein targets, challenges remain. For example, open-source tools do not support the easy setup and simulation of metalloproteins, particularly when ligands directly coordinate to the metal site. Here, we evaluate the performance of RBFE methods for KPC-2, a serine-β-lactamase (SBL), and two nonbonded metal parameter setups for VIM-2, a metallo-β-lactamase (MBL) with two active site zinc ions. We tested two different ways of modeling the ligand-zinc interactions. First, a restraint-based approach, in which FF14SB zinc parameters are combined with harmonic restraints between the zincs and their coordinating residues. The second approach uses an upgraded Amber force field (UAFF) for zinc-metalloproteins with adjusted partial charges and nonbonded terms of zinc-coordinating residues. Molecular mechanics (MM) and quantum mechanics/molecular mechanics (QM/MM) simulations show that the crystallographically observed zinc coordination is not retained in MM simulations with either zinc parameter set for a series of known phosphonic acid-based inhibitors bound to VIM-2. These phosphonic acid-based inhibitors exhibit known cross-class affinity for SBLs and MBLs and serve as a benchmark for RBFE calculations for VIM-2, after validation with KPC-2. The KPC-2 free energy of binding estimates are within expected literature accuracies for the ligand series with a mean absolute error of kcal/mol and a Pearson's correlation coefficient of . For VIM-2, the UAFF approach has improved correlation from to , compared to the restraint approach. The presented strategies for handling ligands coordinating to metal sites highlight that simple metal parameter models can provide some predictive free energy estimates for metalloprotein-ligand systems, but leave room for improvement in their ease of use, modeling of coordination sites and as a result, their accuracy.

Targeting serotonin receptors with phytochemicals – an in-silico study

The potential of natural phytochemicals in mitigating depression has been supported by substantial evidence. This study evaluated a total of 88 natural phytochemicals with potential antidepressant properties by targeting serotonin (5-HT) receptors (5-HT1A, 5-HT4, and 5-HT7) using molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis, internal coordinates normal mode analysis (NMA), molecular dynamics simulation (MDS), and free energy calculation. Five evaluated compounds (Genistein, Kaempferol, Daidzein, Peonidin, and glycitein) exhibited favorable pharmacokinetic properties and improved binding scores, indicating their potential as effective antidepressants. Redocking and superimposition analysis of 5-HT with cocrystal structures validated these findings. Furthermore, NMA, MDS, and free energy calculations confirmed the stability and deformability of the ligand-receptor complexes, suggesting that these phytochemicals can effectively interact with 5-HT receptors to modulate depressive symptoms. These powerful phytochemicals, abundantly found in soybeans, fruits, vegetables, and herbs, represent a promising avenue for developing natural treatments for depression. Further in vitro and in vivo studies are warranted to explore their efficacy in alleviating stress and depression through their interactions with 5-HT receptors.

Geometrical Anatomy for Oxygen Vacancies in Epitaxial Hf0.5Zr0.5O2 Films Grown via Atomic Layer Deposition

AbstractThe selective influence of elastic strain on the formation of oxygen deficiencies in (001)‐, (110)‐, and (111)‐ epitaxial Hf0.5Zr0.5O2 films grown by using atomic layer deposition is reported. Optical spectroscopy, conducted using UV–vis spectroscopic ellipsometry on these Hf0.5Zr0.5O2 films grown on yttria‐stabilized zirconia substrates, revealed a dominant shallow trap level in the (111)‐oriented Hf0.5Zr0.5O2 film. X‐ray photoemission spectroscopy demonstrated that the strong oxygen deficiency is preferred in the (111)‐oriented Hf0.5Zr0.5O2 film. Density functional theory calculations of oxygen vacancy formation energy also showed a pronounced preference for oxygen deficiencies in the (111) orientation. This selective formation of oxygen vacancies in the (111)‐oriented Hf0.5Zr0.5O2 film suggests that the latent phenomena associated with oxygen defects in functional Hf0.5Zr0.5O2 films are partly attributed to the directional strain in the (111) orientation.

On the Use of Medium-Term Forecast Data for the Baikal Rift Zone in Seismic-Hazard Assessments

The article presents the general results of medium- and long-term forecasting of earthquakes with K ≥ 13 (M ≥ 5.0) in the Baikal rift zone. These results have recently been obtained through the joint use of the geoinformation system for predicting the preshock stage of earthquake preparation and its associated two-stage phenomenological model. This model was created based on the analysis of seismological data on the preparation of the most dangerous earthquakes that occurred in the Baikal rift zone. It is consistent with the results obtained in studies of seismic regimes of ice shock preparation on the Baikal ice cover and in conducting full-scale experiments on fault sections with the aim of clarifying physical and mechanical conditions for the emergence of the sources of seismic-induced oscillations.The paper provides an example of practical use of results obtained for earthquake forecasting, as well as the ways of refining seismic hazard assessments relative to the infrastructure in the city of Angarsk 100 km away from the seismically hazardous Main Sayan Fault (MSF), whose zone reveals a “locked” segment with a seismic gap during the seismic regime analysis. In accordance with its linear dimensioning which represents a length of 60 km, two L/M equations served as a basis for potential energy calculations whose maximum values correspond to Mmax 7.1 and 7.8. It is shown that the use of the obtained earthquake-forecast results assists in refining the level of seismic hazard for the nearest time intervals of expected earthquakes with different Mmax values. Consideration is being given to the example of assessing the current seismic hazard using a medium-term forecast for the infrastructure of the city of Angarsk for the probability of seismic shaking from the southeastern section of the MSF zone for the next 10 and 50 years. The comparison with the OSR-16 map showed that the calculations carried out indicate a relatively lower level of seismic hazard for the city of Angarsk for 10- and 50-year expectation periods.

Discovery of novel VEGFR2 inhibitors against non-small cell lung cancer based on fingerprint-enhanced graph attention convolutional network

Despite the proven inhibitory effects of drugs targeting vascular endothelial growth factor receptor 2 (VEGFR2) on solid tumors, including non-small cell lung cancer (NSCLC), the development of anti-NSCLC drugs solely targeting VEGFR2 still faces risks such as off-target effects and limited efficacy. This study aims to develop a novel fingerprint-enhanced graph attention convolutional network (FnGATGCN) model for predicting the activity of anti-NSCLC drugs. Employing a multimodal fusion strategy, the model integrates a feature extraction layer that comprises molecular graph feature extraction and molecular fingerprint feature extraction. The performance evaluation results indicate that the model exhibits high accuracy and stability in predicting activity. Moreover, we explored the relationship between molecular features and biological activity through visualization analysis, thus improving the interpretability of the approach. Utilizing this model, we screened the ZINC database and conducted high-precision molecular docking, leading to the identification of 11 potential active molecules. Subsequently, molecular dynamics simulations and free energy calculations were performed. The results demonstrate that all 11 aforementioned molecules can stably bind to VEGFR2 under dynamic conditions. Among the short-listed compounds, the top six exhibited satisfactory inhibitory activity against VEGFR2 and A549 cells. Especially, compound Z-3 displayed VEGFR2 inhibitory with IC50 values of 0.88 μM, and anti-proliferative activity against A549 cells with IC50 values of 4.23 ± 0.45 μM. This approach combines the advantages of target-based and phenotype-based screening, facilitating the rapid and efficient identification of candidate compounds with dual activity against VEGFR2 and A549 cell lines. It provides new insights and methods for the development of anti-NSCLC drugs. Furthermore, further biological activity tests revealed that Z1-Z3 and Z6 manifested relatively strong antiproliferative activities against NCI-H23 and NCI-H460, and relatively low toxicity towards GES-1. The hit compounds were promising candidates for the further development of novel VEGFR2 inhibitors against NSCLC.

Molecular Decoration and Unconventional Double Bond Migration in Irumamycin Biosynthesis

Irumamycin (Iru) is a complex polyketide with pronounced antifungal activity produced by a type I polyketide (PKS) synthase. Iru features a unique hemiketal ring and an epoxide group, making its biosynthesis and the structural diversity of related compounds particularly intriguing. In this study, we performed a detailed analysis of the iru biosynthetic gene cluster (BGC) to uncover the mechanisms underlying Iru formation. We examined the iru PKS, including the domain architecture of individual modules and the overall spatial structure of the PKS, and uncovered discrepancies in substrate specificity and iterative chain elongation. Two potential pathways for the formation of the hemiketal ring, involving either an olefin shift or electrocyclization, were proposed and assessed using 18O-labeling experiments and reaction activation energy calculations. Based on our findings, the hemiketal ring is likely formed by PKS-assisted double bond migration and TE domain-mediated cyclization. Furthermore, putative tailoring enzymes mediating epoxide formation specific to Iru were identified. The revealed Iru biosynthetic machinery provides insight into the complex enzymatic processes involved in Iru production, including macrocycle sculpting and decoration. These mechanistic details open new avenues for a targeted architecture of novel macrolide analogs through synthetic biology and biosynthetic engineering.