Computational Approach Research Articles

Pyridazinone-substituted Benzenesulfonamides Demonstrate Inhibition of Monoamine Oxidase

Background: The monoamine oxidase (MAO) enzymes are important drug targets. Inhibitors of MAO-A and MAO-B have been used to treat the symptoms of depression and Parkinson’s disease. Methods: A series of seventeen pyridazinone-substituted benzenesulfonamides was synthesized and evaluated as potential inhibitors of human MAO-A and MAO-B. This study is a continuation of our interest in the pharmacological activities of sulfonamide compounds. Results: Among the compounds evaluated, only 10 and 18 demonstrated appreciable inhibition of MAOB with IC50 values of 2.90 and 4.36 μM, respectively. None of the benzenesulfonamides inhibited the MAO-A isoform. Potential binding orientations and interactions of 10 and 18 with the active site of MAO-B were investigated by computational approaches. Conclusion: Although these potencies are modest, this study is the first report on MAO inhibition by this class of compounds. Active MAO-B inhibitors may serve as leads for the future discovery of therapeutic agents for neurodegenerative disorders, such as Parkinson’s disease.

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Exploration and Validation of Lead Molecules against Yellow Fever through High Throughput Virtual Screening and Molecular Dynamics Simulation

Background: Yellow fever (YF) is a mosquito-borne flaviviral hemorrhagic fever (VHF) that causes severe hepatitis, renal failure, bleeding, and quick terminal events such as shock and multi-organ failure. There are currently no particular anti-viral medications for the management of the YF virus (YFV). Despite the availability of a commercial YFV vaccination, there are roughly 30,000 fatalities globally each year, with instances rising over the previous 20 years. After being translocated into the endoplasmic reticulum lumen, glycosylated NS1 resides as a membrane-associated dimer, where it is required for viral genome replication. The secreted hexamer NS1 has a role in immune evasion and pathogenesis and has been discovered as a possible diagnostic marker for the early identification of viral infections. Objective: The main aim of this study is to analyze the small molecule as a potent drug candidate against the target NS1 protein. Methods: In this study, Computational approaches, including high throughput virtual screening, molecular docking, and dynamics simulation, were carried out against the target NS1 protein using three different chemical libraries Enamine, Asinex, and NCI. The selected lead compounds were validated through HOMO-LUMO analysis, ADME prediction, and Toxicity parameters to analyze the biological and pharmacological properties of the lead small molecules. Results: From the result, it was concluded that the leads possessed the highest docking scores, interacting with the binding residues, and were stable in the simulation period. Conclusion: Overall findings revealed that the lead three small molecules could act as the potential drug candidate for the target NS1 protein to inhibit the diseasing efficacy of Yellow fever.

Application of Cell Penetrating Peptides for Intracellular Delivery of Endostatin: A Computational Approach.

Endostatin is an antiangiogenic compound with anticancer activity. The poor stability and low half-life of endostatin are the main barriers to the clinical use of this protein. Cell-penetrating peptides (CPPs) are extensively applied as carrier in the delivery of drugs and different therapeutic agents. Therefore, they can be proper candidates to improve endostatin delivery to the target cells. In this study, we aim to computationally predict appropriate CPPs for the delivery of endostatin. Potential appropriate CPPs for protein delivery were selected based on the literature. The main parameters for detection of best CPP-endostatin fusions, including stability, hydrophobicity, antigenicity, and subcellular localization, were predicted using ProtParam, VaxiJen, and DeepLoc-1.0 servers, respectively. The 3D structures of the best CPP-Endostatin fusions were modeled by the I-TASSER server. The predicted models were validated using PROCHECK, ERRAT, Verify3D and ProSA-Web servers. The best models were visualized by the PyMol molecular graphics system. Considering the principal parameters in the selection of best CPPs for endostatin delivery, endostatin fusions with four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, high stability and hydrophobicity, no antigenicity and extracellular localization were predicted as the best potential fusions for endostatin delivery. Four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, were predicted as the best potential candidates to improve endostatin delivery. Application of these CPPs may overcome the limitation of endostatin therapeutic applications, including poor stability and low half-life. Subsequent experimental studies will contribute to verifying these computational results.

Decision tree-based approach to extrapolate life cycle inventory data of manufacturing processes

Life cycle assessment (LCA) plays a crucial role in green manufacturing to uncover the critical aspects for alleviating the environmental burdens due to manufacturing processes. However, the scarcity of life cycle inventory (LCI) data for the manufacturing processes is a considerable challenge. This paper proposes a novel approach to extrapolate LCI data of manufacturing processes. Taking advantage of LCI data in the Ecoinvent datasets, decision tree-based supervised machine learning models, namely decision tree, random forest, gradient boosting, and adaptive boosting, have been developed to extrapolate the data of GHG emissions, i.e., carbon dioxide, nitrous oxide, methane, and water vapor. Initially, a correlation analysis was conducted to derive the most influential factors on GHG quantities resulting from manufacturing activities. First, the collected data have been preprocessed and split into train and test sets (70% and 30%, respectively). Second, a five-fold cross-validation method was applied to tune the hyperparameters of the models. Then, the models were re-trained using the best hyperparameters and evaluated using the test set. The results reveal that the Gradient Boosting model has a superior predictive performance for extrapolating the GHG emission data, with average coefficients of determination (R2) on the test set <0.95. Moreover, the model predictions involve relatively low values of the average root mean squared error and an average mean percentage of error on the test set. The correlation and feature importance analyses emphasized that the workpiece material and manufacturing technology have a considerable effect on natural resource consumption, i.e., energy, material, and water inflows into the process. Meanwhile, energy consumption, water usage, and raw aluminum depletion were the most influential factors in GHG emissions. Eventually, a case study to extrapolate the inflows and the outflows for new manufacturing activities has been conducted using the validated models. The proposed GraBoost model provides a computational supplementary approach to estimate and extrapolate the GHG emissions for different manufacturing processes when LCI data are incomplete or don't exist within LCI databases.

COVID-19 Pandemic and its Economic Impact on the Global Economy: A Computable General Equilibrium Approach

Introduction: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV- 2, has been a global health crisis since its emergence in late 2019, impacting all aspects of life worldwide. It has revealed vulnerabilities in healthcare systems while also inspiring scientific advancements and cooperation in developing vaccines, highlighting the importance of preparedness and equitable healthcare for future challenges. Method: This paper evaluates the economic impact of the COVID-19 pandemic on the global economy. The study employed the Global Trade Analysis Project model using GTAP database version 10A with 2014 as the base year. The database's 141 regions and 65 sectors are aggregated into 20 regions and 29 sectors. The model used involves a baseline and two policy scenarios. The baseline scenario projected the global economy to 2020 from 2014 using macroeconomic variables, such as real GDP, population, capital stock, and labor supply. Policy scenarios 1 and 2 accounted for the impact of the COVID-19 pandemic and fiscal stimulus package, respectively. Result: The impacts of the pandemic are measured in real GDP, welfare, export and import volumes, trade balance, and output by sector. Simulation results indicate that the real GDP for all regions declined. The impact of COVID-19 on the welfare of countries, including Korea, Japan, the US, Mexico, China, Italy, and Great Britain, varied. In all these countries, the pandemic highlighted existing inequalities and vulnerabilities, affecting countries disproportionately. Government interventions, such as stimulus packages, income support programs, and vaccination campaigns, have aimed to mitigate the impact and support the welfare of the population. The pandemic disrupted global supply chains and increased trade costs, thereby negatively impacting both import and export volumes. The output of some sectors, such as tourism-related sectors, was heavily affected. Conclusion: The COVID-19 pandemic has impacted real GDP, welfare, export and import volumes, trade balance, and output by sector. By examining the specific implications of the pandemic on each of these economic factors, policymakers and researchers can gain valuable insights into the challenges faced and potential avenues for recovery.

Computational investigation of transportation and thermal characteristics in a bi-modal slurry flow through a horizontally placed pipe bend

In this study, we develop a three-dimensional computational model to explore the transportation and thermal characteristics of a bi-modal slurry flowing through a horizontally placed 90° pipe bend. The slurry comprises silica sand (SS) and fly ash (FA) granular, with varying combinations (65:35, 75:25, 85:15, 95:05, and 100:0). The computational investigation has been carried out for different carrier fluid's characteristics, including Prandtl number, and flow velocity (5 m/s), for efflux concentrations of Cw = 40–60% for each mixture. Validated with experimental data, the computational approach employs a two-phase Eulerian-Eulerian RNG k-ε turbulence model and the kinetic theory of granular flow. Results reveal that the 65:35 combination exhibits minimal pressure drop, and the 100:0 combination shows maximum heat transfer characteristics, particularly with low-viscosity fluid (Pr = 2.88) and high particle efflux concentration (Cw = 60%). The study also examines bend loss coefficients (Kt), concentration distribution, convective heat transfer coefficient (h), and Nusselt number (Nu) across different Prandtl fluids and efflux concentrations.

A Computational Investigation on Chitosan Derivatives using Pharmacophore- based Screening, Molecular Docking, and Molecular Dynamics Simulations against Kaposi Sarcoma.

Cancer is one of the most dangerous illnesses to the human body due to its severity and progressive nature. Kaposi's Sarcoma (KS) tumor can appear as painless purple spots on the legs, foot, or face. This cancer develops in the lining of lymph arteries and blood vessels. Along with the enlargement of lymph nodes, the vaginal region and the mouth portion are the additional target areas of KS. DNA-binding proteins known as Sox proteins are found in all mammals and belong to the HMG box superfamily. They controlled a wide range of developmental procedures, such as the formation of the germ layer, the growth of organs, and the selection of the cell type. Human developmental abnormalities and congenital illnesses are frequently caused by the deletion or mutation of the Sox protein. The purpose of this study is to determine the promising Kaposi's sarcoma inhibitors through computational studies. In this present study computational approaches were used to evaluate the anti- carcinogenic efficacy against Kaposi's sarcoma. Ligand-based pharmacophore screening was performed utilising four different chemical libraries (Asinex, Chembridge, Specs, and NCI Natural products (NSC)) depending on the top hypothesis. The top hits were examined using molecular docking, absorption, distribution, metabolism and excretion. Highest occupied molecular orbital and lowest unoccupied molecular orbital were analysed to determine the lead compounds' biological and pharmacological efficacy. The results of the study indicated that the leading candidates were possible SOX protein inhibitors. A pharmacophore model to inhibit the production of SOX protein in Kaposi Sarcoma was generated in this computational experiment using a set of 19 Chitosan compounds. The results revealed that the top hits responded to all of the pharmacological druglikening criteria and had the best interaction residues, fitness scores, and docking scores. The resulting leads might be potential Kaposi's Sarcoma alternative treatments.

Network pharmacology and molecular docking: combined computational approaches to explore the antihypertensive potential of Fabaceae species

Hypertension is a major global public health issue, affecting quarter of adults worldwide. Numerous synthetic drugs are available for treating hypertension; however, they often come with a higher risk of side effects and long-term therapy. Modern formulations with active phytoconstituents are gaining popularity, addressing some of these issues. This study aims to discover novel antihypertensive compounds in Cassia fistula, Senna alexandrina, and Cassia occidentalis from family Fabaceae and understand their interaction mechanism with hypertension targeted genes, using network pharmacology and molecular docking. Total 414 compounds were identified; initial screening was conducted based on their pharmacokinetic and ADMET properties, with a particular emphasis on adherence to Lipinski's rules. 6 compounds, namely Germichrysone, Benzeneacetic acid, Flavan-3-ol, 5,7,3',4'-Tetrahydroxy-6, 8-dimethoxyflavon, Dihydrokaempferol, and Epiafzelechin, were identified as effective agents. Most of the compounds found non-toxic against various indicators with greater bioactivity score. 161 common targets were obtained against these compounds and hypertension followed by compound-target network construction and protein–protein interaction, which showed their role in diverse biological system. Top hub genes identified were TLR4, MMP9, MAPK14, AKT1, VEGFA and HSP90AA1 with their respective associates. Higher binding affinities was found with three compounds Dihydrokaempferol, Flavan-3-ol and Germichrysone, −7.1, −9.0 and −8.0 kcal/mol, respectively. The MD simulation results validate the structural flexibility of two complexes Flavan-MMP9 and Germich-TLR4 based on no. of hydrogen bonds, root mean square deviations and interaction energies. This study concluded that C. fistula (Dihydrokaempferol, Flavan-3-ol) and C. occidentalis (Germichrysone) have potential therapeutic active constituents to treat hypertension and in future novel drug formulation.Graphical

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals

The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, no diagnostic procedures exist for this disease. However, several computational approaches have proven successful in detecting PD at early stages, overcoming the disadvantages of traditional methods of diagnosis. In this study, a machine learning (ML) detection system based on the voice signals of PD patients is proposed. The AdaBoost classifier has been utilized to construct the model and trained on a dataset obtained from the machine learning repository of the University of California, Irvine (UCI). This dataset includes voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrated promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Furthermore, the robustness of the proposed model is rigorously assessed through cross-validation, revealing consistent performance across all iterations. The overarching objective of this study is to contribute to the scientific community by furnishing a robust system for the detection of PD.

Constructing the soliton wave structure to the nonlinear fractional Kairat-X dynamical equation under computational approach

In this paper, the nonlinear fractional Kairat-X equation is investigated on the basis of computational simulation. The nonlinear fractional Kairat-X equation is an integrable equation and is used to explain the differential geometry of curves and equivalence aspects. Several kinds of solitary wave structures of the nonlinear fractional Kairat-X equation are established successfully via the implantation of the extended simple equation method. Here, we explore the interesting, novel and general solutions in trigonometric, exponential, and rational types, which represent periodic wave solitons, mixed solitons in the shape of bright–dark solutions, kink wave solitons, peakon bright and dark solitons, anti-kink wave solutions, bright solitons, dark solitons, and solitary wave structure. The physical structures of secured results, aided by numerical simulation, have numerous applications in applied sciences such as optical fiber, geophysics, laser optics, mathematical physics, nonlinear optics, nonlinear dynamics, communication system, and engineering. This study explores the physical behavior of models through the visualization of solutions in contour, 2D and 3D plots by revealing that these solutions yield profitable results in the field of mathematical physics. The study demonstrates that the proposed technique is more reliable, efficient, and powerful in analyzing nonlinear evolution equations in various domains of science.

Molecular docking based comparative study of antiviral compounds on SARS-CoV-2 spike protein.

The urgent need for effective therapeutic interventions against SARS-CoV-2 has prompted extensive exploration of potential drug candidates. Among the viral proteins, the spike (S) protein presents an attractive target due to its critical role in viral entry and infection. In this study, we employed molecular docking techniques to investigate the binding affinities and interaction profiles of a panel of active compounds against the SARS-CoV-2 spike protein. Utilising computational simulations, we assessed the binding properties of these compounds within the receptor-binding domain (RBD) and other key regions of the spike protein. Our comparative analysis elucidates the differential binding patterns and identifies promising lead compounds with high binding affinity and favourable interaction profiles. Furthermore, we discuss the implications of these findings for the development of potential therapeutics targeting the SARS-CoV-2 spike protein. Using molecular docking and the Lipinski five rule, this study illustrates possible compounds with strong binding affinities, their molecular interactions, for both naturally occurring and man-made drugs. Computational approach is applied, and it is concluded that, drugs like Withanolide, Dihydroergocristine, Fenebrutinib, and Ergotamine shows binding energies between -8.3 and -9.1 kcal/mol, and are possible candidate for anti covid drug.

Inhibition of DEF-p65 Interactions as a Potential Avenue to Suppress Tumor Growth in Pancreatic Cancer.

The limited success of current targeted therapies for pancreatic cancer underscores an urgent demand for novel treatment modalities. The challenge in mitigating this malignancy can be attributed to the digestive organ expansion factor (DEF), a pivotal yet underexplored factor in pancreatic tumorigenesis. The study uses a blend of in vitro and in vivo approaches, complemented by the theoretical analyses, to propose DEF as a promising anti-tumor target. Analysis of clinical samples reveals that high expression of DEF is correlated with diminished survival in pancreatic cancer patients. Crucially, the depletion of DEF significantly impedes tumor growth. The study further discovers that DEF binds to p65, shielding it from degradation mediated by the ubiquitin-proteasome pathway in cancer cells. Based on these findings and computational approaches, the study formulates a DEF-mimicking peptide, peptide-031, designed to disrupt the DEF-p65 interaction. The effectiveness of peptide-031 in inhibiting tumor proliferation has been demonstrated both in vitro and in vivo. This study unveils the oncogenic role of DEF while highlighting its prognostic value and therapeutic potential in pancreatic cancer. In addition, peptide-031 is a promising therapeutic agent with potent anti-tumor effects.

New oxadiazol‐5‐ones derivatives and their performance as angiotensin‐converting enzyme (ACE) inhibitors

AbstractAngiotensin‐converting enzyme inhibitors are widely used in treating arterial hypertension, acting on the renin‐angiotensin‐aldosterone system and controlling blood pressure. We present a novel, greener, and faster methodology to assess the 1,2,4‐oxadiazol‐5‐one ring and perform molecular modifications to obtain angiotensin‐converting enzyme (ACE) inhibitors using this heterocyclic core. Molecular docking simulations indicate that the tested compounds exhibited an affinity for the ACE binding site, with scores comparable to the commercial inhibitor lisinopril. However, in vitro assays revealed that the compounds were ineffective in inhibiting ACE activity. The lack of inhibition may be related to the compounds' more apolar nature. These results emphasize the importance of integrating computational and experimental approaches in developing new drugs, providing valuable insights for planning future studies to optimize the activity of synthesized compounds.

Analysis and Optimization of the Performances of the Tandem Blade Radial Compressor Using the CFD

Centrifugal compressors are frequently used in both military and commercial areas because they can be easily manufactured and reach high-pressure compression ratios. The factor that limits the performance and operating range of compressors is flow instability. Many ideas have been put forward for performance improvement, but tandem blade radial compressors, which do not require an extra air system, have attracted the most interest. In this study, computational fluid dynamics (CFD) analyses were carried out on various parameters of the tandem blade (TB) radial compressor, and an optimization study was carried out to find the best design using a genetic algorithm on a whole operating curve. It was investigated how these parameters affected the efficiency and total pressure ratio between the determined lower and upper limits. The numerical analyses of the optimum design obtained as a result of the iterations were carried out. As a result of the iterations, three optimum designs were obtained and numerical analysis was carried out according to one of them, and then they were compared with the results in the literature. The general agreement of computational fluid dynamics and the literature data served as a validation for the computational approach. The error rates between the numerical analysis results and the experimental results in the literature were calculated for different flow rates and were found to be 1.98% as the highest and 0.35 as the lowest. The work carried out in this article will provide a valuable reference for future advanced tandem blade compressor designs.

Simulation of Chemical Reactions on a Quantum Computer.

Studying chemical reactions, particularly in the gas phase, relies heavily on computing scattering matrix elements. These elements are essential for characterizing molecular reactions and accurately determining reaction probabilities. However, the intricate nature of quantum interactions poses challenges, necessitating the use of advanced mathematical models and computational approaches to tackle the inherent complexities. In this study, we develop and apply a quantum computing algorithm for the calculation of scattering matrix elements. In our approach, we employ the time-dependent method based on the Møller operator formulation where the S-matrix element between the respective reactant and product channels is determined through the time correlation function of the reactant and product Møller wavepackets. We successfully apply our quantum algorithm to calculate scattering matrix elements for 1D semi-infinite square well potential and on the colinear hydrogen exchange reaction. As we navigate the complexities of quantum interactions, this quantum algorithm is general and emerges as a promising avenue, shedding light on new possibilities for simulating chemical reactions on quantum computers.

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data, and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine learning techniques. However, this analysis incurs high computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.

Size-dependent effect on the interaction of surface waves in micropolar thermoelastic medium with dual pore connectivity

This research tackles a critical knowledge gap in Rayleigh surface wave propagation. It offers a comprehensive analysis that surpasses previous limitations. A size-dependent micropolar medium with unique void distributions and thermal effects is considered in this work. The constitutive relations and equations of motion for a nonlocal micropolar thermoelastic medium with double voids (MTMWDV) have been established by using Eringen’s nonlocal elasticity theory. Employing the three-phase-lag thermoelasticity theory (TPLTE), the study utilizes a wave-mode method to derive analytical solutions for Rayleigh waves in a nonlocal MTMWDV. To gain a comprehensive understanding of wave behavior, we solve the characteristic equation and analyze its roots, applying a filter based on the surface wave decay condition. A medium with stress-free and isothermal boundaries is explored through computational simulations to determine the attenuation coefficient and phase velocity. Furthermore, particle motion analysis is conducted to complement the analytical and computational approaches. Moreover, the influence of the nonlocal parameter and various thermoelastic models on these wave phenomena is investigated. The validity of the current mathematical model is confirmed through the derivation of particular scenarios.

Rationalization of a Streptavidin Based Enantioselective Artificial Suzukiase: An Integrative Computational Approach.

An Artificial Metalloenzyme (ArM) built employing the streptavidin-biotin technology has been used for the enantioselective synthesis of binaphthyls by means of asymmetric Suzuki-Miyaura cross-coupling reactions. Despite its success, it remains a challenge to understand how the length of the biotin cofactors or the introduction of mutations to streptavidin leads the preferential synthesis of one atropisomer over the other. In this study, we apply an integrated computational modeling approach, including DFT calculations, protein-ligand dockings and molecular dynamics to rationalize the impact of mutations and length of the biotion cofactor on the enantioselectivities of the biaryl product. The results unravel that the enantiomeric differences found experimentally can be rationalized by the disposition of the first intermediate, coming from the oxidative addition step, and the entrance of the second substrate. The work also showcases the difficulties facing to control the enantioselection when engineering ArM to catalyze enantioselective Suzuki-Miyaura couplings and how the combination of DFT calculations, molecular dockings and MD simulations can be used to rationalize artificial metalloenzymes.

On the History of the Lennard‐Jones Potential

AbstractThis contribution describes two episodes from the history of the Lennard‐Jones (LJ) potential. The first, located in the 1920s and 1930s, is about a computational approach that aimed at pragmatics rather than truth and that remained remarkably robust when quantum theory arrived. The second episode covers the birth of the LJ substance in 1964. Due to increasing interest in computer methods, simulated model substances became objects on their own, the prime targets of investigation. The history of the LJ potential and substance exemplifies the dynamic relationship between prediction, theory, mathematization, and computer instrumentation.