Root Mean Square Deviation Values Research Articles

Exploring novel antitubercular agents: Innovative design of 2,3-diaryl-quinoxalines targeting DprE1 for effective tuberculosis treatment

Abstract The rising instances of drug resistance in Mycobacterium tuberculosis strains pose a significant global health challenge. Conventional tuberculosis (TB) treatments, which typically involve multiple antibiotics, face hurdles like drug resistance, reduced effectiveness, and heightened toxicity. Consequently, there is a pressing need for innovative anti-TB agents with new modes of action. Decaprenyl-phosphoryl-β-d-ribose 2′-epimerase 1(DprE1), a crucial enzyme in Mycobacterium tuberculosis, plays a vital role in cell wall biosynthesis – a critical aspect for the bacterium’s survival. Building on the success of diarylquinolines like bedaquiline, targeting DprE1 presents a promising avenue for developing anti-TB drugs, especially against drug-resistant strains. Our research focused on discovering novel DprE1 inhibitors using a ligand-based drug design strategy, starting with the established non-covalent inhibitor Ty38c. We assembled a library of 16 molecules, modifying them based on factors like drug-like properties, chemical accessibility, and synthetic feasibility. Molecular docking analyses of this library identified three molecules with binding affinities comparable to Ty38c. Among these, KS_QD_05 and KS_QD_04 are promising candidates, which were further validated through molecular dynamics simulation studies where root-mean-square deviation (RMSD) values of all three complexes reached a plateau, measuring around 0.3 nm, indicating that the apoprotein and all complexes stabilized during the simulation. The ligands KS_QD_04 and KS_QD_05 displayed significantly stable deviation. KS_QD_05 reached about 0.1 nm equilibrium value. However, the ligand KS_QD_04 reached an RMSD value of 0.17 nm and showed distress at 70 nm. KS_QD_04 and KS_QD_05 showed an average value of 1-3 H-bond interaction and regarding the RMSF values, both the compounds showed fluctuations less than 0.5 nm in the case of Mtb. DprE1 enzyme. This indicates the potential of both compounds to become lead compounds in the pursuit of DprE1 inhibitors for TB treatment.

Identification of steroidal cardenolides from Calotropis procera as novel HIV-1 PR inhibitors: A molecular docking & molecular dynamics simulation study.

Background & objectives Despite advancements in antiretroviral therapy, drug-resistant strains of HIV (human immunodeficiency virus) remain a global health concern. Natural compounds from medicinal plants offer a promising avenue for developing new HIV-1 PR (protease) inhibitors. This study aimed to explore the potential of compounds derived from Calotropis procera, a medicinal plant, as inhibitors of HIV-1 PR. Methods This in silico study utilized natural compound information and the crystal structure of HIV-1 PR. Molecular docking of 17 steroidal cardenolides from Calotropis procera against HIV-1 PR was performed using AutoDock 4.2 to identify compounds with higher antiviral potential. A dynamic simulation study was performed to provide insights into the stability, binding dynamics, and potential efficacy of the top potential antiviral compound as an HIV-1 therapeutic. Results We found that all tested cardenolides had higher binding affinities than Amprenavir, indicating their potential as potent HIV-1 PR inhibitors. Voruscharin and uscharidin displayed the strongest interactions, forming hydrogen bonds and hydrophobic interactions with HIV-1 PR. Voruscharin showed improved stability with lower RMSD (Root Mean Square Deviation) values and reduced fluctuations in binding site residues but increased flexibility in certain regions. The radius of gyration analysis confirmed a stable binding pose between HIV-1 PR and Voruscharin. Interpretation & conclusions These findings suggest that Calotropis procera could potentially be a source of compounds for developing novel HIV-1 PR inhibitors, contributing to the efforts to combat HIV. Further studies and clinical trials are needed to evaluate the safety and efficacy of these compounds as potential drug candidates for the treatment of HIV-1 infection.

Computational insights into the inhibitory mechanism of type 2 diabetes mellitus by bioactive components of Oryza sativa L. indica (black rice).

Type 2 diabetes mellitus is a metabolic disease categorized by hyperglycemia, resistance to insulin, and ß-cell dysfunction. Around the globe, approximately 422 million people have diabetes, out of which 1.5million die annually. In spite of innovative advancements in the treatment of diabetes, no biological drug has been known to successfully cure and avert its progression. Thereupon, natural drugs derived from plants are emerging as a novel therapeutic strategy to combat diseases like diabetes. The current study aims to investigate the antidiabetic potential of natural compounds of Oryza sativa L. indica (black rice) in disease treatment. Antioxidant activity and alpha amylase assays were performed to evaluate the therapeutic potential of the extract of Oryza sativa L. indica. Gas chromatography-mass spectrometry (GC-MS) was used for identification of constituents from the ethanol extract. ADMET profiling (absorption, distribution, metabolism, excretion, and toxicity), network pharmacology, and molecular dynamics simulation were employed in order to uncover the active ingredients and their therapeutic targets in O. sativa L. indica against type 2 diabetes mellitus. GC-MS of the plant extract provided a list of 184 compounds. Lipinski filter and toxicity parameters screened out 18 compounds. The topological parameters of the protein-protein interaction (PPI) were used to shortlist the nine key proteins (STAT3, HSP90AA1, AKT1, SRC, ESR1, MAPK1, NFKB1, EP300, and CREBBP) in the type 2 diabetes mellitus pathways. Later, molecular docking analysis and simulations showed that C14 (1H-purine-8-propanoic acid, .alpha.-amino-2, 3, 6, 7-tetrahydro-1,3,7-trimethyl-2,6-dioxo-) and C18 (cyclohexane-carboxamide, N-furfuryl) bind with AKT1 and ESR1 with a binding energy of 8.1, 6.9, 7.3, and 7.2kcal/mol, respectively. RMSD (root-mean-square deviation) and RMSF (root-mean-square fluctuation) values for AKT1 and ESR1 have shown very little fluctuation, indicating that proteins were stabilized after ligand docking. This study suggests therapeutic drug candidates against AKT1 and ESR1 to treat type 2 diabetes mellitus. However, further wet-lab analysis is required to discover the best remedy for type 2 diabetes mellitus.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Thymol-1,2,3-triazole derivatives: Network pharmacology, molecular simulations and synthesis targeting breast cancer

Cancer prevention has become a major focus of public health in the 21st century. Despite advancements in treatment, tackling cancer remains a significant challenge due to the different types of cancer. Breast cancer stands out as the leading cause of cancer-related deaths, highlighting its significant impact on female health worldwide. Monoterpenes, exemplified by thymol, exhibit notable potential in cancer treatment, owing to their pharmacological properties and mechanisms of action. Concurrently, 1,2,3-triazoles have garnered attention as promising entities for the development of anticancer agents, showcasing their efficacy and relevance in oncology research. This study describes the synthesis and characterization of a novel library of 1,2,3-triazole derivatives tethered to p-methoxythymol, a natural product. High-resolution mass spectrometry (HRMS) definitively confirmed the structures of the synthesized compounds, which were further elucidated by detailed analysis of their ¹H- and ¹³CNMR spectra. To predict the suitable target, network pharmacology was applied to the synthesized thymol derivatives, resulting in the identification of phosphatidylinositol-3-kinase (PIK3CA) as a key target. Furthermore, these thymol derivatives were screened for ADMET and molecular docking against the phosphatidylinositol-3-kinase (PIK3CA) target. Molecular dynamics simulations were conducted for the top two compounds over a duration of 100 ns to assess their stability. The RMSD (Root-Mean-Square Devia­tion) and RMSF (root-mean-square fluctuation) values for amino acid residues indicated structural integrity and stable protein-ligand interaction. Additionally, all designed compounds displayed favorable physicochemical properties and ADMET profiles, and two compounds, 4b and 4d, were found to be new lead molecules targeting PIK3CA

Repeatability and minimal detectable change including clothing effects for smartphone-based 3D markerless motion capture

OpenCap, a smartphone- and web-based markerless system, has shown acceptable accuracy compared to marker-based systems, but lacks information on repeatability. This study fills this gap by evaluating the intersession repeatability of OpenCap and investigating the effects of clothing on gait kinematics. Twenty healthy volunteers participated in a test–retest study, performing walking and sit-to-stand tasks with minimal clothing and regular street wear. Segment lengths and lower-limb kinematics were compared between both sessions and for both clothing conditions using the root-mean-square-deviation (RMSD) for entire waveforms and the standard error of measurement (SEM) and minimal detectable change (MDC) for discrete kinematic parameters. In general, the RMSD test–retest values were 2.8 degrees (SD: 1.0) for walking and 3.3 degrees (SD: 1.2) for sit-to-stand. The highest intersession variability was observed in the trunk, pelvis, and hip kinematics of the sagittal plane. SEM and MDC values were on average 2.2 and 6.0 degrees, respectively, for walking, and 2.4 and 6.5 degrees for sit-to-stand. Clothing had minimal effects on kinematics by adding on average less than one degree to the RMSD values for most variables. The segment lengths showed moderate to excellent agreement between both sessions and poor to moderate agreement between clothing conditions. The study highlights the reliability of OpenCap for markerless motion capture, emphasizing its potential for large-scale field studies. However, some variables showed high MDC values above 5 degrees and thus warrant further enhancement of the technology. Although clothing had minimal effects, it is still recommended to maintain consistent clothing to minimize overall variability.

Separation of 2-ethoxyethanol from wastewater by four esters: Correlation of LLE thermodynamic modeling, influence of molecular structure, and investigation of component interactions

To prevent the wastewater containing 2-ethoxyethanol (2EE) from causing environmental pollution and economic waste, liquid–liquid extraction was used to recover 2-ethoxyethanol from wastewater in this study. The liquid–liquid equilibrium behavior of water + 2-ethoxyethanol + four solvents (propyl acetate, isopropyl acetate, n-butyl acetate, and isobutyl acetate) was investigated at 303.2 K and atmospheric pressure. The distribution coefficient (D) and separation factor (S) were used to compare and discuss the extraction efficiency of each system. The results indicated that all esters could effectively separate 2-ethoxyethanol from water. Among them, isobutyl acetate exhibited the highest values for D and S, demonstrating its superior separation capability. The experimental data were correlated with the thermodynamic model and interaction parameters were obtained. These parameters were verified using the GUI-MATLAB tool, and all four systems passed validation, confirming the reliability of the obtained binary interaction parameters. Furthermore, the root mean square deviation (RMSD) was used to assess the deviation between the experimental and regression values. The RMSD values for the NRTL and UNIQUAC models were 0.008 and 0.007, respectively. The interactions between 2-ethoxyethanol and water or esters were explored using σ-Profile, and it was found that esters with a branched chain structure provided better extraction. To explain the properties of the extractants microscopically, the interaction energies between 2-ethoxyethanol molecules and water or ester molecules, as well as the non-bonding energies of 2-ethoxyethanol in different media, were calculated.

Exploring KRas Protein Dynamics: An Integrated Molecular Dynamics Analysis of KRas Wild and Mutant Variants.

This study employs a comprehensive approach combining protein retrieval, sequence alignment, and molecular dynamics simulations to investigate the structural dynamics and stability of wild-type KRas and its mutated variants (G12C, G12D, G12V, and G13D). The selected protein structures were retrieved from the Protein Data Bank (PDB) and prepared by using visual molecular dynamics (VMD) software. Sequence alignment using Clustal Omega provided a detailed comparison of the amino acid sequences, focusing on key mutation sites. Molecular dynamics simulations, performed with Gromacs, revealed distinct conformational changes and stability patterns in the wild-type and mutated KRas proteins over 100 ns. Clustering analysis identified higher conformational changes in the second α-helix of the mutated variants. The root-mean-square deviation (RMSD) distribution analysis showed variant-specific conformational dynamics, with G12V and G12D exhibiting slightly higher average RMSD values. Furthermore, clustering and RMSD analyses of specific amino acid residues (12, 13, 51, and 118) highlighted their roles in maintaining overall stability and influencing structural dynamics. The results indicate that mutations at positions 12 and 13 disrupt normal cycling between wild and mutated variants, leading to the persistent activation of KRas. Additionally, principal component analysis (PCA) elucidated unique conformational dynamics in mutated variants. Free energy landscape (FEL) analysis revealed alterations in the thermodynamic stability of mutated variants compared with the wild type. Overall, this study provides a detailed understanding of the structural changes associated with oncogenic mutations in KRas, offering insights crucial for targeted therapeutic strategies in KRas-driven cancers.

Which one is more accurate, BILOG or R program? (a comparison for score test equating)

Evaluation may be carried out using different tests that are not necessarily parallel. Students with lower abilities may get higher scores while those with higher abilities get lower scores. Measurement errors caused by this condition require test equating. Several computer programs, including Bilog and the R program, can be used for test equating. Each program has a different level of accuracy, and the accuracy of the equating results will affect the standard errors of equating. This study aimed to find out the most accurate equating test method and the accuracy of the estimated BILOG and R program. This research used two sets of tests with equivalent group designs. The determination of the most accurate equating method was based on the root mean square deviation (RMSD) value. Equating test packages, A to package B with BILOG program estimation on the mean and Sigma method resulted in RMSD value of 0.320. In the mean and mean method, RMSD value is 0.250. Meanwhile, equating package A to package B using R program on the mean and sigma resulted in RMSD value of 0.300, and the mean and mean method with the RMSD value is 0.272. The mean and mean yield RMSD values smaller than the mean and sigma methods. Therefore, the mean and mean method is more accurate and applicable. Moreover, the estimation results of the BILOG program are more accurate and can be used in test equating.

Validation of upper extremity kinematics using Markerless motion capture

Movement research has typically been performed using three-dimensional (3D) marker-based motion capture, which is considered the “gold-standard” for biomechanical assessment. However, limitations exist due to the lack of portability, extensive preparation for data collection, marker placement training, error due to marker movement, and possible skin irritation due to marker adhesives. There is inherent error due to motion artifact stemming from skin movement and differences in marker placement between testers. Markerless motion capture systems are emerging as a new method of kinematic assessment. These methods require little preparation and there is no need to alter participant clothing. Markerless motion capture has also been validated for the lower extremity in healthy older adults during gait. However, it has not been validated for other populations or for the assessment of upper extremity (UE) motion. Therefore, the purpose of this study was to examine differences in calculated UE kinematics between marker-based and a markerless motion capture system. Participants attended two data collection sessions. Marker-based and markerless motion capture data was collected simultaneously while participants completed the Box and Blocks test (BBT). Kinematic and spatiotemporal data from both systems was exported using identical time series to ensure the same conditions for comparisons. Intraclass Correlation Coefficients (ICCs) were calculated to determine between session reliability for both systems on range of motion and peak joint angular data to ensure movement variability was not affecting measurement consistency. ICCs and Bland Altman statistics were also calculated between the systems. Root mean square deviation (RMSD) values were determined between demeaned UE joint angles for the two systems to examine movement pattern differences. The resulting between-session ICCs for each system showed that the markerless system shared similar reliability during this task as the marker-based system, further supporting the effect of variability on between-session reliability. Between-system ICCs resulted in good (0.7<ICC<0.9) to excellent (ICC>0.9) agreement. Bland Altman results confirmed the existence of measurement bias between the systems. RMSD values for all UE joint angles were found to be less than 6°. Overall, the results from this study support the use of markerless motion capture in clinical settings to examine upper extremity biomechanics in children.

MicronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability.

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN), that can be enumerated to quantify CIN. Despite recent advancements in automation through computer vision and machine learning, the assessment of CIN remains a predominantly manual and time-consuming task, thus hampering important work in the field. Here, we present micronuclAI , a novel pipeline for automated and reliable quantification of MN of varying size, morphology and location from DNA-only stained images. In micronucleAI , single-cell crops are extracted from high-resolution microscopy images with the help of segmentation masks, which are then used to train a convolutional neural network (CNN) to output the number of MN associated with each cell. The pipeline was evaluated against manual single-cell level counts by experts and against routinely used MN ratio within the complete image. The classifier was able to achieve a weighted F1 score of 0.937 on the test dataset and the complete pipeline can achieve close to human-level performance on various datasets derived from multiple human and murine cancer cell lines. The pipeline achieved a root-mean-square deviation (RMSE) value of 0.0041, an R 2 of 0.87 and a Pearson's correlation of 0.938 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and also on a publicly available image data set (obtained at 100X) and achieved an RMSE value of 0.0159, an R 2 of 0.90, and a Pearson's correlation of 0.951. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on routinely obtained images. We release a GUI-implementation for easy access and utilization of the pipeline.

UNIFAC PREDICTION OF VAPOR LIQUID EQUILIBRIA INVOLVING GAMMA-VALEROLACTONE DERIVATIVE SYSTEMS

Researchers have studied the hydrogenation of Gamma-Valerolactone (GVL) over Ru/C to produce 2-methyltetrahydrofuran (2-MTHF), a biomass-based platform chemical with potential as a biofuel and green solvent. Other byproducts of this reaction include 2-butanol (2-BuOH), 2-pentanol (2-PeOH), and 1,4-pentanediol (1,4-PDO). In this study, UNIFAC activity coefficient models were used to predict the vapor-liquid equilibrium of several systems included in the process to explain the phase behavior which is important in purification process. The accuracy of the UNIFAC model is tested by comparing the experimental boiling points for the binary system of 2-propanol + 1-butanol and the vapor-liquid equi-librium of the GVL + 2-MTHF system. From this comparison, the Root Mean Square Deviation (RMSD) values for the boiling point measurements are obtained to be 3.153%, and for the liquid and vapor phases in the vapor-liquid equilibrium measurements, the RMSD values are 1.934% and 0.298% respectively. These RMSD values indicate the level of accuracy of the UNIFAC model in representing the experimental data for both boiling point measurements and vapor-liquid equilibrium phase behavior. The prediction results of vapor-liquid equilibrium data for GVL derivative systems showed that the 2-BuOH + 2-MTHF system does form an azeotrope when the mol fraction of 2-BuOH is 0.0031 at 79.780C. The calculation was performed using ChemCAD commercial software for chemical process modeling and simulation

HSPiP, Computational, and Thermodynamic Model-Based Optimized Solvents for Subcutaneous Delivery of Tolterodine Tartrate and GastroPlus-Based In Vivo Prediction in Humans: Part I.

Tolterodine tartrate (TOTA) is associated with adverse effect, high hepatic access, varied bioavailability, slight aqueous solubility, and short half-life after oral delivery. Hansen solubility parameters (HSP, HSPiP program), experimental solubility (T = 298.2 to 318.2K and p = 0.1MPa), computational (van't Hoff and Apelblat models), and thermodynamic models were used to the select solvent(s). HSPiP predicted PEG400 as the most suitable co-solvent based on HSP values (δd = 17.88, δp = 4.0, and δh = 8.8 of PEG400) and comparable to the drug (δd = 17.6, δp = 2.4, and δh = 4.6 of TOTA). The experimental mole fraction solubility of TOTA was maximum (xe = 0.0852) in PEG400 confirming the best fit of the prediction. The observed highest solubility was attributed to the δp and δh interacting forces. The activity coefficient (ϒi) was found to be increased with temperature. The higher values of r2 (linear regression coefficient) and low RMSD (root mean square deviation) indicated a good correlation between the generated "xe" data for crystalline TOTA and the explored models (modified Apelblat and van't Hoff models). TOTA solubility in "PEG400 + water mixture" was endothermic and entropy-driven. IR (immediate release product) formulation can be tailored using 60% PEG400 in buffer solution for 2mg of TOTA in 0.25mL (dosing volume). The isotonic binary solution was associated with a pH of 7.2 suitable for sub-Q delivery. The approach would be a promising alternative with ease of delivery to children and aged patients.

Design, synthesis, and bioevaluation of novel unsaturated cyanoacetamide derivatives: In vitro and in silico exploration

In this study, we synthesized novel α,β-unsaturated 2-cyanoacetamide derivatives (1–5) using microwave-assisted Knoevenagel condensation. Characterization of these compounds was carried out using FTIR and 1H NMR spectroscopy. We then evaluated their in vitro antibacterial activity against both gram-positive and gram-negative pathogenic bacteria. Additionally, we employed in silico methods, including ADMET prediction and density functional theory (DFT) calculations of molecular orbital properties, to investigate these cyanoacetamide derivatives (1–5). Molecular docking was used to assess the binding interactions of these derivatives (1–5) with seven target proteins (5MM8, 4NZZ, 7FEQ, 5NIJ, ITM2, 6SE1, and 5GVZ) and compared them to the reference standard tyrphostin AG99. Notably, derivative 5 exhibited the most favorable binding affinity, with a binding energy of -7.7 kcal mol−1 when interacting with the staphylococcus aureus (PDB:5MM8), while also meeting all drug-likeness criteria. Additionally, molecular dynamics simulations were carried out to evaluate the stability of the interaction between the protein and ligand, utilizing parameters such as Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), Radius of Gyration (Rg), and Principal Component Analysis (PCA). A 50 nanosecond molecular dynamics (MD) simulation was performed to investigate stability further, incorporating RMSD and RMSF analyses on compound 5 within the active binding site of the modeled protein across different temperatures (300, 305, 310, and 320 K). Among these temperatures, compound 5 exhibited an RMSD value ranging from approximately 0.2 to 0.3 nm at 310 K (body temperature) with the 5MM8 target, which differed from the other temperature conditions. The in silico results suggest that compound 5 maintained significant conformational stability throughout the 50 ns simulation period. It is consistent with its low docking energy and in vitro findings concerning α,β-unsaturated cyanoacetamides.Key insights from this study include:•The creation of innovative α,β-unsaturated 2-cyanoacetamide derivatives (1–5) employing cost-effective, licensed, versatile, and efficient software for both in silico and in vitro assessment of antibacterial activity.•Utilization of FTIR and NMR techniques for characterizing compounds 1–5.

Mammalian maltase-glucoamylase and sucrase-isomaltase inhibitory effects of Artocarpus heterophyllus: An in vitro and in silico approach

Alpha-glucosidase (maltase, sucrase, isomaltase and glucoamylase) activities which are involved in carbohydrate metabolism are present in human intestinal maltase-glucoamylase (MGAM) and sucrase-isomaltase (SI). Hence, these proteins are important targets to identify drugs against postprandial hyperglycemia thereby for diabetes. To find natural-based drugs against MGAM and SI, Artocarpus heterophyllus leaf was explored for MGAM and SI inhibition in in vitro and in silico. A. heterophyllus leaf aqueous active fraction (AHL-AAF) was prepared using Soxhlet extraction followed by silica column chromatography. The phytoconstituents of AHL-AAF were determined using LC-ESI-MS/MS. AHL-AAF showed dose-dependent and mixed inhibition against maltase (IC50 = 460 µg/ml; Ki = 300 µg/ml), glucoamylase (IC50 = 780 µg/ml; Ki = 480 µg/ml), sucrase (IC50 = 900 µg/ml, Ki = 504 µg/ml) and isomaltase (IC50 = 860 µg/ml, Ki = 400 µg/ml). AHL-AAF phytoconstituents interaction with N-terminal (Nt) and C-terminal (Ct) subunits of human MGAM and SI was analyzed using induced-fit docking, molecular dynamics (MD), and binding free energy calculation. In docking studies, rhamnosyl hexosyl methyl quercetin (RHMQ), P-coumaryl-O-16-hydroxy palmitic acid (PCHP), and spirostanol interacted with active site amino acids of human MGAM and SI. Among these RHMQ stably interacted with all the subunits (Nt-MGAM, Ct-MGAM, Nt-SI and Ct-SI) whereas PCHP with Ct-MGAM and Nt-SI during MD analysis. In molecular docking, the docking score of RHMQ with NtMGAM, CtMGAM, NtSI and CtSI was −8.48, −12.88, −11.98 and −11.37 kcal/mol. The docking score of PCHP for CtMGAM and NtSI was −8.59 and −8.4 kcal/mol, respectively. After MD simulation, the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values further confirmed the stable protein-ligand interaction. The RMSD value of all the complexes were around 2.5 Å and the corresponding RMSF values were also quite low. In MM/GBSA analysis, the involvement of Van der Waals and lipophilic energy in the protein/ligand interactions are understood. Further binding free energy for Nt-MGAM-PCHP, Nt-MGAM-RHMQ, Nt-SI-PCHP, Nt-SI-RHMQ, Ct-MGAM-PCHP, Ct-MGAM-RHMQ and Ct-SI-RHMQ complexes was found to be −24.94, −46.60, −46.56, −44.48, −40.3, −41.86 and −19.39 kcal/mol, respectively. Altogether, AHL-AAF showed inhibition of α-glucosidase activities of MGAM and SI. AHL-AAF could be further studied for its effect on diabetes in in vivo.

Computational insights into KRAS G12C inhibition: exploring possible repurposing of Azacitidine and Ribavirin

Kirsten rat sarcoma (KRAS) stands out as the most prevalent mutated oncogene, playing a crucial role in the initiation and progression of various cancer types, including colorectal, lung and pancreatic cancer. The oncogenic modifications of KRAS are intricately linked to tumor development and are identified in 22% of cancer patients. This has spurred the necessity to explore inhibition mechanisms, with the aim of investigating and repurposing existing drugs for diagnosing cancers dependent on KRAS G12C In this investigation, 26 nucleoside-based drugs were collected from literature to assess their effectiveness against KRAS G12C. The study incorporates in-silico molecular simulations and molecular docking examinations of these nucleoside-derived drugs with the KRAS G12C protein using Protein Data Bank (PDB) ID: 5V71. The docking outcomes indicated that two drugs, Azacitidine and Ribavirin, exhibited substantial binding affinities of −8.7 and −8.3 kcal/mol, respectively. These drugs demonstrated stability in binding to the active site of the protein during simulation studies. Root mean square deviation (RMSD) analyses indicated that the complexes closely adhered to an equilibrium RMSD value ranging from 0.17 to 0.2 nm. Additionally, % occupancies, bond angles and the length of hydrogen bonds were calculated. These findings suggest that Azacitidine and Ribavirin may potentially serve as candidates for repurposing in individuals with KRAS-dependent cancers. Communicated by Ramaswamy H. Sarma

Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the deep learning method. For this purpose, a Trained Neural Network (TNN) was used to produce 75 molecules similar to Favipiravir by using Simplified Molecular Input Line Entry System (SMILES) representations. The binding properties of molecules to Viral RNA-dependent RNA polymerase (RdRp) were studied by using molecular docking studies. To confirm the accuracy of this method, compounds were also tested against 3CL protease (3CLpro), which is another important enzyme for the progression of SARS-CoV-2. Compounds having better binding energies and RMSD values than favipiravir were searched with similarity analysis on the ChEMBL drug database in order to find similar structures with RdRp and 3CLpro inhibitory activities. A similarity search found new 200 potential RdRp and 3CLpro inhibitors structurally similar to produced molecules, and these compounds were again evaluated for their receptor interactions with molecular docking studies. Compounds showed better interaction with RdRp protease than 3CLpro. This result presented that artificial intelligence correctly produced structures similar to favipiravir that act more specifically as RdRp inhibitors. In addition, Lipinski's rules were applied to the molecules that showed the best interaction with RdRp, and 7 compounds were determined to be potential drug candidates. Among these compounds, a Molecular Dynamic simulation study was applied for ChEMBL ID:1193133 to better understand the existence and duration of the compound in the receptor site. The results confirmed that the ChEMBL ID:1193133 compound showed good Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), hydrogen bonding, and remaining time in the active site; therefore, it was considered that it could be active against the virus. This compound was also tested for antiviral activity, and it was determined that it did not delay viral infection, although it was cytotoxic between 5mg/mL-1.25mg/mL concentrations. However, if other compounds could be tested, it might provide a chance to obtain activity, and compounds should also be tested against the enzymes as well as the other types of viruses.

In silico simulation of hyperoside, isoquercetin, quercetin, and quercitrin as potential antivirals against the pNP868R protein of African swine fever virus.

African swine fever (ASF) causes disease in pigs with up to 100% mortality rates. There is no effective vaccine to protect against it. This study aimed to perform in silico docking of ASF virus (ASFV) pNP868R protein with potential flavonoid ligands to identify ligands that interfere with mRNA cap formation. The ASFV pNP868R protein was tested with hyperoside, isoquercetin, quercetin, and quercitrin in this in silico simulation. ASFV pNP868R protein was extracted from the Research Collaboration for Structural Bioinformatics Protein Data Bank (RCSB PDB) database with PDB ID 7D8U (https://www.rcsb.org/structure/7D8U). Standard ligands were separated from proteins using UCSF Chimera 1.13. The standard ligand was redocked to protein using AutoDockTools 1.5.6 with the AutoDock4 method for validation. In the docking process, the grid box size was 40 × 40 × 40 Å3 with x, y, and z coordinates of 16.433, -43.826, and -9.496, respectively. The molecular docking process of the proposed ligand-protein complex can proceed if the standard ligand position is not significantly different from its original position in the viral protein's pocket. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (RoG) of the hyperoside with the lowest energy binding need to be analyzed with molecular dynamics using Groningen machine for chemical simulation 5.1.1. Molecular docking and dynamic simulation revealed that hyperoside had the most stable and compact binding to the pNP868R protein. Hyperoside binds to the protein at the minimum energy of -9.07 KJ/mol. The RMSD, RMSF, and RoG values of 0.281 nm, 0.2 nm, and 2.175 nm, respectively, indicate the stability and compactness of this binding. Hyperoside is the most likely antiviral candidate to bind to the pNP868R protein in silico. Therefore, it is necessary to test whether this flavonoid can inhibit mRNA capping in vitro and elicit the host immune response against uncapped viral mRNA.

The Accuracy Verification of Recent Global Geopotential Model based on GNSS/Levelling Data on Ground Control Points in Johor

The external gravitational field of the Earth is mathematically represented using spherical harmonic (SH) coefficients in the global geopotential model. Hence, the appropriate global geopotential model for a region needs to determine a reference surface that refers to geoid heights specific for coastal erosion, flood and many more. The aim of this study is to estimate the best global geopotential model model in Johor region which is selected based on satellite only and combined model for the years 2018 until 2022. Next, the global geopotential models are verified with Global Navigation Satellite System (GNSS) levelling data based on root mean square deviation (RMSE) value in Johor region near coastal area. The findings found that EIGEN-GRGS.RL04. MEAN_FIELD has the lowest RMSE values that represent the highest accuracy with 0.1911m than the other models in their respective categories and represent the best adjustment model. In conclusion, the global geopotential model (GGM) is the most suitable used as a reference for future studies in Johor region from the satellite-only model of EIGEN-GRGS.RL04. MEAN_FIELD with the degree and order 300 compared to other models.

Analysis of Molecular Docking and Dynamics Simulation of Mahogany (Swietenia macrophylla King) Compounds Against the PLpro Enzyme SARS-COV-2

Background: Using natural ingredients as antivirals can be considered a treatment for SARS-CoV-2. One of the potential plants, mahogany (Swietenia macrophylla King), is widely used in various countries as an antiviral treatment. Paparin-like protease (PLpro) is an essential cysteine ​​protease that regulates viral replication and interferes with the regulation of immune sensing. Objective: This study aims to predict which compounds in the mahogany plant have good affinity, patterns, and stability interaction against the target protein of SARS-CoV-2. Methods: The drug-likeness parameter using SwissADME was used to screen compounds that will be docked against PLpro using the Autodock program. The parameters observed in molecular docking analysis are the value of bond energy and interaction model to amino acid residues. The compounds in mahogany plants that have the best interactions were then analyzed using molecular dynamics simulation methods to determine the stability of their bonds based on the values of Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). Results: Twenty-two compounds met the drug-likeness requirements. Molecular docking analysis showed that the compounds predicted to have the best binding affinity and have an interaction pattern similar to natural ligands towards the molecular target of PLpro are 7-deacetoxy-7-oxogedunin and 3β-hydroxy-stigmast-5-en-7-one. The molecular dynamics simulation results revealed that based on the RMSD and RMSF values, the compound 3β-hydroxy-stigmast-5-en-7-one showed higher stability than 7-deacetoxy-7-oxogedunin. Conclusion: 3β-hydroxy-stigmast-5-en-7-one and 7-deacetoxy-7-oxogedunin were predicted to have good interaction with PLPro; however, 3β-hydroxy-stigmast-5-en-7-one showed the higher interaction stability.