RMSD Research Articles

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

A molecular and computational study of galbanic acid as a regulator of Sirtuin1 pathway in inhibiting lipid accumulation in HepG2 cells

Introduction Sirtuin1 (SIRT1) plays a crucial role in the pathophysiology of non-alcoholic fatty liver disease. We investigated the mechanistic role of galbanic acid (Gal) as a regulator of SIRT1 in silico and in vitro. Methods HepG2 cells were treated with Gal in the presence or absence of EX-527, a SIRT1-specific inhibitor, for 24 h. Sirtuin1 gene and protein expression were measured by RT-PCR and Western blotting, respectively. It has been docked to the allosteric reign of SIRT1 (PDB ID: 4ZZJ) to study the effect of Gal on SIRT1, and then the protein and complex molecular dynamic (MD) simulations had been studied in 100 ns. Results The semi-quantitative results of Oil red (p < .03) and TG level (p < .009) showed a significant reduction in lipid accumulation by treatment with Gal. Also, a significant increase was observed in the gene and protein expression of SIRT1 (p < .05). MD studies have shown that the average root mean square deviation (RMSD) was about 0.51 Å for protein structure and 0.66 Å for the complex. The average of radius of gyration (Rg) is 2.33 and 2.32 Å for protein and complex, respectively, and the pattern of root mean square fluctuation (RMSF) was almost similar. Conclusion Computational studies show that Gal can be a great candidate to use as a SIRT1 ligand because it does not interfere with the structure of the protein, and other experimental studies showed that Gal treatment with SIRT1 inhibitor increases fat accumulation in HepG2 cells.

On the Validation of Protein Force Fields Based on Structural Criteria.

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

Radiological and Molecular Analysis of Radioiodinated Anastrozole and Epirubicin as Innovative Radiopharmaceuticals Targeting Methylenetetrahydrofolate Dehydrogenase 2 in Solid Tumors.

In the dynamic field of radiopharmaceuticals, innovating targeted agents for cancer diagnosis and therapy is crucial. Our study enriches this evolving landscape by evaluating the potential of radioiodinated anastrozole ([125I]anastrozole) and radioiodinated epirubicin ([125I]epirubicin) as targeting agents against MTHFD2-driven tumors. MTHFD2, which is pivotal in one-carbon metabolism, is notably upregulated in various cancers, presenting a novel target for radiopharmaceutical application. Through molecular docking and 200 ns molecular dynamics (MD) simulations, we assess the binding efficiency and stability of [125I]anastrozole and [125I]epirubicin with MTHFD2. Molecular docking illustrates that [125I]epirubicin has a superior binding free energy (∆Gbind) of -41.25 kJ/mol compared to -39.07 kJ/mol for [125I]anastrozole and -38.53 kJ/mol for the control ligand, suggesting that it has a higher affinity for MTHFD2. MD simulations reinforce this, showing stable binding, as evidenced by root mean square deviation (RMSD) values within a narrow range, underscoring the structural integrity of the enzyme-ligand complexes. The root mean square fluctuation (RMSF) analysis indicates consistent dynamic behavior of the MTHFD2 complex upon binding with [125I]anastrozole and [125I]epirubicin akin to the control. The radius of gyration (RG) measurements of 16.90 Å for MTHFD2-[125I]anastrozole and 16.84 Å for MTHFD2-[125I]epirubicin confirm minimal structural disruption upon binding. The hydrogen bond analysis reveals averages of two and three stable hydrogen bonds for [125I]anastrozole and [125I]epirubicin complexes, respectively, highlighting crucial stabilizing interactions. The MM-PBSA calculations further endorse the thermodynamic favorability of these interactions, with binding free energies of -48.49 ± 0.11 kJ/mol for [125I]anastrozole and -43.8 kJ/mol for MTHFD2-. The significant contribution of Van der Waals and electrostatic interactions to the binding affinities of [125I]anastrozole and [125I]epirubicin, respectively, underscores their potential efficacy for targeted tumor imaging and therapy. These computational findings lay the groundwork for the future experimental validation of [125I]anastrozole and [125I]epirubicin as MTHFD2 inhibitors, heralding a notable advancement in precision oncology tools. The data necessitate subsequent in vitro and in vivo assays to corroborate these results.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Evaluating nuclear charge radii based on the mean mass-density parameter using BP neural networks

In this paper, we have successfully obtained the mean mass-density parameter ([Formula: see text]) using the nuclear masses AME2020 database and nuclear charge radius (CR) CR2013 database. The empirical formula is derived based on the relationship between [Formula: see text] and [Formula: see text] (the ratio of the number of neutrons to protons). Subsequently, we obtained an empirical formula for the difference in [Formula: see text] between two neighboring isotopes. By utilizing this empirical formula along with the AME2020 and CR2013 databases, we then calculated 625 charge radii for nuclei with [Formula: see text]. The root-mean-square deviation (RMSD) between the calculated values and the experimental values in the CR2013 database is 0.0075[Formula: see text]fm. The predicted nuclear CR values are comparable to those of other researches, which some predictions closely matching the experimental values measured in recent years. Additionally, this work used the Back Propagation (BP) neural network to establish a model for describing and predicting the difference in the mean mass-density parameter between two neighboring isotopes. The RMSD between the calculated and experimental values obtained using this model is 0.0039[Formula: see text]fm. Some of our predicted values have good accuracy and compared well with experimental values. Both of the above methods indicate that the nuclear CR relationship proposed in this paper based on the difference in mean mass-density parameter has simplicity and reliability.

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.

Bioinformatics Study of Flavonoids From Genus Erythrina As Ace2 inhibitor Candidates For Covid-19 Treatment.

This study aimed to screen potential drug candidates from the flavonoids of the genus Erythrina for the Corona Virus Disease 2019 (COVID-19) treatment. A comprehensive screening was conducted on the structures of 473 flavonoids derived from the genus Erythrina, focusing on their potential toxicity and pharmacokinetic profiles. Subsequently, flavonoids that were non-toxic and possessed favorable pharmacokinetic properties underwent further analysis to explore their interactions with the angiotensin-converting enzyme 2 (ACE2) receptor, employing molecular docking and molecular dynamics simulations. Among 473 flavonoids, 104 were predicted to be safe from being mutagenic, hepatotoxic, and inhibitors of the human ether-a-go-go-related gene (hERG). Among these 104 flavonoids, 18 compounds were predicted not to be substrates of P-glycoprotein (P-gp). Among these 18 flavonoids, gangetinin (471) and erybraedin D (310) exhibit low binding affinities and root mean square deviation (RMSD) values, indicating stable binding to the ACE2 receptor. The physicochemical attributes of compounds 310 and 471 suggest that they possess drug-like properties. Gangetinin (471) and erybraedin D (310) may serve as promising candidates for COVID-19 treatment due to their potential to inhibit the ACE2-RBD interaction. This warrants further investigation into their inhibitory effects on ACE2-RBD binding through in vitro experiments.

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches.

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

Evaluation of novel pyridoxal isonicotinoyl hydrazone (PIH) derivatives as potential anti‐tuberculosis agents: An in silico investigation

AbstractThis investigation employed computational methodologies to assess the therapeutic potential of derivatives (1–16) of pyridoxal isonicotinoyl hydrazone (PIH) as potential treatments for tuberculosis. Various computational techniques, including molecular dynamics simulation, molecular docking, density functional theory, and global chemical descriptors, were employed to analyze the interactions between the ligands and target proteins. Docking results indicated that ligands 6, 7, 8, and rifampin exhibited binding affinities of −8.4, −7.4, −9.2, and − 7.2 kcal mol−1, respectively, against mycobacterium tuberculosis enoyl acyl carrier protein reductase (INHA), with ligand 8 demonstrating superior inhibition. Molecular dynamics (MD) simulations were utilized to assess the stability of protein‐ligand interactions. Remarkably, the Root Mean Square Deviation (RMSD) of the INHA‐ligand 8 complex remained minimal, with peak values at .40, .56, .37, and .50 nm at temperatures of 300, 305, 310, and 320 K, respectively. This suggests superior stability compared to the reference drug rifampin and INHA complex, which exhibited an RMSD range of .2 to .8 nm at 300 K. Furthermore, analysis using Frontier Molecular Orbital (FMO) revealed that the Egap value of ligand 8 (4.407 eV) is lower than all the reference drugs except rifampin. This comprehensive theoretical analysis positions ligand 8 as a promising candidate for anti‐tuberculosis drug development, underscoring the need for further exploration through in vitro and in vivo studies.

Study of Cellulose Dissolution in ZnO/NaOH/Water Solvent Solution and Its Temperature-Dependent Effect Using Molecular Dynamics Simulation.

Cellulose is a biopolymer with numerous advantages that make it an ecological, economical, and high-performing choice for various applications. To fully exploit the potential of cellulose, it is often necessary to dissolve it, which poses a current challenge. The aqueous zinc oxide/sodium hydroxide (ZnO/NaOH/Water) system is a preferred solvent for its rapid dissolution, non-toxicity, low cost, and environmentally friendly nature. In this context, the behavior of cellulose chains in the aqueous solution of ZnO/NaOH and the impact of temperature on the solubility of this polymer were examined through a molecular dynamics simulation. The analysis of the root means square deviation (RMSD), interaction energy, hydrogen bond curves, and radial distribution function revealed that cellulose is insoluble in the ZnO/NaOH solvent at room temperature (T = 298 K). Decreasing the temperature in the range of 273 K to 268 K led to a geometric deformation of cellulose chains, accompanied by a decrease in the number of interchain hydrogen bonds over the simulation time, thus confirming the solubility of cellulose in this system between T = 273 K and T = 268 K.

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations.

Protein carbonylation by reactive aldehydes derived from lipid peroxidation leads to cross-linking, oligomerization, and aggregation of proteins, causing intracellular damage, impaired cell functions, and, ultimately, cell death. It has been described in aging and several age-related chronic conditions. However, the basis of structural changes related to the loss of function in protein targets is still not well understood. Hence, a route to the in silico construction of new parameters for amino acids carbonylated with reactive carbonyl species derived from fatty acid oxidation is described. The Michael adducts for Cys, His, and Lys with 4-hydroxy-2-nonenal (HNE), 4-hydroxy-2-hexenal (HHE), and a furan ring form for 4-Oxo-2-nonenal (ONE), were built, while malondialdehyde (MDA) was directly attached to each residue. The protocol describes details for the construction, geometry optimization, assignment of charges, missing bonds, angles, dihedral angles parameters, and its validation for each modified residue structure. As a result, structural effects induced by the carbonylation with these lipid derivatives have been measured by molecular dynamics simulations on different protein systems such as the thioredoxin enzyme, bovine serum albumin and the membrane Zu-5-ankyrin domain employing root-mean-square deviation (RMSD), root mean square fluctuation (RMSF), structural secondary prediction (DSSP) and the solvent-accessible surface area analysis (SASA), among others.

Assessment of the electrostatic binding of ferrocenylmethyl-nitroaniline derivatives to DNA: A combined experimental and theoretical study

The present study investigates the electrostatic binding interactions between ferrocenylmethylnitroaniline derivatives and DNA using a combination of experimental and theoretical approaches. The objective is to elucidate the binding mechanisms and evaluate the potential of these derivatives as DNA-targeting agents. Experimental techniques, including cyclic voltammetry (CV) and UV–Vis spectroscopy (UV–Vis), were employed to assess the binding affinity and conformational changes in DNA upon interaction with the derivatives. Our findings reveal that the ferrocenylmethylnitroaniline derivatives exhibit strong electrostatic interactions with DNA, as confirmed by the negative formal potential shift observed in CV. The binding constants and free binding energies obtained from the docking simulation were found to be consistent with those obtained from CV and UV–Vis spectral analysis. Furthermore, the binding site size was determined from the voltametric data. Complementary theoretical calculations such as geometry optimization, MEP analysis, Mulliken charge distribution, and HOMO-LUMO surface were performed by DFT approach (B3LYP/aug-cc-pVTZ/6-311++G (d,p)) to gain insights into the structural data, active regions, and chemical reactivity of derivatives. Molecular docking simulation verifies that electrostatic attraction plays a fundamental role in the interaction of Fc2N, Fc3N, and Fc4N with DNA. The molecular dynamics simulations elucidated the stability of DNA-ligand complexes formed with Fc2N, Fc3N, and Fc4N compounds. Analysis of root mean square deviation (RMSD) and radius of gyration (Rg) indicated stable binding and maintained compactness of DNA structures, underscoring the robustness of these interactions for potential therapeutic applications.Top of Form

Exploring ionospheric dynamics: a comprehensive analysis of GNSS TEC estimations during the solar phases using linear function model

Abstract The modeling and forecasting of Total Electron Content (TEC) play a major role in influencing signals from satellite-based navigation systems and impact the performance of diverse satellite-dependent technologies. The intensity of solar ionizing radiation and the state of geomagnetic field activity influence the Global Navigation Satellite System (GNSS)-TEC. This paper uses a Linear TEC Function (LTF) climatology model to understand ionospheric behavior under solar and geomagnetic activities that cause variations in the electron distribution of the ionosphere medium. The LTF model integrates representations of solar EUV photon (MgII) and geomagnetic (SYMH) activities, incorporating solar-modulated oscillations (periodic variations) at four seasonal cycles and a linear trend. The LTF model examined the time series of GPS-TEC at a location (geographic 34.95° N, 134.05° E) with a time resolution of 1 h, from 1997 to 2016, covering solar cycles 23 and 24. The Root Mean Square Deviation (RMSD) and correlation coefficient between the GNSS-TEC and model TEC (LTF) was 5.30 TECU and 95 %. The results indicate that solar components, as well as annual and semi-annual variations, have a significant impact on the daily average TEC. Solar activity appears to be the predominant determining factor of TEC during the solar phases of cycles 23 and 24. In contrast, periodic influences primarily outline TEC during periods characterized by minimal solar activity. The geomagnetic component presents an increased influence, particularly during storm periods. The model demonstrates superior performance in Total TEC modeling compared to other state-of-the-art approaches.

DMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks.

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.

Effects of a Larger N‐Heterocyclic Ring in Rofecoxib Analogues on AIE Activity and Multifunctional Applications

AbstractTwo analogues of rofecoxib, 2‐Pyr‐ROF and 2‐Pip‐ROF, were designed according to the strategy of heterocyclic ring enlargement and facilely synthesized in a facile manner by Knoevenagel reactions. The first compound, 2‐Pyr‐ROF, had low quantum yields (QY) of 3.0 % in the solid states. After replacing the pyrrolidinyl with piperidinyl, the resulting 2‐Pip‐ROF had a significantly increased QY, with a value of 28 %. The root‐mean‐square deviation (RMSD) values between S0 and S1 of 2‐Pyr‐ROF and 2‐Pip‐ROF in tetrahydrofuran were 0.51, 0.35 Å, respectively. As compared with 2‐Pyr‐ROF, compound 2‐Pip‐ROF exhibited a smaller RMSD, which indicated a lower non‐radiative transition rate. A single‐crystal X‐ray diffraction study and Hirshfeld surfaces analysis revealed that 2‐Pip‐ROF had a more nonplanar molecule structure and a weaker π‐π interaction, which might result in a higher fluorescence intensity and QY. Owing to its better aggregation‐induced emission (AIE) behavior, compound 2‐Pip‐ROF showed more multi‐functional applications, including mechanochromism, acidochromism, and lipid droplets imaging ability. These results imply that the strategy of heterocyclic ring enlargement provides a new paradigm for the design of new luminogens with AIE properties.

Identification of novel peptide inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR): molecular docking and MD simulation studies

The presence of drug-resistant variants of Plasmodium parasites within the population has presented a substantial obstacle to the eradication of Malaria. As a result, numerous research groups have directed their efforts towards creating new medication candidates that specifically target parasites. In this study, our main objective was to identify tri-peptide inhibitors for Plasmodium falciparum Dihydrofolate Reductase (PfDHFR) with the aim of finding a new peptide that exhibits superior binding properties compared to the current inhibitor, WR99210. In order to achieve this objective, a virtual library consisting of 8000 tripeptides was generated and subjected to computational screening against wild-type PfDHFR. The purpose of this screening was to discover the most effective binders at the active site. The four most optimal tripeptides identified (Trp-Trp-Glu, Trp-Phe-Tyr, Phe-Trp-Trp, Tyr-Trp-Trp) exhibited significant non-covalent interactions inside the active site of PfDHFR and had binding energies ranging from −9.5 to −9.0 kcal/mol and WR99210 had a binding energy of −6.2 kcal/mol. A 250 ns Molecular Dynamics (MD) simulation was performed to investigate the kinetic and thermodynamic characteristics of the protein-ligand complexes. The Root Mean Square Deviation (RMSD) values for the optimal tripeptides fell within the allowed range, indicating the stability of the ligands inside the protein complex. The Ki value for the most effective tripeptide was 0.3482 µM, whereas WR99210 had a Ki value of 1.02 µM. This article presents the initial discovery of peptide inhibitors targeting PfDHFR. In this text, we provide a comprehensive explanation of the interactions that occur between peptides and the enzyme. Communicated by Ramaswamy H. Sarma

Structure-guided engineered urethanase from Candida parapsilosis with pH and ethanol tolerance to efficiently degrade ethyl carbamate in Chinese rice wine

Urethane hydrolase can degrade the carcinogen ethyl carbamate (EC) in fermented food, but its stability and activity limit its application. In this study, a mutant G246A and a double mutant N194V/G246A with improved cpUH activity and stability of Candida parapsilosis were obtained by site-directed mutagenesis. The catalytic efficiency (Kcat/Km) of mutant G246A and double mutant N194V/G246A are 1.95 times and 1.88 times higher than that of WT, respectively. In addition, compared with WT, the thermal stability and pH stability of mutant G246A and double mutant N194V/G246A were enhanced. The ability of mutant G246A and double mutant N194V/G246A to degrade EC in rice wine was also stronger than that of WT. The mutation increased the stability of the enzyme, as evidenced by decreased root mean square deviation (RMSD) and increased hydrogen bonds between the enzyme and substrate by molecular dynamics simulation and molecular docking analysis. The molecule modification of new cpUH promotes the industrial process of EC degradation.

Comparative Analysis of Deep Generative Model for Industrial Enzyme Design

Abstract: Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the design of novel enzymes tailored to specific conditions and requirements. It is beneficial to de novo design industrial enzymes using computational methods. Currently, only one tool explicitly designed for the enzyme-only generation performs unsatisfactorily. We have selected several general protein sequence design tools and systematically evaluated their effectiveness when applied to specific industrial enzymes. We investigated the literature related to protein generation. We summarized the computational methods used for sequence generation into three categories: structure-conditional sequence generation, sequence generation without structural constraints, and co-generation of sequence and structure. To effectively evaluate the ability of six computational tools to generate enzyme sequences, we first constructed a luciferase dataset named Luc_64. Then we assessed the quality of enzyme sequences generated by these methods on this dataset, including amino acid distribution, EC number validation, etc. We also assessed sequences generated by structure-based methods on existing public datasets using sequence recovery rates and root-mean-square deviation (RMSD) from a sequence and structure perspective. In the functionality dataset, Luc_64, ABACUS-R, and ProteinMPNN stood out for producing sequences with amino acid distributions and functionalities closely matching those of naturally occurring luciferase enzymes, suggesting their effectiveness in preserving essential enzymatic characteristics. Across both benchmark datasets, ABACUS-R and ProteinMPNN, have also exhibited the highest sequence recovery rates, indicating their superior ability to generate sequences closely resembling the original enzyme structures. Our study provides a crucial reference for researchers selecting appropriate enzyme sequence design tools, highlighting the strengths and limitations of each tool in generating accurate and functional enzyme sequences. ProteinMPNN and ABACUS-R emerged as the most effective tools in our evaluation, offering high accuracy in sequence recovery and RMSD and maintaining the functional integrity of enzymes through accurate amino acid distribution. Meanwhile, the performance of protein general tools for migration to specific industrial enzymes was fairly evaluated on our specific industrial enzyme benchmark.

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.