Binding Energy Of Molecule Research Articles

Insight of low flammability limit on sustainable aviation fuel blend and prediction by ANN model

Sustainable aviation fuel (SAF) blend has been confirmed to benefit for greenhouse gases reduction, and thus the property of blend fuel should be understanded the detail to support the utilization in aircraft. Low flammability limit (LFL) is a key property of jet fuel which should be sufficiently flammable to burn in combustor of aeroengine and meanwhile should be non-flammable for safety storage in fuel tank of aircraft. LFL of fuel could be influenced by integrating effects including molecule structure, intramolecular chemical bond energy and binding energy of molecule to molecule. Three types of theoretical models, based on different individual view including LFL of every pure hydrocarbon, stoichiometric concentration, and combustion enthalpy, present unsatisfactory simulation results, which can be deduced without integrating all potential influence factors together. The artificial neural network (ANN) approaches have been involved to bridge the relationship of the complex compositions in jet fuels with LFL. For providing adequate and available composition input, the boundary of fuel compositions has been extracted based on constrains of boiling point, flash point and freeze point coupling with statistic petroleum-based jet fuels. By clustering analysis, 43 critical classes of compositions, extracted as surrogate hydrocarbons based on with similar LFL within 1 % deviation, have been deployed as input matrix. ANN-LFL model, trained by only drop-in fuel with feature of Sigmoid function as an activation function, can distinguish drop-in fuel with non-drop-in fuel. ANN LFL model can predict LFL of drop-in fuel with 0.988 accuracy. The predict output value of non-drop-in fuel could present obvious deviation with traditional jet fuel. The optimization methodologies of ANN-LFL model could be improved the understanding of LFL and extend ANN in SAF utilization.

A multi-grain multi-layer astrochemical model with variable desorption energy for surface species

Interstellar surface chemistry is a complex process that occurs in icy layers that have accumulated onto grains of different sizes. The efficiency of the surface processes often depends on the immediate environment of the adsorbed molecules. We investigated how gas-grain chemistry changes when the surface molecule binding energy is modified, depending on the properties of the surface. In a gas-grain astrochemical model, molecular binding energy gradually changes for three different environments -- (1) the bare grain surface, (2) polar water-dominated ices, and (3) weakly polar carbon monoxide-dominated ices. In addition to diffusion, evaporation, and chemical desorption, photodesorption was also made binding energy-dependent, in line with experimental results. These phenomena occur in a collapsing prestellar core model that considers five grain sizes with ices arranged into four layers. Variable desorption energy moderately affects gas-grain chemistry. Bare-grain effects slow down ice accumulation, while easier diffusion of molecules on weakly polar ices promotes the production of carbon dioxide. Efficient chemical desorption from bare grains significantly delays the appearance of the first ice monolayer. The combination of multiple aspects of grain surface chemistry creates a gas-ice balance that is different from simpler models. The composition of the interstellar ices is regulated by several binding-energy dependent desorption mechanisms. Their actions overlap in time and space, explaining the similar proportions of major ice components (water and carbon oxides) observed in all directions.

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA)

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, more than 10 million people were infected worldwide, and 1.3 million were children. The current study considered the in-silico and machine learning (ML) approaches to explore the potential anti-TB molecules from the SelleckChem database against Enoyl-Acyl Carrier Protein Reductase (InhA). Initially, the entire database of ∼ 119000 molecules was sorted out through drug-likeness. Further, the molecular docking study was conducted to reduce the chemical space. The standard TB drug molecule's binding energy was considered a threshold, and molecules found with lower affinity were removed for further analyses. Finally, the molecules were checked for the pharmacokinetic and toxicity studies, and compounds found to have acceptable pharmacokinetic parameters and were non-toxic were considered as final promising molecules for InhA. The above approach further evaluated five molecules for ML-based toxicity and synthetic accessibility assessment. Not a single molecule was found toxic and each of them was revealed as easy to synthesise. The complex between InhA and proposed and standard molecules was considered for molecular dynamics simulation. Several statistical parameters showed the stability between InhA and the proposed molecule. The high binding affinity was also found for each of the molecules towards InhA using the MM-GBSA approach. Hence, the above approaches and findings exposed the potentiality of the proposed molecules against InhA.

Stretching Bonds without Breaking Symmetries in Density Functional Theory.

Kohn-Sham density functional theory (KS-DFT) stands out among electronic structure methods due to its balance of accuracy and computational efficiency. However, to achieve chemically accurate energies, standard density functional approximations in KS-DFT often need to break underlying symmetries, a long-standing "symmetry dilemma". By employing fragment spin densities as the main variables in calculations (rather than total molecular densities, as in KS-DFT), we present an embedding framework in which this symmetry dilemma is understood and partially resolved. The spatial overlap between fragment densities is used as the main ingredient to construct a simple, physically motivated approximation to a universal functional of the fragment densities. This "overlap approximation" is shown to significantly improve semilocal KS-DFT binding energies of molecules without artificially breaking either charge or spin symmetries. The approach is shown to be applicable to covalently bonded molecules and to systems of the "strongly correlated" type.

In-silico evaluation of Bismurrayaquinone-A phytochemical as a potential multifunctional inhibitor targeting dihydrofolate reductase: implications for anticancer and antibacterial drug development

Dihydrofolate reductase (DHFR) has gained significant attention in drug development, primarily due to marked distinctions in its active site among different species. DHFR plays a crucial role in both DNA and amino acid metabolism by facilitating the transfer of monocarbon residues through tetrahydrofolate, which is vital for nucleotide and amino acid synthesis. This considers its potential as a promising target for therapeutic interventions. In this study, our focus was on conducting a virtual screening of phytoconstituents from the IMPPAT2.0 database to identify potential inhibitors of DHFR. The initial criterion involved assessing the binding energy of molecules against DHFR and we screened top 20 compounds ranging energy −13.5 to −11.4 (kcal/Mol) while Pemetrexed disodium bound with less energy −10.2 (kcal/Mol), followed by an analysis of their interactions to identify more effective hits. We prioritized IMPHY007679 (Bismurrayaquinone-A), which displayed a high binding affinity and crucial interaction with DHFR. We also evaluated the drug-like properties and biological activity of IMPHY007679. Furthermore, MD simulation was done, RMSD, RMSF, Rg, SASA, PCA and FEL explore the time-evolution impact of IMPHY007679 comparing it with a reference drug, Pemetrexed disodium. Collectively, our findings suggest that IMPHY007679 recommend further investigation in both in vitro and in vivo settings for its potential in developing anticancer and antibacterial therapies. This compound holds promise as a valuable candidate for advancing drug research and treatment strategies. Communicated by Ramaswamy H. Sarma

Identification of potential drug molecules against fibroblast growth factor receptor 3 (FGFR3) by multi-stage computational-biophysics correlate

The fibroblast growth factor receptor 3 (FGFR3) is warranted as a promising therapeutic target in bladder cancer as it is described in 75% of papillary bladder tumors. Considering this, the present study was conducted to use different approaches of computer-aided drug discovery (CADD) to identify the best binding compounds against the active pocket of FGFR3. Compared to control pyrimidine derivative, the study identified three promising lead structures; BDC_24037121, BDC_21200852, and BDC_21206757 with binding energy value of −14.80 kcal/mol, −12.22 kcal/mol, and −11.67 kcal/mol, respectively. The control molecule binding energy score was −9.85 kcal/mol. The compounds achieved deep pocket binding and produced balanced interactions of hydrogen bonds and van der Waals. The FGFR3 enzyme residues such as Leu478, Lys508, Glu556, Asn562, Asn622, and Asp635. The molecular dynamic (MD) simulation studies additionally validated the docked conformation stability with respect to FGFR3 with a mean root mean square deviation (RMSD) value of < 3 Å. The root mean square fluctuation (RMSF) complements the complexes structural stability and the residues showed less fluctuation in the presence of compounds. The Poisson–Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods revalidated compounds better binding and highlighted van der Waals energy to dominate the overall net energy. The docked stability was additionally confirmed by WaterSwap and AMBER normal mode entropy energy analyses. In a nutshell, the compounds shortlisted in this study are promising in term of theoretical binding affinity for FGFR3 but experimental validation is needed.Communicated by Ramaswamy H. Sarma

Identification of promising inhibitors against breast cancer disease by targeting NUDIX hydrolase 5 (NUDT5) biomolecule

It is well documented that NUDT5 enzyme inhibition in breast cancer cell lines arrest cancer cells growth, invasiveness and migration. The NUDT5 enzyme enhances breast cancer aggressiveness and act as key regulator of oncogenic pathways. Similarly, the NUDT5 enzyme plays a primer role in ATP-dependent cellular processes and proliferation in breast cancer. Thus, the NUDT5 enzyme plays a profound contribution in promoting breast cancers carcinogenesis and could be an ideal target for anti-cancer drug discovery. In this work, LAS_51382001, LAS_51177972 and LAS_51380924 with binding energy of −12.64 kcal/mol, −11.59 kcal/mol and −10.01 kcal/mol, respectively were filtered as lead molecules. The control molecule binding energy was −10.87 kcal/mol. The system dynamics were found uniform in molecular dynamics simulation studies and observed with no major structural changes. Among the leads, the LAS_51177972 showed the most stable binding energy values. The MM-GBSA binding energy of the compound was −37.07 kcal/mol and MM-PBSA binding energy of −43.56 kcal/mol. Similarly, the compound revealed very stable WaterSwap absolute binding energy values; Bennett’s, TI and FEP energy of −36.2 kcal/mol, −36.13 kcal/mol and −36.58 kcal/mol, respectively. Similarly, the leads reported very favorable physicochemical properties, water solubility, pharmacokinetics, druglikeness and medicinal chemistry properties. In a nutshell, the compounds are potent in term of the current computational study however, need to be subjected to experimental studies.Communicated by Ramaswamy H. Sarma

In-silico studies of 2-aminothiazole derivatives as anticancer agents by QSAR, molecular docking, MD simulation and MM-GBSA approaches

Targeting Hec1/Nek2 is considered as crucial target for cancer treatment due to its significant role in cell proliferation. In pursuit of this, a series of twenty-five 2-aminothiazoles derivatives, along with their Hec1/Nek2 inhibitory activities were subjected to QSAR studies utilizing QSARINS software. The significant three descriptor QSAR model was generated, showing noteworthy statistical parameters: a correlation coefficient of cross validation leave one out (Q2 LOO) = 0.7965, coefficient of determination (R2) = 0.8436, (R2ext) = 0.6308, cross validation leave many out (Q2LMO) = 0.7656, Concordance Correlation Coefficient (CCCCV = 0.8875), CCCtr = 0.9151, and CCCext = 0.0.7241. The descriptors integral to generated QSAR model include Moreau-Broto autocorrelation, which represents the spatial autocorrelation of a property along the molecular graph’s topological structure (ATSC1i), Moran autocorrelation at lag 8, which is weighted by charges (MATS8c) and RPSA representing the total molecular surface area. It was noted that these descriptors significantly influence Hec1/Nek2 inhibitory activity of 2-aminothiazoles derivatives. New lead molecules were designed and predicted for their Hec1/Nek2 inhibitory activity based on the developed three descriptor model. Further, the ADMET and Molecular docking studies were carried out for these designed molecules. The three molecules were selected based on their docking score and further subjected for MD simulation studies. Post-MD MM-GBSA analysis were also performed to predicted the free binding energies of molecules. The study helped us to understand the key interactions between 2-aminothiazoles derivatives and Hec1/Nek2 protein that may be necessary to develop new lead molecules against cancer. Communicated by Ramaswamy H. Sarma

Quantum chemical exploration of the binding motifs and binding energies of neutral molecules, radicals and ions with small water clusters: characterisation and astrochemical implications

ABSTRACT Accurate binding energies of molecules to water clusters are relevant for understanding intermolecular interactions and various chemical applications. They enter models of interstellar chemical processes, as binding to icy grains influences surface reactions and thus affects calculated gas-phase abundances. Unfortunately, many astrochemical molecules (especially radicals and ions) are incompletely characterised in these models. To address this, we report computational searches for optimal structures and benchmark binding and condensation energies for sets of neutral, radical, cationic, and anionic molecules of astrochemical relevance with clusters of N = 1 − 4 water molecules. These calculations utilised reliable density functionals for geometry optimisation, and coupled cluster (CCSD(T)) single point calculations with large basis sets. Four energetic binding motifs (weak, intermediate, strong or covalently bonded) were observed depending on the chemical nature of the guest molecule. Neutral closed and open-shell molecules with strong dipoles and a greater potential for hydrogen bonding are more tightly bound to water clusters compared to non-polar ones. For closed-shell cationic and anionic species, barrier-less reactions with water clusters occur, which reveals radical-free routes to molecular processing in the gas phase and on amorphous ice surfaces.

Identification and dynamics of novel scaffolds against Enterococcus faecium serine hydroxymethyltransferase enzyme: a potential target for antibiotics development

Serine hydroxymethyltransferase enzyme is a significant player in purine, thymidylate, and L-serine biosynthesis and has been tagged as a potential target for cancer, viruses, and parasites. However, this enzyme as an anti-bacterial druggable target has not been explored much. Herein, in this work, different computational chemistry and biophysics techniques were applied to identify potential computational predicted inhibitory molecules against Enterococcus faecium serine hydroxymethyltransferase enzyme. By structure based virtual screening process of ASINEX antibacterial library against the enzyme two main compounds: Top-1_BDC_21204033 and Top-2_BDC_20700155 were reported as best binding molecules. The Top-1_BDC_21204033 and Top-2_BDC_20700155 binding energy value is −9.3 and −8.9 kcal/mol, respectively. The control molecule binding energy score is −6.55 kcal/mol. The mean RMSD of Top-1-BDC_21204033, Top-2-BDC_20700155 and control is 3.7 Å (maximum 5.03 Å), 1.7 Å (maximum 3.05 Å), and 3.84 Å (maximum of 6.7 Å), respectively. During the simulation time, the intermolecular docked conformation and interactions were seen stable despite of few small jumps by the compounds/control, responsible for high RMSD in some frames. The MM/GBSA and MM/PBSA binding free energy of lead Top-2-BDC_20700155 complex is −79.52 and −82.63 kcal/mol, respectively. This complex was seen as the most stable compared to the control. Furthermore, the lead molecules and control showed good druglikeness and pharmacokinetics profile. The lead molecules were non-toxic and non-mutagenic. In short, the compounds are promising in terms of binding to the serine hydroxymethyltransferase enzyme and need to be subjected to experimental studies. Communicated by Ramaswamy H. Sarma

Heteroatom-Doped Molybdenum Disulfide Nanomaterials for Gas Sensors, Alkali Metal-Ion Batteries and Supercapacitors.

Molybdenum disulfide (MoS2) is the second two-dimensional material after graphene that received a lot of attention from the research community. Strong S-Mo-S bonds make the sandwich-like layer mechanically and chemically stable, while the abundance of precursors and several developed synthesis methods allow obtaining various MoS2 architectures, including those in combinations with a carbon component. Doping of MoS2 with heteroatom substituents can occur by replacing Mo and S with other cations and anions. This creates active sites on the basal plane, which is important for the adsorption of reactive species. Adsorption is a key step in the gas detection and electrochemical energy storage processes discussed in this review. The literature data were analyzed in the light of the influence of a substitutional heteroatom on the interaction of MoS2 with gas molecules and electrolyte ions. Theory predicts that the binding energy of molecules to a MoS2 surface increases in the presence of heteroatoms, and experiments showed that such surfaces are more sensitive to certain gases. The best electrochemical performance of MoS2-based nanomaterials is usually achieved by including foreign metals. Heteroatoms improve the electrical conductivity of MoS2, which is a semiconductor in a thermodynamically stable hexagonal form, increase the distance between layers, and cause lattice deformation and electronic density redistribution. An analysis of literature data showed that co-doping with various elements is most attractive for improving the performance of MoS2 in sensor and electrochemical applications. This is the first comprehensive review on the influence of foreign elements inserted into MoS2 lattice on the performance of a nanomaterial in chemiresistive gas sensors, lithium-, sodium-, and potassium-ion batteries, and supercapacitors. The collected data can serve as a guide to determine which elements and combinations of elements can be used to obtain a MoS2-based nanomaterial with the properties required for a particular application.

An integrated computational approach towards novel drugs discovery against polyketide synthase 13 thioesterase domain of Mycobacterium tuberculosis

The acquired drug resistance by Mycobacterium tuberculosis (M. tuberculosis) to antibiotics urges the need for developing novel anti-M. tuberculosis drugs that possess novel mechanism of action. Since traditional drug discovery is a labor-intensive and costly process, computer aided drug design is highly appreciated tool as it speeds up and lower the cost of drug development process. Herein, Asinex antibacterial compounds were virtually screened against thioesterase domain of Polyketide synthase 13, a unique enzyme that forms α-alkyl β-ketoesters as a direct precursor of mycolic acids which are essential components of the lipid-rich cell wall of M. tuberculosis. The study identified three drug-like compounds as the most promising leads; BBB_26582140, BBD_30878599 and BBC_29956160 with binding energy value of − 11.25 kcal/mol, − 9.87 kcal/mol and − 9.33 kcal/mol, respectively. The control molecule binding energy score is -9.25 kcal/mol. Also, the docked complexes were dynamically stable with maximum root mean square deviation (RMSD) value of 3 Å. Similarly, the MM-GB\\PBSA method revealed highly stable complexes with mean energy values < − 75 kcal/mol for all three systems. The net binding energy scores are validated by WaterSwap and entropy energy analysis. Furthermore, The in silico druglike and pharmacokinetic investigation revealed that the compounds could be suitable candidates for additional experimentations. In summary, the study findings are significant, and the compounds may be used in experimental validation pipeline to develop potential drugs against drug-resistant tuberculosis.

Exploring the effect of C6H5-x/FxBr (x = 0-3) passivating agent on surface properties at different termination ends: first principles.

To prevent further decomposition of organic-inorganic hybrid perovskite by defects, in this work density functional theory was applied to explore the electronic properties, carrier surface mobility and theoretical photoelectric conversion efficiency (PCE) of passivating molecules with different fluorine atom content at the symmetric site of the benzene ring at different termination ends of MAPbI3, which shed light on the control of perovskite surface passivation by different element atoms in the same molecule. We found that the same molecule acts as a different passivation agent at different termination faces. Passivating molecules on the surface termination end by MAI play a Lewis acid role, with molecules with stronger dipole moments narrowing the band gap from the original 1.77 to 1.73 eV. The exciton binding energy of molecules with stronger dipole moments (0.187-0.292 meV) is significantly lower than that of MAPbI3 (0.332 meV), so the effective separation of interface electrons and holes can be realized. Bromopenta-fluorobenzene has a lower adsorption energy of -0.17 eV, which can stably adsorb on the surface of perovskite and increase visible light absorption. Ultimately, the theoretical PCE increased from 15.8% to 16.16%. In addition, on the surface terminated by PbI2, BrB with a strong dipole moment can provide electrons for Pb2+ and act as a Lewis base. At the surface end, it can form an ionic bond with Pb2+, while the antibonding molecular orbital characteristic is dominant, which increases the band gap from 1.76 to 1.87 eV. After increasing to 4-F-BrB, the fluorine atom has strong electronegativity and can easily bond with Pb2+. The conjugate π cycle intensifies the promotion of electron transfer, reducing the work function from 5.262 to 4.703 eV, reducing the effective electron and hole mass (0.514, 0.204 m0), and improving the photovoltaic performance. Finally, increasing the number of passivation molecules resulted in a decrease in the PCE from 15.93% to 14.75%.

Release of molecules from nanocarriers.

Release of drugs or vaccine molecules from macro-, micro-, and nano-sized carriers is usually considered to be limited by diffusion and/or carrier dissolution and/or erosion. The corresponding experimentally observed kinetics are customarily fitted by using the empirical Weibull and Korsemeyer-Peppas expressions. With decreasing size of carriers down to about 100 nm, the timescale of diffusion decreases, and accordingly the release can be kinetically limited, i.e., controlled by jumps of molecules located near the carrier-solution interface. In addition, nanocarriers (e.g., lipid nanoparticles) are often structurally heterogeneous so that the absorption of molecules there can be interpreted in terms of energetic heterogeneity, i.e., distribution of energies corresponding to binding sites and activation barriers for release. Herein, I present a general kinetic model aimed at such situations. For illustration, the deviation of the molecule binding energy from the maximum value was considered to be about 4-8 kcal mol-1. With this physically reasonable (for non-covalent interaction) scale of energetic heterogeneity, the predicted kinetics (i) are linear in the very beginning and then, with increasing time, become logarithmic and (ii) can be nearly perfectly fitted by employing the Weibull or Korsmeyer-Peppas expressions with the exponent in the range from 0.6 to 0.75. Such values of the exponent are often obtained in experiments and customarily associated with non-Fickian diffusion. My analysis shows that the energetic heterogeneity can be operative here as well.

ДИССОЦИАТИВНЫЙ ЗАХВАТ ЭЛЕКТРОНОВ МОЛЕКУЛАМИ ДИГИДРОКУМАРИНА И 5,6-МЕТИЛЕНДИОКСИ-1-ИНДАНОНА

The molecules of dihydrocoumarin and 5,6-methylenedioxy-1-indanone were studied by the negative ion resonant electron capture mass spectrometry for slow (0-15 eV) electrons. Such studies of the structure of vacant molecular orbitals (VMO) and dissociative electron attachment (DEA) for isolated molecules of prospective organic semiconducting materials, which are of great interest in terms of use as elementary components of organic nanoelectronics devices, are the most promising, since a correspondence between the energies of vacant MO isolated molecules and the maxima of the density of states in the conduction band of thin films was revealed earlier. Also, there is an idea developing in modern science, that the results of studies of vacant orbitals of individual molecules in the gas phase, in particular, using the DEA method, are necessary to identify the features of the energy structure of the conduction band of molecular organic condensates, starting from energies below the vacuum level, by the amount of binding energy of molecules in the film. In particular, it was shown that the substances studied in this work cannot be used as monomeric compounds in the design of electrically conductive polymers in the future, since they do not form negative molecular ions in the gas phase at energies close to thermal.

Predicting binding energies of astrochemically relevant molecules via machine learning

Context. The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is therefore an important parameter for many astrochemical studies. This parameter is usually determined with time-consuming experiments, computationally expensive quantum chemical calculations, or the inexpensive yet relatively inaccurate linear addition method. Aims. In this work, we propose a new method for predicting binding energies (BEs) based on machine learning that is accurate, yet computationally inexpensive. Methods. We created a machine-learning (ML) model based on Gaussian process regression (GPR) and trained it on a database of BEs of molecules collected from laboratory experiments presented in the literature. The molecules in the database are categorised by their features, such as mono- or multilayer coverage, binding surface, functional groups, valence electrons, and H-bond acceptors and donors. Results. We assessed the performance of the model with five-fold and leave-one-molecule-out cross validation. Predictions are generally accurate, with differences between predicted binding energies and values from the literature of less than ±20%. We used the validated model to predict the binding energies of 21 molecules that were recently detected in the interstellar medium, but for which binding energy values are unknown. We used a simplified model to visualise where the snow lines of these molecules would be located in a protoplanetary disk. Conclusions. This work demonstrates that ML can be employed to accurately and rapidly predict BEs of molecules. Machine learning complements current laboratory experiments and quantum chemical computational studies. The predicted BEs will find use in the modelling of astrochemical and planet-forming environments.

Density functional theory and molecular dynamics simulation of the corrosive particle diffusion in pyrimidine and its derivatives films

The search and discovery of corrosion inhibitors to mitigate sweet corrosion in the oil and gas industry has been carried out mainly using tedious and costly experimental approaches. In this study, theoretical approach using density functional theory (DFT) and molecular dynamics (MD) simulation were utilized to investigate the adsorption and diffusion properties of pyrimidine and six (6) of its derivatives on carbon steel corrosion in simulated CO2-containing environment. The DFT calculations revealed that the electrons can easily flow from the HOMO orbitals of all the pyrimidine molecules to the empty substrate orbitals until reaching the equilibrium state as the values of EHOMO (-7.63 to −5.55 eV) are much higher than the Fermi level energy of Fe (-13.16 eV). Besides, the binding energy of molecules on the Fe (110) surface obtained using MD simulation was found to be in the range of 60.67 to 99.28 kcal/mol, indicating the pyrimidine molecules can strongly interact with the iron atoms of the metal surface. In the sweet medium, the diffusion coefficient (D) of HCO3– was in the range of 0.81 to 1.41 m2 s−1, which is about three times less than the diffusion coefficient of HCO3– in water. Self-diffusion coefficient (D'), and fractional free volume (FFV) were used to further investigate the diffusion mechanism of the pyrimidine molecules in CO2 corrosive medium. Pyrimidine containing carboxylic acid and thioamide groups gave the best result as CO2 corrosion inhibitor theoretically. The DFT and MD results provided useful insights and a theoretical basis for the screening and design of new sweet corrosion inhibitors for carbon steel.

In Silico Studies of Bioactive Compounds from Hibiscus rosa-sinensis Against HER2 and ESR1 for Breast Cancer Treatment

The most common type of cancer and the second biggest cause of death is breast cancer. The disease is the leading cause of death in women aged 45 to 55. It's a multi-stage disease in which viruses have a role in one stage. Breast cancer is an illness that affects the sufferer, their family, and the entire community all over the world. Breast cancer has no established cause, however specific risk factors have been found. Age, race, gender, and family history are all fixed and immutable characteristics. Other elements, such as social and familial support, can help to mitigate the harmful effects.The aim of the present study was to investigate the bioactive ligand for the breast cancer treatment against the HER2 and ESR1 proteins by molecular docking studies. HER2 is an oncogene and membrane tyrosine kinase that is overexpressed and gene amplified in about 20% of breast tumours. When active, it sends proliferative and anti-apoptotic signals to the cell. It is the most important factor in the genesis and progression of breast cancer tumours. To carry out this work protein structures were download from the PDB database and ligands three dimensional structures were downloaded from the PubChem database. The docking was performed by using the iGEMDOCK software suit. The binding energies of bioactive molecules from Hibiscus rosa-sinensis were found to be Rutin (-139.779, -102.743), Quercetin (-106.5, -99.7807), Kaempferol (-105.824, -92.5271), Myricetin (-111.913, -99.603) and Methotrexate (-140.69, -130.165) against HER2 and ESR1 proteins respectively. Lowest energy of Rutin compared to Methotrexate showed highest binding among the other bioactive molecules.The binding energies of the molecules are the sum of hydrogen bond, vanderwaal’s and electrostatic energies that inversely linked to protein-ligand interaction. From the result of the current study we can conclude that among the four bioactive molecule rutin has lowest binding energy so it could be used as an inhibitor of HER2 and ESR1 protein for the treatment of breast carcinoma in future.

Mechanism of Myoglobin Molecule Adsorption on Silica: QCM, OWLS and AFM Investigations.

Adsorption kinetics of myoglobin on silica was investigated using the quartz crystal microbalance (QCM) and the optical waveguide light-mode spectroscopy (OWLS). Measurements were carried out for the NaCl concentration of 0.01 M and 0.15 M. A quantitative analysis of the kinetic adsorption and desorption runs acquired from QCM allowed to determine the maximum coverage of irreversibly bound myoglobin molecules. At a pH of 3.5–4 this was equal to 0.60 mg m−2 and 1.3 mg m−2 for a NaCl concentration of 0.01 M and 0.15 M, respectively, which agrees with the OWLS measurements. The latter value corresponds to the closely packed monolayer of molecules predicted from the random sequential adsorption approach. The fraction of reversibly bound protein molecules and their biding energy were also determined. It is observed that at larger pHs, the myoglobin adsorption kinetics was much slower. This behavior was attributed to the vanishing net charge that decreased the binding energy of molecules with the substrate. These results can be exploited to develop procedures for preparing myoglobin layers at silica substrates of well-controlled coverage useful for biosensing purposes.

High level ab initio binding energy distribution of molecules on interstellar ices: Hydrogen fluoride

The knowledge of the binding energy of molecules on astrophysically relevant ices can help to obtain an estimate of the desorption rate, i.e. the molecules residence time on the surface. This represents an important parameter for astrochemical models, and it is crucial to determine the chemical fate of interstellar complex organic molecules formed on the surface of dust grains and observed in the densest regions of the interstellar medium through rich rotational lines. In this work, we propose a new robust procedure to study the interaction of atoms and molecules with interstellar ices, based on ab initio molecular dynamics and density functional theory, validated by high-level ab initio methods at a CCSD(T)/CBS level. We have applied this procedure to a simple but astronomically relevant molecule, hydrogen fluoride (HF), a promising tracer of the molecular content of galaxies. In total we found 13 unique equilibrium structures of HF binding to small water clusters of up to 4 molecules, with binding energies ranging from 1208 K to 7162 K (2.40 to 14.23 kcal mol−1). We computed a 22-molecules model of amorphous solid water (ASW) surface using ab initio molecular dynamics simulations and carried out a systematic analysis of the binding sites of HF, in terms of binding modes and binding energies. Considering 10 different water clusters configurations, we found a binding energy distribution with an average value of 5313±74 K, and a dispersion of 921±115 K (10.56±0.15 kcal mol−1), and a dispersion of 921±115 K (1.83±0.23 kcal mol−1). Finally, the effect of the electrostatic field of the 22 water molecules on the binding energies was investigated incrementally by symmetry adapted perturbation theory, in order to gauge the effect of the water environment on the binding energies. The results indicate that the extent of the electrostatic interaction of HF with ASW depends strongly on the properties of the binding site on the water cluster. We expect that this work will provide a solid foundation for a systematic development of a binding energy distribution database of small molecules on astrophysically relevant surfaces.