ALBERT

Description: A bio-based Silica/Calcium Carbonate (CS–SiO2/CaCO3) nanocomposite was synthesized in this study using waste eggshells (ES) and rice husks (RH). The adsorbents (ESCaCO3, RHSiO2 and, CS-SiO2/CaCO3) characterized using XRD show crystallinity associated with the calcite and quartz phase. The FTIR of ESCaCO3 shows the CO−23 group of CaCO3, while the spectra of RHSiO2 majorly show the siloxane bonds (Si–O–Si) in addition to the asymmetric and symmetric bending mode of SiO2. The spectra for Chitosan (CS) show peaks corresponding to the C=O vibration mode of amides, C–N stretching, and C–O stretching. The CS–SiO2/CaCO3 nanocomposite shows the spectra pattern associated with ESCaCO3 and RHSiO2. The FESEM micrograph shows a near monodispersed and spherical CS–SiO2/CaCO3 nanocomposite morphology, with an average size distribution of 32.15 ± 6.20 nm. The corresponding EDX showed the representative peaks for Ca, C, Si, and O. The highest removal efficiency of phenol over the adsorbents was observed over CS–SiO2/CaCO3 nanocomposite compared to other adsorbents. Adsorbing 84–89% of phenol in 60–90 min at a pH of 5.4, and a dose of 0.15 g in 20 ml of 25 mg/L phenol concentration. The result of the kinetic model shows the adsorption processes to be best described by pseudo-second-order. The highest correlation coefficient (R2) of 0.99 was observed in CS-SiO2/CaCO3 nanocomposite, followed by RHSiO2 and ESCaCO3. The result shows the equilibrium data for all the adsorbents fitting well to the Langmuir isotherm model, and follow the trend CS-SiO2/CaCO3 〉 ESCaCO3 〉 RHSiO2. The Langmuir equation and Freundlich model in this study show a higher correlation coefficient (R2 = 0.9912 and 0.9905) for phenol adsorption onto the CS–SiO2/CaCO3 nanocomposite with a maximum adsorption capacity (qm ) of 14.06 mg/g compared to RHSiO2 (10.64 mg/g) and ESCaCO3 (10.33 mg/g). The results suggest good monolayer coverage on the adsorbent’s surface (Langmuir) and heterogeneous surfaces with available binding sites (Freundlich).

Description: The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e., the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density functional theory to virtually screen over 230 billion substituted DHA molecules to identify promising candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent compound, which should correspond to a half-life of about 10 days—4 months. This is considerably longer than the 3–39 h (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. Our paper makes two main important contributions: (1) a novel and generally applicable methodological approach that makes screening of huge libraries for properties involving chemical reactivity with modest computational resources, and (2) a clear demonstration that the storage capacity of the DHA/VHF thermocouple cannot be increased to 〉0.5 kJ/g by combining simple substituents.

Description: We present a method for the automatic determination of transition states (TSs) that is based on Grimme’s RMSD-PP semiempirical tight binding reaction path method (J. Chem. Theory Comput. 2019, 15, 2847–2862), where the maximum energy structure along the path serves as an initial guess for DFT TS searches. The method is tested on 100 elementary reactions and located a total of 89 TSs correctly. Of the 11 remaining reactions, nine are shown not to be elementary reactions after all and for one of the two true failures the problem is shown to be the semiempirical tight binding model itself. Furthermore, we show that the GFN2-xTB RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol. Thus, GFN2-xTB RMSD-PP barrier heights, which can be estimated at the cost of a single energy minimisation, can be used to quickly identify reactions with low barriers, although it will also produce some false positives.

Description: Mathematical models of the dynamics of infectious disease transmission are used to forecast epidemics and assess mitigation strategies. In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics while providing an exposition on the classic Susceptible–Infectious–Removed (SIR) epidemic model. Particularly, the SIR model resembles a dynamic model of a batch reactor carrying out an autocatalytic reaction with catalyst deactivation. This analogy between disease transmission and chemical reaction enables the exchange of ideas between epidemic and chemical kinetic modeling communities.

Description: Background The interactions between colloidal particles in the binary systems or mixture colloids containing clay minerals and bacteria have important influences on formations and stabilities of soil aggregates, transportations of soil water, as well as biological activities of microorganisms. How the interfacial reaction of metal ions affects their interaction therefore becomes an important scientific issue. Methods Dynamic light scattering studies on the aggregation kinetics of mixture colloids containing kaolinite and Pseudomonas putida (P. putida) were conducted in this study. Results Aggregation could be observed between kaolinite and kaolinite, between kaolinite and P. putida when P. putida content was less than 33.3%. Additionally, aggregation rates decreased with increasing P. putida content. The critical coagulation concentrations and activation energies indicated that there were strong specific ion effects on the aggregation of mixture colloids. Most importantly, the activation energy increased sharply with increasing P. putida content, which might result from the lower Hamaker constant of P. putida compared with that of kaolinite. Contributions (1) Strong specific ion effects on mixture colloids aggregation of kaolinite-P. putida were observed; (2) the aggregation behavior of mixture colloids was determined by the average effects of mixture colloids, rather than the specific component. This finding provides an important methodological guide for further studies on the colloidal aggregation behavior of mixture systems with organic and inorganic materials.

Description: Malaria is a disease with debilitating health and negative economic impacts in regions at high risk of infection. Parasitic resistance and side effects of current antimalarial drugs are major setbacks to the successful campaigns that have reduced malaria incidence by 40% in the last decade. The parasite’s dependence on glycolysis for energy requirements makes pathway enzymes suitable targets for drug development. Specifically, triose phosphate isomerase (TPI) from Plasmodium falciparum (pTPI) and human (hTPI) cells show striking structural features that can be used in development of new antimalarial agents. In this study MD simulations were used to characterize binding sites on hTPI and pTPI interactions with sulfonamides. The molecular mechanics Poisson–Boltzmann surface area (MM–PBSA) method was used to estimate the interaction energies of four sulfonamide-TPI docked complexes. A unique combination of key residues at the dimer interface of pTPI is responsible for the observed selective affinity to pTPI compared to hTPI. The representative sulfonamide; 4-amino-N-(3,5-dimethylphenyl)-3-fluorbenzenesulfonamide (sulfaE) shows a strong affinity with pTPI (dimer interface, −42.91 kJ/mol and active site region, −71.62 kJ/mol), hTPI (dimer interface, −41.32 kJ/mol and active site region, −84.40 kJ/mol). Strong and favorable Van der Waals interactions and increases in non-polar solvation energies explain the difference in affinity between pTPI with sulfaE compared to hTPI at the dimer interface. This is an indication that the dimer interface of TPI glycolytic enzyme is vital for development of sulfonamide based antimalarial drugs.

Description: We explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom- or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.

Description: Adaptive sampling molecular dynamics based on Markov State Models use short parallel MD simulations to accelerate simulations, and are proven to identify hidden conformers. The accuracy of the predictions provided by it depends on the features extracted from the simulated data that is used to construct it. The identification of the most important features in the trajectories of the simulated system has a considerable effect on the results. Methods In this study, we use a combination of Laplacian scoring and genetic algorithms to obtain an optimized feature subset for the construction of the MSM. The approach is validated on simulations of three protein folding complexes, and two protein ligand binding complexes. Results Our experiments show that this approach produces better results when the number of samples is significantly lesser than the number of features extracted. We also observed that this method mitigates over fitting that occurs due to high dimensionality of large biosystems with shorter simulation times.

Description: In regard to the actual public health global emergency and, based on the state of the art about the ways to inhibit the SARS-CoV-2 treating the COVID19, a family of 1,5-disubstituted tetrazole-1,2,3-triazoles, previously synthesized, have been evaluated through in silico assays against the main protease of the mentioned virus (CoV-2-MPro). The results show that three of these compounds present a more favorable interaction with the selected target than the co-crystallized molecule, which is a peptide-like derivative. It was also found that also hydrophobic interactions play a key role in the ligand-target molecular couplings, due to the higher hydrophobic surfaces into the active site. Finally, a pharmacophore model has been proposed based on the results below, and a family of 1,5-DT derivatives has been designed and tested with the same methods employed in this work. It was concluded that the compound with the isatin as a substituent (P8) present the higher ligand-target interaction, which makes this a strong drug candidate against COVID19, due can inhibit the CoV-2-MPro protein.

Description: To assess the accuracy of different quantum mechanical methods for biochemical modeling, the reaction energies of 20 small model reactions (chosen to represent chemical steps catalyzed by commonly studied enzymes) were calculated. The methods tested included several popular Density Functional Theory (DFT) functionals, second-order Møller Plesset perturbation theory (MP2) and its spin-component scaled variant (SCS-MP2), and coupled cluster singles and doubles and perturbative triples (CCSD(T)). Different basis sets were tested. CCSD(T)/aug-cc-pVTZ results for all 20 reactions were used to benchmark the other methods. It was found that MP2 and SCS-MP2 reaction energy calculation results are similar in quality to CCSD(T) (mean absolute error (MAE) of 1.2 and 1.3 kcal mol−1, respectively). MP2 calculations gave a large error in one case, and are more subject to basis set effects, so in general SCS-MP2 calculations are a good choice when CCSD(T) calculations are not feasible. Results with different DFT functionals were of reasonably good quality (MAEs of 2.5–5.1 kcal mol−1), whereas popular semi-empirical methods (AM1, PM3, SCC-DFTB) gave much larger errors (MAEs of 11.6–14.6 kcal mol−1). These results should be useful in guiding methodological choices and assessing the accuracy of QM/MM calculations on enzyme-catalyzed reactions.