Molecular dynamic (MD) simulation and electrochemical assessments of the Satureja Hortensis extract for the construction of effective zinc-based protective film on carbon steel

An Update in Computational Methods for Environmental Monitoring: Theoretical Evaluation of the Molecular and Electronic Structures of Natural Pigment-Metal Complexes

Metals are beneficial to life, but the presence of these elements in excessive amounts can harm both organisms and the environment; therefore, detecting the presence of metals is essential. Currently, metal detection methods employ powerful instrumental techniques that require a lot of time and money. Hence, the development of efficient and effective metal indicators is essential. Several synthetic metal detectors have been made, but due to their risk of harm, the use of natural pigments is considered a potential alternative. Experiments are needed for their development, but they are expensive and time-consuming. This review explores various computational methods and approaches that can be used to investigate metal-pigment interactions because choosing the right methods and approaches will affect the reliability of the results. The results show that quantum mechanical methods (ab initio, density functional theory, and semiempirical approaches) and molecular dynamics simulations have been used. Among the available methods, the density functional theory approach with the B3LYP functional and the LANL2DZ ECP and basis set is the most promising combination due to its good accuracy and cost-effectiveness. Various experimental studies were also in good agreement with the results of computational methods. However, deeper analysis still needs to be carried out to find the best combination of functions and basis sets.

Fibroblast growth factor 2 is a druggable target against glioblastoma: A computational investigation

Fibroblast growth factor 2 (FGF2) is a key player in cancer and tissue homeostasis and regulates renewal of several stem cell types. The FGF2 role in malignant glioma is proven and tagged FGF2, a novel druggable target, is used for developing potent drugs against glioblastoma. In this study, Asinex 51412372, Asinex 51217461, and Asinex 51216586 were filtered to show the best binding affinity for FGF2 with binding energy scores of -8.3 kcal/mol, -8.2 kcal/mol, and -7.8 kcal/mol, respectively. The compounds showed chemical interactions with several vital residues of FGF2 along the compound length. The noticeable residues that interacted with the compounds were Arg15, Asp23, Arg63, and Gln105. In dynamic investigation in solution, the FGF2 reported unstable dynamics in the first 100 ns and gained structural equilibrium in the second phase of 100 ns. The maximum root mean square deviation (RMSD) value touched by the systems is 3 Å. Similarly, the residue flexibility of FGF2 in the presence of compounds was within a stable range and is compact along the simulation time length. The compounds showed robust atomic-level stable energies with FGF2, which are dominated by both van der Waals and electrostatic interactions. The net binding energy of systems varies between -40 kcal/mol and -86 kcal/mol, suggesting the formation of strong intermolecular docked complexes. The drug-likeness and pharmacokinetic properties also pointed toward good structures that are not toxic, have high gastric absorption, showed good distribution, and readily excreted from the body. In summary, the predicted compounds in this study might be ideal hits that might be further optimized for structure and activity during experimental studies.

Computer-Aided Multi-Epitope Vaccine Design against Enterobacter xiangfangensis

Antibiotic resistance is a global public health threat and is associated with high mortality due to antibiotics’ inability to treat bacterial infections. Enterobacter xiangfangensis is an emerging antibiotic-resistant bacterial pathogen from the Enterobacter genus and has the ability to acquire resistance to multiple antibiotic classes. Currently, there is no effective vaccine against Enterobacter species. In this study, a chimeric vaccine is designed comprising different epitopes screened from E. xiangfangensis proteomes using immunoinformatic and bioinformatic approaches. In the first phase, six fully sequenced proteomes were investigated by bacterial pan-genome analysis, which revealed that the pathogen consists of 21,996 core proteins, 3785 non-redundant proteins and 18,211 redundant proteins. The non-redundant proteins were considered for the vaccine target prioritization phase where different vaccine filters were applied. By doing so, two proteins; ferrichrome porin (FhuA) and peptidoglycan-associated lipoprotein (Pal) were shortlisted for epitope prediction. Based on properties of antigenicity, allergenicity, water solubility and DRB*0101 binding ability, three epitopes (GPAPTIAAKR, ATKTDTPIEK and RNNGTTAEI) were used in multi-epitope vaccine designing. The designed vaccine construct was analyzed in a docking study with immune cell receptors, which predicted the vaccine’s proper binding with said receptors. Molecular dynamics analysis revealed that the vaccine demonstrated stable binding dynamics, and binding free energy calculations further validated the docking results. In conclusion, these in silico results may help experimentalists in developing a vaccine against E. xiangfangensis in specific and Enterobacter in general.