Molecular Dynamics on Wood-Derived Lignans Analyzed by Intermolecular Network Theory

Leaf metabolic traits reveal hidden dimensions of plant form and function

The metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization-a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function.

In Silico Study of Thiourea Derivatives as Potential Epidermal Growth Factor Receptor Inhibitors

Over the years, the escalation of cancer cases has been linked to the resistance, less selectivity, and toxicity of available anticancer drugs to normal cells. Therefore, continuous efforts are necessary to find new anticancer drugs with high selectivity of epidermal growth factor receptor tyrosine kinase (EGFR-TK) as a therapeutic target. The EGFR-TK protein has a crucial role in cell proliferation and cancer progression. With about 30% of cancer cases involved with the protein, it has piqued the interest as a therapeutic target. The potential of theoretically designed thiourea derivatives as anticancer agents in this report was evaluated against EGFR-TK via in silico techniques, including molecular docking (AutoDock Vina), molecular dynamics simulations (GROMACS), pharmacokinetics, and drug-likeness properties (SwissADME and Molinspiration). New hybrid molecules of the thiourea derivative moiety were designed in this study based on the fragment-based drug discovery and linked with diverse pharmacophoric fragments with reported anticancer potential ([Formula: see text]) and the modification of the methyl position on phenyl ring ([Formula: see text]). These fragments include pyridine, thiophene, furan, pyrrole and styrene groups. Out of 15 compounds, compound 13 displayed the most potent inhibitory activity, with the lowest binding affinity in docking of [Formula: see text]8.7 kcal/mol compared to the positive control erlotinib of [Formula: see text]6.7 kcal/mol. Our molecular dynamics (MD) simulations revealed that molecule 13, comprising styrene and 2-methylphenyl substituents on [Formula: see text] and [Formula: see text], respectively, showed adequate compactness, uniqueness and satisfactory stability. Subsequently, the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties and drug-likeness properties also indicate that this theoretically designed inhibitor ( 13) is less toxic and contains high druggable properties. Thus, compound 13 could be promising against EGFR-TK.

Distance Degree Index of Some Derived Graphs

Topological indices are numerical values associated with a graph (structure) that can predict many physical, chemical, and pharmacological properties of organic molecules and chemical compounds. The distance degree (DD) index was introduced by Dobrynin and Kochetova in 1994 for characterizing alkanes by an integer. In this paper, we have determined expressions for a DD index of some derived graphs in terms of the parameters of the parent graph. Specifically, we establish expressions for the DD index of a line graph, subdivision graph, vertex-semitotal graph, edge-semitotal graph, total graph, and paraline graph.

Distance and Adjacency Energies of Multi-Level Wheel Networks

Energies of molecular graphs have various applications in chemistry, polymerization, computer networking and pharmacy. In this paper, we give general closed forms of distance and adjacency energies of generalized wheel networks W n , m . Consequently, we give these results for classical wheel graphs. We also give pictorial dependencies of energies on the involved parameters m ≥ 3 and n .