In Silico Research of New Therapeutics Rotenoids Derivatives against Leishmania amazonensis Infection

Yearly, 1,500,000 cases of leishmaniasis are diagnosed, causing thousands of deaths. To advance in its therapy, we present an interdisciplinary protocol that unifies ethnobotanical knowledge of natural compounds and the latest bioinformatics advances to respond to an orphan disease such as leishmaniasis and specifically the one caused by Leishmania amazonensis. The use of ethnobotanical information serves as a basis for the development of new drugs, a field in which computer-aided drug design (CADD) has been a revolution. Taking this information from Amazonian communities, located in the area with a high prevalence of this disease, a protocol has been designed to verify new leads. Moreover, a method has been developed that allows the evaluation of lead molecules, and the improvement of their affinity and specificity against therapeutic targets. Through this approach, deguelin has been identified as a good lead to treat the infection due to its potential as an ornithine decarboxylase (ODC) inhibitor, a key enzyme in Leishmania development. Using an in silico-generated combinatorial library followed by docking approaches, we have found deguelin derivatives with better affinity and specificity against ODC than the original compound, suggesting that this approach could be adapted for developing new drugs against leishmaniasis.

In Silico Research of New Therapeutics Rotenoids Derivatives Against Leishmania Amazonensis Infection

Yearly, 1,500,000 cases of leishmaniasis are diagnosed, causing thousands of deaths. To advance in its therapy, we present an interdisciplinary protocol that unifies ethnobotanical knowledge of natural compounds and the latest bioinformatics advances to respond to an orphan disease such as leishmaniasis and specifically the one caused by Leishmania amazonensis. The use of ethnobotanical information serves as a basis for the development of new drugs, a field in which computer-aided drug design (CADD) has been a revolution. Taking this information from Amazonian communities, located in the area with the highest prevalence of this disease, a protocol has been designed to verify new leads. Moreover, a method has been developed that allows the evaluation of lead molecules, and the improvement of their affinity and specificity against therapeutic targets. Through this approach, deguelin has been identified as a good lead to treat the infection due to its potential as an ornithine decarboxylase (ODC) inhibitor, a key enzyme in Leishmania development. Using an in silico-generated combinatorial library followed by docking approaches, we have found deguelin derivatives with better affinity and specificity against ODC than the original compound, suggesting that this approach could be adapted for developing new drugs against leishmaniasis.

Hit Identification of a Novel Quinazoline Sulfonamide as a Promising EphB3 Inhibitor: Design, Virtual Combinatorial Library, Synthesis, Biological Evaluation, and Docking Simulation Studies

EphB3 is a major key player in a variety of cellular activities, including cell migration, proliferation, and apoptosis. However, the exact role of EphB3 in cancer remains ambiguous. Accordingly, new EphB3 inhibitors can increase the understanding of the exact roles of the receptor and may act as promising therapeutic candidates. Herein, a hybrid approach of structure-based design and virtual combinatorial library generated 34 quinazoline sulfonamides as potential selective EphB3 inhibitors. A molecular docking study over EphB3 predicted the binding affinities of the generated library, and the top seven hit compounds (3a and 4a–f), with GlideScore ≥ −6.20 Kcal/mol, were chosen for further MM-GBSA calculations. Out of the seven top hits, compound 4c showed the highest MM-GBSA binding free energy (−74.13 Kcal/mol). To validate these predicted results, compounds 3a and 4a–f were synthesized and characterized using NMR, HRMS, and HPLC. The biological evaluation revealed compound 4c as a potent EphB3 inhibitory lead (IC50 = 1.04 µM). The screening of 4c over a mini-panel of kinases consisting of EGFR, Aurora A, Aurora B, CDK2/cyclin A, EphB1, EphB2, EphB4, ERBB2/HER2, and KDR/VEGFR2, showed a promising selective profile against EphB3 isoform. A dose-dependent assay of compound 4c and a molecular docking study over the different forms of EphB provided insights into the elicited biological activities and highlighted reasonable explanations of the selectivity.

Combinatorial assembly platform enabling engineering of genetically stable metabolic pathways in cyanobacteria

Cyanobacteria are simple, efficient, genetically-tractable photosynthetic microorganisms which in principle represent ideal biocatalysts for CO2 capture and conversion. However, in practice, genetic instability and low productivity are key, linked problems in engineered cyanobacteria. We took a massively parallel approach, generating and characterising libraries of synthetic promoters and RBSs for the cyanobacterium Synechocystis sp. PCC 6803, and assembling a sparse combinatorial library of millions of metabolic pathway-encoding construct variants. Genetic instability was observed for some variants, which is expected when variants cause metabolic burden. Surprisingly however, in a single combinatorial round without iterative optimisation, 80% of variants chosen at random and cultured photoautotrophically over many generations accumulated the target terpenoid lycopene from atmospheric CO2, apparently overcoming genetic instability. This large-scale parallel metabolic engineering of cyanobacteria provides a new platform for development of genetically stable cyanobacterial biocatalysts for sustainable light-driven production of valuable products directly from CO2, avoiding fossil carbon or competition with food production.

Novel Degree-Based Topological Descriptors of Carbon Nanotubes

The most significant tool of mathematical chemistry is the numerical descriptor called topological index. Topological indices are extensively used in modelling of chemical compounds to analyse the studies on quantitative structure activity/property/toxicity relationships and combinatorial library virtual screening. In this work, an attempt is made in defining three novel descriptors, namely, neighborhood geometric-harmonic, harmonic-geometric, and neighborhood harmonic-geometric indices. Also, the aforementioned three indices along with the geometric-harmonic index are tested for physicochemical properties of octane isomers using linear regression models and computed for some carbon nanotubes.

π-Stacking Stopper-Macrocycle Stabilized Dynamically Interlocked [2]Rotaxanes

The synthesis of mechanically interlocked molecules is valuable due to their unique topologies. With π-stacking intercomponent interaction, e.g., phenanthroline and anthracene, novel [2]rotaxanes have been synthesized by dynamic imine clipping reaction. Their X-ray crystal structures indicate the π-stackings between the anthracene moiety (stopper) on the thread and the (hetero)aromatic rings at the macrocycle of the rotaxanes. Moreover, the length of glycol chains affects the extra π-stacking intercomponent interactions between the phenyl groups and the dimethoxy phenyl groups on the thread. Dynamic combinatorial library has shown at best 84% distribution of anthracene-threaded phenanthroline-based rotaxane, coinciding with the crystallography in that the additional π-stacking intercomponent interactions could increase the thermodynamic stability and selectivity of the rotaxanes.