Targeting a hidden site on class A beta-lactamases

Phytochemical Compound Screening to Identify Novel Small Molecules against Dengue Virus: A Docking and Dynamics Study

The spread of the Dengue virus over the world, as well as multiple outbreaks of different serotypes, has resulted in a large number of deaths and a medical emergency, as no viable medications to treat Dengue virus patients have yet been found. In this paper, we provide an in silico virtual screening and molecular dynamics-based analysis to uncover efficient Dengue infection inhibitors. Based on a Google search and literature mining, a large phytochemical library was generated and employed as ligand molecules. In this investigation, the protein target NS2B/NS3 from Dengue was employed, and around 27 compounds were evaluated in a docking study. Phellodendroside (−63 kcal/mole), quercimeritrin (−59.5 kcal/mole), and quercetin-7-O-rutinoside (−54.1 kcal/mole) were chosen based on their binding free energy in MM-GBSA. The tested compounds generated numerous interactions at Lys74, Asn152, and Gln167 residues in the active regions of NS2B/NS3, which is needed for the protein’s inhibition. As a result, the stable mode of docked complexes is defined by various descriptors from molecular dynamics simulations, such as RMSD, SASA, Rg, RMSF, and hydrogen bond. The pharmacological properties of the compounds were also investigated, and no toxicity was found in computational ADMET properties calculations. As a result, this computational analysis may aid fellow researchers in developing innovative Dengue virus inhibitors.

Inhibition of Class A β-Lactamase (TEM-1) by Narrow Fractions of Humic Substances

Antimicrobial resistance is a global threat. The use of biologically active natural products alone or in combination with the clinically proven antimicrobial agents might be a useful strategy to fight the resistance. The scientific hypotheses of this study were twofold: (1) the natural humic substances rich in dicarboxyl, phenolic, heteroaryl, and other fragments might possess inhibitory activity against β-lactamases, and (2) this inhibitory activity might be linked to the molecular composition of the humic ensemble. To test these hypotheses, we used humic substances (HS) from different sources (coal, peat, and soil) and of different fractional compositions (humic acids, hymatomelanic acids, and narrow fractions from solid-phase extraction) for inhibiting serine β-lactamase TEM-1. Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) was used to characterize the molecular composition of all humic materials used in this study. The kinetic assay with chromogenic substrate CENTA was used for assessment of inhibitory activity. The inhibition data have shown that among all humic materials tested, a distinct activity was observed within apolar fractions of hymatomelanic acid isolated from lignite. The decrease in the hydrolysis rate in the presence of most active fractions was 42% (with sulbactam-87%). Of particular importance is that these very fractions caused a synergistic effect (2-fold) for the combinations with sulbactam. Linking the observed inhibition effects to molecular composition revealed the preferential contribution of low-oxidized aromatic and acyclic components such as flavonoid-, lignin, and terpenoid-like molecules. The binding of single low-molecular-weight components to the cryptic allosteric site along with supramolecular interactions of humic aggregates with the protein surface could be considered as a major contributor to the observed inhibition. We believe that fine fractionation of hydrophobic humic materials along with molecular modeling studies on the interaction between humic molecules and β-lactamases might contribute to the development of novel β-lactamase inhibitors of humic nature.

Can We Exploit β-Lactamases Intrinsic Dynamics for Designing More Effective Inhibitors?

β-lactamases (BLs) represent the most frequent cause of antimicrobial resistance in Gram-negative bacteria. Despite the continuous efforts in the development of BL inhibitors (BLIs), new BLs able to hydrolyze the last developed antibiotics rapidly emerge. Moreover, the insurgence rate of effective mutations is far higher than the release of BLIs able to counteract them. This results in a shortage of antibiotics that is menacing the effective treating of infectious diseases. The situation is made even worse by the co-expression in bacteria of BLs with different mechanisms and hydrolysis spectra, and by the lack of inhibitors able to hit them all. Differently from other targets, BL flexibility has not been deeply exploited for drug design, possibly because of the small protein size, for their apparent rigidity and their high fold conservation. In this mini-review, we discuss the evidence for BL binding site dynamics being crucial for catalytic efficiency, mutation effect, and for the design of new inhibitors. Then, we report on identified allosteric sites in BLs and on possible allosteric inhibitors, as a strategy to overcome the frequent occurrence of mutations in BLs and the difficulty of competing efficaciously with substrates. Nevertheless, allosteric inhibitors could work synergistically with traditional inhibitors, increasing the chances of restoring bacterial susceptibility towards available antibiotics.

Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase

TEM family of enzymes is one of the most commonly encountered β-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM β-lactamases, the binding modes of these enzymes against ligands in different functional catalytic states have been largely overlooked. But the binding modes may play a critical role in the function and even the evolution of these proteins. In this work, a newly developed machine learning analysis approach to the recognition of protein dynamics states was applied to compare the binding modes of TEM-1 β-lactamase with regard to penicillin in different catalytic states. While conventional analysis methods, including principal components analysis (PCA), could not differentiate TEM-1 in different binding modes, the application of a machine learning method led to excellent classification models differentiating these states. It was also revealed that both reactant/product states and apo/product states are more differentiable than the apo/reactant states. The feature importance generated by the training procedure of the machine learning model was utilized to evaluate the contribution from residues at active sites and in different secondary structures. Key active site residues, Ser70 and Ser130, play a critical role in differentiating reactant/product states, while other active site residues are more important for differentiating apo/product states. Overall, this study provides new insights into the different dynamical function states of TEM-1 and may open a new venue for β-lactamases functional and evolutional studies in general.