Blocking the catalytic mechanism of MurC ligase enzyme from Acinetobacter baumannii: An in Silico guided study towards the discovery of natural antibiotics

Identification of novel natural MurD ligase inhibitors as potential antimicrobial agents targeting Acinetobacter baumannii: In silico screening and biological evaluation

The increased multidrug resistance in Acinetobacter baumannii (A. baumannii) to the present-day known antibiotics has stimulated academic and industrial efforts globally for the development of novel antibacterial agents. Natural compounds as potential drug leads are gaining significant attention due to their less toxic and more tolerant nature. In the current study, the natural product-based compounds were explored as probable inhibitors of UDP-N-acetylmuramoyl-L-alanine:D-glutamate (MurD) ligase from A.baumannii (AbMurD) to provide a new class of drug leads. The prepared natural library of 3,16,714 compounds from ZINC database was screened into the active site of AbMurD using in silico high-throughput virtual screening which resulted in 100 compounds having high binding affinities. Further screening through flexible molecular docking yielded four potential compounds selected on the basis of estimated binding affinity (ΔG) and favorable protein-ligand interactions. MD simulation of these four compounds under physiological conditions and free binding energy calculations using MM/PBSA (molecular mechanics with Poisson- Boltzmann and surface area solvation) approach revealed three compounds ZINC08879777, ZINC30726863, and ZINC95486217 as potential binders of AbMurD. The calculated physicochemical and ADME properties of these compounds revealed that they can be exploited and modified to improve their binding affinity with the enzyme. Two compounds were purchased and tested against bacterial cell cultures of A. baumannii, Salmonella Typhi, and Staphylococcus aureus to determine their broad-spectrum antibacterial activity. The results suggest that the identified compounds can be exploited as potential herbal leads to target both Gram-positive and Gram-negative pathogens. Communicated by Ramaswamy H. Sarma.

Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

Aurora kinase B plays an important role in the cell cycle to orchestrate the mitotic process. The amplification and overexpression of this kinase have been implicated in several human malignancies. Therefore, Aurora kinase B is a potential drug target for anticancer therapies. Here, we combine atom-based 3D-QSAR analysis and pharmacophore model generation to identify the principal structural features of acylureidoindolin derivatives that could potentially be responsible for the inhibition of Aurora kinase B. The selected CoMFA and CoMSIA model showed significant results with cross-validation values (q²) of 0.68, 0.641 and linear regression values (r²) of 0.971, 0.933 respectively. These values support the statistical reliability of our model. A pharmacophore model was also generated, incorporating features of reported crystal complex structures of Aurora kinase B. The pharmacophore model was used to screen commercial databases to retrieve potential lead candidates. The resulting hits were analyzed at each stage for diversity based on the pharmacophore model, followed by molecular docking and filtering based on their interaction with active site residues and 3D-QSAR predictions. Subsequently, MD simulations and binding free energy calculations were performed to test the predictions and to characterize interactions at the molecular level. The results suggested that the identified compounds retained the interactions with binding residues. Binding energy decomposition identified residues Glu155, Trp156 and Ala157 of site B and Leu83 and Leu207 of site C as major contributors to binding affinity, complementary to 3D-QSAR results. To best of our knowledge, this is the first comparison of WaterSwap field and 3D-QSAR maps. Overall, this integrated strategy provides a basis for the development of new and potential AK-B inhibitors and is applicable to other protein targets.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Molecular Dynamics Simulation Framework to Probe the Binding Hypothesis of CYP3A4 Inhibitors

The Cytochrome P450 family of heme-containing proteins plays a major role in catalyzing phase I metabolic reactions, and the CYP3A4 subtype is responsible for the metabolism of many currently marketed drugs. Additionally, CYP3A4 has an inherent affinity for a broad spectrum of structurally diverse chemical entities, often leading to drug-drug interactions mediated by the inhibition or induction of the metabolic enzyme. The current study explores the binding of selected highly efficient CYP3A4 inhibitors by docking and molecular dynamics (MD) simulation protocols and their binding free energy calculated using the WaterSwap method. The results indicate the importance of binding pocket residues including Phe57, Arg105, Arg106, Ser119, Arg212, Phe213, Thr309, Ser312, Ala370, Arg372, Glu374, Gly481 and Leu483 for interaction with CYP3A4 inhibitors. The residue-wise decomposition of the binding free energy from the WaterSwap method revealed the importance of binding site residues Arg106 and Arg372 in the stabilization of all the selected CYP3A4-inhibitor complexes. The WaterSwap binding energies were further complemented with the MM(GB/PB)SA results and it was observed that the binding energies calculated by both methods do not differ significantly. Overall, our results could guide towards the use of multiple computational approaches to achieve a better understanding of CYP3A4 inhibition, subsequently leading to the design of highly specific and efficient new chemical entities with suitable ADMETox properties and reduced side effects.