Relative Binding Energies Predict Crystallographic Binding Modes of Ethionamide Booster Lead Compounds

Molecular Periphery Design Allows Control of the New Nitrofurans Antimicrobial Selectivity

A series of 13 new 3-substituted 5-(5-nitro-2-furyl)-1,2,4-oxadiazoles was synthesized from different aminonitriles. All compounds were screened in the disc diffusion test at a 100 μg/mL concentration to determine the bacterial growth inhibition zone presence and diameter, and then the minimum inhibitory concentrations (MICs) were determined for the most active compounds by serial dilution. The compounds showed antibacterial activity against ESKAPE bacteria, predominantly suppressing the growth of 5 species out of the panel. Some compounds had similar or lower MICs against ESKAPE pathogens compared to ciprofloxacin, nitrofurantoin, and furazidin. In particular, 3-azetidin-3-yl-5-(5-nitro-2-furyl)-1,2,4-oxadiazole (2h) inhibited S. aureus at a concentration lower than all comparators. Compound 2e (5-(5-nitro-2-furyl)-3-[4-(pyrrolidin-3-yloxy)phenyl]-1,2,4-oxadiazole) was active against Gram-positive ESKAPE pathogens as well as M. tuberculosis. Differences in the molecular periphery led to high selectivity for the compounds. The induced-fit docking (IFD) modeling technique was applied to in silico research. Molecular docking results indicated the targeting of compounds against various nitrofuran-associated biological targets.

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.