Exploring potent ligand for proteins: insights from knowledge-based scoring functions and molecular interaction energies

A rush to explore protein-ligand electrostatic interaction energy with Charger

The mutual penetration of electron densities between two interacting molecules complicates the computation of an accurate electrostatic interaction energy based on a pseudo-atom representation of electron densities. The numerical exact potential and multipole moment (nEP/MM) method is time-consuming since it performs a 3D integration to obtain the electrostatic energy at short interaction distances. Nguyen et al. [(2018), Acta Cryst. A74, 524-536] recently reported a fully analytical computation of the electrostatic interaction energy (aEP/MM). This method performs much faster than nEP/MM (up to two orders of magnitude) and remains highly accurate. A new program library, Charger, contains an implementation of the aEP/MM method. Charger has been incorporated into the MoProViewer software. Benchmark tests on a series of small molecules containing only C, H, N and O atoms show the efficiency of Charger in terms of execution time and accuracy. Charger is also powerful in a study of electrostatic symbiosis between a protein and a ligand. It determines reliable protein-ligand interaction energies even when both contain S atoms. It easily estimates the individual contribution of every residue to the total protein-ligand electrostatic binding energy. Glutathione transferase (GST) in complex with a benzophenone ligand was studied due to the availability of both structural and thermodynamic data. The resulting analysis highlights not only the residues that stabilize the ligand but also those that hinder ligand binding from an electrostatic point of view. This offers new perspectives in the search for mutations to improve the interaction between the two partners. A proposed mutation would improve ligand binding to GST by removing an electrostatic obstacle, rather than by the traditional increase in the number of favourable contacts.

In silico, theoretical biointerface analysis and in vitro kinetic analysis of amine compounds interaction with acetylcholinesterase and butyrylcholinesterase

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are considered important target for drug design against Alzheimer's disease. In the present study in silico analysis; theoretical analysis of biointerface between ligand and interacting amino acid residues of proteins; and in vitro analysis of enzyme inhibition kinetics were carried out to delineate the inhibitory property of amine compounds against AChE/BChE. High throughput virtual screening of amine compounds identified three compounds (2-aminoquinoline, 2-aminobenzimidazole and 2-amino-1-methylbenzimidazole) that best interacted with AChE/BChE. Molecular docking analysis revealed the interaction of these compounds in the active site gorge of AChE/BChE, in particular with amino acid residues present in the peripheral anionic site. Molecular dynamics simulation confirmed the stable binding of these compounds with AChE/BChE. Binding energy calculated through MMGBSA method identified the non-covalent interactions (electrostatic and Van der Waals interactions) have contributed to the stable binding of the amine compounds with the AChE/BChE. Biointerface between amine compounds and AChE/BChE were visualized through Hirshfeld surface analysis. The inter-fragment interaction energies for the possible contacts between amine compounds and amino acid residues were carried out for the first time. All the amine compounds showed mixed-type of inhibition with moderate Ki value in in vitro analysis.

Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach

Optimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) can improve the accuracy by and large. We select human aldose reductase at 0.66 Å, cyclin dependent kinase 2 at 2.0 Å and estrogen receptor β at 2.7 Å resolutions with active site environment ranging from highly hydrophilic to moderate to highly hydrophobic and several of their known ligands. Overall, the use of KEM-CP alongside the GoldScore resulted superior prediction than the GoldScore alone. Unlike GoldScore, the KEM-CP approach is neither environment-specific nor structural resolution dependent, which highlights its versatility. Further, the ranking of the ligands based on the KEM-CP results correlated well with that of the experimental IC₅₀ values. This computationally inexpensive yet simple approach is expected to ease the process of virtual screening of potent ligands, and it would advance the drug discovery research.