Green tea catechins are polyphenols which are believed to provide health benefits; they are marketed as health supplements and are studied for their potential effects on a variety of medical conditions. However, their mechanisms of action and interaction with the environment at the molecular level are still not well-understood. Here, by means of atomistic simulations, we explore the structural properties of four green tea catechins, in the gas phase and water solution: specifically, (−)-epigallocatechin-3-gallate, which is the most abundant, (−)-epicatechin-3-gallate, (−)-epigallocatechin-3-O-(3-O-methyl)-gallate, and (−)-epigallocatechin. We characterize the free energy conformational landscapes of these catechins at ambient conditions, as a function of the torsional degrees of freedom of the pholyphenolic rings, determining the stable conformers and their connections. We show that these free energy landscapes are only subtly influenced by the interactions with the solvent and by the structural details of the polyphenolic rings. However, the number and position of the hydroxyl groups (or their sustituents) and the presence/absence of the galloyl moiety have significant impact on the selected catechin solvation shells and hydrogen bond capabilities, which are ultimately linked to their ability to interact with and affect the biological environment.

Trans−cis isomerization of a proline peptide bond is a potential mechanism to open the channel of the 5-HT₃ receptor. Here, we have used the metadynamics method to theoretically explore such a mechanism. We have determined the free energy surfaces in aqueous solution of a series of dipeptides of proline analogues and evaluated the free energy difference between the cis and trans isomers. These theoretical results were then compared with data from mutagenesis experiments, in which the response of the 5-HT₃ receptor was measured when the proline at the apex of the M2-M3 transmembrane domain loop was mutated. The strong correlation between the experimental and the theoretical data supports the existence of a trans−cis proline switch for opening the 5-HT₃ receptor ion channel.

Pentameric ligand-gated ion channels (pLGICs) of the Cys-loop superfamily are important neuroreceptors that mediate fast synaptic transmission. They are activated by the binding of a neurotransmitter, but the details of this process are still not fully understood. As a prototypical pLGIC, here we choose the insect resistance to dieldrin (RDL) receptor involved in resistance to insecticides and investigate the binding of the neurotransmitter GABA to its extracellular domain at the atomistic level. We achieve this by means of μ-sec funnel-metadynamics simulations, which efficiently enhance the sampling of bound and unbound states by using a funnel-shaped restraining potential to limit the exploration in the solvent. We reveal the sequence of events in the binding process from the capture of GABA from the solvent to its pinning between the charged residues Arg111 and Glu204 in the binding pocket. We characterize the associated free energy landscapes in the wild-type RDL receptor and in two mutant forms, where the key residues Arg111 and Glu204 are mutated to Ala. Experimentally these mutations produce nonfunctional channels, which is reflected in the reduced ligand binding affinities due to the loss of essential interactions. We also analyze the dynamical behavior of the crucial loop C, whose opening allows the access of GABA to the binding site and closure locks the ligand into the protein. The RDL receptor shares structural and functional features with other pLGICs; hence, our work outlines a valuable protocol to study the binding of ligands to pLGICs beyond conventional docking and molecular dynamics techniques.

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased “downhill” trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand–protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.