Discovery of a hidden transient state in all bromodomain families

Abstract

Bromodomains (BDs) are small protein modules that interact with acetylated marks in histones. These posttranslational modifications are pivotal to regulate gene expression, making BDs promising targets to treat several diseases. While the general structure of BDs is well known, their dynamical features and their interplay with other macromolecules are poorly understood, hampering the rational design of potent and selective inhibitors. Here, we combine extensive molecular dynamics simulations, Markov state modeling, and available structural data to reveal a transiently formed state that is conserved across all BD families. It involves the breaking of two backbone hydrogen bonds that anchor the ZA-loop with the αA helix, opening a cryptic pocket that partially occludes the one associated to histone binding. By analyzing more than 1,900 experimental structures, we unveil just two adopting the hidden state, explaining why it has been previously unnoticed and providing direct structural evidence for its existence. Our results suggest that this state is an allosteric regulatory switch for BDs, potentially related to a recently unveiled BD-DNA–binding mode.

Differential BET Bromodomain Inhibition by Dihydropteridinone and Pyrimidodiazepinone Kinase Inhibitors

“…Lack of pi–pi stacking between the WPF shelf of BRDT-1 and inhibitors was the only noticeable difference in hydrophobic interactions (Supporting Information Figure S8). Sandwiched between the WPF shelf and a conserved leucine residue, optimal positioning of the inhibitor is likely influenced by the dynamics and flexibility of bromodomains . It appears that the conglomerate of small changes in vdW distances between hydrophobic residues and inhibitor atoms beyond the warhead determines the overall shape that each inhibitor preferably adopts upon KAc site occupation (Figure ).…”

Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

“…The current work also provides information about the effectiveness of molecular simulation for identifying cryptic pockets. A number of studies have detailed the discovery of cryptic pockets using molecular simulation [13][14][15][16][17][18] . In this work, we systematically evaluated how well cryptic pockets are identified by simulation across a large set of known cryptic pockets.…”

Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems

“…Sampling metastable states and modeling of kinetics properties, currently lack strategies to generalize and transfer between chemically related systems. There are numerous examples in the literature illustrating that chemically similar systems share similar metastable states yet thermodynamics and kinetics are modulated [Plattner and Noé, 2015, Raich et al, 2021, Sultan et al, 2018a. Early work explores using reaction from one system in other systems [Sultan et al, 2018b] and shows promising results.…”