Structural insights into the subtype-selective antagonist binding to the M2 muscarinic receptor

Allostery in G protein-coupled receptors investigated by molecular dynamics simulations

G-protein-coupled receptors (GPCRs) are allosteric signaling machines that trigger distinct functional responses depending on the particular conformational state they adopt upon binding. This so-called GPCR functional selectivity is prompted by ligands of different efficacy binding at orthosteric or allosteric sites on the receptor, as well as by interactions with intracellular protein partners or other receptor types. Molecular dynamics (MD) simulations can provide important mechanistic, thermodynamic, and kinetic insights into these interactions at a level of molecular detail that is necessary to rightly inform modern drug discovery. Here, we review the most recent MD contributions to understanding GPCR allostery, with an emphasis on their strengths and limitations.

Computational design for thermostabilization of GPCRs

GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.

ZOO: an automatic data-collection system for high-throughput structure analysis in protein microcrystallography

Owing to the development of brilliant microfocus beamlines, rapid-readout detectors and sample changers, protein microcrystallography is rapidly becoming a popular technique for accessing structural information from complex biological samples. However, the method is time-consuming and labor-intensive and requires technical expertise to obtain high-resolution protein crystal structures. At SPring-8, an automated data-collection system named ZOO has been developed. This system enables faster data collection, facilitates advanced data-collection and data-processing techniques, and permits the collection of higher quality data. In this paper, the key features of the functionality put in place on the SPring-8 microbeam beamline BL32XU are described and the major advantages of this system are outlined. The ZOO system will be a major driving force in the evolution of the macromolecular crystallography beamlines at SPring-8.