Conformational transitions of a neurotensin receptor 1-G complex.

Neurotensin receptor 1 (NTSR1) is a G-protein-coupled receptor (GPCR) that engages multiple subtypes of G protein, and is involved in the regulation of blood pressure, body temperature, weight and the response to pain. Here we present structures of human NTSR1 in complex with the agonist JMV449 and the heterotrimeric G protein, at a resolution of 3 Å. We identify two conformations: a canonical-state complex that is similar to recently reported GPCR-G complexes (in which the nucleotide-binding pocket adopts more flexible conformations that may facilitate nucleotide exchange), and a non-canonical state in which the G protein is rotated by about 45 degrees relative to the receptor and exhibits a more rigid nucleotide-binding pocket.

Author Correction: Structural insights into the activation of metabotropic glutamate receptors.

The surname of author Toon Laeremans was misspelled 'Laermans'. This error has been corrected online.

Diverse GPCRs exhibit conserved water networks for stabilization and activation.

G protein-coupled receptors (GPCRs) have evolved to recognize incredibly diverse extracellular ligands while sharing a common architecture and structurally conserved intracellular signaling partners. It remains unclear how binding of diverse ligands brings about GPCR activation, the common structural change that enables intracellular signaling. Here, we identify highly conserved networks of water-mediated interactions that play a central role in activation.

Structural insights into the activation of metabotropic glutamate receptors.

Metabotropic glutamate receptors are family C G-protein-coupled receptors. They form obligate dimers and possess extracellular ligand-binding Venus flytrap domains, which are linked by cysteine-rich domains to their 7-transmembrane domains. Spectroscopic studies show that signalling is a dynamic process, in which large-scale conformational changes underlie the transmission of signals from the extracellular Venus flytraps to the G protein-coupling domains-the 7-transmembrane domains-in the membrane.

qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X-Ray Electron Density Maps.

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data.

Collision-free poisson motion planning in ultra high-dimensional molecular conformation spaces.

The function of protein, RNA, and DNA is modulated by fast, dynamic exchanges between three-dimensional conformations. Conformational sampling of biomolecules with exact and nullspace inverse kinematics, using rotatable bonds as revolute joints and noncovalent interactions as holonomic constraints, can accurately characterize these native ensembles. However, sampling biomolecules remains challenging owing to their ultra-high dimensional configuration spaces, and the requirement to avoid (self-) collisions, which results in low acceptance rates.

Driving Structural Transitions in Molecular Simulations Using the Nonequilibrium Candidate Monte Carlo.

Hybrid simulation procedures which combine molecular dynamics with Monte Carlo are attracting increasing attention as tools for improving the sampling efficiency in molecular simulations. In particular, encouraging results have been reported for nonequilibrium candidate protocols, in which a Monte Carlo move is applied gradually, and interleaved with a process that equilibrates the remaining degrees of freedom. Although initial studies have uncovered a substantial potential of the method, its practical applicability for sampling structural transitions in macromolecules remains incompletely understood.

Frustration-guided motion planning reveals conformational transitions in proteins.

Proteins exist as conformational ensembles, exchanging between substates to perform their function. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers.

Fast, clash-free RNA conformational morphing using molecular junctions.

Motivation: Non-coding ribonucleic acids (ncRNA) are functional RNA molecules that are not translated into protein. They are extremely dynamic, adopting diverse conformational substates, which enables them to modulate their interaction with a large number of other molecules. The flexibility of ncRNA provides a challenge for probing their complex 3D conformational landscape, both experimentally and computationally.

Probing RNA Native Conformational Ensembles with Structural Constraints.

Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules.

Coupled Motions in β2AR:Gαs Conformational Ensembles.

G protein-coupled receptors (GPCRs) act as conduits in the plasma membrane, facilitating cellular responses to physiological events by activating intracellular signal transduction pathways. Extracellular signaling molecules can induce conformational changes in GPCR, which allow it to selectively activate intracellular protein partners such as heterotrimeric protein G. However, a major unsolved problem is how GPCRs and G proteins form complexes and how their interaction results in G protein activation.

Characterizing RNA ensembles from NMR data with kinematic models.

Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging.

Bounding volumes for proteins: a comparative study.

A chain tree is a data structure for representing changing protein conformations. It enables very fast detection of clashes and free potential energy calculations. The efficiency of chain trees is closely related to the bounding volumes associated with chain tree nodes.

Adjustable chain trees for proteins.

A chain tree is a data structure for changing protein conformations. It enables very fast detection of clashes and free energy potential calculations. A modified version of chain trees that adjust themselves to the changing conformations of folding proteins is introduced.

Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks.

Background: Predicting the three-dimensional structure of a protein from its amino acid sequence is currently one of the most challenging problems in bioinformatics. The internal structure of helices and sheets is highly recurrent and help reduce the search space significantly. However, random coil segments make up nearly 40% of proteins and they do not have any apparent recurrent patterns, which complicates overall prediction accuracy of protein structure prediction methods.