Conformational transitions of a neurotensin receptor 1-Gi1 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 Gi1 protein, at a resolution of 3 Å. We identify two conformations: a canonical-state complex that is similar to recently reported GPCR-Gi/o 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. In the non-canonical state, NTSR1 exhibits features of both active and inactive conformations, which suggests that the structure may represent an intermediate form along the activation pathway of G proteins. This structural information, complemented by molecular dynamics simulations and functional studies, provides insights into the complex process of G-protein activation.

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. Using atomic-level simulations of diverse GPCRs, we show that most of the water molecules in GPCR crystal structures are highly mobile. Several water molecules near the G protein-coupling interface, however, are stable. These water molecules form two kinds of polar networks that are conserved across diverse GPCRs: (i) a network that is maintained across the inactive and the active states and (ii) a network that rearranges upon activation. Comparative analysis of GPCR crystal structures independently confirms the striking conservation of water-mediated interaction networks. These conserved water-mediated interactions near the G protein-coupling region, along with diverse water-mediated interactions with extracellular ligands, have direct implications for structure-based drug design and GPCR engineering.

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. Here, using a combination of X-ray crystallography, cryo-electron microscopy and signalling studies, we present a structural framework for the activation mechanism of metabotropic glutamate receptor subtype 5. Our results show that agonist binding at the Venus flytraps leads to a compaction of the intersubunit dimer interface, thereby bringing the cysteine-rich domains into close proximity. Interactions between the cysteine-rich domains and the second extracellular loops of the receptor enable the rigid-body repositioning of the 7-transmembrane domains, which come into contact with each other to initiate signalling.

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. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.

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. Here, we present two novel mechanisms to overcome these limitations. First, we introduce temporary constraints between near-colliding links. The resulting constraint varieties instantaneously redirect the search for collision-free conformations, and couple motions between distant parts of the linkage. Second, we adapt a randomized Poisson-disk motion planner, which prevents local oversampling and widens the search, to ultra-high dimensions. Tests on several model systems show that the sampling acceptance rate can increase from 16% to 70%, and that the conformational coverage in loop modeling measured as average closeness to existing loop conformations doubled. Correlated protein motions identified with our algorithm agree with those from MD simulations. © 2018 Wiley Periodicals, Inc.

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. Here, we address this issue by systematically investigating the efficiency of the nonequilibrium candidate Monte Carlo on the sampling of rotameric distributions of two peptide systems at atomistic resolution both in vacuum and explicit solvent. The studied systems allow us to directly probe the efficiency with which a single or a few slow degrees of freedom can be driven between well-separated free-energy minima and to explore the sensitivity of the method toward the involved free parameters. In line with results on other systems, our study suggests that order-of-magnitude gains can be obtained in certain scenarios but also identifies challenges that arise when applying the procedure in explicit solvent.

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. Here, we present a new, robotics-inspired motion planning procedure called dCC-RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non-native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 Å root mean square deviation (RMSD), our pathways reduced the Cα atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC-RRT to examine how collective, small-scale motions of four side-chains in the active site of cyclophilin A propagate through the protein. dCC-RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non-canonical capsid binding site 25 Å away, rationalizing NMR and multi-temperature crystallography experiments. In all, dCC-RRT can reveal detailed, all-atom molecular mechanisms for small and large amplitude motions. Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/.

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. RESULTS: Despite their conformational diversity, ncRNAs mostly preserve their secondary structure throughout the dynamic ensemble. Here we present a kinematics-based procedure to morph an RNA molecule between conformational substates, while avoiding inter-atomic clashes. We represent an RNA as a kinematic linkage, with fixed groups of atoms as rigid bodies and rotatable bonds as degrees of freedom. Our procedure maintains RNA secondary structure by treating hydrogen bonds between base pairs as constraints. The constraints define a lower-dimensional, secondary-structure constraint manifold in conformation space, where motions are largely governed by molecular junctions of unpaired nucleotides. On a large benchmark set, we show that our morphing procedure compares favorably to peer algorithms, and can approach goal conformations to within a low all-atom RMSD by directing fewer than 1% of its atoms. Our results suggest that molecular junctions can modulate 3D structural rearrangements, while secondary structure elements guide large parts of the molecule along the transition to the correct final conformation. AVAILABILITY AND IMPLEMENTATION: The source code, binaries and data are available at https://simtk.org/home/kgs . CONTACT: amelie.heliou@polytechnique.edu or vdbedem@stanford.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.

Coupled Motions in ß2AR: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. Here, we show that an inactive, agonist-free ß2AR:Gαs complex can collectively sample intermediate states of the receptor on an activation pathway. An in silico conformational ensemble around the inactive state manifests significant conformational coupling between structural elements implicated in G protein activation throughout the complex. While Gαs helix α5 has received much attention as a driver for nucleotide exchange, we also observe interactions between helix αN with Intra Cellular Loop 2, which can be transmitted by ß1 to facilitate nucleotide exchange by disrupting a salt bridge between the P-loop and Switch I. These interactions are moderated in an active state ensemble. Collectively, our results support an alternative view of G protein activation, in which precoupling can allosterically modulate an agonist-free receptor. Subsequent selective agonist recruitment would result in collective activation of the complex. This alternative view can help us understand how distinct extracellular binding partners result in different but interdependent signaling pathways, with broad implications for GPCR drug discovery.

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. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem-loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.

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. A protein subchain associated with a node of a chain tree will clash with another subchain only if their bounding volumes intersect. It is therefore essential that bounding volumes are as tight as possible while intersection tests can be carried out efficiently. We compare the performance of four different types of bounding volumes in connection with the rotation of protein bonds. It is observed that oriented bounding boxes are not as good as could be expected judging by their extensive use in various applications. Both rectangular- and line-swept spheres are shown to have very good tightness of fit but the line-swept, or even simple spheres, are shown to be significantly faster because of quick overlap checks. We also investigate how the performance of the recently introduced adjustable chain trees is affected by different bounding volume types.

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. This results in much tighter bounding volume hierarchies and therefore fewer intersection checks. Computational results indicate that the efficiency of the adjustable chain trees is significantly improved compared to the traditional chain trees.

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. Luckily, previous work has indicated that coil segments are in fact not completely random in structure and flanking residues do seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles. RESULTS: In this paper we develop an artificial neural network that uses an input-window of amino acids to predict a dihedral angle probability distribution for the middle residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30 degrees x 30 degrees area of the dihedral angle space) for all amino acids in the data set compared to baseline statistics. An accuracy comparable to that of secondary structure prediction ( approximately 80%) is achieved by observing the 20 bins with highest output values. CONCLUSION: Many different protein structure prediction methods exist and each uses different tools and auxiliary predictions to help determine the native structure. In this work the sequence is used to predict local context dependent dihedral angle propensities in coil-regions. This predicted distribution can potentially improve tertiary structure prediction methods that are based on sampling the backbone dihedral angles of individual amino acids. The predicted distribution may also help predict local structure fragments used in fragment assembly methods.