Scalable computation of anisotropic vibrations for large macromolecular assemblies.

The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.

Constitutive activation mechanism of a class C GPCR.

Class C G-protein-coupled receptors (GPCRs) are activated through binding of agonists to the large extracellular domain (ECD) followed by rearrangement of the transmembrane domains (TMDs). GPR156, a class C orphan GPCR, is unique because it lacks an ECD and exhibits constitutive activity. Impaired GPR156-Gi signaling contributes to loss of hearing. Here we present the cryo-electron microscopy structures of human GPR156 in the Go-free and Go-coupled states. We found that an endogenous phospholipid molecule is located within each TMD of the GPR156 dimer. Asymmetric binding of Gα to the phospholipid-bound GPR156 dimer restructures the first and second intracellular loops and the carboxy-terminal part of the elongated transmembrane 7 (TM7) without altering dimer conformation. Our findings reveal that GPR156 is a transducer for phospholipid signaling. Constant binding of abundant phospholipid molecules and the G-protein-induced reshaping of the cytoplasmic face provide a basis for the constitutive activation of GPR156.

Ligand and G-protein selectivity in the κ-opioid receptor.

The κ-opioid receptor (KOR) represents a highly desirable therapeutic target for treating not only pain but also addiction and affective disorders1. However, the development of KOR analgesics has been hindered by the associated hallucinogenic side effects2. The initiation of KOR signalling requires the Gi/o-family proteins including the conventional (Gi1, Gi2, Gi3, GoA and GoB) and nonconventional (Gz and Gg) subtypes. How hallucinogens exert their actions through KOR and how KOR determines G-protein subtype selectivity are not well understood. Here we determined the active-state structures of KOR in a complex with multiple G-protein heterotrimers-Gi1, GoA, Gz and Gg-using cryo-electron microscopy. The KOR-G-protein complexes are bound to hallucinogenic salvinorins or highly selective KOR agonists. Comparisons of these structures reveal molecular determinants critical for KOR-G-protein interactions as well as key elements governing Gi/o-family subtype selectivity and KOR ligand selectivity. Furthermore, the four G-protein subtypes display an intrinsically different binding affinity and allosteric activity on agonist binding at KOR. These results provide insights into the actions of opioids and G-protein-coupling specificity at KOR and establish a foundation to examine the therapeutic potential of pathway-selective agonists of KOR.

Structural details of a Class B GPCR-arrestin complex revealed by genetically encoded crosslinkers in living cells.

Understanding the molecular basis of arrestin-mediated regulation of GPCRs is critical for deciphering signaling mechanisms and designing functional selectivity. However, structural studies of GPCR-arrestin complexes are hampered by their highly dynamic nature. Here, we dissect the interaction of arrestin-2 (arr2) with the secretin-like parathyroid hormone 1 receptor PTH1R using genetically encoded crosslinking amino acids in live cells. We identify 136 intermolecular proximity points that guide the construction of energy-optimized molecular models for the PTH1R-arr2 complex. Our data reveal flexible receptor elements missing in existing structures, including intracellular loop 3 and the proximal C-tail, and suggest a functional role of a hitherto overlooked positively charged region at the arrestin N-edge. Unbiased MD simulations highlight the stability and dynamic nature of the complex. Our integrative approach yields structural insights into protein-protein complexes in a biologically relevant live-cell environment and provides information inaccessible to classical structural methods, while also revealing the dynamics of the system.

Structure of the dopamine D3 receptor bound to a bitopic agonist reveals a new specificity site in an expanded allosteric pocket.

Although aminergic GPCRs are the target for ~25% of approved drugs, developing subtype selective drugs is a major challenge due to the high sequence conservation at their orthosteric binding site. Bitopic ligands are covalently joined orthosteric and allosteric pharmacophores with the potential to boost receptor selectivity, driven by the binding of the secondary pharmacophore to non-conserved regions of the receptor. Although bitopic ligands have great potential to improve current medications by reducing off-target side effects, the lack of structural information on their binding mode impedes rational design. Here we determine the cryo-EM structure of the hD3R coupled to a GO heterotrimer and bound to the D3R selective bitopic agonist FOB02-04A. Structural, functional and computational analyses provide new insights into its binding mode and point to a new TM2-ECL1-TM1 region, which requires the N-terminal ordering of TM1, as a major determinant of subtype selectivity in aminergic GPCRs. This region is underexploited in drug development, expands the established secondary binding pocket in aminergic GPCRs and could potentially be used to design novel and subtype selective drugs.

Structural basis of GABA reuptake inhibition.

γ-Aminobutyric acid (GABA) transporter 1 (GAT1)1 regulates neuronal excitation of the central nervous system by clearing the synaptic cleft of the inhibitory neurotransmitter GABA upon its release from synaptic vesicles. Elevating the levels of GABA in the synaptic cleft, by inhibiting GABA reuptake transporters, is an established strategy to treat neurological disorders, such as epilepsy2. Here we determined the cryo-electron microscopy structure of full-length, wild-type human GAT1 in complex with its clinically used inhibitor tiagabine3, with an ordered part of only 60 kDa. Our structure reveals that tiagabine locks GAT1 in the inward-open conformation, by blocking the intracellular gate of the GABA release pathway, and thus suppresses neurotransmitter uptake. Our results provide insights into the mixed-type inhibition of GAT1 by tiagabine, which is an important anticonvulsant medication. Its pharmacodynamic profile, confirmed by our experimental data, suggests initial binding of tiagabine to the substrate-binding site in the outward-open conformation, whereas our structure presents the drug stalling the transporter in the inward-open conformation, consistent with a two-step mechanism of inhibition4. The presented structure of GAT1 gives crucial insights into the biology and pharmacology of this important neurotransmitter transporter and provides blueprints for the rational design of neuromodulators, as well as moving the boundaries of what is considered possible in single-particle cryo-electron microscopy of challenging membrane proteins.

Structure of the full-length human Pannexin1 channel and insights into its role in pyroptosis.

Pannexin1 (PANX1) is a large-pore ATP efflux channel with a broad distribution, which allows the exchange of molecules and ions smaller than 1 kDa between the cytoplasm and extracellular space. In this study, we show that in human macrophages PANX1 expression is upregulated by diverse stimuli that promote pyroptosis, which is reminiscent of the previously reported lipopolysaccharide-induced upregulation of PANX1 during inflammasome activation. To further elucidate the function of PANX1, we propose the full-length human Pannexin1 (hPANX1) model through cryo-electron microscopy (cryo-EM) and molecular dynamics (MD) simulation studies, establishing hPANX1 as a homo-heptamer and revealing that both the N-termini and C-termini protrude deeply into the channel pore funnel. MD simulations also elucidate key energetic features governing the channel that lay a foundation to understand the channel gating mechanism. Structural analyses, functional characterizations, and computational studies support the current hPANX1-MD model, suggesting the potential role of hPANX1 in pyroptosis during immune responses.

Self-assembling tetrameric peptides allow in situ 3D bioprinting under physiological conditions.

We have developed an in situ bioprinting method that allows the printing of cells under true physiological conditions by applying self-assembling ultrashort peptides as bioinks. This method avoids cell stressing methods, such as UV-treatment, chemical crosslinking and viscous bioink printing methods. We further demonstrate that different nanomaterials can easily be synthesized or incorporated in the 3D bioprinted peptide scaffolds which opens up the possibility of functionalized 3D scaffolds.

Structural basis of the activation of a metabotropic GABA receptor.

Metabotropic γ-aminobutyric acid receptors (GABAB) are involved in the modulation of synaptic responses in the central nervous system and have been implicated in neuropsychological conditions that range from addiction to psychosis1. GABAB belongs to class C of the G-protein-coupled receptors, and its functional entity comprises an obligate heterodimer that is composed of the GB1 and GB2 subunits2. Each subunit possesses an extracellular Venus flytrap domain, which is connected to a canonical seven-transmembrane domain. Here we present four cryo-electron microscopy structures of the human full-length GB1-GB2 heterodimer: one structure of its inactive apo state, two intermediate agonist-bound forms and an active form in which the heterodimer is bound to an agonist and a positive allosteric modulator. The structures reveal substantial differences, which shed light on the complex motions that underlie the unique activation mechanism of GABAB. Our results show that agonist binding leads to the closure of the Venus flytrap domain of GB1, triggering a series of transitions, first rearranging and bringing the two transmembrane domains into close contact along transmembrane helix 6 and ultimately inducing conformational rearrangements in the GB2 transmembrane domain via a lever-like mechanism to initiate downstream signalling. This active state is stabilized by a positive allosteric modulator binding at the transmembrane dimerization interface.

A deep learning framework to predict binding preference of RNA constituents on protein surface.

Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.