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Determining the principal energy-transfer pathways responsible for allosteric communication in biomolecules remains challenging, partially due to the intrinsic complexity of the systems and the lack of effective characterization methods. In this work, we introduce the eigenvector centrality metric based on mutual information to elucidate allosteric mechanisms that regulate enzymatic activity. Moreover, we propose a strategy to characterize the range of correlations that underlie the allosteric processes. We use the V-type allosteric enzyme imidazole glycerol phosphate synthase (IGPS) to test the proposed methodology. The eigenvector centrality method identifies key amino acid residues of IGPS with high susceptibility to effector binding. The findings are validated by solution NMR measurements yielding important biological insights, including direct experimental evidence for interdomain motion, the central role played by helix hα 1, and the short-range nature of correlations responsible for the allosteric mechanism. Beyond insights on IGPS allosteric pathways and the nature of residues that could be targeted by therapeutic drugs or site-directed mutagenesis, the reported findings demonstrate the eigenvector centrality analysis as a general cost-effective methodology to gain fundamental understanding of allosteric mechanisms at the molecular level.

Mu opioid receptor agonists are among the most powerful analgesic medications but also among the most addictive. The current opioid crisis has energized a quest to develop opioid analgesics that are devoid of untoward effects. Since their discovery in the 1970’s, there have been major advances in our understanding of the endogenous opioid systems that these drugs target. Yet many questions remain and the development of non-addictive opioid analgesics has not been achieved. However, access to new molecular, genetic and computational tools have begun to elucidate the structural dynamics of opioid receptors, the scaffolding that links them to intracellular signaling cascades, their cellular trafficking and the distinct ways that various opioid drugs modify them. This mini-review highlights some of the chemical and pharmacological findings and new perspectives that have arisen from studies using these tools. They reveal multiple layers of complexity of opioid receptor function, including a spatiotemporal specificity in opioid receptor-induced cellular signaling, ligand-directed biased signaling, allosteric modulation of ligand interactions, heterodimerization of different opioid receptors, and the existence of slice variants with different ligand specificity. By untangling these layers, basic research into the chemistry and pharmacology of opioid receptors is guiding the way towards deciphering the mysteries of tolerance and physical dependence that have plagued the field and is providing a platform for the development of more effective and safer opioids.

Thousands of proteins have been validated genetically as therapeutic targets for human diseases 1 . However, very few have been successfully targeted, and many are considered ‘undruggable’. This is particularly true for proteins that function via protein–protein interactions—direct inhibition of binding interfaces is difficult and requires the identification of allosteric sites. However, most proteins have no known allosteric sites, and a comprehensive allosteric map does not exist for any protein. Here we address this shortcoming by charting multiple global atlases of inhibitory allosteric communication in KRAS. We quantified the effects of more than 26,000 mutations on the folding of KRAS and its binding to six interaction partners. Genetic interactions in double mutants enabled us to perform biophysical measurements at scale, inferring more than 22,000 causal free energy changes. These energy landscapes quantify how mutations tune the binding specificity of a signalling protein and map the inhibitory allosteric sites for an important therapeutic target. Allosteric propagation is particularly effective across the central β-sheet of KRAS, and multiple surface pockets are genetically validated as allosterically active, including a distal pocket in the C-terminal lobe of the protein. Allosteric mutations typically inhibit binding to all tested effectors, but they can also change the binding specificity, revealing the regulatory, evolutionary and therapeutic potential to tune pathway activation. Using the approach described here, it should be possible to rapidly and comprehensively identify allosteric target sites in many proteins. Analysis of the effects of more than 26,000 KRAS mutations on abundance and interactions with six other proteins is used to construct an energy landscape of KRAS and identify allosteric drug target sites.

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development 1 – 6 . An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes—here we examine binding and protein abundance—in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering ‘edgetic’ variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped. An approach that combines deep mutational scanning with neural network-based thermodynamic modelling is used to provide comprehensive maps of the energetic and allosteric effects of mutations in two common protein domains.

G protein-coupled receptors (GPCRs) are the most common proteins targeted by approved drugs. A complete mechanistic elucidation of large-scale conformational transitions underlying the activation mechanisms of GPCRs is of critical importance for therapeutic drug development. Here, we apply a combined computational and experimental framework integrating extensive molecular dynamics simulations, Markov state models, site-directed mutagenesis, and conformational biosensors to investigate the conformational landscape of the angiotensin II (AngII) type 1 receptor (AT 1 receptor) — a prototypical class A GPCR—activation. Our findings suggest a synergistic transition mechanism for AT 1 receptor activation. A key intermediate state is identified in the activation pathway, which possesses a cryptic binding site within the intracellular region of the receptor. Mutation of this cryptic site prevents activation of the downstream G protein signaling and β-arrestin-mediated pathways by the endogenous AngII octapeptide agonist, suggesting an allosteric regulatory mechanism. Together, these findings provide a deeper understanding of AT 1 receptor activation at an atomic level and suggest avenues for the design of allosteric AT 1 receptor modulators with a broad range of applications in GPCR biology, biophysics, and medicinal chemistry. G protein-coupled receptors (GPCRs) are a critical target in modern drug development across a wide range of indications. Here the authors provide a comprehensive characterization of a typical GPCR, the angiotensin II (AngII) type 1 receptor (AT1R), and provide insight into its activation mechanism that suggest avenues for the design of allosteric GPCR modulators.

Protein allostery is a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid change at a distant site affects the active site remotely. Molecular dynamics (MD) simulation provides a powerful computational approach to probe the allosteric effect. However, current MD simulations cannot reach the time scales of whole allosteric processes. The advent of deep learning made it possible to evaluate both spatially short and long-range communications for understanding allostery. For this purpose, we applied a neural relational inference model based on a graph neural network, which adopts an encoder-decoder architecture to simultaneously infer latent interactions for probing protein allosteric processes as dynamic networks of interacting residues. From the MD trajectories, this model successfully learned the long-range interactions and pathways that can mediate the allosteric communications between distant sites in the Pin1, SOD1, and MEK1 systems. Furthermore, the model can discover allostery-related interactions earlier in the MD simulation trajectories and predict relative free energy changes upon mutations more accurately than other methods. Here, the authors apply a neural relational inference model to infer dynamic networks of interacting residues in protein molecular dynamics simulations. The model can predict allosteric communication pathways and relative free energy changes upon mutations.

Psychedelics produce fast and persistent antidepressant effects and induce neuroplasticity resembling the effects of clinically approved antidepressants. We recently reported that pharmacologically diverse antidepressants, including fluoxetine and ketamine, act by binding to TrkB, the receptor for BDNF. Here we show that lysergic acid diethylamide (LSD) and psilocin directly bind to TrkB with affinities 1,000-fold higher than those for other antidepressants, and that psychedelics and antidepressants bind to distinct but partially overlapping sites within the transmembrane domain of TrkB dimers. The effects of psychedelics on neurotrophic signaling, plasticity and antidepressant-like behavior in mice depend on TrkB binding and promotion of endogenous BDNF signaling but are independent of serotonin 2A receptor (5-HT 2A ) activation, whereas LSD-induced head twitching is dependent on 5-HT 2A and independent of TrkB binding. Our data confirm TrkB as a common primary target for antidepressants and suggest that high-affinity TrkB positive allosteric modulators lacking 5-HT 2A activity may retain the antidepressant potential of psychedelics without hallucinogenic effects. Moliner et al. show that psychedelics directly bind to the BDNF receptor TrkB with high affinity and promote BDNF-mediated plasticity and antidepressant-like effects, whereas their hallucinogenic-like effects are independent of TrkB binding.

Class A G-protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology. Understanding receptor activation mechanism is critical for discovering novel therapeutics since about one-third of all marketed drugs target members of this family. GPCR activation is an allosteric process that couples agonist binding to G-protein recruitment, with the hallmark outward movement of transmembrane helix 6 (TM6). However, what leads to TM6 movement and the key residue level changes of this movement remain less well understood. Here, we report a framework to quantify conformational changes. By analyzing the conformational changes in 234 structures from 45 class A GPCRs, we discovered a common GPCR activation pathway comprising of 34 residue pairs and 35 residues. The pathway unifies previous findings into a common activation mechanism and strings together the scattered key motifs such as CWxP, DRY, Na+ pocket, NPxxY and PIF, thereby directly linking the bottom of ligand-binding pocket with G-protein coupling region. Site-directed mutagenesis experiments support this proposition and reveal that rational mutations of residues in this pathway can be used to obtain receptors that are constitutively active or inactive. The common activation pathway provides the mechanistic interpretation of constitutively activating, inactivating and disease mutations. As a module responsible for activation, the common pathway allows for decoupling of the evolution of the ligand binding site and G-protein-binding region. Such an architecture might have facilitated GPCRs to emerge as a highly successful family of proteins for signal transduction in nature.

Chemokines and their receptors mediate cell migration, which influences multiple fundamental biological processes and disease conditions such as inflammation and cancer 1 . Although ample effort has been invested into the structural investigation of the chemokine receptors and receptor–chemokine recognition 2 – 4 , less is known about endogenous chemokine-induced receptor activation and G-protein coupling. Here we present the cryo-electron microscopy structures of interleukin-8 (IL-8, also known as CXCL8)-activated human CXC chemokine receptor 2 (CXCR2) in complex with G i  protein, along with a crystal structure of CXCR2 bound to a designed allosteric antagonist. Our results reveal a unique shallow mode of binding between CXCL8 and CXCR2, and also show the interactions between CXCR2 and G i  protein. Further structural analysis of the inactive and active states of CXCR2 reveals a distinct activation process and the competitive small-molecule antagonism of chemokine receptors. In addition, our results provide insights into how a G-protein-coupled receptor is activated by an endogenous protein molecule, which will assist in the rational development of therapeutics that target the chemokine system for better pharmacological profiles. Structures of the G i -coupled CXC chemokine receptor 2 (CXCR2) in complex with CXCL8 and in complex with an allosteric antagonist provide insight into the ligand binding and activation of CXCR2 and its mode of G-protein coupling.

Besides genome editing, CRISPR-Cas12a has recently been used for DNA detection applications with attomolar sensitivity but, to our knowledge, it has not been used for the detection of small molecules. Bacterial allosteric transcription factors (aTFs) have evolved to sense and respond sensitively to a variety of small molecules to benefit bacterial survival. By combining the single-stranded DNA cleavage ability of CRISPR-Cas12a and the competitive binding activities of aTFs for small molecules and double-stranded DNA, here we develop a simple, supersensitive, fast and high-throughput platform for the detection of small molecules, designated CaT-SMelor ( C RISPR-Cas12a- and aT F-mediated s mall m ol e cu l e detect or ). CaT-SMelor is successfully evaluated by detecting nanomolar levels of various small molecules, including uric acid and p -hydroxybenzoic acid among their structurally similar analogues. We also demonstrate that our CaT-SMelor directly measured the uric acid concentration in clinical human blood samples, indicating a great potential of CaT-SMelor in the detection of small molecules. Bacterial allosteric transcription factors can sense and respond to a variety of small molecules. Here the authors present CaT-SMelor which uses Cas12a and allosteric transcription factors to detect small molecules in the nanomolar range.