Atomistic description of the OCTN1 recognition mechanism via in silico methods.

The Organic Cation Transporter Novel 1 (OCTN1), also known as SLC22A4, is widely expressed in various human tissues, and involved in numerous physiological and pathological processes remains. It facilitates the transport of organic cations, zwitterions, with selectivity for positively charged solutes. Ergothioneine, an antioxidant compound, and acetylcholine (Ach) are among its substrates. Given the lack of experimentally solved structures of this protein, this study aimed at generating a reliable 3D model of OCTN1 to shed light on its substrate-binding preferences and the role of sodium in substrate recognition and transport. A chimeric model was built by grafting the large extracellular loop 1 (EL1) from an AlphaFold-generated model onto a homology model. Molecular dynamics simulations revealed domain-specific mobility, with EL1 exhibiting the highest impact on overall stability. Molecular docking simulations identified cytarabine and verapamil as highest affinity ligands, consistent with their known inhibitory effects on OCTN1. Furthermore, MM/GBSA analysis allowed the categorization of substrates into weak, good, and strong binders, with molecular weight strongly correlating with binding affinity to the recognition site. Key recognition residues, including Tyr211, Glu381, and Arg469, were identified through interaction analysis. Ach demonstrated a low interaction energy, supporting the hypothesis of its one-directional transport towards to outside of the membrane. Regarding the role of sodium, our model suggested the involvement of Glu381 in sodium binding. Molecular dynamics simulations of systems at increasing levels of Na+ concentrations revealed increased sodium occupancy around Glu381, supporting experimental data associating Na+ concentration to molecule transport. In conclusion, this study provides valuable insights into the 3D structure of OCTN1, its substrate-binding preferences, and the role of sodium in the recognition. These findings contribute to the understanding of OCTN1 involvement in various physiological and pathological processes and may have implications for drug development and disease management.

Asymmetric allostery in estrogen receptor-α homodimers drives responses to the ensemble of estrogens in the hormonal milieu.

The estrogen receptor-α (ER) is thought to function only as a homodimer but responds to a variety of environmental, metazoan, and therapeutic estrogens at subsaturating doses, supporting binding mixtures of ligands as well as dimers that are only partially occupied. Here, we present a series of flexible ER ligands that bind to receptor dimers with individual ligand poses favoring distinct receptor conformations-receptor conformational heterodimers-mimicking the binding of two different ligands. Molecular dynamics simulations showed that the pairs of different ligand poses changed the correlated motion across the dimer interface to generate asymmetric communication between the dimer interface, the ligands, and the surface binding sites for epigenetic regulatory proteins. By examining the binding of the same ligand in crystal structures of ER in the agonist vs. antagonist conformers, we also showed that these allosteric signals are bidirectional. The receptor conformer can drive different ligand binding modes to support agonist vs. antagonist activity profiles, a revision of ligand binding theory that has focused on unidirectional signaling from the ligand to the coregulator binding site. We also observed differences in the allosteric signals between ligand and coregulator binding sites in the monomeric vs. dimeric receptor, and when bound by two different ligands, states that are physiologically relevant. Thus, ER conformational heterodimers integrate two different ligand-regulated activity profiles, representing different modes for ligand-dependent regulation of ER activity.

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452 .

In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations.

AIM: This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations. METHODOLOGY: Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets. RESULTS: The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles. CONCLUSION: This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance.

TRPV3 activation by different agonists accompanied by lipid dissociation from the vanilloid site.

TRPV3 represents both temperature- and ligand-activated transient receptor potential (TRP) channel. Physiologically relevant opening of TRPV3 channels by heat has been captured structurally, while opening by agonists has only been observed in structures of mutant channels. Here, we present cryo-EM structures that illuminate opening and inactivation of wild-type human TRPV3 in response to binding of two types of agonists: either the natural cannabinoid tetrahydrocannabivarin (THCV) or synthetic agonist 2-aminoethoxydiphenylborane (2-APB). We found that THCV binds to the vanilloid site, while 2-APB binds to the S1-S4 base and ARD-TMD linker sites. Despite binding to distally located sites, both agonists induce similar pore opening and cause dissociation of a lipid that occupies the vanilloid site in their absence. Our results uncover different but converging allosteric pathways through which small-molecule agonists activate TRPV3 and provide a framework for drug design and understanding the role of lipids in ion channel function.

A unified classification system for HIV-1 5' long terminal repeats.

The HIV-1 provirus mainly consists of internal coding region flanked by 1 long terminal repeats (LTRs) at each terminus. The LTRs play important roles in HIV-1 reverse transcription, integration, and transcription. However, despite of the significant study advances of the internal coding regions of HIV-1 by using definite reference classification, there are no systematic and phylogenetic classifications for HIV-1 5' LTRs, which hinders our elaboration on 5' LTR and a better understanding of the viral origin, spread and therapy. Here, by analyzing all available resources of 5' LTR sequences in public databases following 4 recognized principles for the reference classification, 83 representatives and 14 consensus sequences were identified as representatives of 2 groups, 6 subtypes, 6 sub-subtypes, and 9 CRFs. To test the reliability of the supplemented classification system, the constructed references were applied to identify the 5' LTR assignment of the 22 clinical isolates in China. The results revealed that 16 out of 22 tested strains showed a consistent subtype classification with the previous LTR-independent classification system. However, 6 strains, for which recombination events within 5' LTR were demonstrated, unexpectedly showed a different subtype classification, leading a significant change of binding sites for important transcription factors including SP1, p53, and NF-κB. The binding change of these transcriptional factors would probably affect the transcriptional activity of 5' LTR. This study supplemented a unified classification system for HIV-1 5' LTRs, which will facilitate HIV-1 characterization and be helpful for both basic and clinical research fields.

BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved. This paper proposes a bidirectional encoder representations from transformers (BERT)-based model, called BERT-TFBS, to predict TFBSs solely based on DNA sequences. The model consists of a pre-trained BERT module (DNABERT-2), a convolutional neural network (CNN) module, a convolutional block attention module (CBAM) and an output module. The BERT-TFBS model utilizes the pre-trained DNABERT-2 module to acquire the complex long-term dependencies in DNA sequences through a transfer learning approach, and applies the CNN module and the CBAM to extract high-order local features. The proposed model is trained and tested based on 165 ENCODE ChIP-seq datasets. We conducted experiments with model variants, cross-cell-line validations and comparisons with other models. The experimental results demonstrate the effectiveness and generalization capability of BERT-TFBS in predicting TFBSs, and they show that the proposed model outperforms other deep-learning models. The source code for BERT-TFBS is available at https://github.com/ZX1998-12/BERT-TFBS.

Structural basis and synergism of ATP and Na+ activation in bacterial K+ uptake system KtrAB.

The K+ uptake system KtrAB is essential for bacterial survival in low K+ environments. The activity of KtrAB is regulated by nucleotides and Na+. Previous studies proposed a putative gating mechanism of KtrB regulated by KtrA upon binding to ATP or ADP. However, how Na+ activates KtrAB and the Na+ binding site remain unknown. Here we present the cryo-EM structures of ATP- and ADP-bound KtrAB from Bacillus subtilis (BsKtrAB) both solved at 2.8 Å. A cryo-EM density at the intra-dimer interface of ATP-KtrA was identified as Na+, as supported by X-ray crystallography and ICP-MS. Thermostability assays and functional studies demonstrated that Na+ binding stabilizes the ATP-bound BsKtrAB complex and enhances its K+ flux activity. Comparing ATP- and ADP-BsKtrAB structures suggests that BsKtrB Arg417 and Phe91 serve as a channel gate. The synergism of ATP and Na+ in activating BsKtrAB is likely applicable to Na+-activated K+ channels in central nervous system.

Structural basis for the intracellular regulation of ferritin degradation.

The interaction between nuclear receptor coactivator 4 (NCOA4) and the iron storage protein ferritin is a crucial component of cellular iron homeostasis. The binding of NCOA4 to the FTH1 subunits of ferritin initiates ferritinophagy-a ferritin-specific autophagic pathway leading to the release of the iron stored inside ferritin. The dysregulation of NCOA4 is associated with several diseases, including neurodegenerative disorders and cancer, highlighting the NCOA4-ferritin interface as a prime target for drug development. Here, we present the cryo-EM structure of the NCOA4-FTH1 interface, resolving 16 amino acids of NCOA4 that are crucial for the interaction. The characterization of mutants, designed to modulate the NCOA4-FTH1 interaction, is used to validate the significance of the different features of the binding site. Our results explain the role of the large solvent-exposed hydrophobic patch found on the surface of FTH1 and pave the way for the rational development of ferritinophagy modulators.

MARK2 phosphorylates KIF13A at a 14-3-3 binding site to polarize vesicular transport of transferrin receptor within dendrites.

Neurons regulate the microtubule-based transport of certain vesicles selectively into axons or dendrites to ensure proper polarization of function. The mechanism of this polarized vesicle transport is still not fully elucidated, though it is known to involve kinesins, which drive anterograde transport on microtubules. Here, we explore how the kinesin-3 family member KIF13A is regulated such that vesicles containing transferrin receptor (TfR) travel only to dendrites. In experiments involving live-cell imaging, knockout of KIF13A, BioID assay, we found that the kinase MARK2 phosphorylates KIF13A at a 14-3-3 binding motif, strengthening interaction of KIF13A with 14-3-3 such that it dissociates from TfR-containing vesicles, which therefore cannot enter axons. Overexpression of KIF13A or knockout of MARK2 leads to axonal transport of TfR-containing vesicles. These results suggest a unique kinesin-based mechanism for polarized transport of vesicles to dendrites.

PharmaRedefine: A database server for repurposing drugs against pathogenic bacteria.

Pathogenic bacteria represent a formidable threat to human health, necessitating substantial resources for prevention and treatment. With the escalating concern regarding antibiotic resistance, there is a pressing need for innovative approaches to combat these pathogens. Repurposing existing drugs offers a promising solution. Our present work hypothesizes that proteins harboring ligand-binding pockets with similar chemical environments may be able to bind the same drug. To facilitate this drug-repurposing strategy against pathogenic bacteria, we introduce an online server, PharmaRedefine. Leveraging a combination of sequence and structure alignment and protein pocket similarity analysis, this platform enables the prediction of potential targets in representative bacteria for specific FDA-approved drugs. This novel approach holds tremendous potential for drug repositioning that effectively combat infections caused by pathogenic bacteria. PharmaRedefine is freely available at http://guolab.mpu.edu.mo/pharmredefine.

Integrating Explicit and Implicit Fullerene Models into UNRES Force Field for Protein Interaction Studies.

Fullerenes, particularly C60, exhibit unique properties that make them promising candidates for various applications, including drug delivery and nanomedicine. However, their interactions with biomolecules, especially proteins, remain not fully understood. This study implements both explicit and implicit C60 models into the UNRES coarse-grained force field, enabling the investigation of fullerene-protein interactions without the need for restraints to stabilize protein structures. The UNRES force field offers computational efficiency, allowing for longer timescale simulations while maintaining accuracy. Five model proteins were studied: FK506 binding protein, HIV-1 protease, intestinal fatty acid binding protein, PCB-binding protein, and hen egg-white lysozyme. Molecular dynamics simulations were performed with and without C60 to assess protein stability and investigate the impact of fullerene interactions. Analysis of contact probabilities reveals distinct interaction patterns for each protein. FK506 binding protein (1FKF) shows specific binding sites, while intestinal fatty acid binding protein (1ICN) and uteroglobin (1UTR) exhibit more generalized interactions. The explicit C60 model shows good agreement with all-atom simulations in predicting protein flexibility, the position of C60 in the binding pocket, and the estimation of effective binding energies. The integration of explicit and implicit C60 models into the UNRES force field, coupled with recent advances in coarse-grained modeling and multiscale approaches, provides a powerful framework for investigating protein-nanoparticle interactions at biologically relevant scales without the need to use restraints stabilizing the protein, thus allowing for large conformational changes to occur. These computational tools, in synergy with experimental techniques, can aid in understanding the mechanisms and consequences of nanoparticle-biomolecule interactions, guiding the design of nanomaterials for biomedical applications.

Ensemble-Based Virtual Screening Led to the Discovery of Novel Lead Molecules as Potential NMBAs.

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs' active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations.

Carbonic Anhydrases: Different Active Sites, Same Metal Selectivity Rules.

Carbonic anhydrases are mononuclear metalloenzymes catalyzing the reversible hydration of carbon dioxide in organisms belonging to all three domains of life. Although the mechanism of the catalytic reaction is similar, different families of carbonic anhydrases do not have a common ancestor nor do they exhibit significant resemblance in the amino acid sequence or the structure and composition of the metal-binding sites. Little is known about the physical principles determining the metal affinity and selectivity of the catalytic centers, and how well the native metal is protected from being dislodged by other metal species from the local environment. Here, we endeavor to shed light on these issues by studying (via a combination of density functional theory calculations and polarizable continuum model computations) the thermodynamic outcome of the competition between the native metal cation and its noncognate competitor in various metal-binding sites. Typical representatives of the competing cations from the cellular environments of the respective classes of carbonic anhydrases are considered. The calculations reveal how the Gibbs energy of the metal competition changes when varying the metal type, structure, composition, and solvent exposure of the active center. Physical principles governing metal competition in different carbonic anhydrase metal-binding sites are delineated.

Structural basis of human NOX5 activation.

NADPH oxidase 5 (NOX5) catalyzes the production of superoxide free radicals and regulates physiological processes from sperm motility to cardiac rhythm. Overexpression of NOX5 leads to cancers, diabetes, and cardiovascular diseases. NOX5 is activated by intracellular calcium signaling, but the underlying molecular mechanism of which - in particular, how calcium triggers electron transfer from NADPH to FAD - is still unclear. Here we capture motions of full-length human NOX5 upon calcium binding using single-particle cryogenic electron microscopy (cryo-EM). By combining biochemistry, mutagenesis analyses, and molecular dynamics (MD) simulations, we decode the molecular basis of NOX5 activation and electron transfer. We find that calcium binding to the EF-hand domain increases NADPH dynamics, permitting electron transfer between NADPH and FAD and superoxide production. Our structural findings also uncover a zinc-binding motif that is important for NOX5 stability and enzymatic activity, revealing modulation mechanisms of reactive oxygen species (ROS) production.

Understanding the interactions between repurposed drugs sertindole and temoporfin with receptor for advanced glycation endproducts: Therapeutic implications in cancer and metabolic diseases.

CONTEXT: In the pursuit of novel therapeutic possibilities, repurposing existing drugs has gained prominence as an efficient strategy. The findings from our study highlight the potential of repurposed drugs as promising candidates against receptor for advanced glycation endproducts (RAGE) that offer therapeutic implications in cancer, neurodegenerative conditions and metabolic syndromes. Through careful analyses of binding affinities and interaction patterns, we identified a few promising candidates, ultimately focusing on sertindole and temoporfin. These candidates exhibited exceptional binding affinities, efficacy, and specificity within the RAGE binding pocket. Notably, they displayed a pronounced propensity to interact with the active site of RAGE. Our investigation further revealed that sertindole and temoporfin possess desirable pharmacological properties that highlighted them as attractive candidates for targeted drug development. Overall, our integrated computational approach provides a comprehensive understanding of the interactions between repurposed drugs, sertindole and temoporfin and RAGE that pave the way for future experimental validation and drug development endeavors. METHODS: We present an integrated approach utilizing molecular docking and extensive molecular dynamics (MD) simulations to evaluate the potential of FDA-approved drugs, sourced from DrugBank, against RAGE. To gain deeper insights into the binding mechanisms of the elucidated candidate repurposed drugs, sertindole and temoporfin with RAGE, we conducted extensive all-atom MD simulations, spanning 500 nanoseconds (ns). These simulations elucidated the conformational dynamics and stability of the RAGE-sertindole and RAGE-temoporfin complexes.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries.

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Molecular mechanism of trehalose 6-phosphate inhibition of the plant metabolic sensor kinase SnRK1.

SUCROSE-NON-FERMENTING1-RELATED PROTEIN KINASE1 (SnRK1), a central plant metabolic sensor kinase, phosphorylates its target proteins, triggering a global shift from anabolism to catabolism. Molecular modeling revealed that upon binding of KIN10 to GEMINIVIRUS REP-INTERACTING KINASE1 (GRIK1), KIN10's activation T-loop reorients into GRIK1's active site, enabling its phosphorylation and activation. Trehalose 6-phosphate (T6P) is a proxy for cellular sugar status and a potent inhibitor of SnRK1. T6P binds to KIN10, a SnRK1 catalytic subunit, weakening its affinity for GRIK1. Here, we investigate the molecular details of T6P inhibition of KIN10. Molecular dynamics simulations and in vitro phosphorylation assays identified and validated the T6P binding site on KIN10. Under high-sugar conditions, T6P binds to KIN10, blocking the reorientation of its activation loop and preventing its phosphorylation and activation by GRIK1. Under these conditions, SnRK1 maintains only basal activity levels, minimizing phosphorylation of its target proteins, thereby facilitating a general shift from catabolism to anabolism.

Computational investigation of cis-1,4-polyisoprene binding to the latex-clearing protein LcpK30.

Latex clearing proteins (Lcps) catalyze the oxidative cleavage of the C = C bonds in cis-1,4-polyisoprene (natural rubber), producing oligomeric compounds that can be repurposed to other materials. The active catalytic site of Lcps is buried inside the protein structure, thus raising the question of how the large hydrophobic rubber chains can access the catalytic center. To improve our understanding of hydrophobic polymeric substrate binding to Lcps and subsequent catalysis, we investigated the interaction of a substrate model containing ten carbon-carbon double bonds with the structurally characterized LcpK30, using multiple computational tools. Prediction of the putative tunnels and cavities in the LcpK30 structure, using CAVER-Pymol plugin 3.0.3, fpocket and Molecular Dynamic (MD) simulations provided valuable insights on how substrate enters from the surface to the buried active site. Two dominant tunnels were discovered that provided feasible routes for substrate binding, and the presence of two hydrophobic pockets was predicted near the heme cofactor. The larger of these pockets is likely to accommodate the substrate and to determine the size distribution of the oligomers. Protein-ligand docking was carried out using GOLD software to predict the conformations and interactions of the substrate within the protein active site. Deeper insight into the protein-substrate interactions, including close-contacts, binding energies and potential cleavage sites in the cis-1,4-polyisoprene, were obtained from MD simulations. Our findings provide further justification that the protein-substrate complexation in LcpK30 is mainly driven by the hydrophobic interactions accompanied by mutual conformational changes of both molecules. Two potential binding modes were identified, with the substrate in either extended or folded conformations. Whilst binding in the extended conformation was most favorable, the folded conformation suggested a preference for cleavage of a central double bond, leading to a preference for oligomers with 5 to 6 C = C bonds. The results provide insight into further enzyme engineering studies to improve catalytic activity and diversify the substrate and product scope of Lcps.

Interplay between Mg2+ and Ca2+ at multiple sites of the ryanodine receptor.

RyR1 is an intracellular Ca2+ channel important in excitable cells such as neurons and muscle fibers. Ca2+ activates it at low concentrations and inhibits it at high concentrations. Mg2+ is the main physiological RyR1 inhibitor, an effect that is overridden upon activation. Despite the significance of Mg2+-mediated inhibition, the molecular-level mechanisms remain unclear. In this work we determined two cryo-EM structures of RyR1 with Mg2+ up to 2.8 Å resolution, identifying multiple Mg2+ binding sites. Mg2+ inhibits at the known Ca2+ activating site and we propose that the EF hand domain is an inhibitory divalent cation sensor. Both divalent cations bind to ATP within a crevice, contributing to the precise transmission of allosteric changes within the enormous channel protein. Notably, Mg2+ inhibits RyR1 by interacting with the gating helices as validated by molecular dynamics. This structural insight enhances our understanding of how Mg2+ inhibition is overcome during excitation.