Structure-Based De Novo Design for the Discovery of Miniprotein Inhibitors Targeting Oncogenic Mutant BRAF.

Being a component of the Ras/Raf/MEK/ERK signaling pathway crucial for cellular responses, the VRAF murine sarcoma viral oncogene homologue B1 (BRAF) kinase has emerged as a promising target for anticancer drug discovery due to oncogenic mutations that lead to pathway hyperactivation. Despite the discovery of several small-molecule BRAF kinase inhibitors targeting oncogenic mutants, their clinical utility has been limited by challenges such as off-target effects and suboptimal pharmacological properties. This study focuses on identifying miniprotein inhibitors for the oncogenic V600E mutant BRAF, leveraging their potential as versatile drug candidates. Using a structure-based de novo design approach based on binding affinity to V600E mutant BRAF and hydration energy, 39 candidate miniprotein inhibitors comprising three helices and 69 amino acids were generated from the substructure of the endogenous ligand protein (14-3-3). Through in vitro binding and kinase inhibition assays, two miniproteins (63 and 76) were discovered as novel inhibitors of V600E mutant BRAF with low-micromolar activity, with miniprotein 76 demonstrating a specific impediment to MEK1 phosphorylation in mammalian cells. These findings highlight miniprotein 76 as a potential lead compound for developing new cancer therapeutics, and the structural features contributing to its biochemical potency against V600E mutant BRAF are discussed in detail.

Concurrent Optimizations of Efficacy and Blood-Brain Barrier Permeability in New Macrocyclic LRRK2 Inhibitors for Potential Parkinson's Disease Therapeutics.

The elevated activity of leucine-rich repeat kinase 2 (LRRK2) is implicated in the pathogenesis of Parkinson's disease (PD). The quest for effective LRRK2 inhibitors has been impeded by the formidable challenge of crossing the blood-brain barrier (BBB). We leveraged structure-based de novo design and developed robust three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict BBB permeability, enhancing the likelihood of the inhibitor's brain accessibility. Our strategy involved the synthesis of macrocyclic molecules by linking the two terminal nitrogen atoms of HG-10-102-01 with an alkyl chain ranging from 2 to 4 units, laying the groundwork for innovative LRRK2 inhibitor designs. Through meticulous computational and synthetic optimization of both biochemical efficacy and BBB permeability, 9 out of 14 synthesized candidates demonstrated potent low-nanomolar inhibition and significant BBB penetration. Further assessments of in vitro and in vivo effectiveness, coupled with pharmacological profiling, highlighted 8 as the promising new lead compound for PD therapeutics.

Machine Learning-Assisted Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine.

Marine mussels adhere to virtually any surface via 3,4-dihydroxyphenyl-L-alanines (L-DOPA), an amino acid largely contained in their foot proteins. The biofriendly, water-repellent, and strong adhesion of L-DOPA are unparalleled by any synthetic adhesive. Inspired by this, we computationally designed diverse derivatives of DOPA and studied their potential as adhesives or coating materials. We used first-principles calculations to investigate the adsorption of the DOPA derivatives on graphite. The presence of an electron-withdrawing group, such as nitrogen dioxide, strengthens the adsorption by increasing the π-π interaction between DOPA and graphite. To quantify the distribution of electron charge and to gain insights into the charge distribution at interfaces, we performed Bader charge analysis and examined charge density difference plots. We developed a quantitative structure-property relationship (QSPR) model using an artificial neural network (ANN) to predict the adsorption energy. Using the three-dimensional and quantum mechanical electrostatic potential of a molecule as a descriptor, the present quantum NN model shows promising performance as a predictive QSPR model.

Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm.

The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.

Structure-Based Virtual Screening and De Novo Design to Identify Submicromolar Inhibitors of G2019S Mutant of Leucine-Rich Repeat Kinase 2.

Missense mutations of leucine-rich repeat kinase 2 (LRRK2), including the G2019S mutant, are responsible for the pathogenesis of Parkinson's disease. In this work, structure-based virtual screening of a large chemical library was carried out to identify a number of novel inhibitors of the G2019S mutant of LRRK2, the biochemical potencies of which ranged from the low micromolar to the submicromolar level. The discovery of these potent inhibitors was made possible due to the modification of the original protein-ligand binding energy function in order to include an accurate ligand dehydration energy term. The results of extensive molecular docking simulations indicated that the newly identified inhibitors were bound to the ATP-binding site of the G2019S mutant of LRRK2 through the multiple hydrogen bonds with backbone amide groups in the hinge region as well as the hydrophobic interactions with the nonpolar residues in the P-loop, hinge region, and interdomain region. Among 18 inhibitors derived from virtual screening, 4-(2-amino-5-phenylpyrimidin-4-yl)benzene-1,3-diol (Inhibitor 2) is most likely to serve as a new molecular scaffold to optimize the biochemical potency, because it revealed submicromolar inhibitory activity in spite of its low molecular weight (279.3 amu). Indeed, a highly potent inhibitor (Inhibitor 2n) of the G2019S mutant was derived via the structure-based de novo design using the structure of Inhibitor 2 as the molecular core. The biochemical potency of Inhibitor 2n surged to the nanomolar level due to the strengthening of hydrophobic interactions in the ATP-binding site, which were presumably caused by the substitutions of small nonpolar moieties. Due to the high biochemical potency against the G2019S mutant of LRRK2 and the putatively good physicochemical properties, Inhibitor 2n is anticipated to serve as a new lead compound for the discovery of antiparkinsonian medicines.

Two-Track Virtual Screening Approach to Identify the Dual Inhibitors of Wild Type and C481S Mutant of Bruton's Tyrosine Kinase.

Bruton's tyrosine kinase (BTK) is responsible for the pathogenesis of various autoimmune diseases and chronic lymphocytic leukemia. However, the discovery of efficient medicines has seen limited success due to the constitutively active mutants that acquired the drug resistance. To disclose the dual inhibitors against the wild-type BTK and the problematic drug-resistant C481S mutant, a large chemical library was virtually screened with extensive molecular docking simulations using two target proteins. As a consequence of imposing the configurational restraint to make a hydrogen bond in the hinge region of BTK as well as modifying the ligand dehydration term in the scoring function, a total of 20 dual inhibitors were discovered with the range of the associated IC50 values from 2.5 to 15 µM. All these dual inhibitors revealed the inhibitory activity against the C481S mutant to a comparable extent to that measured for the wild type. Among the new inhibitors, N-(3,5-dimethoxyphenyl)-6,7-dimethoxyquinazolin-4-amine (1) appeared to be most suitable as a starting point of the lead optimization due to the highest biochemical potency against the C481S mutant as well as the lowest molecular weight. To increase the potential of a drug candidate, 1 was modified into 6,7-dimethoxy-N-(pyridin-3-yl)quinazolin-4-amine (12) via chemical synthesis so as to possess better physicochemical properties without loss of the biochemical potency. 12 is suggested as a new effective molecular core from which numerous druggable dual inhibitors of the wild-type BTK and the C481S mutant would be derivatized.

A Dynamic Substrate Pool Revealed by cryo-EM of a Lipid-Preserved Respiratory Supercomplex.

Aims: Mitochondrial respiratory supercomplexes mediate redox electron transfer, generating a proton gradient for ATP synthesis. To provide structural information on the function of supercomplexes in physiologically relevant conditions, we conducted cryoelectron microscopy studies with supercomplexes in a lipid-preserving state. Results: Here, we present cryoelectron microscopy structures of bovine respiratory supercomplex I1III2IV1 by using a lipid-preserving sample preparation. The preparation greatly enhances the intercomplex quinone transfer activity. The structures reveal large intercomplex motions that result in different shapes and sizes of the intercomplex space between complexes I and III, forming a dynamic substrate pool. Biochemical and structural analyses indicated that intercomplex phospholipids mediate the intercomplex motions. An analysis of the different classes of focus-refined complex I showed that structural switches due to quinone reduction led to the formation of a novel channel that could transfer reduced quinones to the intercomplex substrate pool. Innovation and Conclusion: Our results indicate potential mechanism for the facilitated electron transfer involving a dynamic substrate pool and intercomplex movement by which supercomplexes play an active role in the regulation of metabolic flux and reactive oxygen species.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood-brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Development of antibody against drug-resistant respiratory syncytial virus: Rapid detection of mutant virus using split superfolder green fluorescent protein-antibody system.

Respiratory syncytial virus (RSV) infections are associated with severe bronchiolitis or pneumonia. Although palivizumab is used to prevent RSV infections, the occurrence of palivizumab-resistant RSV strains is increasing, and these strains pose a threat to public health. Herein, we report an antibody with affinity to the S275F RSV antigen, enabling the specific detection of palivizumab-resistant RSV strains. Experimental and simulation results confirmed the affinity of the antibody to the S275F RSV antigen. Furthermore, we developed a rapid S275F RSV antigen detection method using a split superfolder green fluorescent protein (ssGFP) that can interact with the antibody. In the presence of the mutant virus antigen, ssGFP emitted fluorescence within 1 min, allowing the rapid identification of S275F RSV. We anticipate that the developed antibody would be useful for the precise diagnosis of antiviral drug-resistant RSV strains and help treat patients with RSV infections.

Development of 6E3 antibody-mediated SERS immunoassay for drug-resistant influenza virus.

Influenza viruses are responsible for several pandemics and seasonal epidemics and pose a major public health threat. Even after a major outbreak, the emergence of drug-resistant influenza viruses can pose disease control problems. Here we report a novel 6E3 monoclonal antibody capable of recognizing and binding to the H275Y neuraminidase (NA) mutation, which has been associated with reduced susceptibility of influenza viruses to NA inhibitors. The 6E3 antibody had a KD of 72.74 µM for wild-type NA and 32.76 pM for H275Y NA, suggesting that it can identify drug-resistant pandemic H1N1 (pH1N1) influenza virus. Molecular modeling studies also suggest the high-affinity binding of this antibody to pH1N1 H275Y NA. This antibody was also subject to dot-blot, enzyme-linked immunosorbent assay, bare-eye detection, and lateral flow assay to demonstrate its specificity to drug-resistant pH1N1. Furthermore, it was immobilized on Au nanoplate and nanoparticles, enabling surface-enhanced Raman scattering (SERS)-based detection of the H275Y mutant pH1N1. Using 6E3 antibody-mediated SERS immunoassay, the drug-resistant influenza virus can be detected at a low concentration of 102 plaque-forming units/mL. We also detected pH1N1 in human nasopharyngeal aspirate samples, suggesting that the 6E3-mediated SERS assay has the potential for diagnostic application. We anticipate that this newly developed antibody and SERS-based immunoassay will contribute to the diagnosis of drug-resistant influenza viruses and improve treatment strategies for influenza patients.

Structure-Based Virtual Screening and De Novo Design of PIM1 Inhibitors with Anticancer Activity from Natural Products.

BACKGROUND: the proviral insertion site of Moloney murine leukemia (PIM) 1 kinase has served as a therapeutic target for various human cancers due to the enhancement of cell proliferation and the inhibition of apoptosis. METHODS: to identify effective PIM1 kinase inhibitors, structure-based virtual screening of natural products of plant origin and de novo design were carried out using the protein-ligand binding free energy function improved by introducing an adequate dehydration energy term. RESULTS: as a consequence of subsequent enzyme inhibition assays, four classes of PIM1 kinase inhibitors were discovered, with the biochemical potency ranging from low-micromolar to sub-micromolar levels. The results of extensive docking simulations showed that the inhibitory activity stemmed from the formation of multiple hydrogen bonds in combination with hydrophobic interactions in the ATP-binding site. Optimization of the biochemical potency by chemical modifications of the 2-benzylidenebenzofuran-3(2H)-one scaffold led to the discovery of several nanomolar inhibitors with antiproliferative activities against human breast cancer cell lines. CONCLUSIONS: these new PIM1 kinase inhibitors are anticipated to serve as a new starting point for the development of anticancer medicine.

Rational Computational Design of Fourth-Generation EGFR Inhibitors to Combat Drug-Resistant Non-Small Cell Lung Cancer.

Although the inhibitors of singly mutated epidermal growth factor receptor (EGFR) kinase are effective for the treatment of non-small cell lung cancer (NSCLC), their clinical efficacy has been limited due to the emergence of various double and triple EGFR mutants with drug resistance. It has thus become urgent to identify potent and selective inhibitors of triple mutant EGFRs resistant to first-, second-, and third-generation EGFR inhibitors. Herein, we report the discovery of potent and highly selective inhibitors of EGFR exon 19 p.E746_A750del/EGFR exon 20 p.T790M/EGFR exon 20 p.C797S (d746-750/T790M/C797S) mutant, which were derived via two-track virtual screening and de novo design. This two-track approach was performed so as to maximize and minimize the inhibitory activity against the triple mutant and the wild type, respectively. Extensive chemical modifications of the initial hit compounds led to the identification of several low-nanomolar inhibitors of the d746-750/T790M/C797S mutant. Among them, two compounds exhibited more than 104-fold selectivity in the inhibition of EGFRd746-750/T790M/C797S over the wild type. The formations of a hydrogen bond with the mutated residue Ser797 and the van der Waals contact with the mutated residue Met790 were found to be a common feature in the interactions between EGFRd746-750/T790M/C797S and the fourth-generation inhibitors. Such an exceptionally high selectivity could also be attributed to the formation of the hydrophobic contact with a Gly loop residue or the hydrogen bond with Asp855 in the activation loop. The discovery of the potent and selective EGFRd746-750/T790M/C797S inhibitors were actually made possible by virtue of the modified protein-ligand binding free energy function involving a new hydration free energy term with enhanced accuracy. The fourth-generation EGFR inhibitors found in this work are anticipated to serve as a new starting point for the discovery of anti-NSCLC medicines to overcome the problematic drug resistance.

Kinase and GPCR polypharmacological approach for the identification of efficient anticancer medicines.

Discovery of an anticancer medicine using a single target protein has often been unsuccessful due to the complexity of pathogenic mechanisms as well as the presence of redundant signaling pathways. In this work, we attempted to find promising anticancer drug candidates by simultaneously targeting casein kinase 1 delta (CK1δ) and muscarinic acetylcholine receptor M3 (M3R). Through the structure-based virtual screening and de novo design with the modified potential function for protein-ligand binding, a series of benzo[4,5]imidazo[1,2-a][1,3,5]triazine-2-amine (BITA) derivatives were identified as CK1δ inhibitors and also as M3R antagonists. The biochemical potencies of these bifunctional molecules reached the nanomolar and low-micromolar levels with respect to CK1δ and M3R, respectively. A common interaction feature in the calculated CK1δ-inhibitor and M3R-antagonist complexes is that the BITA moiety is well-stabilized in the orthosteric site of M3R and the hinge region of CK1δ through the establishment of the three hydrogen bonds and the hydrophobic contacts in the vicinity. The computational and experimental results found in this work exemplify the efficiency of kinase and GPCR polypharmacology in developing anticancer medicines.

Development of A4 antibody for detection of neuraminidase I223R/H275Y-associated antiviral multidrug-resistant influenza virus.

The emergence and spread of antiviral drug-resistant viruses have been a worldwide challenge and a great concern for patient care. We report A4 antibody specifically recognizing and binding to the mutant I223R/H275Y neuraminidase and prove the applicability of A4 antibody for direct detection of antiviral multidrug-resistant viruses in various sensing platforms, including naked-eye detection, surface-enhanced Raman scattering-based immunoassay, and lateral flow system. The development of the A4 antibody enables fast, simple, and reliable point-of-care assays of antiviral multidrug-resistant influenza viruses. In addition to current influenza virus infection testing methods that do not provide information on the antiviral drug-resistance of the virus, diagnostic tests for antiviral multidrug-resistant viruses will improve clinical judgment in the treatment of influenza virus infections, avoid the unnecessary prescription of ineffective drugs, and improve current therapies.

A human protein hydroxylase that accepts D-residues.

Factor inhibiting hypoxia-inducible factor (FIH) is a 2-oxoglutarate-dependent protein hydroxylase that catalyses C3 hydroxylations of protein residues. We report FIH can accept (D)- and (L)-residues for hydroxylation. The substrate selectivity of FIH differs for (D) and (L) epimers, e.g., (D)- but not (L)-allylglycine, and conversely (L)- but not (D)-aspartate, undergo monohydroxylation, in the tested sequence context. The (L)-Leu-containing substrate undergoes FIH-catalysed monohydroxylation, whereas (D)-Leu unexpectedly undergoes dihydroxylation. Crystallographic, mass spectrometric, and DFT studies provide insights into the selectivity of FIH towards (L)- and (D)-residues. The results of this work expand the potential range of known substrates hydroxylated by isolated FIH and imply that it will be possible to generate FIH variants with altered selectivities.

Structural basis for recognition of the tumor suppressor protein PTPN14 by the oncoprotein E7 of human papillomavirus.

Human papillomaviruses (HPVs) are causative agents of various diseases associated with cellular hyperproliferation, including cervical cancer, one of the most prevalent tumors in women. E7 is one of the two HPV-encoded oncoproteins and directs recruitment and subsequent degradation of tumor-suppressive proteins such as retinoblastoma protein (pRb) via its LxCxE motif. E7 also triggers tumorigenesis in a pRb-independent pathway through its C-terminal domain, which has yet been largely undetermined, with a lack of structural information in a complex form with a host protein. Herein, we present the crystal structure of the E7 C-terminal domain of HPV18 belonging to the high-risk HPV genotypes bound to the catalytic domain of human nonreceptor-type protein tyrosine phosphatase 14 (PTPN14). They interact directly and potently with each other, with a dissociation constant of 18.2 nM. Ensuing structural analysis revealed the molecular basis of the PTPN14-binding specificity of E7 over other protein tyrosine phosphatases and also led to the identification of PTPN21 as a direct interacting partner of E7. Disruption of HPV18 E7 binding to PTPN14 by structure-based mutagenesis impaired E7's ability to promote keratinocyte proliferation and migration. Likewise, E7 binding-defective PTPN14 was resistant for degradation via proteasome, and it was much more effective than wild-type PTPN14 in attenuating the activity of downstream effectors of Hippo signaling and negatively regulating cell proliferation, migration, and invasion when examined in HPV18-positive HeLa cells. These results therefore demonstrated the significance and therapeutic potential of the intermolecular interaction between HPV E7 and host PTPN14 in HPV-mediated cell transformation and tumorigenesis.

Synthesis and biological evaluation of acylthiourea against DUSP1 inhibition.

Structure based virtual screening attempts to discover DUSP1 inhibitors have yielded a scaffold featuring benzoxazole and acylthiourea pharmacophore. A series of its analogues were synthesized to explore structure activity relationship (SAR) of DUSP1 inhibition.

Identifying New AMP-Activated Protein Kinase Inhibitors That Protect against Ischemic Brain Injury.

We recently reported that AMP-activated protein kinase (AMPK) contributes to zinc-induced neuronal death by inducing Bim, a pro-apoptotic Bcl-2 homology domain 3-only protein, in a liver kinase B1 (LKB1)-dependent manner. Current data suggest AMPK plays key roles in excitotoxicity and ischemic brain injury, with zinc neurotoxicity representing at least one mechanism of ischemic neuronal death. Inhibition of AMPK could be a viable therapeutic strategy to prevent ischemic brain injury following stroke. This prompted our search for novel inhibitors of AMPK activity and zinc-induced neuronal death using cultured mouse cortex and a rat model of brain injury after middle cerebral artery occlusion (MCAO). In structure-based virtual screening, 118 compounds were predicted to bind the active site of AMPK α2, and 40 showed in vitro AMPK α2 inhibitory activity comparable to compound C (a well-known, potent AMPK inhibitor). In mouse cortical neuronal cultures, 7 of 40 compound reduced zinc-induced neuronal death at levels comparable to compound C. Ultimately, only agents 2G11 and 1H10 significantly attenuated various types of neuronal death, including oxidative stress, excitotoxicity, and apoptosis. When administered as intracerebroventricular injections prior to permanent MCAO in rats, 2G11 and 1H10 reduced brain infarct volumes, whereas compound C did not. Therefore, these novel AMPK inhibitors could be drug development candidates to treat stroke.

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties.

We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.

Graphite as a Long-Life Ca2+-Intercalation Anode and its Implementation for Rocking-Chair Type Calcium-Ion Batteries.

Herein, graphite is proposed as a reliable Ca2+-intercalation anode in tetraglyme (G4). When charged (reduced), graphite accommodates solvated Ca2+-ions (Ca-G4) and delivers a reversible capacity of 62 mAh g-1 that signifies the formation of a ternary intercalation compound, Ca-G4·C72. Mass/volume changes during Ca-G4 intercalation and the evolution of in operando X-ray diffraction studies both suggest that Ca-G4 intercalation results in the formation of an intermediate phase between stage-III and stage-II with a gallery height of 11.41 Å. Density functional theory calculations also reveal that the most stable conformation of Ca-G4 has a planar structure with Ca2+ surrounded by G4, which eventually forms a double stack that aligns with graphene layers after intercalation. Despite large dimensional changes during charge/discharge (C/D), both rate performance and cyclic stability are excellent. Graphite retains a substantial capacity at high C/D rates (e.g., 47 mAh g-1 at 1.0 A g-1 s vs 62 mAh g-1 at 0.05 A g-1) and shows no capacity decay during as many as 2000 C/D cycles. As the first Ca2+-shuttling calcium-ion batteries with a graphite anode, a full-cell is constructed by coupling with an organic cathode and its electrochemical performance is presented.