Positive allosteric mechanisms of adenosine A1 receptor-mediated analgesia.

The adenosine A1 receptor (A1R) is a promising therapeutic target for non-opioid analgesic agents to treat neuropathic pain1,2. However, development of analgesic orthosteric A1R agonists has failed because of a lack of sufficient on-target selectivity as well as off-tissue adverse effects3. Here we show that [2-amino-4-(3,5-bis(trifluoromethyl)phenyl)thiophen-3-yl)(4-chlorophenyl)methanone] (MIPS521), a positive allosteric modulator of the A1R, exhibits analgesic efficacy in rats in vivo through modulation of the increased levels of endogenous adenosine that occur in the spinal cord of rats with neuropathic pain. We also report the structure of the A1R co-bound to adenosine, MIPS521 and a Gi2 heterotrimer, revealing an extrahelical lipid-detergent-facing allosteric binding pocket that involves transmembrane helixes 1, 6 and 7. Molecular dynamics simulations and ligand kinetic binding experiments support a mechanism whereby MIPS521 stabilizes the adenosine-receptor-G protein complex. This study provides proof of concept for structure-based allosteric drug design of non-opioid analgesic agents that are specific to disease contexts.

GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.

Mechanistic insights into ligand dissociation from the SARS-CoV-2 spike glycoprotein.

The COVID-19 pandemic, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spurred an urgent need for effective therapeutic interventions. The spike glycoprotein of the SARS-CoV-2 is crucial for infiltrating host cells, rendering it a key candidate for drug development. By interacting with the human angiotensin-converting enzyme 2 (ACE2) receptor, the spike initiates the infection of SARS-CoV-2. Linoleate is known to bind the spike glycoprotein, subsequently reducing its interaction with ACE2. However, the detailed mechanisms underlying the protein-ligand interaction remain unclear. In this study, we characterized the pathways of ligand dissociation and the conformational changes associated with the spike glycoprotein by using ligand Gaussian accelerated molecular dynamics (LiGaMD). Our simulations resulted in eight complete ligand dissociation trajectories, unveiling two distinct ligand unbinding pathways. The preference between these two pathways depends on the gate distance between two α-helices in the receptor binding domain (RBD) and the position of the N-linked glycan at N343. Our study also highlights the essential contributions of K417, N121 glycan, and N165 glycan in ligand unbinding, which are equally crucial in enhancing spike-ACE2 binding. We suggest that the presence of the ligand influences the motions of these residues and glycans, consequently reducing accessibility for spike-ACE2 binding. These findings enhance our understanding of ligand dissociation from the spike glycoprotein and offer significant implications for drug design strategies in the battle against COVID-19.

Mechanism of Ligand Binding to Theophylline RNA Aptamer.

Studying RNA-ligand interactions and quantifying their binding thermodynamics and kinetics are of particular relevance in the field of drug discovery. Here, we combined biochemical binding assays and accelerated molecular simulations to investigate ligand binding and dissociation in RNA using the theophylline-binding RNA as a model system. All-atom simulations using a Ligand Gaussian accelerated Molecular Dynamics method (LiGaMD) have captured repetitive binding and dissociation of theophylline and caffeine to RNA. Theophylline's binding free energy and kinetic rate constants align with our experimental data, while caffeine's binding affinity is over 10,000 times weaker, and its kinetics could not be determined. LiGaMD simulations allowed us to identify distinct low-energy conformations and multiple ligand binding pathways to RNA. Simulations revealed a "conformational selection" mechanism for ligand binding to the flexible RNA aptamer, which provides important mechanistic insights into ligand binding to the theophylline-binding model. Our findings suggest that compound docking using a structural ensemble of representative RNA conformations would be necessary for structure-based drug design of flexible RNA.

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks.

BACKGROUND: Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE: To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS: Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS: The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION: CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

Recognition of single-stranded nucleic acids by small-molecule splicing modulators.

Risdiplam is the first approved small-molecule splicing modulator for the treatment of spinal muscular atrophy (SMA). Previous studies demonstrated that risdiplam analogues have two separate binding sites in exon 7 of the SMN2 pre-mRNA: (i) the 5'-splice site and (ii) an upstream purine (GA)-rich binding site. Importantly, the sequence of this GA-rich binding site significantly enhanced the potency of risdiplam analogues. In this report, we unambiguously determined that a known risdiplam analogue, SMN-C2, binds to single-stranded GA-rich RNA in a sequence-specific manner. The minimum required binding sequence for SMN-C2 was identified as GAAGGAAGG. We performed all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method, which captured spontaneous binding of a risdiplam analogue to the target nucleic acids. We uncovered, for the first time, a ligand-binding pocket formed by two sequential GAAG loop-like structures. The simulation findings were highly consistent with experimental data obtained from saturation transfer difference (STD) NMR and structure-affinity-relationship studies of the risdiplam analogues. Together, these studies illuminate us to understand the molecular basis of single-stranded purine-rich RNA recognition by small-molecule splicing modulators with an unprecedented binding mode.

Mechanism of Tripeptide Trimming of Amyloid ß-Peptide 49 by γ-Secretase.

The membrane-embedded γ-secretase complex processively cleaves within the transmembrane domain of amyloid precursor protein (APP) to produce 37-to-43-residue amyloid ß-peptides (Aß) of Alzheimer's disease (AD). Despite its importance in pathogenesis, the mechanism of processive proteolysis by γ-secretase remains poorly understood. Here, mass spectrometry and Western blotting were used to quantify the efficiency of tripeptide trimming of wild-type (WT) and familial AD (FAD) mutant Aß49. In comparison to WT Aß49, the efficiency of tripeptide trimming was similar for the I45F, A42T, and V46F Aß49 FAD mutants but substantially diminished for the I45T and T48P mutants. In parallel with biochemical experiments, all-atom simulations using a novel peptide Gaussian accelerated molecular dynamics (Pep-GaMD) method were applied to investigate the tripeptide trimming of Aß49 by γ-secretase. The starting structure was the active γ-secretase bound to Aß49 and APP intracellular domain (AICD), as generated from our previous study that captured the activation of γ-secretase for the initial endoproteolytic cleavage of APP (Bhattarai, A., ACS Cent. Sci. 2020, 6, 969-983). Pep-GaMD simulations captured remarkable structural rearrangements of both the enzyme and substrate, in which hydrogen-bonded catalytic aspartates and water became poised for tripeptide trimming of Aß49 to Aß46. These structural changes required a positively charged N-terminus of endoproteolytic coproduct AICD, which could dissociate during conformational rearrangements of the protease and Aß49. The simulation findings were highly consistent with biochemical experimental data. Taken together, our complementary biochemical experiments and Pep-GaMD simulations have enabled elucidation of the mechanism of tripeptide trimming of Aß49 by γ-secretase.

Predictive value of time-variant color-coded multiphase CT angiography (mCTA) regarding clinical outcome of acute ischemic stroke: in comparison with conventional mCTA and CT perfusion.

BACKGROUND: Color-coded multiphase computed tomography angiography (mCTA) can provide time-variant blood flow information of collateral circulation for acute ischemic stroke (AIS). PURPOSE: To compare the predictive values of color-coded mCTA, conventional mCTA, and CT perfusion (CTP) for the clinical outcomes of patients with AIS. MATERIAL AND METHODS: Consecutive patients with anterior circulation AIS were retrospectively reviewed at our center. Baseline collateral scores of color-coded mCTA and conventional mCTA were assessed by a 6-point scale. The reliabilities between junior and senior observers were assessed by weighted Kappa coefficients. Receiver operating characteristic (ROC) curves and multivariate logistic regression model were applied to evaluate the predictive capabilities of color-coded mCTA and conventional mCTA scores, and CTP parameters (hypoperfusion and infarct core volume) for a favorable outcome of AIS. RESULTS: A total of 138 patients (including 70 cases of good outcomes) were included in our study. Patients with favorable prognoses were correlated with better collateral circulations on both color-coded and conventional mCTA, and smaller hypoperfusion and infarct core volume (all P < 0.05) on CTP. ROC curves revealed no significant difference between the predictive capability of color-coded and conventional mCTA (P = 0.427). The predictive value of CTP parameters tended to be inferior to that of color-coded mCTA score (all P < 0.001). Both junior and senior observers had consistently excellent performances (κ = 0.89) when analyzing color-coded mCTA maps. CONCLUSION: Color-coded mCTA provides prognostic information of patients with AIS equivalent to or better than that of conventional mCTA and CTP. Junior radiologists can reach high diagnostic accuracy when interpreting color-coded mCTA images.

Molecular Simulations and Drug Discovery of Adenosine Receptors.

G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the A1AR, A2AAR, A2BAR and A3AR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The A1AR and A3AR preferentially couple to the Gi/o proteins, while the A2AAR and A2BAR prefer coupling to the Gs proteins. Adenosine receptors were the first subclass of GPCRs that had experimental structures determined in complex with distinct G proteins. Here, we will review recent studies in molecular simulations and computer-aided drug discovery of the adenosine receptors and also highlight their future research opportunities.

Discovery of potent and selective Bcl-2 inhibitors with acyl sulfonamide skeleton.

The antiapoptotic protein B-cell lymphoma 2 (Bcl-2), overexpressed in many tumor cells, is an attractive target for potential small molecule anticancer drug discovery. Herein, a series of novel derivatives with acyl sulfonamide skeleton was designed, synthesized, and evaluated as Bcl-2 inhibitors by means of bioisosteric replacement. Among them, compound 24g demonstrated equal efficient inhibition activity against RS4;11 cell line compared to positive control ABT-199. Moreover, it showed improved selectivity for Bcl-2/Bcl-xL inhibitory effects, the result of which was consistent with platelet toxicity studies. In vitro and in vivo pharmacokinetic properties of compound 24g had a significantly improved profiles. Taken together, those results suggested it as a promising candidate for development of novel therapeutics targeting Bcl-2 in cancer.

Specific Engineered G Protein Coupling to Histamine Receptors Revealed from Cellular Assay Experiments and Accelerated Molecular Dynamics Simulations.

G protein-coupled receptors (GPCRs) are targets of extracellular stimuli and hence occupy a key position in drug discovery. By specific and not yet fully elucidated coupling profiles with α subunits of distinct G protein families, they regulate cellular responses. The histamine H2 and H4 receptors (H2R and H4R) are prominent members of Gs- and Gi-coupled GPCRs. Nevertheless, promiscuous G protein and selective Gi signaling have been reported for the H2R and H4R, respectively, the molecular mechanism of which remained unclear. Using a combination of cellular experimental assays and Gaussian accelerated molecular dynamics (GaMD) simulations, we investigated the coupling profiles of the H2R and H4R to engineered mini-G proteins (mG). We obtained coupling profiles of the mGs, mGsi, or mGsq proteins to the H2R and H4R from the mini-G protein recruitment assays using HEK293T cells. Compared to H2R-mGs expressing cells, histamine responses were weaker (pEC50, Emax) for H2R-mGsi and -mGsq. By contrast, the H4R selectively bound to mGsi. Similarly, in all-atom GaMD simulations, we observed a preferential binding of H2R to mGs and H4R to mGsi revealed by the structural flexibility and free energy landscapes of the complexes. Although the mG α5 helices were consistently located within the HR binding cavity, alternative binding orientations were detected in the complexes. Due to the specific residue interactions, all mG α5 helices of the H2R complexes adopted the Gs-like orientation toward the receptor transmembrane (TM) 6 domain, whereas in H4R complexes, only mGsi was in the Gi-like orientation toward TM2, which was in agreement with Gs- and Gi-coupled GPCRs structures resolved by X-ray/cryo-EM. These cellular and molecular insights support (patho)physiological profiles of the histamine receptors, especially the hitherto little studied H2R function in the brain, as well as of the pharmacological potential of H4R selective drugs.

Exploring Conformational Change of Adenylate Kinase by Replica Exchange Molecular Dynamic Simulation.

Replica exchange molecular dynamics (REMD) simulation is a popular enhanced sampling method that is widely used for exploring the atomic mechanism of protein conformational change. However, the requirement of huge computational resources for REMD, especially with the explicit solvent model, largely limits its application. In this study, the availability and efficiency of a variant of velocity-scaling REMD (vsREMD) was assessed with adenylate kinase as an example. Although vsREMD achieved results consistent with those from conventional REMD and experimental studies, the number of replicas required for vsREMD (30) was much less than that for conventional REMD (80) to achieve a similar acceptance rate (∼0.2), demonstrating high efficiency of vsREMD to characterize the protein conformational change and associated free-energy profile. Thus, vsREMD is a highly efficient approach for studying the large-scale conformational change of protein systems.

G-Protein-Coupled Receptor-Membrane Interactions Depend on the Receptor Activation State.

G-protein-coupled receptors (GPCRs) are the largest family of human membrane proteins and serve as primary targets of approximately one-third of currently marketed drugs. In particular, adenosine A1 receptor (A1 AR) is an important therapeutic target for treating cardiac ischemia-reperfusion injuries, neuropathic pain, and renal diseases. As a prototypical GPCR, the A1 AR is located within a phospholipid membrane bilayer and transmits cellular signals by changing between different conformational states. It is important to elucidate the lipid-protein interactions in order to understand the functional mechanism of GPCRs. Here, all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method were performed on both the inactive (antagonist bound) and active (agonist and G-protein bound) A1 AR, which was embedded in a 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) lipid bilayer. In the GaMD simulations, the membrane lipids played a key role in stabilizing different conformational states of the A1 AR. Our simulations further identified important regions of the receptor that interacted distinctly with the lipids in highly correlated manner. Activation of the A1 AR led to differential dynamics in the upper and lower leaflets of the lipid bilayer. In summary, GaMD enhanced simulations have revealed strongly coupled dynamics of the GPCR and lipids that depend on the receptor activation state. © 2019 Wiley Periodicals, Inc.

Accurate prediction of relative binding affinities of a series of HIV-1 protease inhibitors using semi-empirical quantum mechanical charge.

A major challenge in computer-aided drug design is the accurate estimation of ligand binding affinity. Here, a new approach that combines the adaptive steered molecular dynamics (ASMD) and partial atomic charges calculated by semi-empirical quantum mechanics (SQMPC), namely ASMD-SQMPC, is suggested to predict the ligand binding affinities, with 24 HIV-1 protease inhibitors as testing examples. In the ASMD-SQMPC, the relative binding free energy (ΔG) is reflected by the average maximum potential of mean force (max ) between bound and unbound states. The correlation coefficient (R2 ) between the max and experimentally determined ΔG is 0.86, showing a significant improvement compared with the conventional ASMD (R2 = 0.52). Therefore, this study provides an efficient approach to predict the relative ΔG and reveals the significance of precise partial atomic charges in the theoretical simulations.

Peptide Gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding.

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method "Pep-GaMD," which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 µs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.

Association of hepatocellular carcinoma risk with polymorphisms in tumour necrosis factor alpha gene in a Chinese Han population.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Studies have shown that the tumour necrosis factor alpha (TNF-α) plays an important role in the development of HCC; however, the association between genetic variations of TNF-α and HCC is not yet fully understood. To evaluate the correlation of TNF-α polymorphisms with HCC, we randomly selected 327 HCC patients and 432 healthy controls, all these subjects reported Han nationality. Genotyping of four TNF-α SNPs (rs1799724, rs1800629, rs1799964 and rs1800610) was performed using the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) method. Distributions of rs1799964 genotypes and rs1800610 alleles were found to be significantly different between cases and controls (p = .011, p = .001). The recessive model of rs1799964 significantly increased HCC risk (p = .0015), while the dominant and over-dominant models of rs1800610 significantly reduced HCC risk (p = .0096, p = .014). Haplotype analysis of the four TNF-α SNPs revealed that the TGTA haplotype was associated with a reduced HCC risk (p = .0033, OR = 0.53), while the TGTG haplotype was associated with an increased HCC risk (p = .0032, OR = 9.69). These findings indicated that specific TNF-α polymorphisms may be associated with the susceptibility to HCC.

Weak-binding molecules are not drugs?-toward a systematic strategy for finding effective weak-binding drugs.

Designing maximally selective ligands that act on individual drug targets with high binding affinity has been the central dogma of drug discovery and development for the past two decades. However, many low-affinity drugs that aim for several targets at the same time are found more effective than the high-affinity binders when faced with complex disease conditions, such as cancers, Alzheimer's disease and cardiovascular diseases. The aim of this study was to appreciate the importance and reveal the features of weak-binding drugs and propose an integrated strategy for discovering them. Weak-binding drugs can be characterized by their high dissociation rates and transient interactions with their targets. In addition, network topologies and dynamics parameters involved in the targets of weak-binding drugs also influence the effects of the drugs. Here, we first performed a dynamics analysis for 33 elementary subgraphs to determine the desirable topology and dynamics parameters among targets. Then, by applying the elementary subgraphs to the mitogen-activated protein kinase (MAPK) pathway, several optimal target combinations were obtained. Combining drug-target interaction prediction with molecular dynamics simulation, we got two potential weak-binding drug candidates, luteolin and tanshinone IIA, acting on these targets. Further, the binding affinity of these two compounds to their targets and the anti-inflammatory effects of them were validated through in vitro experiments. In conclusion, weak-binding drugs have real opportunities for maximum efficiency and may show reduced adverse reactions, which can offer a bright and promising future for new drug discovery.

D3Pockets: A Method and Web Server for Systematic Analysis of Protein Pocket Dynamics.

The intrinsic dynamic properties of the ligand-binding pockets of proteins are important for the protein function mechanism and thus are useful to drug discovery and development. Few methods are available to study the dynamic properties, such as pocket stability, continuity, and correlation. In this work, we develop a method and web server, namely, D3Pockets, for exploring the dynamic properties of the protein pocket based on either molecular dynamics (MD) simulation trajectories or conformational ensembles. Application of D3Pockets on five target proteins as examples, namely, HIV-1 protease, BACE1, L-ABP, GPX4, and GR, uncovers more information on the dynamic properties of the ligand-binding pockets, which should be helpful to understanding protein function mechanism and drug design. The D3Pockets web server is available at http://www.d3pharma.com/D3Pocket/index.php .

Halogen bonding in differently charged complexes: basic profile, essential interaction terms and intrinsic σ-hole.

Studies on halogen bonds (XB) between organohalogens and their acceptors in crystal structures revealed that the XB donor and acceptor could be differently charged, making it difficult to understand the nature of the interaction, especially the negatively charged donor's electrophilicity and positively charged acceptor's nucleophilicity. In this paper, 9 XB systems mimicking all possibly charged halogen bonding interactions were designed and explored computationally. The results revealed that all XBs could be stable, with binding energies after removing background interaction as strong as -1.2, -3.4, and -8.3 kcal mol-1 for Cl, Br, and I involved XBs respectively. Orbital and dispersion interactions are found to be always attractive while unidirectional intermolecular electron transfer from a XB acceptor to a XB donor occurs in all XB complexes. These observations could be attributed to the intrinsic σ-hole of the XB donor and the intrinsic electronic properties of the XB acceptor regardless of their charge states. Intramolecular charge redistribution inside both the donor and the acceptor is found to be system-dependent but always leads to a more stable XB. Accordingly, this study demonstrates that the orbital-based origin of halogen bonds could successfully interpret the complicated behaviour of differently charged XB complexes, while electrostatic interaction may dramatically change the overall bonding strength. The results should further promote the application of halogens in all related areas.

Structural basis for DNA recognition by STAT6.

STAT6 participates in classical IL-4/IL-13 signaling and stimulator of interferon genes-mediated antiviral innate immune responses. Aberrations in STAT6-mediated signaling are linked to development of asthma and diseases of the immune system. In addition, STAT6 remains constitutively active in multiple types of cancer. Therefore, targeting STAT6 is an attractive proposition for treating related diseases. Although a lot is known about the role of STAT6 in transcriptional regulation, molecular details on how STAT6 recognizes and binds specific segments of DNA to exert its function are not clearly understood. Here, we report the crystal structures of a homodimer of phosphorylated STAT6 core fragment (STAT6CF) alone and bound with the N3 and N4 DNA binding site. Analysis of the structures reveals that STAT6 undergoes a dramatic conformational change on DNA binding, which was further validated by performing molecular dynamics simulation studies and small angle X-ray scattering analysis. Our data show that a larger angle at the intersection where the two protomers of STAT meet and the presence of a unique residue, H415, in the DNA-binding domain play important roles in discrimination of the N4 site DNA from the N3 site by STAT6. H415N mutation of STAT6CF decreased affinity of the protein for the N4 site DNA, but increased its affinity for N3 site DNA, both in vitro and in vivo. Results of our structure-function studies on STAT6 shed light on mechanism of DNA recognition by STATs in general and explain the reasons underlying STAT6's preference for N4 site DNA over N3.