RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

High-throughput virtual search of small molecules for controlling the mechanical stability of human CD4.

Protein mechanical stability determines the function of a myriad of proteins, especially proteins from the extracellular matrix. Failure to maintain protein mechanical stability may result in diseases and disorders such as cancer, cardiomyopathies, or muscular dystrophy. Thus, developing mutation-free approaches to enhance and control the mechanical stability of proteins using pharmacology-based methods may have important implications in drug development and discovery. Here, we present the first approach that employs computational high-throughput virtual screening and molecular docking to search for small molecules in chemical libraries that function as mechano-regulators of the stability of human cluster of differentiation 4, receptor of HIV-1. Using single-molecule force spectroscopy, we prove that these small molecules can increase the mechanical stability of CD4D1D2 domains over 4-fold in addition to modifying the mechanical unfolding pathways. Our experiments demonstrate that chemical libraries are a source of mechanoactive molecules and that drug discovery approaches provide the foundation of a new type of molecular function, that is, mechano-regulation, paving the way toward mechanopharmacology.

The small molecule activator S3969 stimulates the epithelial sodium channel by interacting with a specific binding pocket in the channel's ß-subunit.

The epithelial sodium channel (ENaC) is essential for mediating sodium absorption in several epithelia. Its impaired function leads to severe disorders, including pseudohypoaldosteronism type 1 and respiratory distress. Therefore, pharmacological ENaC activators have potential therapeutic implications. Previously, a small molecule ENaC activator (S3969) was developed. So far, little is known about molecular mechanisms involved in S3969-mediated ENaC stimulation. Here, we identified an S3969-binding site in human ENaC by combining structure-based simulations with molecular biological methods and electrophysiological measurements of ENaC heterologously expressed in Xenopus laevis oocytes. We confirmed a previous observation that the extracellular loop of ß-ENaC is essential for ENaC stimulation by S3969. Molecular dynamics simulations predicted critical residues in the thumb domain of ß-ENaC (Arg388, Phe391, and Tyr406) that coordinate S3969 within a binding site localized at the ß-γ-subunit interface. Importantly, mutating each of these residues reduced (R388H; R388A) or nearly abolished (F391G; Y406A) the S3969-mediated ENaC activation. Molecular dynamics simulations also suggested that S3969-mediated ENaC stimulation involved a movement of the α5 helix of the thumb domain of ß-ENaC away from the palm domain of γ-ENaC. Consistent with this, the introduction of two cysteine residues (ßR437C - γS298C) to form a disulfide bridge connecting these two domains prevented ENaC stimulation by S3969 unless the disulfide bond was reduced by DTT. Finally, we demonstrated that S3969 stimulated ENaC endogenously expressed in cultured human airway epithelial cells (H441). These new findings may lead to novel (patho-)physiological and therapeutic concepts for disorders associated with altered ENaC function.

GPDOCK: highly accurate docking strategy for metalloproteins based on geometric probability.

Accurately predicting the interaction modes for metalloproteins remains extremely challenging in structure-based drug design and mechanism analysis of enzymatic catalysis due to the complexity of metal coordination in metalloproteins. Here, we report a docking method for metalloproteins based on geometric probability (GPDOCK) with unprecedented accuracy. The docking tests of 10 common metal ions with 9360 metalloprotein-ligand complexes demonstrate that GPDOCK has an accuracy of 94.3% in predicting binding pose. What is more, it can accurately realize the docking of metalloproteins with ligand when one or two water molecules are engaged in the metal ion coordination. Since GPDOCK only depends on the three-dimensional structure of metalloprotein and ligand, structure-based machine learning model is employed for the scoring of binding poses, which significantly improves computational efficiency. The proposed docking strategy can be an effective and efficient tool for drug design and further study of binding mechanism of metalloproteins. The manual of GPDOCK and the code for the logistical regression model used to re-rank the docking results are available at https://github.com/wangkai-zhku/GPDOCK.git.

A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.

Development of innovative multi-epitope mRNA vaccine against Pseudomonas aeruginosa using in silico approaches.

The rising issue of antibiotic resistance has made treating Pseudomonas aeruginosa infections increasingly challenging. Therefore, vaccines have emerged as a viable alternative to antibiotics for preventing P. aeruginosa infections in susceptible individuals. With its superior accuracy, high efficiency in stimulating cellular and humoral immune responses, and low cost, mRNA vaccine technology is quickly replacing traditional methods. This study aimed to design a novel mRNA vaccine by using in silico approaches against P. aeruginosa. The research team identified five surface and antigenic proteins and selected their appropriate epitopes with immunoinformatic tools. These epitopes were then examined for toxicity, allergenicity and homology. The researchers also checked their presentation and identification by major histocompatibility complex cells and other immune cells through valuable tools like molecular docking. They subsequently modeled a multi-epitope protein and optimized it. The mRNA was analyzed in terms of structure and stability, after which the immune system's response against the new vaccine was simulated. The results indicated that the designed mRNA construct could be an effective and promising vaccine that requires laboratory and clinical trials.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening.

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

HCovDock: an efficient docking method for modeling covalent protein-ligand interactions.

Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.

Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations.

Natural products have successfully treated several diseases using a multi-component, multi-target mechanism. However, a precise mechanism of action (MOA) has not been identified. Systems pharmacology methods have been used to overcome these challenges. However, there is a limitation as those similar mechanisms of similar components cannot be identified. In this study, comparisons of physicochemical descriptors, molecular docking analysis and RNA-seq analysis were performed to compare the MOA of similar compounds and to confirm the changes observed when similar compounds were mixed and used. Various analyses have confirmed that compounds with similar structures share similar MOA. We propose an advanced method for in silico experiments in herbal medicine research based on the results. Our study has three novel findings. First, an advanced network pharmacology research method was suggested by partially presenting a solution to the difficulty in identifying multi-component mechanisms. Second, a new natural product analysis method was proposed using large-scale molecular docking analysis. Finally, various biological data and analysis methods were used, such as in silico system pharmacology, docking analysis and drug response RNA-seq. The results of this study are meaningful in that they suggest an analysis strategy that can improve existing systems pharmacology research analysis methods by showing that natural product-derived compounds with the same scaffold have the same mechanism.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

The novel function of an orphan pheromone receptor reveals the sensory specializations of two potential distinct types of sex pheromones in noctuid moth.

Sex pheromones play crucial role in mating behavior of moths, involving intricate recognition mechanisms. While insect chemical biology has extensively studied type I pheromones, type II pheromones remain largely unexplored. This study focused on Helicoverpa armigera, a representative species of noctuid moth, aiming to reassess its sex pheromone composition. Our research unveiled two previously unidentified candidate type II sex pheromones-3Z,6Z,9Z-21:H and 3Z,6Z,9Z-23:H-in H. armigera. Furthermore, we identified HarmOR11 as an orphan pheromone receptor of 3Z,6Z,9Z-21:H. Through AlphaFold2 structural prediction, molecular docking, and molecular dynamics simulations, we elucidated the structural basis and key residues governing the sensory nuances of both type I and type II pheromone receptors, particularly HarmOR11 and HarmOR13. This study not only reveals the presence and recognition of candidate type II pheromones in a noctuid moth, but also establishes a comprehensive structural framework for PRs, contributing to the understanding of connections between evolutionary adaptations and the emergence of new pheromone types.

Structural Premise of Selective Deubiquitinase USP30 Inhibition by Small-Molecule Benzosulfonamides.

Dampening functional levels of the mitochondrial deubiquitylating enzyme Ubiquitin-specific protease 30 (USP30) has been suggested as an effective therapeutic strategy against neurodegenerative disorders such as Parkinson's Disease. USP30 inhibition may counteract the deleterious effects of impaired turnover of damaged mitochondria, which is inherent to both familial and sporadic forms of the disease. Small-molecule inhibitors targeting USP30 are currently in development, but little is known about their precise nature of binding to the protein. We have integrated biochemical and structural approaches to gain novel mechanistic insights into USP30 inhibition by a small-molecule benzosulfonamide-containing compound, USP30inh. Activity-based protein profiling mass spectrometry confirmed target engagement, high selectivity, and potency of USP30inh for USP30 against 49 other deubiquitylating enzymes in a neuroblastoma cell line. In vitro characterization of USP30inh enzyme kinetics inferred slow and tight binding behavior, which is comparable with features of covalent modification of USP30. Finally, we blended hydrogen-deuterium exchange mass spectrometry and computational docking to elucidate the molecular architecture and geometry of USP30 complex formation with USP30inh, identifying structural rearrangements at the cleft of the USP30 thumb and palm subdomains. These studies suggest that USP30inh binds to this thumb-palm cleft, which guides the ubiquitin C terminus into the active site, thereby preventing ubiquitin binding and isopeptide bond cleavage, and confirming its importance in the inhibitory process. Our data will pave the way for the design and development of next-generation inhibitors targeting USP30 and associated deubiquitinylases.

Enhancement of intestinal calcium transport by short-chain fatty acids: roles of Na+/H+ exchanger 3 and transient receptor potential vanilloid subfamily 6.

Small organic molecules in the intestinal lumen, particularly short-chain fatty acids (SCFAs) and glucose, have long been postulated to enhance calcium absorption. Here, we used 45Ca radioactive tracer to determine calcium fluxes across the rat intestine after exposure to glucose and SCFAs. Confirming previous reports, glucose was found to increase the apical-to-basolateral calcium flux in the cecum. Under apical glucose-free conditions, SCFAs (e.g., butyrate) stimulated the cecal calcium fluxes by approximately twofold, while having no effect on proximal colon. Since SCFAs could be absorbed into the circulation, we further determined whether basolateral SCFA exposure rendered some positive actions. It was found that exposure of duodenum and cecum on the basolateral side to acetate or butyrate increased calcium fluxes. Under butyrate-rich conditions, cecal calcium transport was partially diminished by Na+/H+ exchanger 3 (NHE3) inhibitor (tenapanor) and nonselective transient receptor potential vanilloid subfamily 6 (TRPV6) inhibitor (miconazole). To confirm the contribution of TRPV6 to SCFA-stimulated calcium transport, we synthesized another TRPV6 inhibitor that was demonstrated by in silico molecular docking and molecular dynamics to occlude TRPV6 pore and diminish the glucose- and butyrate-induced calcium fluxes. Therefore, besides corroborating the importance of luminal molecules in calcium absorption, our findings provided foundation for development of more effective calcium-rich nutraceuticals in combination with various absorptive enhancers, e.g., glucose and SCFAs.NEW & NOTEWORTHY Organic molecules in the intestinal lumen, e.g., glucose and short-chain fatty acids (SCFAs), the latter of which are normally produced by microfloral fermentation, can stimulate calcium absorption dependent on transient receptor potential vanilloid subfamily 6 (TRPV6) and Na+/H+ exchanger 3 (NHE3). A selective TRPV6 inhibitor synthesized and demonstrated by in silico docking and molecular dynamics to specifically bind to the pore domain of TRPV6 was used to confirm a significant contribution of this channel. Our findings corroborate physiological significance of nutrients and SCFAs in enhancing calcium absorption.

Synthesis, in vitro Anti-HIV-1RT evaluation, molecular modeling, DFT and acute oral toxicity studies of some benzotriazole derivatives.

This study synthesized and evaluated a series of benzotriazole derivatives denoted 3(a-j) and 6(a-j) for their anti-HIV-1 RT activities compared to the standard drug efavirenz. Notably, compound 3 h, followed closely by 6 h, exhibited significant anti-HIV-1 RT efficacy relative to the standard drug. In vivo oral toxicity studies were conducted for the most active compound 3 h, confirming its nontoxic nature to ascertain the safety profile. By employing molecular docking techniques, we explored the potential interactions between the synthesized compounds (ligands) and a target biomolecule (protein)(PDB ID 1RT2) at the molecular level. We undertook the molecular dynamics study of 3 h, the most active compound, within the active binding pocket of the cocrystallized structure of HIV-1 RT (PDB ID 1RT2). We aimed to learn more about how biomolecular systems behave, interact, and change at the atomic or molecular level over time. Finally, the DFT-derived HOMO and LUMO orbitals, as well as analysis of the molecular electrostatic potential map, aid in discerning the reactivity characteristics of our molecule.

Integrated analyses for identification of a three-gene signature associated with Chaihu Shugan San formula for hepatocellular carcinoma treatment.

Chaihu Shugan San (CSS) is a well-known traditional herbal formula that has the potential to ameliorate hepatocellular carcinoma (HCC); however, its mechanism of action remains unknown. Here, we identified the key targets of CSS against HCC and developed a prognostic model to predict the survival of patients with HCC. The effect of CSS plus sorafenib on HCC cell proliferation was evaluated using the MTT assay. LASSO-Cox regression was used to establish a three-gene signature model targeting CSS. Correlations between immune cells, immune checkpoints and risk score were determined to evaluate the immune-related effects of CSS. The interactions between the components and targets were validated using molecular docking and Surface Plasmon Resonance (SPR) assays. CSS and sorafenib synergistically inhibited HCC cell proliferation. Ten core compounds and 224 targets were identified using a drug compound-target network. The prognostic model of the three CSS targets (AKT1, MAPK3 and CASP3) showed predictive ability. Risk scores positively correlated with cancer-promoting immune cells and high expression of immune checkpoint proteins. Molecular docking and SPR analyses confirmed the strong binding affinities of the active components and the target genes. Western blot analysis confirmed the synergistic effect of CSS and sorafenib in inhibiting the expression of these three targets. In conclusion, CSS may regulate the activity of immune-related factors in the tumour microenvironment, reverse immune escape, enhance immune responses through AKT1, MAPK3, and CASP3, and synergistically alleviate HCC. The co-administration of sorafenib with CSS has a strong clinical outlook against HCC.

Exploring the pharmacological mechanism of Glycyrrhiza uralensis against KOA through integrating network pharmacology and experimental assessment.

Knee osteoarthritis (KOA), a major health and economic problem facing older adults worldwide, is a degenerative joint disease. Glycyrrhiza uralensis Fisch. (GC) plays an integral role in many classic Chinese medicine prescriptions for treating knee osteoarthritis. Still, the role of GC in treating KOA is unclear. To explore the pharmacological mechanism of GC against KOA, UPLC-Q-TOF/MS was conducted to detect the main compounds in GC. The therapeutic effect of GC on DMM-induced osteoarthritic mice was assessed by histomorphology, µCT, behavioural tests, and immunohistochemical staining. Network pharmacology and molecular docking were used to predict the potential targets of GC against KOA. The predicted results were verified by immunohistochemical staining Animal experiments showed that GC had a protective effect on DMM-induced KOA, mainly in the improvement of movement disorders, subchondral bone sclerosis and cartilage damage. A variety of flavonoids and triterpenoids were detected in GC via UPLC-Q-TOF/MS, such as Naringenin. Seven core targets (JUN, MAPK3, MAPK1, AKT1, TP53, RELA and STAT3) and three main pathways (IL-17, NF-κB and TNF signalling pathways) were discovered through network pharmacology analysis that closely related to inflammatory response. Interestingly, molecular docking results showed that the active ingredient Naringenin had a good binding effect on anti-inflammatory-related proteins. In the verification experiment, after the intervention of GC, the expression levels of pp65 and F4/80 inflammatory indicators in the knee joint of KOA model mice were significantly downregulated. GC could improve the inflammatory environment in DMM-induced osteoarthritic mice thus alleviating the physiological structure and dysfunction of the knee joint. GC might play an important role in the treatment of knee osteoarthritis.

Mechanism of Astragalus membranaceus (Huangqi, HQ) for treatment of heart failure based on network pharmacology and molecular docking.

Heart failure is a leading cause of death in the elderly. Traditional Chinese medicine, a verified alternative therapeutic regimen, has been used to treat heart failure, which is less expensive and has fewer adverse effects. In this study, a total of 15 active ingredients of Astragalus membranaceus (Huangqi, HQ) were obtained; among them, Isorhamnetin, Quercetin, Calycosin, Formononetin, and Kaempferol were found to be linked to heart failure. Ang II significantly enlarged the cell size of cardiomyocytes, which could be partially reduced by Quercetin, Isorhamnetin, Calycosin, Kaempferol, or Formononetin. Ang II significantly up-regulated ANP, BNP, ß-MHC, and CTGF expressions, whereas Quercetin, Isorhamnetin, Calycosin, Kaempferol or Formononetin treatment partially downregulated ANP, BNP, ß-MHC and CTGF expressions. Five active ingredients of HQ attenuated inflammation in Ang II-induced cardiomyocytes by inhibiting the levels of TNF-α, IL-1ß, IL-18 and IL-6. Molecular docking shows Isorhamnetin, Quercetin, Calycosin, Formononetin and Kaempferol can bind with its target protein ESR1 in a good bond by intermolecular force. Quercetin, Calycosin, Kaempferol or Formononetin treatment promoted the expression levels of ESR1 and phosphorylated ESR1 in Ang II-stimulated cardiomyocytes; however, Isorhamnetin treatment had no effect on ESR1 and phosphorylated ESR1 expression levels. In conclusion, our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of HQ against heart failure. Isorhamnetin, Quercetin, Calycosin, Formononetin and Kaempferol might be the primary active ingredients of HQ, dominating its cardioprotective effects against heart failure through regulating ESR1 expression, which provided a basis for the clinical application of HQ to regulate cardiac hypertrophy and heart failure.

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches.

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

SAMHD1 impairs type I interferon induction through the MAVS, IKKε, and IRF7 signaling axis during viral infection.

Sterile alpha motif and HD domain-containing protein 1 (SAMHD1) restricts human immunodeficiency virus type 1 (HIV-1) infection by reducing the intracellular dNTP pool. We have shown that SAMHD1 suppresses nuclear factor kappa-B activation and type I interferon (IFN-I) induction by viral infection and inflammatory stimuli. However, the mechanism by which SAMHD1 inhibits IFN-I remains unclear. Here, we show that SAMHD1 inhibits IFN-I activation induced by the mitochondrial antiviral-signaling protein (MAVS). SAMHD1 interacted with MAVS and suppressed MAVS aggregation in response to Sendai virus infection in human monocytic THP-1 cells. This resulted in increased phosphorylation of TANK binding kinase 1 (TBK1), inhibitor of nuclear factor kappa-B kinase epsilon (IKKε), and IFN regulatory factor 3 (IRF3). SAMHD1 suppressed IFN-I activation induced by IKKε and prevented IRF7 binding to the kinase domain of IKKε. We found that SAMHD1 interaction with the inhibitory domain (ID) of IRF7 (IRF7-ID) was necessary and sufficient for SAMHD1 suppression of IRF7-mediated IFN-I activation in HEK293T cells. Computational docking and molecular dynamics simulations revealed possible binding sites between IRF7-ID and full-length SAMHD1. Individual substitution of F411, E416, or V460 in IRF7-ID significantly reduced IRF7 transactivation activity and SAMHD1 binding. Furthermore, we investigated the role of SAMHD1 inhibition of IRF7-mediated IFN-I induction during HIV-1 infection. We found that THP-1 cells lacking IRF7 expression had reduced HIV-1 infection and viral transcription compared to control cells, indicating a positive role of IRF7 in HIV-1 infection. Our findings suggest that SAMHD1 suppresses IFN-I induction through the MAVS, IKKε, and IRF7 signaling axis.

Molecular mechanism for endo-type action of glycoside hydrolase family 55 endo-ß-1,3-glucanase on ß1-3/1-6-glucan.

The glycoside hydrolase family 55 (GH55) includes inverting exo-ß-1,3-glucosidases and endo-ß-1,3-glucanases, acting on laminarin, which is a ß1-3/1-6-glucan consisting of a ß1-3/1-6-linked main chain and ß1-6-linked branches. Despite their different modes of action toward laminarin, endo-ß-1,3-glucanases share with exo-ß-1,3-glucosidases conserved residues that form the dead-end structure of subsite -1. Here, we investigated the mechanism of endo-type action on laminarin by GH55 endo-ß-1,3-glucanase MnLam55A, identified from Microdochium nivale. MnLam55A, like other endo-ß-1,3-glucanases, degraded internal ß-d-glucosidic linkages of laminarin, producing more reducing sugars than the sum of d-glucose and gentiooligosaccharides detected. ß1-3-Glucans lacking ß1-6-linkages in the main chain were not hydrolyzed. NMR analysis of the initial degradation of laminarin revealed that MnLam55A preferentially cleaved the nonreducing terminal ß1-3-linkage of the laminarioligosaccharide moiety at the reducing end side of the main chain ß1-6-linkage. MnLam55A liberates d-glucose from laminaritriose and longer laminarioligosaccharides, but kcat/Km values to laminarioligosaccharides (≤4.21 s-1 mM-1) were much lower than to laminarin (5920 s-1 mM-1). These results indicate that ß-glucan binding to the minus subsites of MnLam55A, including exclusive binding of the gentiobiosyl moiety to subsites -1 and -2, is required for high hydrolytic activity. A crystal structure of MnLam55A, determined at 2.4 Å resolution, showed that MnLam55A adopts an overall structure and catalytic site similar to those of exo-ß-1,3-glucosidases. However, MnLam55A possesses an extended substrate-binding cleft that is expected to form the minus subsites. Sequence comparison suggested that other endo-type enzymes share the extended cleft. The specific hydrolysis of internal linkages in laminarin is presumably common to GH55 endo-ß-1,3-glucanases.