Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive

Proposal of pharmacophore model for HIV reverse transcriptase inhibitors: Combined mutational effect analysis, molecular dynamics, molecular docking and pharmacophore modeling study

OBJECTIVES

Antiretroviral therapy (ART) efficacy is jeopardized by the emergence of drug resistance mutations in HIV, compromising treatment effectiveness. This study aims to propose novel analogs of Effavirenz (EFV) as potential direct inhibitors of HIV reverse transcriptase, employing computer-aided drug design methodologies.

METHODS

Three key approaches were applied: a mutational profile study, molecular dynamics simulations, and pharmacophore development. The impact of mutations on the stability, flexibility, function, and affinity of target proteins, especially those associated with NRTI, was assessed. Molecular dynamics analysis identified G190E as a mutation significantly altering protein properties, potentially leading to therapeutic failure. Comparative analysis revealed that among six first-line antiretroviral drugs, EFV exhibited notably low affinity with viral reverse transcriptase, further reduced by the G190E mutation. Subsequently, a search for EFV-similar inhibitors yielded 12 promising molecules based on their affinity, forming the basis for generating a pharmacophore model.

RESULTS

Mutational analysis pinpointed G190E as a crucial mutation impacting protein properties, potentially undermining therapeutic efficacy. EFV demonstrated diminished affinity with viral reverse transcriptase, exacerbated by the G190E mutation. The search for EFV analogs identified 12 high-affinity molecules, culminating in a pharmacophore model elucidating key structural features crucial for potent inhibition.

CONCLUSION

This study underscores the significance of EFV analogs as potential inhibitors of HIV reverse transcriptase. The findings highlight the impact of mutations on drug efficacy, particularly the detrimental effect of G190E. The generated pharmacophore model serves as a pivotal reference for future drug development efforts targeting HIV, providing essential structural insights for the design of potent inhibitors based on EFV analogs identified in vitro.

Protective effect of Amauroderma rugosum ethanol extract and its primary bioactive compound, ergosterol, against acute gastric ulcers based on LXR-mediated gastric mucus secretions

BACKGROUND

Amauroderma rugosum (Blume & T. Nees) Torrend (Ganodermataceae) is an edible mushroom with a wide range of medicinal values. Our previous publication demonstrated the therapeutic effects of the water extract of A. rugosum (WEA) against gastric ulcers. However, the protective effects of the ethanol extract of A. rugosum (EEA) on gastric mucosa and its major active constituents have not yet been elucidated.

PURPOSE

This study aims to evaluate the gastroprotective effects and underlying mechanisms of EEA and its fat-soluble constituent, ergosterol, in acute gastric ulcers.

STUDY DESIGN AND METHOD

SD rats were pre-treated with EEA (50, 100, and 200 mg/kg) or ergosterol (5, 10, and 20 mg/kg), and acute gastric ulcer models were constructed using ethanol, gastric mucus secretion inhibitor (indomethacin) or pyloric-ligation. The gastric ulcer area, histological structure alterations (H&E staining), and mucus secretion (AB-PAS staining) were recorded. Additionally, Q-PCR, western blotting, immunohistochemistry, ELISA, molecular docking, molecular dynamics simulations, MM-GBSA analysis, and surface plasmon resonance assay (SPR) were used to investigate the underlying mechanisms of the gastroprotective effect.

RESULT

Compared with WEA, which primarily exerts its anti-ulcer effects by inhibiting inflammation, EEA containing fat-soluble molecules showed more potent gastroprotective effect through the promotion of gastric mucus secretion, as the anti-ulcer activity was partly blocked by indomethacin. Meanwhile, EEA exhibited anti-inflammatory effects by suppressing the production of IL-6, IL-1β, TNF-α, and NO, thereby inhibiting the MAPK pathway. Significantly, ergosterol (20 mg/kg), the bioactive water-insoluble compound in EEA, exhibited a gastroprotective effect comparable to that of lansoprazole (30 mg/kg). The promotion of gastric mucus secretion contributed to the effects of ergosterol, as indomethacin can completely block it. The upregulations of COX1-PGE2 and C-fos, an activator protein 1 (AP-1) transcription factor, were observed after the ergosterol treatment. Ergosterol acted as an LXRβ agonist via van der Waals binding and stabilizing the LXRβ protein without compromising its flexibility, thereby inducing the upregulation of AP-1 and COX-1.

CONCLUSION

EEA and its primary bioactive compound, ergosterol, exert anti-ulcer effects by promoting gastric mucus secretion through the LXRβ/C-fos/COX-1/PGE2 pathway.

Accelerating Molecular Dynamics Simulations for Drug Discovery

Accurate prediction of ligand binding thermodynamics and kinetics is crucial in drug design. However, it remains challenging for conventional molecular dynamics (MD) simulations due to sampling issues. Gaussian accelerated MD (GaMD) is an enhanced sampling method that adds a harmonic boost to overcome energy barriers, which has demonstrated significant benefits in exploring protein-ligand interactions. Especially, the ligand GaMD (LiGaMD) applies a selective boost potential to the ligand nonbonded potential energy, significantly improving sampling for ligand binding and dissociation. Furthermore, a selective boost potential is applied to the potential of both ligand and protein residues around binding pocket in LiGaMD2 to further increase the sampling of protein-ligand interaction. LiGaMD and LiGaMD2 simulations could capture repetitive ligand binding and unbinding events within microsecond simulations, allowing to simultaneously characterize ligand binding thermodynamics and kinetics, which is expected to greatly facilitate drug design. In this chapter, we provide a brief review of the status of LiGaMD in drug discovery and outline its usage.

Development of AKR1B10 inhibitors from Ajuga nipponensis based on diseases and targets

Ten compounds (1-10) including one new neoclerodane diterpenoid (1) and nine known compounds were isolated from the whole plants of Ajuga nipponensis. Their structures were established by performing detailed analysis of NMR, the structure of 1 was determined using HRESIMS, 1D and 2D NMR, UV, and IR. Compounds 1 and 4-10 were isolated from Ajuga nipponensis for the first time. And it was the first time to report compounds 9 and 10 as natural products. Based on network pharmacology methods, 45 key targets were selected, which were compounds mapping to diseases. And compounds 2, 3, 7, and a (ajugacumbin B) exhibited excellent AKR1B10 inhibitory activities, with IC50 values of 53.05 ± 0.75, 87.22 ± 0.85, 61.85 ± 0.66, and 85.19±1.02 nM respectively, with Epalrestat used as the positive control (82.09 ± 1.62 nM). Additionally, the interaction between active compounds and AKR1B10 had been discussed according to the molecular docking results. Ultimately, the analysis of GO and KEGG enrichment indicated that the key signaling pathway of the active compounds may be related to prostate cancer. Our study results demonstrate the hypoglycemic and anti-tumor properties of A. nipponensis for the first time, and provide a comprehensive understanding of its application in traditional medicine. Furthermore, this article establishes a reference for further research on the optimized experimental design of novel AKR1B10 inhibitors.

Exploration of human pancreatic alpha-amylase inhibitors from Physalis peruviana for the treatment of type 2 diabetes

Type 2 Diabetes (T2D), a chronic metabolic disorder characterized by persistent hyperglycemia, accounts for ∼90% of all types of diabetes. Pancreatic α-amylase is a potential drug target for preventing postprandial hyperglycemia and inhibiting T2D in humans. Although many synthetic drugs have been identified against pancreatic α-amylase, however, reported several side effects, and plant-derived natural products are less explored against T2D. This study tested 34 flavonoids derived from the plant Physalis peruviana against the human pancreatic α-amylase (HPA) using in silico computational approaches such as molecular docking and molecular dynamics simulation approaches. Schrödinger, a drug discovery package with modules applicable for molecular docking, protein-ligand interaction analysis, molecular dynamics, post-dynamics simulation, and binding free energy calculation, was employed for all computational studies. Four flavonoids, namely, Chlorogenic acid, Withaperuvin F, Withaperuvin H, and Rutin, were picked based on their docking score ranging between -7.03 kcal/mol and -11.35 kcal/mol compared to the docking score -7.3 kcal/mol of reference ligand, i.e. Myricetin. The molecular dynamics analysis suggested that all flavonoids showed considerable stability within the protein's catalytic pocket, except chlorogenic acid, which showed high deviation during the last 15 ns. However, the interactions observed in initial docking and extracted from the simulation trajectory involved > 90% identical residues, indicating the affinity and stability of the docked flavonoids with the protein. Therefore, all four compounds identified in this study are proposed as promising antidiabetic candidates and should be further considered for their in vitro and in vivo validation.Communicated by Ramaswamy H. Sarma.

Discovery of druggable potent inhibitors of serine proteases and farnesoid X receptor by ligand-based virtual screening to obstruct SARS-CoV-2

The coronavirus, a subfamily of the coronavirinae family, is an RNA virus with over 40 variations that can infect humans, non-human mammals and birds. There are seven types of human coronaviruses, including SARS-CoV-2, is responsible for the recent COVID-19 pandemic. The current study is focused on the identification of drug molecules for the treatment of COVID-19 by targeting human proteases like transmembrane serine protease 2 (TMPRSS2), furin, cathepsin B, and a nuclear receptor named farnesoid X receptor (FXR). TMPRSS2 and furin help in cleaving the spike protein of the SARS-CoV-2 virus, while cathepsin B plays a critical role in the entry and pathogenesis. FXR, on the other hand, regulates the expression of ACE2, and its inhibition can reduce SARS-CoV-2 infection. By inhibiting these four protein targets with non-toxic inhibitors, the entry of the infectious agent into host cells and its pathogenesis can be obstructed. We have used the BioSolveIT suite for pharmacophore-based computational drug designing. A total of 1611 ligands from the ligand library were docked with the target proteins to obtain potent inhibitors on the basis of pharmacophore. Following the ADMET analysis and protein ligand interactions, potent and druggable inhibitors of the target proteins were obtained. Additionally, toxic substructures and the less toxic route of administration of the most potent inhibitors in rodents were also determined computationally. Compounds namely N-(diaminomethylene)-2-((3-((1R,3R)-3-(2-(methoxy(methyl)amino)-2-oxoethyl)cyclopentyl)propyl)amino)-2-oxoethan-1-aminium (26), (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((4-propyl-1H-imidazol-2-yl)methyl)piperidin-1-ium (29) and (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((1-propyl-1H-pyrazol-4-yl)methyl)piperidin-1-ium (30) were found as the potent inhibitors of TMPRSS2, whereas, 1-(1-(1-(1H-tetrazol-1-yl)cyclopropane-1‑carbonyl)piperidin-4-yl)azepan-2-one (6), (2R)-4-methyl-1-oxo-1-((7R,11S)-4-oxo-6,7,8,9,10,11-hexahydro-4H-7,11-methanopyrido[1,2-a]azocin-9-yl)pentan-2-aminium (12), 4-((1-(3-(3,5-dimethylisoxazol-4-yl)propanoyl)piperidin-4-yl)methyl)morpholin-4-ium (13), 1-(4,6-dimethylpyrimidin-2-yl)-N-(3-oxocyclohex-1-en-1-yl)piperidine-4-carboxamide (14), 1-(4-(1,5-dimethyl-1H-1,2,4-triazol-3-yl)piperidin-1-yl)-3-(3,5-dimethylisoxazol-4-yl)propan-1-one (25) and N,N-dimethyl-4-oxo-4-((1S,5R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl)butanamide (31) inhibited the FXR preferentially. In case of cathepsin B, N-((5-benzoylthiophen-2-yl)methyl)-2-hydrazineyl-2-oxoacetamide (2) and N-([2,2'-bifuran]-5-ylmethyl)-2-hydrazineyl-2-oxoacetamide (7) were identified as the most druggable inhibitors whereas 1-amino-2,7-diethyl-3,8-dioxo-6-(p-tolyl)-2,3,7,8-tetrahydro-2,7-naphthyridine-4‑carbonitrile (5) and (R)-6-amino-2-(2,3-dihydroxypropyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (20) were active against furin.

Principal Component Analysis (PCA) of Molecular Descriptors for Improving Permeation through the Blood-Brain Barrier of Quercetin Analogues

Despite its beneficial pharmacological effects in the brain, partly by modulating inositol phosphate multikinase (IPMK) activity, the therapeutic use of quercetin is limited due to its poor solubility, low oral bioavailability, and low permeability through the blood-brain barrier (BBB). We aimed to identify quercetin analogues with improved BBB permeability and preserved binding affinities towards IPMK and to identify the molecular characteristics required for them to permeate the BBB. Binding affinities of quercetin analogues towards IPMK were determined by molecular docking. Principal component analysis (PCA) was applied to identify the molecular descriptors contributing to efficient permeation through the BBB. Among 34 quercetin analogues, 19 compounds were found to form more stable complexes with IPMK, and the vast majority were found to be more lipophilic than quercetin. Using two distinct in silico techniques, insufficient BBB permeation was determined for all quercetin analogues. However, using the PCA method, the descriptors related to intrinsic solubility and lipophilicity (logP) were identified as mainly responsible for clustering four quercetin analogues (trihydroxyflavones) with the highest BBB permeability. The application of PCA revealed that quercetin analogues could be classified with respect to their structural characteristics, which may be utilized in further analogue syntheses and lead optimization of BBB-penetrating IPMK modulators as neuroprotective agents.

Potential Hepatoprotective Effects of Chamaecyparis lawsoniana against Methotrexate-Induced Liver Injury: Integrated Phytochemical Profiling, Target Network Analysis, and Experimental Validation

Methotrexate (MTX) therapy encounters significant limitations due to the significant concern of drug-induced liver injury (DILI), which poses a significant challenge to its usage. To mitigate the deleterious effects of MTX on hepatic function, researchers have explored plant sources to discover potential hepatoprotective agents. This study investigated the hepatoprotective effects of the ethanolic extract derived from the aerial parts of Chamaecyparis lawsoniana (CLAE) against DILI, specifically focusing on MTX-induced hepatotoxicity. UPLC-ESI-MS/MS was used to identify 61 compounds in CLAE, with 31 potential bioactive compounds determined through pharmacokinetic analysis. Network pharmacology analysis revealed 195 potential DILI targets for the bioactive compounds, including TP53, IL6, TNF, HSP90AA1, EGFR, IL1B, BCL2, and CASP3 as top targets. In vivo experiments conducted on rats with acute MTX-hepatotoxicity revealed that administering CLAE orally at 200 and 400 mg/kg/day for ten days dose-dependently improved liver function, attenuated hepatic oxidative stress, inflammation, and apoptosis, and reversed the disarrayed hepatic histological features induced by MTX. In general, the findings of the present study provide evidence in favor of the hepatoprotective capabilities of CLAE in DILI, thereby justifying the need for additional preclinical and clinical investigations.

AlphaFold2 has more to learn about protein energy landscapes

Recent work suggests that AlphaFold2 (AF2)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. Using several implementations of AF2, including two published enhanced sampling approaches, we generated >280,000 models of 93 fold-switching proteins whose experimentally determined conformations were likely in AF2's training set. Combining all models, AF2 predicted fold switching with a modest success rate of ~25%, indicating that it does not readily sample both experimentally characterized conformations of most fold switchers. Further, AF2's confidence metrics selected against models consistent with experimentally determined fold-switching conformations in favor of inconsistent models. Accordingly, these confidence metrics-though suggested to evaluate protein energetics reliably-did not discriminate between low and high energy states of fold-switching proteins. We then evaluated AF2's performance on seven fold-switching proteins outside of its training set, generating >159,000 models in total. Fold switching was accurately predicted in one of seven targets with moderate confidence. Further, AF2 demonstrated no ability to predict alternative conformations of two newly discovered targets without homologs in the set of 93 fold switchers. These results indicate that AF2 has more to learn about the underlying energetics of protein ensembles and highlight the need for further developments of methods that readily predict multiple protein conformations.

A deep learning method for drug-target affinity prediction based on sequence interaction information mining

Background

A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In recent years, deep learning has emerged as a promising technique for DTA prediction, leveraging the substantial computational power of modern computers.

Methods

We proposed a novel sequence-based approach, named KC-DTA, for predicting drug-target affinity (DTA). In this approach, we converted the target sequence into two distinct matrices, while representing the molecule compound as a graph. The proposed method utilized k-mers analysis and Cartesian product calculation to capture the interactions and evolutionary information among various residues, enabling the creation of the two matrices for target sequence. For molecule, it was represented by constructing a molecular graph where atoms serve as nodes and chemical bonds serve as edges. Subsequently, the obtained target matrices and molecule graph were utilized as inputs for convolutional neural networks (CNNs) and graph neural networks (GNNs) to extract hidden features, which were further used for the prediction of binding affinity.

Results

In order to evaluate the effectiveness of the proposed method, we conducted several experiments and made a comprehensive comparison with the state-of-the-art approaches using multiple evaluation metrics. The results of our experiments demonstrated that the KC-DTA method achieves high performance in predicting drug-target affinity (DTA). The findings of this research underscore the significance of the KC-DTA method as a valuable tool in the field of in silico drug discovery, offering promising opportunities for accelerating the drug development process. All the data and code are available for access on https://github.com/syc2017/KCDTA.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

Structural organization of the retriever-CCC endosomal recycling complex

The recycling of membrane proteins from endosomes to the cell surface is vital for cell signaling and survival. Retriever, a trimeric complex of vacuolar protein-sorting-associated protein (VPS)35L, VPS26C and VPS29, together with the CCC complex comprising coiled-coil domain-containing (CCDC)22, CCDC93 and copper metabolism domain-containing (COMMD) proteins, plays a crucial role in this process. The precise mechanisms underlying retriever assembly and its interaction with CCC have remained elusive. Here, we present a high-resolution structure of retriever in humans determined using cryogenic electron microscopy. The structure reveals a unique assembly mechanism, distinguishing it from its remotely related paralog retromer. By combining AlphaFold predictions and biochemical, cellular and proteomic analyses, we further elucidate the structural organization of the entire retriever-CCC complex across evolution and uncover how cancer-associated mutations in humans disrupt complex formation and impair membrane protein homeostasis. These findings provide a fundamental framework for understanding the biological and pathological implications associated with retriever-CCC-mediated endosomal recycling.

Comprehensive analysis reveals key genes and environmental toxin exposures underlying treatment response in ulcerative colitis based on in-silico analysis and Mendelian randomization

BACKGROUND

UC is increasingly prevalent worldwide and represents a significant global disease burden. Although medical therapeutics are employed, they often fall short of being optimal, leaving patients struggling with treatment non-responsiveness and many related complications.

MATERIALS AND METHODS

The study utilized gene microarray data and clinical information from GEO. Gene enrichment and differential expression analyses were conducted using Metascape and Limma, respectively. Lasso Regression Algorithm was constructed using glmnet and heat maps were generated using pheatmap. ROC curves were used to assess diagnostic parameter capability, while XSum was employed to screen for small-molecule drugs exacerbating UC. Molecular docking was carried out using Autodock Vina. The study also performed Mendelian randomization analysis based on TwoSampleMR and used CTD to investigate the relationship between exposure to environmental chemical toxicants and UC therapy responsiveness.

RESULTS

Six genes (ELL2, DAPP1, SAMD9L, CD38, IGSF6, and LYN) were found to be significantly overexpressed in UC patient samples that did not respond to multiple therapies. Lasso analysis identified ELL2 and DAPP1 as key genes influencing UC treatment response. Both genes accurately predicted intestinal inflammation in UC and impacted the immunological infiltration status. Clofibrate showed therapeutic potential for UC by binding to ELL2 and DAPP1 proteins. The study also reviews environmental toxins and drug exposures that could impact UC progression.

CONCLUSIONS

We used microarray technology to identify DAPP1 and ELL2 as key genes that impact UC treatment response and inflammatory progression. Clofibrate was identified as a promising UC treatment. Our review also highlights the impact of environmental toxins on UC treatment response, providing valuable insights for personalized clinical management.

Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning

Protein-peptide interactions play a key role in biological processes. Understanding the interactions that occur within a receptor-peptide complex can help in discovering and altering their biological functions. Various computational methods for modeling the structures of receptor-peptide complexes have been developed. Recently, accurate structure prediction enabled by deep learning methods has significantly advanced the field of structural biology. AlphaFold (AF) is among the top-performing structure prediction methods and has highly accurate structure modeling performance on single-chain targets. Shortly after the release of AlphaFold, AlphaFold-Multimer (AFM) was developed in a similar fashion as AF for prediction of protein complex structures. AFM has achieved competitive performance in modeling protein-peptide interactions compared to previous computational methods; however, still further improvement is needed. Here, we present DistPepFold, which improves protein-peptide complex docking using an AFM-based architecture through a privileged knowledge distillation approach. DistPepFold leverages a teacher model that uses native interaction information during training and transfers its knowledge to a student model through a teacher-student distillation process. We evaluated DistPepFold's docking performance on two protein-peptide complex datasets and showed that DistPepFold outperforms AFM. Furthermore, we demonstrate that the student model was able to learn from the teacher model to make structural improvements based on AFM predictions.

Ribosomal stalk-captured CARF-RelE ribonuclease inhibits translation following CRISPR signaling

Prokaryotic type III CRISPR-Cas antiviral systems employ cyclic oligoadenylate (cAn) signaling to activate a diverse range of auxiliary proteins that reinforce the CRISPR-Cas defense. Here we characterize a class of cAn-dependent effector proteins named CRISPR-Cas-associated messenger RNA (mRNA) interferase 1 (Cami1) consisting of a CRISPR-associated Rossmann fold sensor domain fused to winged helix-turn-helix and a RelE-family mRNA interferase domain. Upon activation by cyclic tetra-adenylate (cA4), Cami1 cleaves mRNA exposed at the ribosomal A-site thereby depleting mRNA and leading to cell growth arrest. The structures of apo-Cami1 and the ribosome-bound Cami1-cA4 complex delineate the conformational changes that lead to Cami1 activation and the mechanism of Cami1 binding to a bacterial ribosome, revealing unexpected parallels with eukaryotic ribosome-inactivating proteins.

Study on tumour cell-derived hybrid exosomes as dasatinib nanocarriers for pancreatic cancer therapy

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death. Therefore, we intend to explore novel strategies against PDAC. The exosomes-based biomimetic nanoparticle is an appealing candidate served as a drug carrier in cancer treatment, due to its inherit abilities. In the present study, we designed dasatinib-loaded hybrid exosomes by fusing human pancreatic cancer cells derived exosomes with dasatinib-loaded liposomes, followed by characterization for particle size (119.9 ± 6.10 nm) and zeta potential (-11.45 ± 2.24 mV). Major protein analysis from western blot techniques reveal the presence of exosome marker proteins CD9 and CD81. PEGylated hybrid exosomes showed pH-sensitive drug release in acidic condition, benefiting drug delivery to acidic cancer environment. Dasatinib-loaded hybrid exosomes exhibited significantly higher uptake rates and cytotoxicity to parent PDAC cells by two-sample t-test or by one-way ANOVA analysis of variance, as compared to free drug or liposomal formulations. The results from our computational analysis demonstrated that the drug-likeness, ADMET, and protein-ligand binding affinity of dasatinib are verified successfully. Cancer derived hybrid exosomes may serve as a potential therapeutic candidate for pancreatic cancer treatment.

SSDraw: Software for generating comparative protein secondary structure diagrams

The program SSDraw generates publication-quality protein secondary structure diagrams from three-dimensional protein structures. To depict relationships between secondary structure and other protein features, diagrams can be colored by conservation score, B-factor, or custom scoring. Diagrams of homologous proteins can be registered according to an input multiple sequence alignment. Linear visualization allows the user to stack registered diagrams, facilitating comparison of secondary structure and other properties among homologous proteins. SSDraw can be used to compare secondary structures of homologous proteins with both conserved and divergent folds. It can also generate one secondary structure diagram from an input protein structure of interest. The source code can be downloaded (https://github.com/ncbi/SSDraw) and run locally for rapid structure generation, while a Google Colab notebook allows easy use.

High-throughput virtual screening of potential inhibitors of GPR52 using docking and biased sampling method for Huntington's disease therapy

Huntington's disease (HD) is a rare and progressive neurodegenerative disorder caused by polyglutamine (poly-Q) mutations of the huntingtin (HTT) gene resulting in chorea, cognitive, and psychiatric dysfunctions. Being a monogenic condition, reducing the levels of the mutated huntingtin protein (mHTT) holds promise as an effective therapeutic approach. GPR52, an orphan G-protein coupled receptor (GPCR), enriched in the striatum, is a novel target for slowing down the progression of HD by lowering the mHTT levels. Therefore, the study focuses on identifying potent small-molecule inhibitors for GPR52 using a combination of robust high-throughput virtual screening (HTVS) and pharmacokinetics profiling followed by fast pulling of ligand (FPL) and umbrella sampling (US) simulations. Initially, screening a library of 2,36,545 compounds was done against the binding pocket of GPR52. Based on binding affinity, stereochemical and non-bonded interactions, and pharmacokinetic profiling, 50 compounds were shortlisted. Selected hit compounds 1, 2, and 3 were subjected to FPL simulations with applied external bias potential to investigate their unique dissociation pathways and intermolecular interactions over time. Subsequently, the US simulations were performed on the selected hit compounds to estimate their binding free energy (ΔG). The analysis of the trajectories obtained from simulations revealed that the residues TYR34, TYR185, GLY187, ASP188, ILE189, SER299, PHE300, and THR303 within the active site of GPR52 were significant for efficient ligand binding through the formation of various hydrogen bond interactions and hydrophobic contacts. Out of the three hit compounds, compound 3 had the lowest ΔG of - 20.82 ± 0.44 kcal/mol. The study identified compounds 1, 2, and 3 as potential molecules that can be developed as GPR52 inhibitors holding promise for lowering mHTT levels.

Network pharmacology identifies the inhibitory effect of Yiqiyangyinquyu prescription on salivary gland inflammation in Sjögren's syndrome

This study aimed to explore the mode of action of Yiqiyangyinquyu prescription (YP) against Sjögren's syndrome (SS) by combining network pharmacology with molecular docking techniques. YP's active components and target proteins were identified using the BATMAN-traditional Chinese medicine database. Concurrently, targets associated with SS were extracted from databases, including Genecards, Online Mendelian Inheritance in Man, and Therapeutic Target Database. The standard targets were then imported into the STRING database to construct a protein-protein interaction network. We then conducted gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses, which were succeeded by molecular docking studies to validate core active components and key targets. Finally, in vitro experiments and molecular dynamics simulation were conducted to substantiate the therapeutic efficacy of YP in treating SS. A total of 206 intersection targets and 46 active compounds were identified. Gene ontology analysis unveiled that YP targets were primarily enriched in cellular responses to chemical stress, inflammation, and cell proliferation. Key enriched signaling pathways encompassed the interleukin 17, hypoxia-inducible factor-1, tumor necrosis factor (TNF-α), and advanced glycation end products-receptor for AGEs (AGE-RAGE) signaling pathways. Molecular docking results demonstrated high-affinity between neotanshinone C, tanshiquinone B, miltionone I, TNF-α, interleukin 1 beta (IL-1β), and interleukin 6 (IL-6). Noteworthy, TNF-α, considered the most important gene in YP against SS, binds to YP most stably, which was further validated by molecular dynamics simulation. In vitro experiments confirmed YP's capacity to reduce TNF-α, IL-1β, and IL-6 expression, effectively alleviating SS-related inflammation. YP demonstrated a significant anti-inflammatory effect by suppressing inflammatory cytokines (TNF-α, IL-6, and IL-1β), providing experimental evidence for its clinical application in treating SS.

Qi Fu Yin ameliorates neuroinflammation through inhibiting RAGE and TLR4/NF-κB pathway in AD model rats

The purpose of this study is to investigate the therapeutic effect of Qi Fu Yin (QFY) on Alzheimer's disease (AD) both computationally and experimentally. Network pharmacology analysis and molecular docking were conducted to identify potential targets and signaling pathways involved in QFY treating AD. Streptozotocin-induced AD rat model was used to verify important targets and predicted pathways. The components of QFY were identified using liquid chromatography-tandem mass spectrometry. The results indicate that the potential targets of QFY are highly enriched for anti-inflammatory pathways. Molecular docking analysis revealed stable structures formed between QFY's active compounds, including stigmasterol, β-sitosterol, and isorhamnetin, and the identified targets. In vivo, QFY improved cognitive memory in AD rats and reduced the mRNA expression levels of toll-like receptor 4 (TLR4), the receptor for advanced glycation end products (AGER), and the inflammatory factors interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) in the brains of AD rats. Furthermore, QFY effectively reduced nuclear translocation of nuclear factor-kappa B (NF-κB) and inhibited NF-κB and microglia activation. In conclusion, QFY can ameliorate neuroinflammation in AD model rats, partly via the inhibition of TLR4 and RAGE/NF-κB pathway and microglia activation, thereby enhancing learning and memory in AD model rats.

Exploring the Potential of Plant Bioactive Compounds against Male Infertility: An In Silico and In Vivo Study

Infertility is a well-recognized multifactorial problem affecting the majority of people who struggle with infertility issues. In recent times, among infertility cases, the male factor has acquired importance, and now it contributes to approximately half of the infertility cases because of different abnormalities. In the current study, we used natural phytochemicals as potential drug-lead compounds to target different receptor proteins that are involved in the onset of male infertility. A set of 210 plant phytochemicals were docked counter to active site residues of sex hormone-binding globulin, a disintegrin and metalloproteinase 17, and DNase I as receptor proteins. On the basis of binding scores and molecular dynamics simulation, the phytochemicals tricin, quercetin, malvidin, rhamnetin, isorhamnetin, gallic acid, kaempferol, esculin, robinetin, and okanin were found to be the potential drug candidates to treat male infertility. Molecular dynamics simulation showed tricin as a strong inhibitor of all selected receptor proteins because the ligand-protein complexes remained stabilized during the entire simulation time of 100 ns. Further, an in vivo study was designed to evaluate the effect of tricin in male rats with nicotine-induced infertility. It was explored that a high dose of tricin significantly reduced the levels of alanine transaminase, aspartate transaminase, urea, creatinine, cholesterol, triglyceride, and low-density lipoprotein and raised the level of high-density lipoprotein in intoxicated male rats. A high dose of tricin also increased the reproductive hormones (i.e., testosterone, luteinizing hormone, follicle-stimulating hormone, and prolactin) and reduced the level of DHEA-SO4. The phytochemical (tricin, 10 mg/kg body weight) also showed significant improvement in the histo-architecture after nicotine intoxication in rats. From the current study, it is concluded that the phytochemical tricin could serve as a potential drug candidate to cure male infertility.

Structural similarities between SAM and ATP recognition motifs and detection of ATP binding in a SAM binding DNA methyltransferase

S-adenosylmethionine (SAM) is a ubiquitous co-factor that serves as a donor for methylation reactions and additionally serves as a donor of other functional groups such as amino and ribosyl moieties in a variety of other biochemical reactions. Such versatility in function is enabled by the ability of SAM to be recognized by a wide variety of protein molecules that vary in their sequences and structural folds. To understand what gives rise to specific SAM binding in diverse proteins, we set out to study if there are any structural patterns at their binding sites. A comprehensive analysis of structures of the binding sites of SAM by all-pair comparison and clustering, indicated the presence of 4 different site-types, only one among them being well studied. For each site-type we decipher the common minimum principle involved in SAM recognition by diverse proteins and derive structural motifs that are characteristic of SAM binding. The presence of the structural motifs with precise three-dimensional arrangement of amino acids in SAM sites that appear to have evolved independently, indicates that these are winning arrangements of residues to bring about SAM recognition. Further, we find high similarity between one of the SAM site types and a well known ATP binding site type. We demonstrate using in vitro experiments that a known SAM binding protein, HpyAII.M1, a type 2 methyltransferase can bind and hydrolyse ATP. We find common structural motifs that explain this, further supported through site-directed mutagenesis. Observation of similar motifs for binding two of the most ubiquitous ligands in multiple protein families with diverse sequences and structural folds presents compelling evidence at the molecular level in favour of convergent evolution.

Impact of N221S missense mutation in human ribonucleotide reductase small subunit b on mitochondrial DNA depletion syndrome

The impact of N221S mutation in hRRM2B gene, which encodes the small subunit of human ribonucleotide reductase (RNR), on RNR activity and the pathogenesis of mitochondrial DNA depletion syndrome (MDDS) was investigated. Our results demonstrate that N221 mutations significantly reduce RNR activity, suggesting its role in the development of MDDS. We proposed an allosteric regulation pathway involving a chain of three phenylalanine residues on the αE helix of RNR small subunit β. This pathway connects the C-terminal loop of β2, transfers the activation signal from the large catalytic subunit α to β active site, and controls access of oxygen for radical generation. N221 is near this pathway and likely plays a role in regulating RNR activity. Mutagenesis studies on residues involved in the phenylalanine chain and the regulation pathway were conducted to confirm our proposed mechanism. We also performed molecular dynamic simulation and protein contact network analysis to support our findings. This study sheds new light on RNR small subunit regulation and provides insight on the pathogenesis of MDDS.

CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures

The field of bioimage analysis is currently impacted by a profound transformation, driven by the advancements in imaging technologies and artificial intelligence. The emergence of multi-modal AI systems could allow extracting and utilizing knowledge from bioimaging databases based on information from other data modalities. We leverage the multi-modal contrastive learning paradigm, which enables the embedding of both bioimages and chemical structures into a unified space by means of bioimage and molecular structure encoders. This common embedding space unlocks the possibility of querying bioimaging databases with chemical structures that induce different phenotypic effects. Concretely, in this work we show that a retrieval system based on multi-modal contrastive learning is capable of identifying the correct bioimage corresponding to a given chemical structure from a database of ~2000 candidate images with a top-1 accuracy >70 times higher than a random baseline. Additionally, the bioimage encoder demonstrates remarkable transferability to various further prediction tasks within the domain of drug discovery, such as activity prediction, molecule classification, and mechanism of action identification. Thus, our approach not only addresses the current limitations of bioimaging databases but also paves the way towards foundation models for microscopy images.

SSDraw: software for generating comparative protein secondary structure diagrams

The program SSDraw generates publication-quality protein secondary structure diagrams from three-dimensional protein structures. To depict relationships between secondary structure and other protein features, diagrams can be colored by conservation score, B-factor, or custom scoring. Diagrams of homologous proteins can be registered according to an input multiple sequence alignment. Linear visualization allows the user to stack registered diagrams, facilitating comparison of secondary structure and other properties among homologous proteins. SSDraw can be used to compare secondary structures of homologous proteins with both conserved and divergent folds. It can also generate one secondary structure diagram from an input protein structure of interest. The source code can be downloaded (https://github.com/ethanchen1301/SSDraw) and run locally for rapid structure generation, while a Google Colab notebook allows easy use.

Glucose restriction in Saccharomyces cerevisiae modulates the phosphorylation pattern of the 20S proteasome and increases its activity

Caloric restriction is known to extend the lifespan and/or improve diverse physiological parameters in a vast array of organisms. In the yeast Saccharomyces cerevisiae, caloric restriction is performed by reducing the glucose concentration in the culture medium, a condition previously associated with increased chronological lifespan and 20S proteasome activity in cell extracts, which was not due to increased proteasome amounts in restricted cells. Herein, we sought to investigate the mechanisms through which glucose restriction improved proteasome activity and whether these activity changes were associated with modifications in the particle conformation. We show that glucose restriction increases the ability of 20S proteasomes, isolated from Saccharomyces cerevisiae cells, to degrade model substrates and whole proteins. In addition, threonine 55 and/or serine 56 of the α5-subunit, were/was consistently found to be phosphorylated in proteasomes isolated from glucose restricted cells, which may be involved in the increased proteolysis capacity of proteasomes from restricted cells. We were not able to observe changes in the gate opening nor in the spatial conformation in 20S proteasome particles isolated from glucose restricted cells, suggesting that the changes in activity were not accompanied by large conformational alterations in the 20S proteasome but involved allosteric activation of proteasome catalytic site.

Identification and screening of potential inhibitors obtained from Plumeria rubra L. compounds against type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is the inability of the body's cells to retaliate to insulin, which can periodically culminate into absolute insulin deficiency. Hyperinsulinemia can be alleviated by administering oral medications or insulin. Prevailing medicaments engender repercussions with prolonged use, as they transmute to an inefficacious form. Hence, it will be advantageous to design plant-derived antihyperglycemic drugs with remarkable efficacy and safety quotients to address T2DM and associated comorbidities. Based on prior research, we have identified 7 novel phytocompounds from Plumeria rubra L. and 5 co-crystals that serve as an important residence for T2DM. The compounds are assessed for their inhibitory activity and dynamic stability against five major receptors which are responsible for T2DM. Additionally, in silico ADMET assessment followed by GPU-enabled GROMACS was performed on the selected compounds. The results demonstrated that β-d-Hexaglucoside had the highest binding affinity, hydrophobicity and bond length in contrast to all the targeted receptors. β-d-Hexaglucoside was subjected to dynamic simulation to analyze the root mean square deviation and root mean square fluctuation graph rates using the GROMOS force field in GROMACS software. Furthermore, β-d-Hexaglucoside exhibited inhibitory activity against diabetic receptors with a docking score of -9.5 kcal/mol. The current study proposes β-d-Hexaglucoside as a potential candidate for in-vitro or pre-clinical investigations to ameliorate T2DM management.Communicated by Ramaswamy H. Sarma.

Obacunone targets macrophage migration inhibitory factor (MIF) to impede osteoclastogenesis and alleviate ovariectomy-induced bone loss

INTRODUCTION

Osteoporosis is the most common bone disorder where the hyperactive osteoclasts represent the leading role during the pathogenesis. Targeting hyperactive osteoclasts is currently the primary therapeutic strategy. However, concerns about the long-term efficacy and side effects of current frontline treatments persist. Alternative therapeutic agents are still needed.

OBJECTIVES

Obacunone (OB) is a small molecule with a broad spectrum of biological activities, particularly antioxidant and anti-inflammatory effects. This study aims to examine OB's therapeutic potential on osteoporosis and explore the rudimentary mechanisms.

METHODS

Osteoclast formation and osteoclastic resorption assays were carried out to examine OB's inhibitory effects in vitro, followed by the in-vivo studies of OB's therapeutic effects on ovariectomy-induced osteoporotic preclinical model. To further study the underlying mechanisms, mRNA sequencing and analysis were used to investigate the changes of downstream pathways. The molecular targets of OB were predicted, and in-silico docking analysis was performed. Ligand-target binding was verified by surface plasmon resonance (SPR) assay and Western Blotting assay.

RESULTS

The results indicated that OB suppressed the formation of osteoclast and its resorptive function in vitro. Mechanistically, OB interacts with macrophage migration inhibitory factor (MIF) which attenuates receptor activator of nuclear factor kappa B (NF-κB) ligand (RANKL)-induced signaling pathways, including reactive oxygen species (ROS), NF-κB pathway, and mitogen-activated protein kinases (MAPKs). These effects eventually caused the diminished expression level of the master transcriptional factor of osteoclastogenesis, nuclear factor of activated T cells 1 (NFATc1), and its downstream osteoclast-specific proteins. Furthermore, our data revealed that OB alleviated estrogen deficiency-induced osteoporosis by targeting MIF and thus inhibiting hyperactive osteoclasts in vivo.

CONCLUSION

These results together implicated that OB may represent as a therapeutic candidate for bone disorders caused by osteoclasts, such as osteoporosis.

Klebsiella phage KP34gp57 capsular depolymerase structure and function: from a serendipitous finding to the design of active mini-enzymes against K. pneumoniae

IMPORTANCE

In this work, we determined the structure of Klebsiella phage KP34p57 capsular depolymerase and dissected the role of individual domains in trimerization and functional activity. The crystal structure serendipitously revealed that the enzyme can exist in a monomeric state once deprived of its C-terminal domain. Based on the crystal structure and site-directed mutagenesis, we localized the key catalytic residues in an intra-subunit deep groove. Consistently, we show that C-terminally trimmed KP34p57 variants are monomeric, stable, and fully active. The elaboration of monomeric, fully active phage depolymerases is innovative in the field, as no previous example exists. Indeed, mini phage depolymerases can be combined in chimeric enzymes to extend their activity ranges, allowing their use against multiple serotypes.

PASSerRank: Prediction of allosteric sites with learning to rank

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, that is, how well a pocket meets the characteristics of known allosteric sites. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top three positions for 83.6% and 80.5% of test proteins, respectively. The model outperforms other common machine learning models with higher F1 scores (0.662 in ASD and 0.608 in CASBench) and Matthews correlation coefficients (0.645 in ASD and 0.589 in CASBench). The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

Evaluation of the pH effect on complex formation between bovine β-lactoglobulin and aflatoxin M1: a molecular dynamic simulation and molecular docking study

The aim of this work was to evaluate interaction between aflatoxin M1 (AFM1) and structural models of β-lactoglobulin (β-LG) at pH 4.0 and 6.5. This information would provide an explanation of the variability in AFM1 during cheese production. Once β-LG models were optimized using molecular dynamic (MD) simulation, it was found that a region of the Calyx cavity underwent conformational changes, at the E-F loop, from the closed conformation at pH 6.5 to the open at pH 4.0. No differences in Site C conformation were observed at both pH. The binding free energy (ΔGb) of the β-LG-AFM1 complexes at the different pHs were determined by molecular docking. The ΔGb values obtained for the Calyx cavity showed that at pH 4.0 there is a more stable complex formation compared to pH 6.5 with values of -42.6 and -32.0 kJ mol-1, respectively. On the contrary, in the complexes formed in Site C at both pH´s there were no differences. Likewise, the ΔGb in the dimer interface was evaluated, obtaining a value of -29.3 kJ mol-1, like those obtained at Site C. In addition, by the MD simulations of the β-LG-AFM1 complexes, it was observed that at acidic pH the binding of AFM1 with β-LG is more stable. In conclusion, the computational tools showed that the most stable complex was formed at the Calyx cavity at pH 4.0. This suggests that during cheese production using acidic coagulation, the whey proteins show higher affinity toward AFM1 which may explain the observed variability of mycotoxin.Communicated by Ramaswamy H. Sarma.

Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models.

A pharmacophore-guided deep learning approach for bioactive molecular generation

The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.

Learning on topological surface and geometric structure for 3D molecular generation

Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes. The omission of these fine details leads to many models having difficulty in outputting reasonable molecules for a variety of therapeutic targets. Here, to address this challenge, we formulate a model, called SurfGen, that designs molecules in a fashion closely resembling the figurative key-and-lock principle. SurfGen comprises two equivariant neural networks, Geodesic-GNN and Geoatom-GNN, which capture the topological interactions on the pocket surface and the spatial interaction between ligand atoms and surface nodes, respectively. SurfGen outperforms other methods in a number of benchmarks, and its high sensitivity on the pocket structures enables an effective generative-model-based solution to the thorny issue of mutation-induced drug resistance.

The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15

In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

SARS-CoV-2 external structures interacting with nanospheres using docking and molecular dynamics

Coronavirus is caused by the SARS-CoV-2 virus has shown rapid proliferation and scarcity of treatments with proven effectiveness. In this way, we simulated the hospitalization of carbon nanospheres, with external active sites of the SARS-CoV-2 virus (M-Pro, S-Gly and E-Pro), which can be adsorbed or inactivated when interacting with the nanospheres. The computational procedures performed in this work were developed with the SwissDock server for molecular docking and the GROMACS software for molecular dynamics, making it possible to extract relevant data on affinity energy, distance between molecules, free Gibbs energy and mean square deviation of atomic positions, surface area accessible to solvents. Molecular docking indicates that all ligands have an affinity for the receptor's active sites. The nanospheres interact favorably with all proteins, showing promising results, especially C60, which presented the best affinity energy and RMSD values ​​for all protein macromolecules investigated. The C60 with E-Pro exhibited the highest affinity energy of -9.361 kcal/mol, demonstrating stability in both molecular docking and molecular dynamics simulations. Our RMSD calculations indicated that the nanospheres remained predominantly stable, fluctuating within a range of 2 to 3 Å. Additionally, the analysis of other structures yielded promising results that hold potential for application in other proteases.Communicated by Ramaswamy H. Sarma.

Interaction of Vanadium Complexes with Proteins: Revisiting the Reported Structures in the Protein Data Bank (PDB) since 2015

The structural determination and characterization of molecules, namely proteins and enzymes, is crucial to gaining a better understanding of their role in different chemical and biological processes. The continuous technical developments in the experimental and computational resources of X-ray diffraction (XRD) and, more recently, cryogenic Electron Microscopy (cryo-EM) led to an enormous growth in the number of structures deposited in the Protein Data Bank (PDB). Bioinorganic chemistry arose as a relevant discipline in biology and therapeutics, with a massive number of studies reporting the effects of metal complexes on biological systems, with vanadium complexes being one of the relevant systems addressed. In this review, we focus on the interactions of vanadium compounds (VCs) with proteins. Several types of binding are established between VCs and proteins/enzymes. Considering that the V-species that bind may differ from those initially added, the mentioned structural techniques are pivotal to clarifying the nature and variety of interactions of VCs with proteins and to proposing the mechanisms involved either in enzymatic inhibition or catalysis. As such, we provide an account of the available structural information of VCs bound to proteins obtained by both XRD and/or cryo-EM, mainly exploring the more recent structures, particularly those containing organic-based vanadium complexes.

Exploring the role and mechanism of Astragalus membranaceus and radix paeoniae rubra in idiopathic pulmonary fibrosis through network pharmacology and experimental validation

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic disease with an unclear etiology and no effective treatment. This study aims to elucidate the pathogenic mechanism networks involving multiple targets and pathways in IPF. Extracts and metabolites of Astragalus membranaceus (AM) and Radix paeoniae rubra (RPR), two well-known traditional Chinese medicines, have demonstrated therapeutic effects on IPF. However, the underlying mechanisms of AM and RPR remain unclear. Utilizing network pharmacology analysis, differentially expressed genes (DEGs) associated with IPF were obtained from the GEO database. Targets of AM and RPR were identified using the TCM Systems Pharmacology Database and Analysis Platform and SwissTargetPrediction. A protein-protein interaction (PPI) network was subsequently constructed and analyzed using the STRING database and Cytoscape software. Gene ontology enrichment analysis and kyoto encyclopedia of genes and genomes analysis were conducted using Metascape. Additionally, a component-target-pathway network and a Sankey diagram were employed to identify the main active components, and molecular docking was performed between these components and proteins encoded by key targets. Finally, in vivo studies were conducted based on network pharmacology. A total of 117 common targets between DEGs of IPF and drug targets were identified and included in the PPI network, in which AKT1, MAPK3, HSP90AA1, VEGFA, CASP3, JUN, HIF1A, CCND1, PTGS2, and MDM2 were predicted as key targets. These 117 targets were enriched in the PI3K-AKT pathway, HIF-1 signaling pathway, apoptosis, and microRNAs in cancer. Astragaloside III, (R)-Isomucronulatol, Astragaloside I, Paeoniflorin, and β-sitosterol were selected as the main active components. Docking scores ranged from - 4.7 to - 10.7 kcal/mol, indicating a strong binding affinity between the main active compounds and key targets. In vivo studies have indeed shown that AM and RPR can alleviate the pathological lung fibrotic damage caused by bleomycin treatment. The treatment with AM and RPR resulted in a reduction of mRNA levels for key targets AKT1, HSP90AA1, CASP3, MAPK3, and VEGFA. Additionally, the protein expression levels of AKT1, HSP90AA1, and VEGFA were also reduced. These results support the therapeutic potential of AM and RPR in ameliorating pulmonary fibrosis and provide insight into the molecular mechanisms involved in their therapeutic effects.

The impact of AlphaFold Protein Structure Database on the fields of life sciences

Arguably, 2020 was the year of high-accuracy protein structure predictions, with AlphaFold 2.0 achieving previously unseen accuracy in the Critical Assessment of Protein Structure Prediction (CASP). In 2021, DeepMind and EMBL-EBI developed the AlphaFold Protein Structure Database to make an unprecedented number of reliable protein structure predictions easily accessible to the broad scientific community. We provide a brief overview and describe the latest developments in the AlphaFold database. We highlight how the fields of data services, bioinformatics, structural biology, and drug discovery are directly affected by the influx of protein structure data. We also show examples of cutting-edge research that took advantage of the AlphaFold database. It is apparent that connections between various fields through protein structures are now possible, but the amount of data poses new challenges. Finally, we give an outlook regarding the future direction of the database, both in terms of data sets and new functionalities.

Fluid protein fold space and its implications

Fold-switching proteins, which remodel their secondary and tertiary structures in response to cellular stimuli, suggest a new view of protein fold space. For decades, experimental evidence has indicated that protein fold space is discrete: dissimilar folds are encoded by dissimilar amino acid sequences. Challenging this assumption, fold-switching proteins interconnect discrete groups of dissimilar protein folds, making protein fold space fluid. Three recent observations support the concept of fluid fold space: (1) some amino acid sequences interconvert between folds with distinct secondary structures, (2) some naturally occurring sequences have switched folds by stepwise mutation, and (3) fold switching is evolutionarily selected and likely confers advantage. These observations indicate that minor amino acid sequence modifications can transform protein structure and function. Consequently, proteomic structural and functional diversity may be expanded by alternative splicing, small nucleotide polymorphisms, post-translational modifications, and modified translation rates.

RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding

Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.

Spatial Distribution of Missense Variants within Complement Proteins Associates with Age Related Macular Degeneration

Purpose

Genetic variants in complement genes are associated with age-related macular degeneration (AMD). However, many rare variants have been identified in these genes, but have an unknown significance, and their impact on protein function and structure is still unknown. We set out to address this issue by evaluating the spatial placement and impact on protein structureof these variants by developing an analytical pipeline and applying it to the International AMD Genomics Consortium (IAMDGC) dataset (16,144 AMD cases, 17,832 controls).

Methods

The IAMDGC dataset was imputed using the Haplotype Reference Consortium (HRC), leading to an improvement of over 30% more imputed variants, over the original 1000 Genomes imputation. Variants were extracted for the CFH , CFI , CFB , C9 , and C3 genes, and filtered for missense variants in solved protein structures. We evaluated these variants as to their placement in the three-dimensional structure of the protein (i.e. spatial proximity in the protein), as well as AMD association. We applied several pipelines to a) calculate spatial proximity to known AMD variants versus gnomAD variants, b) assess a variant's likelihood of causing protein destabilization via calculation of predicted free energy change (ddG) using Rosetta, and c) whole gene-based testing to test for statistical associations. Gene-based testing using seqMeta was performed using a) all variants b) variants near known AMD variants or c) with a ddG >|2|. Further, we applied a structural kernel adaptation of SKAT testing (POKEMON) to confirm the association of spatial distributions of missense variants to AMD. Finally, we used logistic regression on known AMD variants in CFI to identify variants leading to >50% reduction in protein expression from known AMD patient carriers of CFI variants compared to wild type (as determined by in vitro experiments) to determine the pipeline's robustness in identifying AMD-relevant variants. These results were compared to functional impact scores, ie CADD values > 10, which indicate if a variant may have a large functional impact genomewide, to determine if our metrics have better discriminative power than existing variant assessment methods. Once our pipeline had been validated, we then performed a priori selection of variants using this pipeline methodology, and tested AMD patient cell lines that carried those selected variants from the EUGENDA cohort (n=34). We investigated complement pathway protein expression in vitro , looking at multiple components of the complement factor pathway in patient carriers of bioinformatically identified variants.

Results

Multiple variants were found with a ddG>|2| in each complement gene investigated. Gene-based tests using known and novel missense variants identified significant associations of the C3 , C9 , CFB , and CFH genes with AMD risk after controlling for age and sex (P=3.22×10 -5 ;7.58×10 -6 ;2.1×10 -3 ;1.2×10 -31 ). ddG filtering and SKAT-O tests indicate that missense variants that are predicted to destabilize the protein, in both CFI and CFH, are associated with AMD (P=CFH:0.05, CFI:0.01, threshold of 0.05 significance). Our structural kernel approach identified spatial associations for AMD risk within the protein structures for C3, C9, CFB, CFH, and CFI at a nominal p-value of 0.05. Both ddG and CADD scores were predictive of reduced CFI protein expression, with ROC curve analyses indicating ddG is a better predictor (AUCs of 0.76 and 0.69, respectively). A priori in vitro analysis of variants in all complement factor genes indicated that several variants identified via bioinformatics programs PathProx/POKEMON in our pipeline via in vitro experiments caused significant change in complement protein expression (P=0.04) in actual patient carriers of those variants, via ELISA testing of proteins in the complement factor pathway, and were previously unknown to contribute to AMD pathogenesis.

Conclusion

We demonstrate for the first time that missense variants in complement genes cluster together spatially and are associated with AMD case/control status. Using this method, we can identify CFI and CFH variants of previously unknown significance that are predicted to destabilize the proteins. These variants, both in and outside spatial clusters, can predict in-vitro tested CFI protein expression changes, and we hypothesize the same is true for CFH . A priori identification of variants that impact gene expression allow for classification for previously classified as VUS. Further investigation is needed to validate the models for additional variants and to be applied to all AMD-associated genes.

In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease

Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold's performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer's disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

Perspectives of Proteomics in Respiratory Allergic Diseases

Proteomics in respiratory allergic diseases has such a battery of techniques and programs that one would almost think there is nothing impossible to find, invent or mold. All the resources that we document here are involved in solving problems in allergic diseases, both diagnostic and prognostic treatment, and immunotherapy development. The main perspectives, according to this version, are in three strands and/or a lockout immunological system: (1) Blocking the diapedesis of the cells involved, (2) Modifications and blocking of paratopes and epitopes being understood by modifications to antibodies, antagonisms, or blocking them, and (3) Blocking FcεRI high-affinity receptors to prevent specific IgEs from sticking to mast cells and basophils. These tools and targets in the allergic landscape are, in our view, the prospects in the field. However, there are still many allergens to identify, including some homologies between allergens and cross-reactions, through the identification of structures and epitopes. The current vision of using proteomics for this purpose remains a constant; this is also true for the basis of diagnostic and controlled systems for immunotherapy. Ours is an open proposal to use this vision for treatment.

Fabrication and Evaluation of Anticancer Potential of Eugenol Incorporated Chitosan-Silver Nanocomposites: In Vitro, In Vivo, and In Silico Studies

The expanding global cancer burden necessitates a comprehensive strategy to promote possible therapeutic interventions. Nanomedicine is a cutting-edge approach for treating cancer with minimal adverse effects. In the present study, chitosan-silver nanoparticles (ChAgNPs) containing Eugenol (EGN) were synthesized and evaluated for their anticancer activity against breast cancer cells (MCF-7). The physical, pharmacological, and molecular docking studies were used to characterize these nanoparticles. EGN had been effectively entrapped into hybrid NPs (84 ± 7%). The EGN-ChAgNPs had a diameter of 128 ± 14 nm, a PDI of 0.472 ± 0.118, and a zeta potential of 30.58 ± 6.92 mV. Anticancer activity was measured in vitro using an SRB assay, and the findings revealed that EGN-ChAgNPs demonstrated stronger anticancer activity against MCF-7 cells (IC50 = 14.87 ± 5.34 µg/ml) than pure EGN (30.72 ± 4.91 µg/ml). To support initial cytotoxicity findings, advanced procedures such as cell cycle analysis and genotoxicity were performed. Tumor weight reduction and survival rate were determined using different groups of mice. Both survival rates and tumor weight reduction were higher in the EGN-ChAgNPs (12.5 mg/kg) treated group than in the pure EGN treated group. Based on protein-ligand interactions, it might be proposed that eugenol had a favorable interaction with Aurora Kinase A. It was observed that C9 had the highest HYDE score of any sample, measuring at -6.8 kJ/mol. These results, in conjunction with physical and pharmacological evaluations, implies that EGN-ChAgNPs may be a suitable drug delivery method for treating breast cancer in a safe and efficient way.

Synthesis, DFT and molecular docking of novel (Z)-4-bromo-N-(4-butyl-3 (quinolin-3-yl)thiazol-2(3H)-ylidene)benzamide as elastase inhibitor

A new compound, C23H20BrN3OS, containing a quinoline-based iminothiazoline with a thiazoline ring, was synthesized and its crystal and molecular structures were analyzed through single crystal X-ray analysis. The compound belongs to the triclinic system P - 1 space group, with dimensions of a = 9.2304 (6) Å, b = 11.1780 (8) Å, c = 11.3006 (6) Å, α = 107.146 (5)°, β = 93.701 (5)°, γ = 110.435 (6)°, Z = 2 and V = 1025.61 (12) Å3. The crystal structure showed that C-H···N and C-H···O hydrogen bond linkages, forming infinite double chains along the b-axis direction, and enclosing R22(14) and R22(16) ring motifs. The Hirshfeld surface analysis revealed that H…H (44.1%) and H…C/C…H (15.3%) interactions made the most significant contribution. The newly synthesized (Z)-4-bromo-N-(4-butyl-3 (quinolin-3-yl)thiazol-2(3H)-ylidene)benzamide, in comparison to oleanolic acid, exhibited more strong potential against elastase with an inhibition value of 1.21 µM. Additionally, the derivative was evaluated using molecular docking and molecular dynamics simulation studies, which showed that the quinoline based iminothiazoline derivative has the potential to be a novel inhibitor of elastase enzyme. Both theoretical and experimental findings suggested that this compound could have a number of biological activities.

Prognostic significance and mechanisms of CXCL genes in clear cell renal cell carcinoma

This study aimed to investigate the clinical significance, biological functions, and underlying mechanisms of CXCL genes in clear cell renal cell carcinoma (ccRcc) based on patient datasets and pan-cancer analysis. The interaction between CXCL genes in ccRcc and immune components, particularly in relation to neutrophil recruitment and polarization mechanisms, was also evaluated. Furthermore, a risk score was developed using a signature for neutrophil polarization. The role of CXCL2 was assessed through in vitro experiments. Results showed that five CXCL genes (CXCL 2, 5, 9, 10, and 11) were upregulated in renal cancer tissue, while seven genes (CXCL 1, 2, 3, 5, 8, 13, and 14) significantly impacted patient survival. Moreover, CXCL 1, 5, and 13 affected progression-free survival. Besides, differences in mRNA expression and immune components affected renal cancer outcomes. Furthermore, three pairs of CXCL gene-immune cell interactions (CXCL13-CD8+ T cells, CXCL9/10-M1 cells, CXCL1/2/3/8-neutrophils) were identified through single-cell and pan-cancer analysis. A TAN risk score with prognostic value for KIRC patients was constructed using 11 genes and a TAN signature. Neutrophil polarization significantly impacted survival. Notably, CXCL2 was involved in neutrophil recruitment and polarization, thus promoting ccRcc progression. In conclusion, seven prognostic CXCL genes (CXCL 1/2/3/5/8/13/14) for ccRcc patients and three pairs of CXCL gene-immune cell interactions were identified. Furthermore, results showed that CXCL 2 promotes ccRcc progression through neutrophil recruitment and polarization.

A soft-computation hybrid method for search of the antibiotic-resistant gene in Mycobacterium tuberculosis for promising drug target identification and antimycobacterial lead discovery

Tuberculosis (TB) control programs were already piloted before the COVID-19 pandemic commenced and the global TB response was amplified by the pandemic. To combat the global TB epidemic, drug repurposing, novel drug discovery, identification and targeting of the antimicrobial resistance (AMR) genes, and addressing social determinants of TB are required. The study aimed to identify AMR genes in Mycobacterium tuberculosis (MTB) and a new anti-mycobacterial drug candidate. In this research, we used a few software to explore some AMR genes as a target protein in MTB and identified some potent antimycobacterial agents. We used Maestro v12.8 software, along with STRING v11.0, KEGG and Pass Server databases to gain a deeper understanding of MTB AMR genes as drug targets. Computer-aided analysis was used to identify mtrA and katG AMR genes as potential drug targets to depict some antimycobacterial drug candidates. Based on docking scores of -4.218 and -6.161, carvacrol was identified as a potent inhibitor against both drug targets. This research offers drug target identification and discovery of antimycobacterial leads, a unique and promising approach to combating the challenge of antibiotic resistance in Mycobacterium, and contributes to the development of a potential futuristic solution.

Systems pharmacology dissection of pharmacological mechanisms of Xiaochaihu decoction against human coronavirus

BACKGROUND

Although coronavirus disease 2019 (COVID-19) pandemic is still rage worldwide, there are still very limited treatments for human coronaviruses (HCoVs) infections. Xiaochahu decoction (XCHD), which is one of the traditional Chinese medicine (TCM) prescriptions in Qingfeipaidu decoction (QFPDD), is widely used for COVID-19 treatment in China and able to relieve the symptoms of fever, fatigue, anorexia, and sore throat. To explore the role and mechanisms of XCHD against HCoVs, we presented an integrated systems pharmacology framework in this study.

METHODS

We constructed a global herb-compound-target (H-C-T) network of XCHD against HCoVs. Multi-level systems pharmacology analyses were conducted to highlight the key XCHD-regulated proteins, and reveal multiple HCoVs relevant biological functions affected by XCHD. We further utilized network-based prediction, drug-likeness analysis, combining with literature investigations to uncover the key ani-HCoV constituents in XCHD, whose effects on anit-HCoV-229E virus were validated using cytopathic effect (CPE) assay. Finally, we proposed potential molecular mechanisms of these compounds against HCoVs via subnetwork analysis.

RESULTS

Based on the systems pharmacology framework, we identified 161 XCHD-derived compounds interacting with 37 HCoV-associated proteins. An integrated pathway analysis revealed that the mechanism of XCHD against HCoVs is related to TLR signaling pathway, RIG-I-like receptor signaling pathway, cytoplasmic DNA sensing pathway, and IL-6/STAT3 pro-inflammatory signaling pathway. Five compounds from XCHD, including betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1 exerted inhibitory activity against HCoV-229E virus in Huh7 cells using in vitro CPE assay.

CONCLUSION

Our work presented a comprehensive systems pharmacology approach to identify the effective molecules and explore the molecular mechanism of XCHD against HCoVs.