DrugBank: a knowledgebase for drugs, drug actions and drug targets

Fault-tolerant quantum chemical calculations with improved machine-learning models

Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [J. Comput. Chem. 2023, 44, 1174], we further propose (1) the improved ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; (2) the idea of coded computation, that is, the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and (3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states.

Exploring potential therapeutic targets for asthma: a proteome-wide Mendelian randomization analysis

BACKGROUND

Asthma poses a significant global health challenge, characterized by high rates of morbidity and mortality. Despite available treatments, many severe asthma patients remain poorly managed, highlighting the need for novel therapeutic strategies. This study aims to identify potential drug targets for asthma by examining the influence of circulating plasma proteins on asthma risk.

METHODS

This study employs summary-data-based Mendelian randomization (MR) and two-sample MR methods to investigate the association between 2940 plasma proteins from the UK Biobank study and asthma. The analysis includes discovery (FinnGen cohort) and replication (GERA cohort) phases, with Bayesian colocalization used to validate the relationships between proteins and asthma. Furthermore, protein-protein interaction and druggability assessments were conducted on high-evidence strength protein biomarkers, and candidate drug prediction and molecular docking were performed for proteins without targeted drugs. Given the complexity of asthma pathogenesis, the study also explores the relationships between plasma proteins and asthma-related endpoints (e.g., obesity-related asthma, infection-related asthma, childhood asthma) to identify potential therapeutic targets for different subtypes.

RESULTS

In the discovery cohort, 75 plasma proteins were associated with asthma, including IL1RAP, IL1RL1, IL6, CXCL5, and CXCL8. Additionally, 6 proteins (IL4R, LTB, CASP8, MAX, PCDH12, and SCLY) were validated through co-localization analysis and validation cohort. The assessment of drug targetability revealed potential drug targets for IL4R, CASP8, and SCLY, while candidate drugs were predicted for LTB and MAX proteins. MAX exhibited strong binding affinity with multiple small molecules indicating a highly stable interaction and significant druggability potential. Analysis of the 75 proteins with 9 asthma-related endpoints highlighted promising targets such as DOK2, ITGAM, CA1, BTN2A1, and GZMB.

CONCLUSION

These findings elucidate the link between asthma, its related endpoints, and plasma proteins, advancing our understanding of molecular pathogenesis and treatment strategies. The discovery of potential therapeutic targets offers new insights into asthma drug target research.

Structure-based virtual screening and drug repurposing studies indicate potential inhibitors of bovine papillomavirus E6 oncoprotein

Bovine papillomavirus type 1 (BPV1) is an oncogenic virus that causes lesions and cancer in infected cattle. Despite being one of the most studied genotypes in the family and occurring in herds worldwide, there are currently no vaccines or drugs for its control. The viral E6 oncoprotein plays a crucial role in infection by this virus, making it a promising target for the development of new therapies. In this regard, we integrated structure-based virtual screening approaches, drug repositioning, and molecular dynamics to identify approved drugs with the potential to inhibit BPV1 E6. Our results reveal that Lumacaftor and MK-3207 are promising candidates for controlling BPV1 infection. The findings of this study may contribute to the development of E6 oncoprotein blockers in an accelerated and cost-effective manner.

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN's utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.

AI-DPAPT: a machine learning framework for predicting PROTAC activity

Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challenge to predict the proteins accurately. This study used machine learning algorithms and molecular fingerprinting techniques to build an AI-powered PROTAC Activity Prediction Tool that could predict PROTAC activity by examining chemical structures. The chemical structures of a diverse set of PROTAC drugs and their corresponding activities are selected as a dataset for training the tool. The processes used in this study included data preparation, feature extraction, and model training. Further, evaluation was done for the performance of the various classifiers, such as AdaBoost, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron. The findings show that the methods selected here depict accurate PROTAC activities. All the models in this study showed an ROC curve better than 0.9, while the random forest on the test set of the AI-DPAPT had an area under the curve score of 0.97, thus showing accurate results. Furthermore, the study revealed significant insights into the molecular features that can influence the functions of the PROTAC. These findings can potentially increase the understanding of the structure-activity correlations involved in the TPD. Overall, the investigation contributes to computational drug development by introducing this platform powered by artificial intelligence that predicts the function of PROTAC. In addition, it sped up the processes of identifying and improving previously unknown medications. The AI-DPAPT platform can be accessed online using a web server at https://ai-protac.streamlit.app/ .

DMTSF-mediated electrophilic cyclization for the synthesis of 3-thiomethyl-substituted benzo[b]furan derivatives

Benzofuran is an important backbone for molecules that make up several pharmaceuticals, herbicides/pesticides, and organo-electronics. An environmentally benign dimethyl(methylthio)sulfonium tetrafluoroborate salt was used as an electrophile to induce cyclization of o-alkynyl anisoles to form 2,3-disubstituted benzofurans. The cyclization is performed at ambient reaction conditions, only takes 12 hours to get excellent yields, and shows a high tolerance for various substituted alkynes. Also, a sulfurmethyl group obtained after the cyclization reactions allows for a cascade cyclization, and an alkyne is used in the reaction to create a thieno[3,2-b]benzofuran core structure.

Proteomic and network pharmacology analyses reveal S100A8 as the anti-inflammatory target of Yunpi Jiedu Tongluo Qushi Granule in the treatment of rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by synovial inflammation. RA has a global prevalence between 0.5 % and 1 % although its pathogenesis is not completely understood. Chinese herbal medicine such as Yunpi Jiedu Tongluo Qushi Granule (YJTQG) is one of the treatments for RA. However, the underlying mechanism of action is unclear. Here, analysis of clinical samples reveals that YJTQG can reduce the inflammatory factors and alleviate the symptoms of RA patients. Quantitative proteomic analysis of serum proteomes of RA patients identifies the potential therapeutic targets of YJTQG. We use biochemical experiments to validate several differentially expressed proteins, discover S100A8 as a possible therapeutic target of YJTQG, and analyze the correlation between S100A8 and several known RA biomarkers. Network pharmacology analysis discloses COX1/2 and NOS2 as potential targets of key compounds in YJTQG and protein-protein interaction network analysis reveals TNFα, IL-6, and STAT3 as possible core targets of YJTQG. Bioinformatic and patient sample analyses indicate that YJTQG may reduce S100A8 expression by suppressing its transcription. Mechanistically, we find that kaempferol and quercetin in YJTQG may reduce the expression of S100A8 by inhibiting the phosphorylation, nuclear translocation, and transcriptional activity of p65 in the lipopolysaccharide-stimulated RAW264.7 cells. Therefore, our work demonstrates that S100A8 is a potential therapeutic target of YJTQG for RA, which may provide a new direction for developing new treatments for RA patients.

Chemical coverage of human biological pathways

Chemical probes and chemogenomic compounds are valuable tools to link gene to phenotype, explore human biology, and uncover novel targets for precision medicine. The mission of the Target 2035 initiative is to discover chemical tools for all human proteins by the year 2035. Here, we draw a landscape of the current chemical coverage of human biological pathways. Although available chemical tools target only 3% of the human proteome, they already cover 53% of human biological pathways and represent a versatile toolkit to dissect a vast portion of human biology. Pathways targeted by existing drugs may be enriched in unknown but valid drug targets and could be prioritized in future Target 2035 efforts.

Molecular analysis of dUTPase of Helicobacter pylori for identification of novel inhibitors using in silico studies

The human gastric pathogen Helicobacter pylori chronically affects the gastric mucosal layer of approximately half of world's population. The emergence of resistant strains urges the need for identification of novel and selective drug against new molecular targets. A ubiquitous enzyme, Deoxyuridine 5'-triphosphate nucleotidohydrolase (dUTPase), is considered as first line of defense against uracil mis-incorporation into DNA, and essential for genome integrity. Lack of dUTPase triggers an elevated recombination frequency, DNA breaks and ultimately cell death. Hence, dUTPase can be considered as a promising target for development of novel lead inhibitor compounds in H. pylori treatment. Herein, we report the generation of three-dimensional model of the target protein using comparative modelling and its validation. To identify dUTPase inhibitors, a high throughput virtual screening approach utilizing Knowledge-based inhibitors and DrugBank database was implemented. Top ranked compounds were scrutinized based on investigations of the protein-ligand interaction fingerprints, molecular interaction maps and binding affinities and the drug potentiality. The best ligands were studied further for complex stability and intermolecular interaction profiling with respect to time under 100 ns classical molecular dynamic stimulation, establishing significant stability in dynamic states as observed from RMSD and RMSF parameters and interactions with the catalytic site residues. The binding free energy calculation computed using MM-GBSA method from the MD simulation trajectories demonstrated that our molecules possess strong binding affinity towards the Helicobacter pylori dUTPase protein. We conclude that our proposed molecules may be potential lead molecules for effective inhibition against the H. pylori dUTPase protein subject to experimental validation.Communicated by Ramaswamy H. Sarma.

MMCL-CPI: A multi-modal compound-protein interaction prediction model incorporating contrastive learning pre-training

MOTIVATION

Compound-protein interaction (CPI) prediction plays a crucial role in drug discovery and drug repositioning. Early researchers relied on time-consuming and labor-intensive wet laboratory experiments. However, the advent of deep learning has significantly accelerated this progress. Most existing deep learning methods utilize deep neural networks to extract compound features from sequences and graphs, either separately or in combination. Our team's previous research has demonstrated that compound images contain valuable information that can be leveraged for CPI task. However, there is a scarcity of multimodal methods that effectively combine sequence and image representations of compounds in CPI. Currently, the use of text-image pairs for contrastive language-image pre-training is a popular approach in the multimodal field. Further research is needed to explore how the integration of sequence and image representations can enhance the accuracy of CPI task.

RESULTS

This paper presents a novel method called MMCL-CPI, which encompasses two key highlights: 1) Firstly, we propose extracting compound features from two modalities: one-dimensional SMILES and two-dimensional images. This approach enables us to capture both sequence and spatial features, enhancing the prediction accuracy for CPI. Based on this, we design a novel multimodal model. 2) Secondly, we introduce a multimodal pre-training strategy that leverages comparative learning on a large-scale unlabeled dataset to establish the correspondence between SMILES string and compound's image. This pre-training approach significantly improves compound feature representations for downstream CPI task. Our method has shown competitive results on multiple datasets.

Revitalizing antimicrobial strategies: paromomycin and dicoumarol repurposed as potent inhibitors of M.tb's replication machinery via targeting the vital protein DnaN

Despite the WHO's recommended treatment regimen, challenges such as patient non-adherence and the emergence of drug-resistant strains persist with TB claiming 1.5 million lives annually. In this study, we propose a novel approach by targeting the DNA replication-machinery of M.tb through drug-repurposing. The β2-Sliding clamp (DnaN), a key component of this complex, emerges as a potentially vulnerable target due to its distinct structure and lack of human homology. Leveraging TBVS, we screened ∼2600 FDA-approved drugs, identifying five potential DnaN inhibitors, by employing computational studies, including molecular-docking and molecular-dynamics simulations. The shortlisted compounds were subjected to in-vitro and ex-vivo studies, evaluating their anti-mycobacterial potential. Notably, Dicoumarol, Paromomycin, and Posaconazole exhibited anti-TB properties with a MIC value of 6.25, 3.12 and 50 μg/ml respectively, with Dicoumarol and Paromomycin, demonstrating efficacy in reducing live M.tb within macrophages. Biophysical analyses confirmed the strong binding-affinity of DnaNdrug complexes, validating our in-silico predictions. Moreover, RNA-Seq data revealed the upregulation of proteins associated with DNA repair and replication mechanisms upon Paromomycin treatment. This study explores repurposing FDA-approved drugs to target TB via the mycobacterial DNA replication-machinery, showing promising inhibitory effects. It sets the stage for further clinical research, demonstrating the potential of drug repurposing in TB treatment.

Recursive dynamics of GspE through machine learning enabled identification of inhibitors

Type II secretion System has been increasingly recognized as a key driver of virulence in many pathogenic bacteria including Achromobacter xylosoxidans. ATPase GspE is the powerhouse of the T2SS. It powers the entire secretion process by binding with ATP and hydrolyzing it. Therefore, targeting it was thought to have a profound effect on the normal functioning of the whole T2SS. A. xylosoxidans is a Gram-negative bacterium that poses a rising concern to immunocompromised people. It is responsible for many opportunistic infections mostly in people with cystic fibrosis. Due to its intrinsic and acquired resistance mechanisms, it is challenging to treat. In this current study, an extensive machine learning-enabled computational investigation was carried out. Drug libraries were screened using machine learning random forest algorithm trained on non-redundant dataset of 8722 antibacterial compounds with reported IC50 values. Active compounds were then further subjected to molecular docking. To unravel the dynamics and better understand the stability of complexes, the top complexes were subjected to MD Simulations followed by various post-simulation analyses including Trajectory analysis, Atom Contacts, SASA, Hydrogen Bond, RDF, binding free energy calculations, PCA, and AFD analysis. Findings from the study unanimously unveiled Asinex-BAS00263070-28551 as the best inhibitor as it instigated the recursive dynamics of the target by making key hydrogen bond interactions with Walker A motif, suggesting it could serve as the promising drug candidate against GspE. Further experimental in-vivo and in-vitro validation is still required to authenticate the therapeutic effects of these drugs.

Computational drug repurposing for viral infectious diseases: a case study on monkeypox

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

In Silico Modeling of Fabry Disease Pathophysiology for the Identification of Early Cellular Damage Biomarker Candidates

Fabry disease (FD) is an X-linked lysosomal disease whose ultimate consequences are the accumulation of sphingolipids and subsequent inflammatory events, mainly at the endothelial level. The outcomes include different nervous system manifestations as well as multiple organ damage. Despite the availability of known biomarkers, early detection of FD remains a medical need. This study aimed to develop an in silico model based on machine learning to identify candidate vascular and nervous system proteins for early FD damage detection at the cellular level. A combined systems biology and machine learning approach was carried out considering molecular characteristics of FD to create a computational model of vascular and nervous system disease. A data science strategy was applied to identify risk classifiers by using 10 K-fold cross-validation. Further biological and clinical criteria were used to prioritize the most promising candidates, resulting in the identification of 36 biomarker candidates with classifier abilities, which are easily measurable in body fluids. Among them, we propose four candidates, CAMK2A, ILK, LMNA, and KHSRP, which have high classification capabilities according to our models (cross-validated accuracy ≥ 90%) and are related to the vascular and nervous systems. These biomarkers show promise as high-risk cellular and tissue damage indicators that are potentially applicable in clinical settings, although in vivo validation is still needed.

A comprehensive survey of drug-target interaction analysis in allopathy and siddha medicine

Effective drug delivery is the cornerstone of modern healthcare, ensuring therapeutic compounds reach their intended targets efficiently. This paper explores the potential of personalized and holistic healthcare, driven by the synergy between traditional and allopathic medicine systems, with a specific focus on the vast reservoir of medicinal compounds found in plants rooted in the historical legacy of traditional medicine. Motivated by the desire to unlock the therapeutic potential of medicinal plants and bridge the gap between traditional and allopathic medicine, this survey delves into in-silico computational approaches for studying Drug-Target Interactions (DTI) within the contexts of allopathy and siddha medicine. The contributions of this survey are multifaceted: it offers a comprehensive overview of in-silico methods for DTI analysis in both systems, identifies common challenges in DTI studies, provides insights into future directions to advance DTI analysis, and includes a comparative analysis of DTI in allopathy and siddha medicine. The findings of this survey highlight the pivotal role of in-silico computational approaches in advancing drug research and development in both allopathy and siddha medicine, emphasizing the importance of integrating these methods to drive the future of personalized healthcare.

Combination therapy synergism prediction for virus treatment using machine learning models

Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models showed the highest performance, and the predicted values were validated by a t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature.

Identification of Zika virus NS2B-NS3 protease and NS5 polymerase inhibitors by structure-based virtual screening of FDA-approved drugs

Zika virus (ZIKV) is a mosquito-borne human flavivirus responsible that causing emergency outbreaks in Brazil. ZIKV is suspected of causing Guillain-Barre syndrome in adults and microcephaly. The NS2B-NS3 protease and NS5 RNA-dependent RNA polymerase (RdRp), central to ZIKV multiplication, have been identified as attractive molecular targets for drugs. We performed a structure-based virtual screening of 2,659 FDA-approved small molecule drugs in the DrugBank database using AutoDock Vina in PyRx v0.8. Accordingly, 15 potential drugs were selected as ZIKV inhibitors because of their high values (binding affinity - binding energy) and we analyzed the molecular interactions between the active site amino acids and the compounds. Among these drugs, tamsulosin was found to interact most efficiently with NS2B/NS3 protease, as indicated by the lowest binding energy value (-8.27 kJ/mol), the highest binding affinity (-5.7 Kcal/mol), and formed H-bonds with amino acid residues TYRB130, SERB135, TYRB150. Furthermore, biotin was found to interact most efficiently with NS5 RdRp with a binding energy of -150.624 kJ/mol, a binding affinity of -5.6 Kcal/mol, and formed H-bonds with the amino acid residues ASPA665 and ASPA540. In vitro, in vivo, and clinical studies are needed to demonstrate anti-ZIKV safety and the efficacy of these FDA-approved drug candidates.Communicated by Ramaswamy H. Sarma.

MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism

Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they rarely consider multimodal features of drugs. Moreover, learning the interaction representations between drugs and targets needs further exploration. Here, we proposed a simple M ulti-modal G ating N etwork for DTI prediction, MGNDTI, based on multimodal representation learning and the gating mechanism. MGNDTI first learns the sequence representations of drugs and targets using different retentive networks. Next, it extracts molecular graph features of drugs through a graph convolutional network. Subsequently, it devises a multimodal gating network to obtain the joint representations of drugs and targets. Finally, it builds a fully connected network for computing the interaction probability. MGNDTI was benchmarked against seven state-of-the-art DTI prediction models (CPI-GNN, TransformerCPI, MolTrans, BACPI, CPGL, GIFDTI, and FOTF-CPI) using four data sets (i.e., Human, C. elegans, BioSNAP, and BindingDB) under four different experimental settings. Through evaluation with AUROC, AUPRC, accuracy, F1 score, and MCC, MGNDTI significantly outperformed the above seven methods. MGNDTI is a powerful tool for DTI prediction, showcasing its superior robustness and generalization ability on diverse data sets and different experimental settings. It is freely available at https://github.com/plhhnu/MGNDTI.

Semisupervised Learning to Boost hERG, Nav1.5, and Cav1.2 Cardiac Ion Channel Toxicity Prediction by Mining a Large Unlabeled Small Molecule Data Set

Predicting drug toxicity is a critical aspect of ensuring patient safety during the drug design process. Although conventional machine learning techniques have shown some success in this field, the scarcity of annotated toxicity data poses a significant challenge in enhancing models' performance. In this study, we explore the potential of leveraging large unlabeled small molecule data sets using semisupervised learning to improve drug cardiotoxicity predictive performance across three cardiac ion channel targets: the voltage-gated potassium channel (hERG), the voltage-gated sodium channel (Nav1.5), and the voltage-gated calcium channel (Cav1.2). We extensively mined the ChEMBL database, comprising approximately 2 million small molecules, and then employed semisupervised learning to construct robust classification models for this purpose. We achieved a performance boost on highly diverse (i.e., structurally dissimilar) test data sets across all three targets. Using our built models, we screened the whole ChEMBL database and a large set of FDA-approved drugs, identifying several compounds with potential cardiac ion channel activity. To ensure broad accessibility and usability for both technical and nontechnical users, we developed a cross-platform graphical user interface that allows users to make predictions and gain insights into the cardiotoxicity of drugs and other small molecules. The software is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred2.

GLP-1 mimetics and diabetic ketoacidosis: possible interactions and clinical consequences

Diabetic ketoacidosis is a serious diabetes-related consequence that occurs in type 1 diabetes and less commonly in type 2 diabetes and is a major cause of death. It results from the metabolic consequences due to a lack of insulin secretion or impaired insulin activity in diabetes leading to dysregulated pathophysiologic pathways resulting in excessive ketone body formation. While ketone bodies are physiologic molecules, their high levels reduce the physiological pH of the blood and induce ketoacidosis, leading to increasing metabolic dysfunction. Glucagon-like peptide-1 (GLP-1) mimetics are a class of recently developed diabetes therapy that do not lead to hypoglycemic, but some reports have suggested a relationship between GLP-1 mimetics and ketogenesis. To clarify the possible interactions between GLP-1 mimetics and ketogenesis in diabetes, this review was undertaken to collate and interpret the literature.

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses

The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug design. To contribute to this field, we developed an accurate predictive classifier for druggable cancer-driving proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. The optimal classifier was achieved with the support vector machine method, utilizing 200 tri-amino acid composition descriptors. The high performance of the model is evident from an area under the receiver operating characteristics (AUROC) of 0.975 ± 0.003 and an accuracy of 0.929 ± 0.006 (threefold cross-validation). The machine learning prediction model was enhanced with multi-omics approaches, including the target-disease evidence score, the shortest pathways to cancer hallmarks, structure-based ligandability assessment, unfavorable prognostic protein analysis, and the oncogenic variome. Additionally, we performed a drug repurposing analysis to identify drugs with the highest affinity capable of targeting the best predicted proteins. As a result, we identified 79 key druggable cancer-driving proteins with the highest ligandability, and 23 of them demonstrated unfavorable prognostic significance across 16 TCGA PanCancer types: CDKN2A, BCL10, ACVR1, CASP8, JAG1, TSC1, NBN, PREX2, PPP2R1A, DNM2, VAV1, ASXL1, TPR, HRAS, BUB1B, ATG7, MARK3, SETD2, CCNE1, MUTYH, CDKN2C, RB1, and SMARCA4. Moreover, we prioritized 11 clinically relevant drugs targeting these proteins. This strategy effectively predicts and prioritizes biomarkers, therapeutic targets, and drugs for in-depth studies in clinical trials. Scripts are available at https://github.com/muntisa/machine-learning-for-druggable-proteins .

Mendelian Randomization and Bayesian Colocalization Analysis Implicate Glycoprotein VI as a Potential Drug Target for Cardioembolic Stroke in South Asian Populations

BACKGROUND

Circulating plasma proteins are clinically useful biomarkers for stroke risk. We examined the causal links between plasma proteins and stroke risk in individuals of South Asian ancestry.

METHODS AND RESULTS

We applied proteome-wide Mendelian randomization and colocalization approaches to understand causality of 2922 plasma proteins on stroke risk in individuals of South Asian ancestry. We obtained genetic instruments (proxies) for plasma proteins from the UK Biobank (N=920). Genome-wide association studies summary data for strokes (N≤11 312) were sourced from GIGASTROKE consortium. Our primary approach involved the Wald ratio or inverse-variance-weighted methods, with statistical significance set at false discovery rate <0.1. Additionally, a Bayesian colocalization approach assessed shared causal variants among proteome, transcriptome, and stroke phenotypes to minimize bias from linkage disequilibrium. We found evidence of a potential causal effect of plasma GP6 (glycoprotein VI) levels on cardioembolic stroke (odds ratio [OR]Wald ratio=2.53 [95% CI, 1.59-4.03]; P=9.2×10-5, false discovery rate=0.059). Generalized Mendelian randomization accounting for correlated single nucleotide polymorphisms (SNPs), with the P value threshold at P<5×10-8 and clumped at r2=0.3, showed consistent direction of effect of GP6 on cardioembolic stroke (ORgeneralized inverse-variance-weighted=2.21 [95% CI, 1.46-3.33]; P=1.6×10-4). Colocalization analysis indicated that plasma GP6 levels colocalize with cardioembolic stroke (posterior probability=91.4%). Multitrait colocalization combining transcriptome, proteome, and cardioembolic stroke showed moderate to strong evidence that these 2 traits colocalize with GP6 expression in the coronary artery and brain tissues (multitrait posterior probability>50%). The potential causal effect of GP6 on cardioembolic stroke was not significant in European populations (ORinverse-variance-weighted=1.08 [95% CI, 0.93-1.26]; P=0.29).

CONCLUSIONS

Our joint Mendelian randomization and colocalization analyses suggest that genetically predicted GP6 is potentially causally associated with cardioembolic stroke risk in individuals of South Asian ancestry. As genetic data on individuals of South Asian ancestry increase, future Mendelian randomization studies with larger sample size for plasma GP6 levels should be implemented to further validate our findings. Additionally, clinical studies will be necessary to verify GP6 as a therapeutic target for cardioembolic stroke in South Asians.

Formononetin inhibits IgE by huPlasma/PBMCs and mast cells/basophil activation via JAK/STAT/PI3-Akt pathways

Rationale

Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation.

Methods

In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin's effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines.

Results

Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R.

Conclusion

These findings comprehensively present formononetin's mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.

Computational analysis of pathogen-host interactome for fast and low-risk in-silico drug repurposing in emerging viral threats like Mpox

Monkeypox (Mpox), a zoonotic illness triggered by the monkeypox virus (MPXV), poses a significant threat since it may be transmitted and has no cure. This work introduces a computational method to predict Protein-Protein Interactions (PPIs) during MPXV infection. The objective is to discover prospective drug targets and repurpose current potential Food and Drug Administration (FDA) drugs for therapeutic purposes. In this work, ensemble features, comprising 2-5 node graphlet attributes and protein composition-based features are utilized for Deep Learning (DL) models to predict PPIs. The technique that is used here demonstrated an excellent prediction performance for PPI on both the Human Integrated Protein-Protein Interaction Reference (HIPPIE) and MPXV-Human PPI datasets. In addition, the human protein targets for MPXV have been identified accurately along with the detection of possible therapeutic targets. Furthermore, the validation process included conducting docking research studies on potential FDA drugs like Nicotinamide Adenine Dinucleotide and Hydrogen (NADH), Fostamatinib, Glutamic acid, Cannabidiol, Copper, and Zinc in DrugBank identified via research on drug repurposing and the Drug Consensus Score (DCS) for MPXV. This has been achieved by employing the primary crystal structures of MPXV, which are now accessible. The docking study is also supported by Molecular Dynamics (MD) simulation. The results of our study emphasize the effectiveness of using ensemble feature-based PPI prediction to understand the molecular processes involved in viral infection and to aid in the development of repurposed drugs for emerging infectious diseases such as, but not limited to, Mpox. The source code and link to data used in this work is available at: https://github.com/CMATERJU-BIOINFO/In-Silico-Drug-Repurposing-Methodology-To-Suggest-Therapies-For-Emerging-Threats-like-Mpox .

Deciphering tumour microenvironment and elucidating the origin of cancer cells in ovarian clear cell carcinoma

Ovarian clear cell carcinoma (CCC) has an East Asian preponderance. It is associated with endometriosis, a benign condition where endometrial (inner lining of the uterus) tissue is found outside the uterus and on the peritoneal surface, in the abdominal or pelvic space. CCC is relatively more resistant to conventional chemotherapy compared to other ovarian cancer subtypes and is associated with a poorer prognosis. In this study, we recruited and obtained tumour tissues from seven patients across the four stages of CCC. The tumour and the tumour microenvironment (TME) from 7 CCC patients spanning clinical stages 1-4 were transcriptionally profiled using high-resolution scRNA-seq to gain insight into CCC's biological mechanisms. Firstly, we built a scRNA-seq resource for the CCC tumour microenvironment (TME). Secondly, we identified the different cell type proportions and found high levels of immune infiltration in CCC. Thirdly, since CCC is associated with endometriosis, we compared CCC with two publicly available endometriosis scRNA-seq datasets. The CCC malignant cells showed similarities with glandular secretory and ciliated epithelial cells found in endometriosis. Finally, we determined the differences in cell-cell communication between various cell types present in CCC TME and endometriosis conditions to gain insights into the transformations in CCC.

An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology

Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.

Investigation of F508del CFTR unfolding and a search for stabilizing small molecules

Mutation of phenylalanine at position 508 in the cystic fibrosis transmembrane conductance regulator (F508del CFTR) yields a protein unstable at physiological temperatures that is rapidly degraded in the cell. This mutation is present in about 90% of cystic fibrosis patients, hence there is great interest in compounds reversing its instability. We have previously reported the expression of the mutated protein at low temperature and its purification in detergent. Here we describe the use of the protein to screen compounds present in a library of Federal Drug Administration (FDA) - approved drugs and also in a small natural product library. The kinetics of unfolding of F508del CFTR at 37 °C were probed by the increase in solvent-exposed cysteine residues accessible to a fluorescent reporter molecule. This occurred in a bi-exponential manner with a major (≈60%) component of half-life around 5 min and a minor component of around 60 min. The faster kinetics match those observed for loss of channel activity of F508del CFTR in cells at 37 °C. Most compounds tested had no effect on the fluorescence increase, but some were identified that significantly slowed the kinetics. The general properties of these compounds, and any likely mechanisms for inducing stability in purified CFTR are discussed. These experimental data may be useful for artificial intelligence - aided design of CFTR-specific drugs and in the identification of stabilizing additives for membrane proteins (in general).

Repurposing DrugBank compounds as potential Plasmodium falciparum class 1a aminoacyl tRNA synthetase multi-stage pan-inhibitors with a specific focus on mitomycin

Plasmodium falciparum aminoacyl tRNA synthetases (PfaaRSs) are potent antimalarial targets essential for proteome fidelity and overall parasite survival in every stage of the parasite's life cycle. So far, some of these proteins have been singly targeted yielding inhibitor compounds that have been limited by incidences of resistance which can be overcome via pan-inhibition strategies. Hence, herein, for the first time, we report the identification and in vitro antiplasmodial validation of Mitomycin (MMC) as a probable pan-inhibitor of class 1a (arginyl(A)-, cysteinyl(C), isoleucyl(I)-, leucyl(L), methionyl(M), and valyl(V)-) PfaaRSs which hypothetically may underlie its previously reported activity on the ribosomal RNA to inhibit protein translation and biosynthesis. We combined multiple in silico structure-based discovery strategies that first helped identify functional and druggable sites that were preferentially targeted by the compound in each of the plasmodial proteins: Ins1-Ins2 domain in Pf-ARS; anticodon binding domain in Pf-CRS; CP1-editing domain in Pf-IRS and Pf-MRS; C-terminal domain in Pf-LRS; and CP-core region in Pf-VRS. Molecular dynamics studies further revealed that MMC allosterically induced changes in the global structures of each protein. Likewise, prominent structural perturbations were caused by the compound across the functional domains of the proteins. More so, MMC induced systematic alterations in the binding of the catalytic nucleotide and amino acid substrates which culminated in the loss of key interactions with key active site residues and ultimate reduction in the nucleotide-binding affinities across all proteins, as deduced from the binding energy calculations. These altogether confirmed that MMC uniformly disrupted the structure of the target proteins and essential substrates. Further, MMC demonstrated IC50 < 5 μM against the Dd2 and 3D7 strains of parasite making it a good starting point for malarial drug development. We believe that findings from our study will be important in the current search for highly effective multi-stage antimalarial drugs.

Exploring the molecular landscape of osteosarcoma through PTTG family genes using a detailed multi-level methodology

Background

Osteosarcoma (OS) poses a significant clinical challenge, necessitating a comprehensive exploration of its molecular underpinnings.

Methods

This study explored the roles of PTTG family genes (PTTG1, PTTG2, and PTTG3P) in OS, employing a multifaceted approach encompassing molecular experiments, including OS cell lines culturing, RT-qPCR, bisulfite and Whole Exome Sequencing (WES) and in silico experiments, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets-based validation, overall survival, gene enrichment, functional assays, and molecular docking analyses.

Results

Our findings reveal a consistent up-regulation of PTTG genes in OS cell lines, supported by RT-qPCR experiments and corroborated across various publically available expression datasets databases. Importantly, ROC curve analyses highlight their potential as diagnostic markers. Moving beyond expression profiles, we unveil the epigenetic landscape by demonstrating significant hypomethylation of CpG islands associated with PTTG genes in OS. The negative correlation between methylation status and mRNA expression emphasizes the regulatory role of promoter methylation in PTTG gene expression. Contrary to expectations, genetic mutations in PTTG genes are rare in OS, with only benign mutations observed. Moreover, functional assays also confirmed the oncogenic roles of the PTTG gene in the development of OS. Lastly, we also revealed that Calcitriol is the most appropriate drug that can be utilized to treat OS in the context of PTTG genes.

Conclusion

The identification of PTTG genes as potential diagnostic markers and their association with epigenetic alterations opens new avenues for understanding OS pathogenesis and developing targeted therapies. As we navigate the complex landscape of OS, this study contributes essential insights that may pave the way for improved diagnostic and therapeutic strategies in its management.

Explainable drug repurposing via path based knowledge graph completion

Drug repurposing aims to find new therapeutic applications for existing drugs in the pharmaceutical market, leading to significant savings in time and cost. The use of artificial intelligence and knowledge graphs to propose repurposing candidates facilitates the process, as large amounts of data can be processed. However, it is important to pay attention to the explainability needed to validate the predictions. We propose a general architecture to understand several explainable methods for graph completion based on knowledge graphs and design our own architecture for drug repurposing. We present XG4Repo (eXplainable Graphs for Repurposing), a framework that takes advantage of the connectivity of any biomedical knowledge graph to link compounds to the diseases they can treat. Our method allows methapaths of different types and lengths, which are automatically generated and optimised based on data. XG4Repo focuses on providing meaningful explanations to the predictions, which are based on paths from compounds to diseases. These paths include nodes such as genes, pathways, side effects, or anatomies, so they provide information about the targets and other characteristics of the biomedical mechanism that link compounds and diseases. Paths make predictions interpretable for experts who can validate them and use them in further research on drug repurposing. We also describe three use cases where we analyse new uses for Epirubicin, Paclitaxel, and Predinisone and present the paths that support the predictions.

Drug-Target Interaction Prediction Based on an Interactive Inference Network

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

Alzheimer's Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

Background

Alzheimer's disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers.

Results

We annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer's Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves.

Conclusion

The ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD's disease research and treatment strategies.

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

An insight into MDR Acinetobacter baumannii infection and its pathogenesis: Potential therapeutic targets and challenges

Acinetobacter baumannii is observed as a common species of Gram-negative bacteria that exist in soil and water. Despite being accepted as a typical component of human skin flora, it has become an important opportunistic pathogen, especially in healthcare settings. The pathogenicity of A. baumannii is attributed to its virulence factors, which include adhesins, pili, lipopolysaccharides, outer membrane proteins, iron uptake systems, autotransporter, secretion systems, phospholipases etc. These elements provide the bacterium the ability to cling to and penetrate host cells, get past the host immune system, and destroy tissue. Its infection is a major contributor to human pathophysiological conditions including pneumonia, bloodstream infections, urinary tract infections, and surgical site infections. It is challenging to treat infections brought on by this pathogen since this bacterium has evolved to withstand numerous drugs and further emergence of drug-resistant A. baumannii results in higher rates of morbidity and mortality. The long-term survival of this bacterium on surfaces of medical supplies and hospital furniture facilitates its frequent spread in humans from one habitat to another. There is a need for urgent investigations to find effective drug targets for A. baumannii as well as designing novel drugs to reduce the survival and spread of infection. In the current review, we represent the specific features, pathogenesis, and molecular intricacies of crucial drug targets of A. baumannii. This would also assist in proposing strategies and alternative therapies for the prevention and treatment of A. baumannii infections and their spread.

Quality assessment and Q-markers discovery in Citri Sarcodactylis Fructus by integrating serum pharmacochemistry and network pharmacology

INTRODUCTION

Citri Sarcodactylis Fructus (CSF), a common fruit and traditional Chinese medicine (TCM), has been hindered in its further development and research owing to the lack of comprehensive and specific quality evaluation standards.

OBJECTIVE

This study aimed to establish clear TCM quality standards related to the therapeutic mechanisms of CSF and to provide a basis for subsequent research and development.

METHODS

Ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap high-resolution mass spectrometry (UPLC-Q-orbitrap HRMS) technology was used to comprehensively identify CSF components and explore their absorbance levels in rat serum. Network pharmacology research methods were employed to investigate the potential mechanisms of action of the identified components in the treatment of major clinical diseases. Subsequently, a combination of HPLC chromatographic fingerprinting for qualitative analysis and multi-index content determination was used to evaluate the detectability of the identified quality markers (Q-markers).

RESULTS

Twenty-six prototype components were tentatively characterized in rat serum. Network pharmacology analysis showed six effective components, namely 7-hydroxycoumarin, isoscopoletin, diosmin, hesperidin, 5,7-dimethoxycoumarin, and bergapten, which played important roles in the treatment of chronic gastritis, functional dyspepsia, peptic ulcer, and depression and were preliminarily identified as Q-markers. The results of content determination in 15 batches of CSF indicated significant differences in the content of medicinal materials from different origins. However, compared with the preliminarily determined Q-markers, all six components could be measured and were determined as Q-markers of CSF.

CONCLUSION

The chemical Q-markers obtained in this study could be used for effective quality control of CSF.

A novel bioinformatics strategy to uncover the active ingredients and molecular mechanisms of Bai Shao in the treatment of non-alcoholic fatty liver disease

Introduction: As a new discipline, network pharmacology has been widely used to disclose the material basis and mechanism of Traditional Chinese Medicine in recent years. However, numerous researches indicated that the material basis of TCMs identified based on network pharmacology was the mixtures of beneficial and harmful substances rather than the real material basis. In this work, taking the anti-NAFLD (non-alcoholic fatty liver disease) effect of Bai Shao (BS) as a case, we attempted to propose a novel bioinformatics strategy to uncover the material basis and mechanism of TCMs in a precise manner. Methods: In our previous studies, we have done a lot work to explore TCM-induced hepatoprotection. Here, by integrating our previous studies, we developed a novel computational pharmacology method to identify hepatoprotective ingredients from TCMs. Then the developed method was used to discover the material basis and mechanism of Bai Shao against Non-alcoholic fatty liver disease by combining with the techniques of molecular network, microarray data analysis, molecular docking, and molecular dynamics simulation. Finally, literature verification method was utilized to validate the findings. Results: A total of 12 ingredients were found to be associated with the anti-NAFLD effect of BS, including monoterpene glucosides, flavonoids, triterpenes, and phenolic acids. Further analysis found that IL1-β, IL6, and JUN would be the key targets. Interestingly, molecular docking and molecular dynamics simulation analysis showed that there indeed existed strong and stable binding affinity between the active ingredients and the key targets. In addition, a total of 23 NAFLD-related KEGG pathways were enriched. The major biological processes involved by these pathways including inflammation, apoptosis, lipid metabolism, and glucose metabolism. Of note, there was a great deal of evidence available in the literature to support the findings mentioned above, indicating that our method was reliable. Discussion: In summary, the contributions of this work can be summarized as two aspects as follows. Firstly, we systematically elucidated the material basis and mechanism of BS against NAFLD from multiple perspectives. These findings further enhanced the theoretical foundation of BS on NAFLD. Secondly, a novel computational pharmacology research strategy was proposed, which would assist network pharmacology to uncover the scientific connotation TCMs in a more precise manner.

Identification and validation of key genes in gastric cancer: insights from in silico analysis, clinical samples, and functional assays

INTRODUCTION

The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.

METHODS

Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and in vitro assays for cellular behaviors.

RESULTS

A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in in vivo and exhibited mutations in GC patients.

CONCLUSION

We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.

Genetically proxied low-density lipoprotein cholesterol lowering via PCSK9-inhibitor drug targets and risk of congenital malformations

AIMS

Current guidelines advise against the use of lipid-lowering drugs during pregnancy. This is based only on previous observational evidence demonstrating an association between statin use and congenital malformations, which is increasingly controversial. In the absence of clinical trial data, we aimed to use drug-target Mendelian randomization to model the potential impact of fetal LDL-lowering, overall and through PCSK9 drug targets, on congenital malformations.

METHODS AND RESULTS

Instrumental variants influencing LDL levels overall and through PCSK9-inhibitor drug targets were extracted from genome-wide association study (GWAS) summary data for LDL on 1 320 016 individuals. Instrumental variants influencing circulating PCSK9 levels (pQTLs) and liver PCSK9 gene expression levels (eQTLs) were extracted, respectively, from a GWAS on 10 186 individuals and from the genotype-tissue expression project. Gene-outcome association data was extracted from the 7th release of GWAS summary data on the FinnGen cohort (n = 342 499) for eight categories of congenital malformations affecting multiple systems. Genetically proxied LDL-lowering through PCSK9 was associated with higher odds of malformations affecting multiple systems [OR 2.70, 95% confidence interval (CI) 1.30-5.63, P = 0.018], the skin (OR 2.23, 95% CI 1.33-3.75, P = 0.007), and the vertebral, anorectal, cardiovascular, tracheo-esophageal, renal, and limb association (VACTERL) (OR 1.51, 95% CI 1.16-1.96, P = 0.007). An association was also found with obstructive defects of the renal pelvis and ureter, but this association was suggestive of horizontal pleiotropy. Lower PCSK9 pQTLs were associated with the same congenital malformations.

CONCLUSION

These data provide genetic evidence supporting current manufacturer advice to avoid the use of PCSK9 inhibitors during pregnancy.

COVID-19 Drug Treatments Are Prone to Sequestration in Extracorporeal Membrane Oxygenation Circuits: An Ex Vivo Extracorporeal Membrane Oxygenation Study

Drug treatments for coronavirus disease 2019 (COVID-19) dramatically improve patient outcomes, and although extracorporeal membrane oxygenation (ECMO) has significant use in these patients, it is unknown whether ECMO affects drug dosing. We used an ex vivo adult ECMO model to measure ECMO circuit effects on concentrations of specific COVID-19 drug treatments. Three identical ECMO circuits used in adult patients were set up. Circuits were primed with fresh human blood (temperature and pH maintained within normal limits). Three polystyrene jars with 75 ml fresh human blood were used as controls. Remdesivir, GS-441524, nafamostat, and tocilizumab were injected in the circuit and control jars at therapeutic concentrations. Samples were taken from circuit and control jars at predefined time points over 6 h and drug concentrations were measured using validated assays. Relative to baseline, mean (± standard deviation [SD]) study drug recoveries in both controls and circuits at 6 h were significantly lower for remdesivir (32.2% [±2.7] and 12.4% [±2.1], p < 0.001), nafamostat (21.4% [±5.0] and 0.0% [±0.0], p = 0.018). Reduced concentrations of COVID-19 drug treatments in ECMO circuits is a clinical concern. Remdesivir and nafamostat may need dose adjustments. Clinical pharmacokinetic studies are suggested to guide optimized COVID-19 drug treatment dosing during ECMO.

Anti-inflammatory benefits of semaglutide: State of the art

Individuals with diabetes often have chronic inflammation and high levels of inflammatory cytokines, leading to insulin resistance and complications. Anti-inflammatory agents are proposed to prevent these issues, including using antidiabetic medications with anti-inflammatory properties like semaglutide, a GLP-1 analogue. Semaglutide not only lowers glucose but also shows potential anti-inflammatory effects. Studies suggest it can modulate inflammatory responses and benefit those with diabetes. However, the exact mechanisms of its anti-inflammatory effects are not fully understood. This review aims to discuss the latest findings on semaglutide's anti-inflammatory effects and the potential pathways involved.

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein's crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions

Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug-target interaction predictions.

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

In Search for Low-Molecular-Weight Ligands of Human Serum Albumin That Affect Its Affinity for Monomeric Amyloid β Peptide

An imbalance between production and excretion of amyloid β peptide (Aβ) in the brain tissues of Alzheimer's disease (AD) patients leads to Aβ accumulation and the formation of noxious Aβ oligomers/plaques. A promising approach to AD prevention is the reduction of free Aβ levels by directed enhancement of Aβ binding to its natural depot, human serum albumin (HSA). We previously demonstrated the ability of specific low-molecular-weight ligands (LMWLs) in HSA to improve its affinity for Aβ. Here we develop this approach through a bioinformatic search for the clinically approved AD-related LMWLs in HSA, followed by classification of the candidates according to the predicted location of their binding sites on the HSA surface, ranking of the candidates, and selective experimental validation of their impact on HSA affinity for Aβ. The top 100 candidate LMWLs were classified into five clusters. The specific representatives of the different clusters exhibit dramatically different behavior, with 3- to 13-fold changes in equilibrium dissociation constants for the HSA-Aβ40 interaction: prednisone favors HSA-Aβ interaction, mefenamic acid shows the opposite effect, and levothyroxine exhibits bidirectional effects. Overall, the LMWLs in HSA chosen here provide a basis for drug repurposing for AD prevention, and for the search of medications promoting AD progression.

Anti-inflammatory action of new hybrid N-acyl-[1,2]dithiolo-[3,4-c]quinoline-1-thione

Most of pharmaceutical agents display a number of biological activities. It is obvious that testing even one compound for thousands of biological activities is not practically possible. A computer-aided prediction is therefore the method of choice in this case to select the most promising bioassays for particular compounds. Using the PASS Online software, we determined the probable anti-inflammatory action of the 12 new hybrid dithioloquinolinethiones derivatives. Chemical similarity search in the World-Wide Approved Drugs (WWAD) and DrugBank databases did not reveal close structural analogues with the anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in the carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds (2a, 3a-3k except for 3j) varied between 52.97% and 68.74%, being higher than the reference drug indomethacin (47%). The most active compounds appeared to be 3h (68.74%), 3k (66.91%) and 3b (63.74%) followed by 3e (61.50%). Thus, based on the in silico predictions a novel class of anti-inflammatory agents was discovered.

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.

The Efficacy & Molecular Mechanisms of a Terpenoid Compound Ganoderic Acid C1 on Corticosteroid-Resistant Neutrophilic Airway Inflammation: In vivo and in vitro Validation

Introduction

Neutrophil predominant airway inflammation is associated with severe and steroid-resistant asthma clusters. Previously, we reported efficacy of ASHMI, a three-herb TCM asthma formula in a steroid-resistant neutrophil-dominant murine asthma model and further identified Ganoderic Acid C1 (GAC1) as a key ASHMI active compound in vitro. The objective of this study is to investigate GAC1 effect on neutrophil-dominant, steroid-resistant asthma in a murine model.

Methods

In this study, Balb/c mice were systematically sensitized with ragweed (RW) and alum and intranasally challenged with ragweed. Unsensitized/PBS challenged mice served as normal controls. Post sensitization, mice were given 4 weeks of oral treatment with GAC1 or acute dexamethasone (Dex) treatment at 48 hours prior to challenge. Pulmonary cytokines were measured by ELISA, and lung sections were processed for histology by H&E staining. Furthermore, GAC1 effect on MUC5AC expression and on reactive oxygen species (ROS) production in human lung epithelial cell line (NCI-H292) was determined by qRT-PCR and ROS assay kit, respectively. Computational analysis was applied to select potential targets of GAC1 in steroid-resistant neutrophil-dominant asthma. Molecular docking was performed to predict binding modes between GAC1 and Dex with TNF-α.

Results

The result of the study showed that chronic GAC1 treatment, significantly reduced pulmonary inflammation (P < 0.01-0.001 vs Sham) and airway neutrophilia (P < 0.01 vs Sham), inhibited TNF-α, IL-4 and IL-5 levels (P < 0.05-0.001 vs Sham). Acute Dex treatment reduced eosinophilic inflammation and IL-4, IL-5 levels, but had no effect on neutrophilia and TNF-α production. GAC1 treated H292 cells showed decreased MUC5AC gene expression and production of ROS (P < 0.001 vs stimulated/untreated cells). Molecular docking results showed binding energy of complex GAC1-TNF was -10.8 kcal/mol.

Discussion

GAC1 may be a promising anti-asthma botanical drug for treatment of steroid-resistant asthma.