STITCH 4: integration of protein-chemical interactions with user data

A review of machine learning-based methods for predicting drug-target interactions

The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

Fritillaria steroidal alkaloids and their multi-target therapeutic mechanisms: insights from network pharmacology

Medicinal Fritillaria herbs, known for their rich content of steroidal alkaloids, have emerged as promising candidates in the treatment of chronic diseases due to their diverse pharmacological properties. Leveraging advancements in network pharmacology and molecular docking, this study explores the multi-target mechanisms through which these alkaloids exert therapeutic effects. The integration of bioinformatics, systems biology, and pharmacology in drug discovery has provided insights into the molecular interactions and pathways influenced by Fritillaria steroidal alkaloids. This review synthesizes comprehensive literature from 1985 to 2024, revealing the potential of these compounds in addressing respiratory diseases, inflammation, and cancer. The integration of traditional Chinese medicine (TCM) with modern pharmacological techniques underscores the relevance of these compounds in next-generation drug discovery. While initial findings are promising, further empirical validation is necessary to fully harness the therapeutic potential of Fritillaria steroidal alkaloids.

Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency

BACKGROUND

Safe drug treatment requires an understanding of the potential side effects. Identifying the frequency of drug side effects can reduce the risks associated with drug use. However, existing computational methods for predicting drug side effect frequencies heavily depend on known drug side effect frequency information. Consequently, these methods face challenges when predicting the side effect frequencies of new drugs. Although a few methods can predict the side effect frequencies of new drugs, they exhibit unreliable performance owing to the exclusion of drug-side effect relationships.

RESULTS

This study proposed CrossFeat, a model based on convolutional neural network-transformer architecture with cross-feature learning that can predict the occurrence and frequency of drug side effects for new drugs, even in the absence of information regarding drug-side effect relationships. CrossFeat facilitates the concurrent learning of drugs and side effect information within its transformer architecture. This simultaneous exchange of information enables drugs to learn about their associated side effects, while side effects concurrently acquire information about the respective drugs. Such bidirectional learning allows for the comprehensive integration of drug and side effect knowledge. Our five-fold cross-validation experiments demonstrated that CrossFeat outperforms existing studies in predicting side effect frequencies for new drugs without prior knowledge.

CONCLUSIONS

Our model offers a promising approach for predicting the drug side effect frequencies, particularly for new drugs where prior information is limited. CrossFeat's superior performance in cross-validation experiments, along with evidence from case studies and ablation experiments, highlights its effectiveness.

Network medicine and systems pharmacology approaches to predicting adverse drug effects

Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.

Metabolomics and proteomics reveal the inhibitory effect of Lactobacillus crispatus on cervical cancer

Cervical cancer remains a significant global health issue due to its high morbidity and mortality rates. Recently, Lactobacillus crispatus has been recognized for its crucial role in maintaining cervical health. While some studies have explored the use of L. crispatus to mitigate cervical cancer, the underlying mechanisms remain largely unknown. In this study, we employed non-targeted proteomics and metabolomics to investigate how L. crispatus affects the growth of cervical cancer cells (SiHa) and normal cervical cells (Ect1/E6E7). Our findings indicated that the inhibitory effect of L. crispatus on SiHa cells was associated with various biological processes, notably the ferroptosis pathway. Specifically, L. crispatus was found to regulate the expression of proteins such as HMOX1, SLC39A14, VDAC2, ACSL4, and LPCAT3 by SiHa cells, which are closely related to ferroptosis. Additionally, it activated the tricarboxylic acid (TCA) cycle in SiHa cells, leading to increased levels of reactive oxygen species (ROS) and lipid peroxides (LPO). These results revealed the therapeutic potential of L. crispatus in targeting the ferroptosis pathway for cervical cancer treatment, opening new avenues for research and therapy in cervical cancer.

Decoding the Foodome: Molecular Networks Connecting Diet and Health

Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Using protein turnover assay to explore the drug mechanism of Carfilzomib

Carfilzomib (CFZ) is the second-generation proteasome inhibitor that is approved by Food and Drug Administration (FDA) of USA for the treatment of relapsed and refractory multiple myeloma. Although the preclinical and clinical efficacy of CFZ is obvious, the mechanism by which CFZ leads to cell death has not been fully elucidated. Since CFZ primarily functions as a proteasome inhibitor, profiling CFZ-induced changes in protein turnover at the systematic level is sufficient and necessary. In this study, we characterize the effects of CFZ on the stability of 15,000 human proteins using Protein Turnover Assay (ProTA). CFZ affects fundamental cellular glycolysis, nitric oxide production and proteasome subunit homeostasis in multiple myeloma cells. In addition, LY294002 or KU-0063794 has synergistic effects with CFZ in multiple myeloma treatment. A profound understanding of how cells respond to chemotherapeutic agents provides insights into the basic mechanism of drug function and the rationale for CFZ combination therapy.

Network pharmacology: an efficient but underutilized approach in oral, head and neck cancer therapy-a review

The application of network pharmacology (NP) has advanced our understanding of the complex molecular mechanisms underlying diseases, including neck, head, and oral cancers, as well as thyroid carcinoma. This review aimed to explore the therapeutic potential of natural network pharmacology using compounds and traditional Chinese medicines for combating these malignancies. NP serves as a pivotal tool that provides a comprehensive view of the interactions among compounds, genes, and diseases, thereby contributing to the advancement of disease treatment and management. In parallel, this review discusses the significance of publicly accessible databases in the identification of oral, head, and neck cancer-specific genes. These databases, including those for head and neck oral cancer, head and neck cancer, oral cancer, and genomic variants of oral cancer, offer valuable insights into the genes, miRNAs, drugs, and genetic variations associated with these cancers. They serve as indispensable resources for researchers, clinicians, and drug developers, contributing to the pursuit of precision medicine and improved treatment of these challenging malignancies. In summary, advancements in NP could improve the globalization and modernization of traditional medicines and prognostic targets as well as aid in the development of innovative drugs. Furthermore, this review will be an eye-opener for researchers working on drug development from traditional medicines by applying NP approaches.

Hair Thickness Growth Effect of Adenosine Complex in Male-/Female-Patterned Hair Loss via Inhibition of Androgen Receptor Signaling

Aging (senescence) is an unavoidable biological process that results in visible manifestations in all cutaneous tissues, including scalp skin and hair follicles. Previously, we evaluated the molecular function of adenosine in promoting alopecia treatment in vitro. To elucidate the differences in the molecular mechanisms between minoxidil (MNX) and adenosine, gene expression changes in dermal papilla cells were examined. The androgen receptor (AR) pathway was identified as a candidate target of adenosine for hair growth, and the anti-androgenic activity of adenosine was examined in vitro. In addition, ex vivo examination of human hair follicle organ cultures revealed that adenosine potently elongated the anagen stage. According to the severity of alopecia, the ratio of the two peaks (terminal hair area/vellus hair area) decreased continuously. We further investigated the adenosine hair growth promoting effect in vivo to examine the hair thickness growth effects of topical 5% MNX and the adenosine complex (0.75% adenosine, 1% penthenol, and 2% niacinamide; APN) in vivo. After 4 months of administration, both the MNX and APN group showed significant increases in hair density (MNX + 5.01% (p < 0.01), APN + 6.20% (p < 0.001)) and thickness (MNX + 5.14% (p < 0.001), APN + 10.32% (p < 0.001)). The inhibition of AR signaling via adenosine could have contributed to hair thickness growth. We suggest that the anti-androgenic effect of adenosine, along with the evaluation of hair thickness distribution, could help us to understand hair physiology and to investigate new approaches for drug development.

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.

Computational drug repositioning with attention walking

Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.

Responses to lead stress in Scrophularia striata: insights into antioxidative defence mechanisms and changes in flavonoids profile

Lead (Pb) induces oxidative stress in plants, which results in different responses, including the production of antioxidants and changes in the profile of secondary metabolites. In this study, the responses of Scrophularia striata exposed to 250mgL-1 Pb (NO3 )2 in a hydroponic environment were determined. Growth parameters, oxidative and antioxidative responses, redox status, and the concentration of Pb were analysed in roots and shoots. Malondialdehyde and hydrogen peroxide (H2 O2 ) levels in the roots were significantly increased and reached their highest value at 72h after Pb treatment. Superoxide dismutase, catalase, and peroxidase, as an enzymatic antioxidant system, were responsible for reactive oxygen species scavenging, where their activities were increased in the shoot and root of Pb-treated plants. Enzymatic antioxidant activities were probably not enough to remove a significant H2 O2 content in response to Pb treatment. Therefore, other defence responses were activated. The results stated that the flavonoid components of S. striata progressed towards the increase of isoflavone, flavanol, and stilbenoid contents under Pb treatment. In general, S. striata stimulates the enzymatic defence system and activates the non-enzymatic system by modulating the profile of flavonoids toward the production of flavonoids with high antioxidant activity, such as quercetin and myricetin in response to Pb stress.

A network pharmacological approach for the identification of potential therapeutic targets of Brahmi Nei - a complex traditional Siddha formulation

Brahmi Nei (BN), a traditional Indian polyherbal formulation has been described in classical texts for the treatment of anxiety and depression, as well as to fortify the immune system. The individual herbs of BN have been used for treatment of wide range of disorders including cognition, inflammation, skin ailments and cancer etc., This diverse basket of therapeutic activity suggests that BN may possess therapeutic benefits to other disorders. So, the present study aims to identify the potential therapeutic targets of BN using a network pharmacological approach to comprehend the multi target action of its multiple phytoconstituents. We have employed Randić Index for the first time to calculate the contribution score of module segregated targets towards diseases. Our results suggests that BN targets could also be effective in other diseases such as lysosomal storage disorders, respiratory disorders etc., apart from neurological disorders. The key targets with highest topological measures of Targets-(Pathway)-Targets network were identified as potential therapeutic targets of BN. And the top hit target PTGS2, a gene encoding for cyclooxygenase-2 was further evaluated using molecular docking, molecular dynamic simulation and in vitro studies. Our findings open up new therapeutic facets for BN that can be explored systematically in future.Communicated by Ramaswamy H. Sarma.

Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents

The underdevelopment of adjuvant discovery and diversity, compared to core vaccine technology, is evident. On the other hand, antibiotic resistance is on the list of the top ten threats to global health. Immunomodulatory peptides that target a pathogen and modulate the immune system simultaneously are promising for the development of preventive and therapeutic molecules. Since investigating innate immunity in insects has led to prominent achievements in human immunology, such as toll-like receptor (TLR) discovery, we used the capacity of the immunomodulatory peptides of arthropods with concomitant antimicrobial or antitumor activity. An SVM-based machine learning classifier identified short immunomodulatory sequences encrypted in 643 antimicrobial peptides from 55 foe-to-friend arthropods. The critical features involved in efficacy and safety were calculated. Finally, 76 safe immunomodulators were identified. Then, molecular docking and simulation studies defined the target of the most optimal peptide ligands among all human cell-surface TLRs. SPalf2-453 from a crab is a cell-penetrating immunoadjuvant with antiviral properties. The peptide interacts with the TLR1/2 heterodimer. SBsib-711 from a blackfly is a TLR4/MD2 ligand used as a cancer vaccine immunoadjuvant. In addition, SBsib-711 binds CD47 and PD-L1 on tumor cells, which is applicable in cancer immunotherapy as a checkpoint inhibitor. MRh4-679 from a shrimp is a broad-spectrum or universal immunoadjuvant with a putative Th1/Th2-balanced response. We also implemented a pathway enrichment analysis to define fingerprints or immunological signatures for further in vitro and in vivo immunogenicity and reactogenicity measurements. Conclusively, combinatorial machine learning, molecular docking, and simulation studies, as well as systems biology, open a new opportunity for the discovery and development of multifunctional prophylactic and therapeutic lead peptides.

Calycosin ameliorates osteoarthritis by regulating the imbalance between chondrocyte synthesis and catabolism

Osteoarthritis (OA) is a severe chronic inflammatory disease. As the main active component of Astragalus mongholicus Bunge, a classic traditional ethnic herb, calycosin exhibits anti-inflammatory action and its mechanism of exact targets for OA have yet to be determined. In this study, we established an anterior cruciate ligament transection (ACLT) mouse model. Mice were randomized to sham, OA, and calycosin groups. Cartilage synthesis markers type II collagen (Col-2) and SRY-Box Transcription Factor 9 (Sox-9) increased significantly after calycosin gavage. While cartilage matrix degradation index cyclooxygenase-2 (COX-2), phosphor-epidermal growth factor receptor (p-EGFR), and matrix metalloproteinase-9 (MMP9) expression were decreased. With the help of network pharmacology and molecular docking, these results were confirmed in chondrocyte ADTC5 cells. Our results indicated that the calycosin treatment significantly improved cartilage damage, this was probably attributed to reversing the imbalance between chondrocyte synthesis and catabolism.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach

INTRODUCTION

Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief.

METHODS

Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing.

RESULTS

In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity.

CONCLUSION

Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.

Vitamin D analog calcitriol for breast cancer therapy; an integrated drug discovery approach

As breast cancer remains leading cause of cancer death globally, it is essential to develop an affordable breast cancer therapy in underdeveloped countries. Drug repurposing offers potential to address gaps in breast cancer treatment. Molecular networking studies were performed for drug repurposing approach by using heterogeneous data. The PPI networks were built to select the target genes from the EGFR overexpression signaling pathway and its associated family members. The selected genes EGFR, ErbB2, ErbB4 and ErbB3 were allowed to interact with 2637 drugs, leads to PDI network construction of 78, 61, 15 and 19 drugs, respectively. As drugs approved for treating non cancer-related diseases or disorders are clinically safe, effective, and affordable, these drugs were given considerable attention. Calcitriol had shown significant binding affinities with all four receptors than standard neratinib. The RMSD, RMSF, and H-bond analysis of protein-ligand complexes from molecular dynamics simulation (100 ns), confirmed the stable binding of calcitriol with ErbB2 and EGFR receptors. In addition, MMGBSA and MMP BSA also affirmed the docking results. These in-silico results were validated with in-vitro cytotoxicity studies in SK-BR-3 and Vero cells. The IC50 value of calcitriol (43.07 mg/ml) was found to be lower than neratinib (61.50 mg/ml) in SK-BR-3 cells. In Vero cells the IC50 value of calcitriol (431.05 mg/ml) was higher than neratinib (404.95 mg/ml). It demonstrates that calcitriol suggestively downregulated the SK-BR-3 cell viability in a dose-dependent manner. These implications revealed calcitriol has shown better cytotoxicity and decreased the proliferation rate of breast cancer cells than neratinib.Communicated by Ramaswamy H. Sarma.

HDAC inhibition by Nigella sativa L. sprouts extract in hepatocellular carcinoma: an approach to study anti-cancer potential

A wide variety of natural products have been widely used in chemoprevention therapy because they have antioxidant, anti-inflammatory, and anticancer activity. In the present study, we shed light on the 5th day germinated sprouts of N. sativa seeds and evaluated them against HDAC inhibition and antioxidant activity. The extract from the seed and sprout was extracted and characterised by LC-MS/MS, FTIR, and NMR to reveal its chemical composition, especially thymol (THY) and thymoquinone (TQ). Hepatocellular carcinoma (HCC) is a global health concern as it is a major lifestyle disease. Hence, incorporating herbal-based therapeutic compounds into everyday routines has become an attractive alternative for preventing hepatic diseases. Histone deacetylase (HDAC) inhibition (HDACi) is emerging as a promising therapeutic strategy for managing various carcinomas including HCC. Therefore, the 5th day of N. sativa can be used as a potential anticancer agent by inhibiting HDAC activity, as it is reported to have an important role in the management of oxidative stress. The bioactive compound of N. sativa, i.e. thymoquinone, also showed a good binding affinity with the HDAC protein (3MAX) with a stable interaction in an in silico study as compared to the standard drug (Trichostatin A) and thymol.Communicated by Ramaswamy H. Sarma.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

Prioritization of therapeutic targets for dyslipidemia using integrative multi-omics and multi-trait analysis

Drug targets with genetic support are several-fold more likely to succeed in clinical trials. We introduce a genetic-driven approach based on causal inferences that can inform drug target prioritization, repurposing, and adverse effects of using lipid-lowering agents. Given that a multi-trait approach increases the power to detect meaningful variants/genes, we conduct multi-omics and multi-trait analyses, followed by network connectivity investigations, and prioritize 30 potential therapeutic targets for dyslipidemia, including SORT1, PSRC1, CELSR2, PCSK9, HMGCR, APOB, GRN, HFE2, FJX1, C1QTNF1, and SLC5A8. 20% (6/30) of prioritized targets from our hypothesis-free drug target search are either approved or under investigation for dyslipidemia. The prioritized targets are 22-fold higher in likelihood of being approved or under investigation in clinical trials than genome-wide association study (GWAS)-curated targets. Our results demonstrate that the genetic-driven approach used in this study is a promising strategy for prioritizing targets while informing about the potential adverse effects and repurposing opportunities.

Deciphering the Mechanism of Xijiao Dihuang Decoction in Treating Psoriasis by Network Pharmacology and Experimental Validation

Purpose

This study aims to confirm the efficacy of Xijiao Dihuang decoction (XJDHT), a classic prescription, in treating psoriasis and to explore the potential therapeutic mechanism.

Methods

For pharmacodynamic analysis, a mouse model of imiquimod cream (IMQ)-induced psoriasis was constructed. Active ingredients and genes of XJDHT, as well as psoriasis-related targets, were obtained from public databases. Intersecting genes (IGEs) of XJDHT and psoriasis were collected by Venn Diagram. A protein-protein interaction (PPI) network of IGEs is constructed through the STRING database. The Molecular Complex Detection (MCODE) and Cytohubba plug-ins of Cytoscape software were used to identified hub genes. In addition, we conducted enrichment analysis of IGEs using the R package clusterProfiler. Hub genes were validated via external GEO databases. The influence of XJDHT on Hub gene expression was examined by qPCR and ELISA, and molecular docking was used to evaluate the binding efficacy between active ingredients and hub genes.

Results

The results revealed that XJDHT possesses 92 potential genes for psoriasis, and 8 Hub genes were screened. Enrichment analysis suggested that XJDHT ameliorate psoriasis through multiple pathways, including AGE-RAGE, HIF-1, IL-17 and TNF signaling pathway. Validation data confirmed the differential expression of IL6, VEGFA, TNF, MMP9, STAT3, and TLR4. Molecular docking revealed a strong affinity between active ingredients and Hub genes. The efficacy of XJDHT in improving psoriatic lesions in model mice was demonstrated by PASI score and HE staining, potentially attributed to the down-regulation of VEGFA, MMP9, STAT3, TNF, and IL-17A, as evidenced by ELISA and qPCR.

Conclusion

This study employed network pharmacology and in vitro experiments to identify the potential mechanisms underlying the therapeutic effects of XJDHT on psoriasis, providing a new theoretical basis for its clinical application in the treatment of psoriasis.

Leveraging decagonal in-silico strategies for uncovering IL-6 inhibitors with precision

Interleukin-6 upregulation leads to various acute phase reactions such as local inflammation and systemic inflammation in many diseases like cancer, multiple sclerosis, rheumatoid arthritis, anemia, and Alzheimer's disease stimulating JAK/STAT3, Ras/MAPK, PI3K-PKB/Akt pathogenic pathways. Since no small molecules are available in the market against IL-6 till now, we have designed a class of small bioactive 1,3 - indanedione (IDC) molecules for inhibiting IL-6 using a decagonal approach computational studies. The IL-6 mutations were mapped in the IL-6 protein (PDB ID: 1ALU) from thorough pharmacogenomic and proteomics studies. The protein-drug interaction networking analysis for 2637 FFDA-approved drugs with IL-6 protein using Cytoscape software showed that 14 drugs have prominent interactions with IL-6. Molecular docking studies showed that the designed compound IDC-24 (-11.8 kcal/mol) and methotrexate (-5.20) bound most strongly to the 1ALU south asian population mutated protein. MMGBSA results indicated that IDC-24 (-41.78 kcal/mol) and methotrexate (-36.81 kcal/mol) had the highest binding energy when compared to the standard molecules LMT-28 (-35.87 kcal/mol) and MDL-A (-26.18 kcal/mol). These results we substantiated by the molecular dynamic studies in which the compound IDC-24 and the methotrexate had the highest stability. Further, the MMPBSA computations produced energies of -28 kcal/mol and -14.69 kcal/mol for IDC-24 and LMT-28. KDeep absolute binding affinity computations revealed energies of -5.81 kcal/mol and -4.74 kcal/mol for IDC-24 and LMT-28 respectively. Finally, our decagonal approach established the compound IDC-24 from the designed 1,3-indanedione library and methotrexate from protein drug interaction networking as suitable HITs against IL-6.

Protein-based salivary biomarkers for the diagnosis of periodontal diseases: Systematic review and meta-analysis

Objective

This systematic review and meta-analysis was aimed at determining differentially expressed protein-based biomarkers detectable in the saliva for the diagnosis of major periodontal diseases.

Methods

A literature review was conducted through January 31, 2022. The methodological quality and risk of bias were assessed with the Newcastle-Ottawa scale for case-control studies. Heterogeneity among studies was analysed with the Q statistical test and the I2 test. p-values lower than 0.10 and I2 values higher than 50% indicated high heterogeneity among studies; therefore, the random-effects model was used. The analysis of biological pathways associated with the differentially expressed protein markers was performed with the STITCH integration analysis tool and was limited to interactions with high confidence levels (0.7).

Results

Of all protein-based biomarkers detected, 12 were suitable for meta-analysis: IL-1β, MIP-1α, albumin, TNF-α, ICTP, Ig-A, lactoferrin, MMP-8, IL-6, IL-8, IL-17 and PGE2. The salivary markers with high applicability were IL-1β for differentiating patients with chronic periodontal disease from patients with gingivitis with an OE = 73.5 pg/mL; ICTP for differentiating patients with chronic periodontal disease from healthy control patients with an OE = 0.091 ng/mL; and PGE2 for differentiating patients with chronic periodontal disease from healthy control patients with an OE = 36.3 pg/mL.

Conclusions

The biomarkers with the highest differential expression and the greatest potential for clinical applicability are IL-1β for differentiating periodontitis from gingivitis, and ICTP and PGE2 for differentiating periodontitis from healthy status.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

Inferring drug-disease associations by a deep analysis on drug and disease networks

Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.

CXCL10 as a shared specific marker in rheumatoid arthritis and inflammatory bowel disease and a clue involved in the mechanism of intestinal flora in rheumatoid arthritis

This study aimed to identify shared specific genes associated with rheumatoid arthritis (RA) and inflammatory bowel disease (IBD) through bioinformatic analysis and to examine the role of the gut microbiome in RA. The data were extracted from the 3 RA and 1 IBD gene expression datasets and 1 RA gut microbiome metagenomic dataset. Weighted correlation network analysis (WGCNA) and machine learnings was performed to identify candidate genes associated with RA and IBD. Differential analysis and two different machine learning algorithms were used to investigate RA's gut microbiome characteristics. Subsequently, the shared specific genes related to the gut microbiome in RA were identified, and an interaction network was constructed utilizing the gutMGene, STITCH, and STRING databases. We identified 15 candidates shared genes through a joint analysis of the WGCNA for RA and IBD. The candidate gene CXCL10 was identified as the shared hub gene by the interaction network analysis of the corresponding WGCNA module gene to each disease, and CXCL10 was further identified as the shared specific gene by two machine learning algorithms. Additionally, we identified 3 RA-associated characteristic intestinal flora (Prevotella, Ruminococcus, and Ruminococcus bromii) and built a network of interactions between the microbiomes, genes, and pathways. Finally, it was discovered that the gene CXCL10 shared between IBD and RA was associated with the three gut microbiomes mentioned above. This study demonstrates the relationship between RA and IBD and provides a reference for research into the role of the gut microbiome in RA.

Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula

ETHNOPHARMACOLOGICAL RELEVANCE

Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds.

AIM OF THE STUDY

The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine.

MATERIALS AND METHODS

Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022.

RESULTS

Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend.

CONCLUSION

Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.

Explainable drug side effect prediction via biologically informed graph neural network

Early detection of potential side effects (SE) is a critical and challenging task for drug discovery and patient care. In-vitro or in-vivo approach to detect potential SEs is not scalable for many drug candidates during the preclinical stage. Recent advances in explainable machine learning may facilitate detecting potential SEs of new drugs before market release and elucidating the critical mechanism of biological actions. Here, we leverage multi-modal interactions among molecules to develop a biologically informed graph-based SE prediction model, called HHAN-DSI. HHAN-DSI predicted frequent and even uncommon SEs of the unseen drug with higher or comparable accuracy against benchmark methods. When applying HHAN-DSI to the central nervous system, the organs with the largest number of SEs, the model revealed diverse psychiatric medications' previously unknown but probable SEs, together with the potential mechanisms of actions through a network of genes, biological functions, drugs, and SEs.

Comprehensive investigation of the prognostic values and molecular mechanisms of syntaxin binding protein 5 antisense RNA 1 in patients with colon adenocarcinoma based on RNA sequencing dataset

Objective: The main purpose of this study is to perform a comprehensive investigation of the prognostic value and molecular mechanism of syntaxin binding protein 5 antisense RNA 1 (STXBP5-AS1) through the whole genome RNA sequencing data of the The Cancer Genome Atlas (TCGA) colon adenocarcinoma (COAD) cohort. Methods: There were 438 COAD patients were fit into current study for survival analysis. Gene expression profiling interactive analysis 2.0, Database for Annotation, Visualization and Integrated Discovery v6.8, gene set enrichment analysis (GSEA) and connectivity map (CMap) are used to investigate the molecular mechanisms and targeted drugs of STXBP5-AS1 in COAD. Results: By comparing the expression level of tumor and non-tumor tissues, we found that STXBP5-AS1 was notablely down-regulated in COAD tumor tissues. Survival analysis suggested that low STXBP5-AS1 expression was significantly related to poor overall survival (OS) of COAD (log-rank P=0.035, adjusted P=0.005, HR=0.545, 95%CI=0.356-0.836). The enrichment analysis of STXBP5-AS1 co-expressed genes, GSEA and differentially expressed genes suggests that STXBP5-AS1 may play a part in COAD by regulating the following biological processes or pathways: cell junction, DNA replication, apoptosis, cell cycle, metastasis, tumor protein 53, Wnt, mTORC1, MCM, notch receptor 4, transforming growth factor beta receptor, and cGMP-PKG signaling pathway. CMap analysis was screened out four small molecule drugs (anisomycin, cephaeline, NU-1025 and quipazine) that may be used as STXBP5-AS1 targeted therapy drugs in COAD. The co-expression analysis of STXBP5-AS1 and immune cell gene signature indicated that STXBP5-AS1 was significantly related to immune cell gene set in normal intestinal tissues, but not in COAD tumor tissues. Conclusion: Our results revealed that STXBP5-AS1 is notablely down-regulated in COAD tumor tissues, and may act as a novel prognostic biomarker for COAD.

Spectroscopic studies on a natural biomarker for the identification of origin and quality of tea extracts for the development of a portable and field deployable prototype

Even in the era of smart technologies and IoT enabled devices, tea testing technique continues to be a person specific subjective task. In this study, we have employed optical spectroscopy-based detection technique for the quantitative validation of tea quality. In this regard, we have employed the external quantum yield of quercetin at 450 nm (λ_ex = 360 nm), which is an enzymatic product generated by the activity of β-glucosidase on rutin, a naturally occurring metabolite responsible for tea-flavour (quality). We have found that a specific point in a graph representing Optical Density and external Quantum Yield as independent and dependent variables respectively of an aqueous tea extract objectively indicates a specific variety of the tea. A variety of tea samples from various geographical origin have been analysed with the developed technique and found to be useful for the tea quality assessment. The principal component analysis distinctly showed the tea samples originated from Nepal and Darjeeling having similar external quantum yield, while the tea samples from Assam region had a lower external quantum yield. Furthermore, we have employed experimental and computational biology techniques for the detection of adulteration and health benefit of the tea extracts. In order to assure the portability/field use, we have also developed a prototype which confirms the results obtained in the laboratory. We are of the opinion that the simple user interface and almost zero maintenance cost of the device will make it useful and attractive with minimally trained manpower at low resource setting.

Effective holistic characterization of small molecule effects using heterogeneous biological networks

The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.

A novel molecular reporter for probing protein DNA recognition: An optical spectroscopic and molecular modeling study

A novel benzo[a]phenoxazine-based fluorescent dye LV2 has been employed as a molecular reporter to probe recognition of a linker histone protein H1 by calf-thymus DNA (DNA). Fluorescence lifetime of LV2 buried in the globular domain of H1 (∼2.1 ns) or in the minor groove of DNA (∼0.93 ns) increases significantly to 2.65 ns upon interaction of the cationic protein with DNA indicating formation of the H1-DNA complex. The rotational relaxation time of the fluorophore buried in the globular domain of H1 increases significantly from 2.2 ns to 8.54 ns in the presence of DNA manifesting the recognition of H1 by DNA leading to formation of the H1-DNA complex. Molecular docking and molecular dynamics (MD) simulations have shown that binding of LV2 is energetically most favourable in the interface of the H1-DNA complex than in the globular domain of H1 or in the minor groove of DNA. As a consequence, orientational relaxation of the LV2 is significantly hindered in the protein-DNA interface compared to H1 or DNA giving rise to a much longer rotational relaxation time (8.54 ns) in the H1-DNA complex relative to that in pure H1 (2.2 ns) or DNA (5.7 ns). Thus, via a significant change of fluorescence lifetime and rotational relaxation time, the benzo[a]phenoxazine-based fluorescent dye buried within the globular domain of the cationic protein, or within the minor groove of DNA, reports on recognition of H1 by DNA.

Prospects for the application of traditional Chinese medicine network pharmacology in food science research

There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.

Multilingual translation for zero-shot biomedical classification using BioTranslator

Existing annotation paradigms rely on controlled vocabularies, where each data instance is classified into one term from a predefined set of controlled vocabularies. This paradigm restricts the analysis to concepts that are known and well-characterized. Here, we present the novel multilingual translation method BioTranslator to address this problem. BioTranslator takes a user-written textual description of a new concept and then translates this description to a non-text biological data instance. The key idea of BioTranslator is to develop a multilingual translation framework, where multiple modalities of biological data are all translated to text. We demonstrate how BioTranslator enables the identification of novel cell types using only a textual description and how BioTranslator can be further generalized to protein function prediction and drug target identification. Our tool frees scientists from limiting their analyses within predefined controlled vocabularies, enabling them to interact with biological data using free text.

Inflammation produced by senescent osteocytes mediates age-related bone loss

Purpose

The molecular mechanisms of age-related bone loss are unclear and without valid drugs yet. The aims of this study were to explore the molecular changes that occur in bone tissue during age-related bone loss, to further clarify the changes in function, and to predict potential therapeutic drugs.

Methods

We collected bone tissues from children, middle-aged individuals, and elderly people for protein sequencing and compared the three groups of proteins pairwise, and the differentially expressed proteins (DEPs) in each group were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). K-means cluster analysis was then used to screen out proteins that continuously increased/decreased with age. Canonical signaling pathways that were activated or inhibited in bone tissue along with increasing age were identified by Ingenuity Pathway Analysis (IPA). Prediction of potential drugs was performed using the Connectivity Map (CMap). Finally, DEPs from sequencing were verified by Western blot, and the drug treatment effect was verified by quantitative real-time PCR.

Results

The GO and KEGG analyses show that the DEPs were associated with inflammation and bone formation with aging, and the IPA analysis shows that pathways such as IL-8 signaling and acute-phase response signaling were activated, while glycolysis I and EIF2 signaling were inhibited. A total of nine potential drugs were predicted, with rapamycin ranking the highest. In cellular experiments, rapamycin reduced the senescence phenotype produced by the H₂O₂-stimulated osteocyte-like cell MLO-Y4.

Conclusion

With age, inflammatory pathways are activated in bone tissue, and signals that promote bone formation are inhibited. This study contributes to the understanding of the molecular changes that occur in bone tissue during age-related bone loss and provides evidence that rapamycin is a drug of potential clinical value for this disease. The therapeutic effects of the drug are to be further studied in animals.

Identification of potential pathways and biomarkers linked to progression in ALS

OBJECTIVE

To identify potential diagnostic and prognostic biomarkers for clinical management and clinical trials in amyotrophic lateral sclerosis.

METHODS

We analysed proteomics data of ALS patient-induced pluripotent stem cell-derived motor neurons available through the AnswerALS consortium. After stratifying patients using clinical ALSFRS-R and ALS-CBS scales, we identified differentially expressed proteins indicative of ALS disease severity and progression rate as candidate ALS-related and prognostic biomarkers. Pathway analysis for identified proteins was performed using STITCH. Protein sets were correlated with the effects of drugs using the Connectivity Map tool to identify compounds likely to affect similar pathways. RNAi screening was performed in a Drosophila TDP-43 ALS model to validate pathological relevance. A statistical classification machine learning model was constructed using ridge regression that uses proteomics data to differentiate ALS patients from controls.

RESULTS

We identified 76, 21, 71 and 1 candidate ALS-related biomarkers and 22, 41, 27 and 64 candidate prognostic biomarkers from patients stratified by ALSFRS-R baseline, ALSFRS-R progression slope, ALS-CBS baseline and ALS-CBS progression slope, respectively. Nineteen proteins enhanced or suppressed pathogenic eye phenotypes in the ALS fly model. Nutraceuticals, dopamine pathway modulators, statins, anti-inflammatories and antimicrobials were predicted starting points for drug repurposing using the connectivity map tool. Ten diagnostic biomarker proteins were predicted by machine learning to identify ALS patients with high accuracy and sensitivity.

INTERPRETATION

This study showcases the powerful approach of iPSC-motor neuron proteomics combined with machine learning and biological confirmation in the prediction of novel mechanisms and diagnostic and predictive biomarkers in ALS.

Integrated multiomics analysis to infer COVID-19 biological insights

Three years after the pandemic, we still have an imprecise comprehension of the pathogen landscape and we are left with an urgent need for early detection methods and effective therapy for severe COVID-19 patients. The implications of infection go beyond pulmonary damage since the virus hijacks the host's cellular machinery and consumes its resources. Here, we profiled the plasma proteome and metabolome of a cohort of 57 control and severe COVID-19 cases using high-resolution mass spectrometry. We analyzed their proteome and metabolome profiles with multiple depths and methodologies as conventional single omics analysis and other multi-omics integrative methods to obtain the most comprehensive method that portrays an in-depth molecular landscape of the disease. Our findings revealed that integrating the knowledge-based and statistical-based techniques (knowledge-statistical network) outperformed other methods not only on the pathway detection level but even on the number of features detected within pathways. The versatile usage of this approach could provide us with a better understanding of the molecular mechanisms behind any biological system and provide multi-dimensional therapeutic solutions by simultaneously targeting more than one pathogenic factor.

Network Pharmacological Analysis on the Herbal Combinations for Mitigating Inflammation in Respiratory Tracts and Experimental Evaluation

The regulation of inflammatory mediators, such as TNF-α, IL-6, IL-1β, and leukotriene B4, could play a crucial role in suppressing inflammatory diseases such as COVID-19. In this study, we investigated the potential mechanisms of drug combinations comprising Ephedrae Herba, Schisandra Fructus, Platycodonis Radix, and Ginseng Radix; validated the anti-inflammatory effects of these drugs; and determined the optimal dose of the drug combinations. By constructing a herb-compound-target network, associations were identified between the herbs and tissues (such as bronchial epithelial cells and lung) and pathways (such as the TNF, NF-κB, and calcium signaling pathways). The drug combinations exerted anti-inflammatory effects in the RAW264.7 cell line treated with lipopolysaccharide by inhibiting the production of nitric oxide and inflammatory mediators, including TNF-α, IL-6, IL-1β, and leukotriene B4. Notably, the drug combinations inhibited PMA-induced MUC5AC mRNA expression in NCI-H292 cells. A design space analysis was carried out to determine the optimal herbal medicine combinations using the design of experiments and synergy score calculation. Consequently, a combination study of the herbal preparations confirmed their mitigating effect on inflammation in COVID-19.

A model with deep analysis on a large drug network for drug classification

Drugs are an important means to treat various diseases. They are classified into several classes to indicate their properties and effects. Those in the same class always share some important features. The Kyoto Encyclopedia of Genes and Genomes (KEGG) DRUG recently reported a new drug classification system that classifies drugs into 14 classes. Correct identification of the class for any possible drug-like compound is helpful to roughly determine its effects for a particular type of disease. Experiments could be conducted to confirm such latent effects, thus accelerating the procedures for discovering novel drugs. In this study, this classification system was investigated. A classification model was proposed to assign one of the classes in the system to any given drug for the first time. Different from traditional fingerprint features, which indicated essential drug properties alone and were very popular in investigating drug-related problems, drugs were represented by novel features derived from a large drug network via a well-known network embedding algorithm called Node2vec. These features abstracted the drug associations generated from their essential properties, and they could overview each drug with all drugs as background. As class sizes were of great differences, synthetic minority over-sampling technique (SMOTE) was employed to tackle the imbalance problem. A balanced dataset was fed into the support vector machine to build the model. The 10-fold cross-validation results suggested the excellent performance of the model. This model was also superior to models using other drug features, including those generated by another network embedding algorithm and fingerprint features. Furthermore, this model provided more balanced performance across all classes than that without SMOTE.

TCM-Suite: A comprehensive and holistic platform for Traditional Chinese Medicine component identification and network pharmacology analysis

DNA-based biological ingredient identification and downstream pharmacology network analysis are commonly used in research for Traditional Chinese Medicine preparations (TCM formulas). Advancements in bioinformatics tools and the accumulation of related data have become driving forces for progress in this field. However, a lack of a platform integrating biological ingredient identification and downstream pharmacology network analysis hinders the deep understanding of TCM. In this study, we developed the TCM-Suite platform composed of two sub-databases, Holmes-Suite and Watson-Suite, for TCM biological ingredient identification and network pharmacology investigation, respectively, both are among the most complete: In the Holmes-Suite, we collected and processed six types of marker gene sequences, accounting for 1,251,548 marker gene sequences. In the Watson-Suite, we curated and integrated a massive number of entries from more than 10 public databases. Importantly, we developed a comprehensive pipeline to integrate TCM biological ingredient identification and downstream network pharmacology research, allowing users to simultaneously identify components of a TCM formula and analyze its potential pharmacology mechanism. Furthermore, we designed search engines and a user-friendly platform to better search and visualize these rich resources. TCM-Suite is a comprehensive and holistic platform for TCM-based drug discovery and repurposing. TCM-Suite website: http://TCM-Suite.AImicrobiome.cn.

Multi-omics approaches for in-depth understanding of therapeutic mechanism for Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) is extensively utilized in clinical practice due to its therapeutic and preventative treatments for various diseases. With the development of high-throughput sequencing and systems biology, TCM research was transformed from traditional experiment-based approaches to a combination of experiment-based and omics-based approaches. Numerous academics have explored the therapeutic mechanism of TCM formula by omics approaches, shifting TCM research from the "one-target, one-drug" to "multi-targets, multi-components" paradigm, which has greatly boosted the digitalization and internationalization of TCM. In this review, we concentrated on multi-omics approaches in principles and applications to gain a better understanding of TCM formulas against various diseases from several aspects. We first summarized frequently used TCM quality assessment methods, and suggested that incorporating both chemical and biological ingredients analytical methods could lead to a more comprehensive assessment of TCM. Secondly, we emphasized the significance of multi-omics approaches in deciphering the therapeutic mechanism of TCM formulas. Thirdly, we focused on TCM network analysis, which plays a vital role in TCM-diseases interaction, and serves for new drug discovery. Finally, as an essential source for storing multi-omics data, we evaluated and compared several TCM databases in terms of completeness and reliability. In summary, multi-omics approaches have infiltrated many aspects of TCM research. With the accumulation of omics data and data-mining resources, deeper understandings of the therapeutic mechanism of TCM have been acquired or will be gained in the future.

Deep learning in drug discovery: an integrative review and future challenges

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

Drug-Protein interaction prediction by correcting the effect of incomplete information in heterogeneous information

MOTIVATION

Large-scale heterogeneous data provide diverse perspectives for predicting drug-protein interactions (DPIs). However, the available information on molecular interactions and clinical associations related to drugs or proteins is incomplete because there may be unproven interactions and associations. This incomplete information in the available data is presented in the form of non-interaction and non-correlation, which may mislead the prediction model. Existing methods fuse incomplete and complete information without considering their integrity, so the negative effects of incomplete information still exist.

RESULTS

We develop a network-based DPI prediction method named BRWCP, which uses the complete information network to correct the prediction results acquired by the incomplete information network. By integrating relevant heterogeneous information that may be incomplete, the feature similarities of drugs and proteins are obtained. Combining the feature similarities and known DPIs, an incomplete information-based drug-protein heterogeneous network is constructed. Then, a bidirectional random walk with pruning algorithm is adopted in this heterogeneous network to predict potential DPIs. Next, the predicted DPIs are combined with the chemical fingerprint similarity of drugs and amino acid sequence similarity of proteins to construct the complete information network. The bidirectional random walk with pruning algorithm is applied in the new network to obtain the final prediction results until it converges. Experimental results show that BRWCP is superior to several state-of-the-art DPI prediction methods, and case studies further confirm its ability to tap potential DPIs.

AVAILABILITY AND IMPLEMENTATION

The code and data used in BRWCP are available at https://github.com/lyfdomain/BRWCP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular co-localization of multiple drugs in a nanoscopic delivery vehicle for potential synergistic remediation of multi-drug resistant bacteria

Anti-microbial resistant infection is predicted to be alarming in upcoming years. In the present study, we proposed co-localization of two model drugs viz., rifampicin and benzothiazole used in anti-tuberculosis and anti-fungal agents respectively in a nanoscopic cationic micelle (cetyl triethyl ammonium bromide) with hydrodynamic diameter of 2.69 nm. Sterilization effect of the co-localized micellar formulation against a model multi-drug resistant bacterial strain viz., Methicillin resistant Staphylococcus aureus was also investigated. 99.88% decrease of bacterial growth in terms of colony forming unit was observed using the developed formulation. While Dynamic Light Scattering and Forsters Resonance Energy Transfer between benzothiazole and rifampicin show co-localization of the drugs in the nanoscopic micellar environment, analysis of time-resolved fluorescence decays by Infelta-Tachiya model and the probability distribution of the donor-acceptor distance fluctuations for 5 μM,10 μM and 15 μM acceptor concentrations confirm efficacy of the co-localization. Energy transfer efficiency and the donor acceptor distance are found to be 46% and 20.9 Å respectively. We have also used a detailed computational biology framework to rationalize the sterilization effect of our indigenous formulation. It has to be noted that the drugs used in our studies are not being used for their conventional indication. Rather the co-localization of the drugs in the micellar environment shows a completely different indication of their use in the remediation of multi-drug resistant bacteria revealing the re-purposing of the drugs for potential use in hospital-born multi-drug resistant bacterial infection.

Eugenol reduces serum testosterone levels and sperm viability in adult Wistar rats

Eugenol is the main constituent of clove extract. It is a remarkably versatile molecule incorporated as a functional ingredient in several food products and widely applied in the pharmaceutical industry. Men consume natural products enriched with eugenol for treating sexual disorders and using as aphrodisiacs. Nevertheless, there is no information about the impact of eugenol intake on the reproductive parameters of healthy males. Therefore, we provided 10, 20, and 40 mg kg^-1 pure eugenol to adult Wistar rats for 60 days. Testis, epididymis, and spermatozoa were analyzed under microscopic, biochemical, and functional approaches. This phenolic compound did not alter testicular and epididymal biometry and microscopy. However, 20 and 40 mg kg^-1 eugenol reduced serum testosterone levels. The highest dose altered lactate and glucose concentrations in the epididymis. All the eugenol concentrations diminished CAT activity and MDA levels in the testis and increased FRAP and CAT activity in the epididymis. Epididymal sperm from rats receiving 10, 20, and 40 mg kg^-1 eugenol presented high Ca²⁺ ATPase activity and low motility. In conclusion, eugenol at low and high doses negatively impacted the competence of epididymal sperm and modified oxidative parameters in male organs, with no influence on their microscopy.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.