NPASS: natural product activity and species source database for natural product research, discovery and tool development

A consortium of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via data-driven analysis

Despite many recent studies on non-alcoholic fatty liver disease (NAFLD) therapeutics, the optimal treatment has yet to be determined. In this unfinished project, we combined secondary metabolites (SMs) from the gut microbiota (GM) and Hordeum vulgare (HV) to investigate their combinatorial effects via network pharmacology (NP). Additionally, we analyzed GM or barley - signalling pathways - targets - metabolites (GBSTMs) in combinatorial perspectives (HV, and GM). A total of 31 key targets were analysed via a protein-protein interaction (PPI) network, and JUN was identified as the uppermost target in NAFLD. On a bubble plot, we revealed that apelin signalling pathway, which had the lowest enrichment factor antagonize NAFLD. Holistically, we scrutinized GBSTM to identify key components (GM, signalling pathways, targets, and metabolites) associated with the Apelin signalling pathway. Consequently, we found that the primary GMs (Eubacterium limosum, Eggerthella sp. SDG-2, Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, Paraprevotella xylaniphila YIT 11841) to ameliorate NAFLD. The molecular docking test (MDT) suggested that tryptanthrin-JUN is an agonist, conversely, dihydroglycitein-HDAC5, 1,3-diphenylpropan-2-ol-NOS1, and (10[(Acetyloxy)methyl]-9-anthryl)methyl acetate-NOS2, which are antagonistic conformers in the apelin signalling pathway. Overall, these results suggest that combination therapy could be an effective strategy for treating NAFLD.

Structure based virtual screening and discovery of novel inhibitors against FabD protein of Mycobacterium tuberculosis

The highly flexible nature of Mycobacterium tuberculosis (Mtb) can be owed to its tough cell wall and multiple gene interaction system which makes it resistant to frontline TB drugs. Mycolic acids are the key components of the unique cell wall that protects the organism from external threats. Proteins of the fatty acid synthesis pathway are evolutionarily conserved that enables cellular survival in harsh conditions and hence have become attractive targets. Malonyl Co-A Acyl carrier protein transacylase (FabD; MCAT, EC2.3.1.39) is an enzyme in the branching point of the unique and vast fatty acid synthase (FAS-I and FAS-II) systems of Mtb. In the present investigation, in-silico structure based drug discovery with the compounds from an open source library (NPASS) is used for target fishing and employed to understand the interaction with the target protein FabD. The potential hit compounds were filtered using exhaustive docking, considering the binding energy, key residue interaction and drug likeness property. Three compounds from the library namely NPC475074 (Hit 1), NPC260631 (Hit 2) and NPC313985 (Hit 3) with binding energies -14.45, -13.29 and -12.37 respectively were taken for molecular dynamic simulation. The results suggested that Hit 3 (NPC313985) has stable interaction with FabD protein. This article further elaborates the interaction of the identified novel compounds Hit 1 and Hit 3 along with the other known compound (Hit 2) against Mtb FabD protein. The hit compounds identified from this study could be further evaluated against mutated FabD protein and considered for in-vitro evaluation.Communicated by Ramaswamy H. Sarma.

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.

Marine alkaloid rigidin analogues as potential selective inhibitors of SHP1, a new strategy for cancer immunotherapeutics

SHP1 is a protein tyrosine phosphatase playing a central role in immunity, cell growth, development, and survival. The inhibition of SHP1 can help in better prognosis in various disorders like breast and ovarian cancer, melanoma, atherosclerosis, hypoxia, hypoactive immune response, and familial dysautonomia. The currently available inhibitors of SHP1 have the side effect of inhibiting the activity of SHP2, which shares >60% sequence similarity with SHP1 but has distinct biological functions. Thus, there is a need to search for novel specific inhibitors of SHP1. The current study uses a combination of virtual screening and molecular dynamic simulations, followed by PCA and MM-GBSA analysis, to screen about 35000 compounds; to predict that two rigidin analogues can potentially selectively inhibit SHP1 but not SHP2. Our studies demonstrate that these rigidin analogues are more potent at inhibiting SHP1 than the commercially available inhibitor NSC-87877. Further, cross-binding studies with SHP2 exhibited poor binding efficiency and lower stability of the complex, thus indicating a specificity of the rigidin analogues for SHP1, which is crucial in preventing side effects due to the diverse physiological functions of SHP2 in cellular signaling, proliferation, and hematopoiesis. Additionally, SHP1 is essential in mediating the inhibitory signaling in antitumor immune cells like NK and T cells. Hence, the rigidin analogues that inhibit SHP1 will potentiate the anti-tumor immune response by the release of inhibitory function of NK cells, thus driving NK activating response, in addition to their intrinsic anti-tumor function. Thus, SHP1 inhibition is a novel double-blade approach towards anti-cancer immunotherapeutics.Communicated by Ramaswamy H. Sarma.

Predicting herb-disease associations using network-based measures in human protein interactome

BACKGROUND

Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations.

RESULTS

In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences.

CONCLUSIONS

This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.

Structure-based drug design-guided identification of estrogen receptor binders

Cancer is one of the life-threatening diseases and the second leading cause of death in the world. The estrogen receptor can be considered as one of the significant drug targets for cancer. A large number of clinically used anticancer drugs were identified from phytochemicals. Multiple literatures suggested that extracts of Datura sp. significantly inhibit estrogen receptors associated with human cancer. In the present study, all reported natural products present in Datura sp. were subjected to molecular docking against estrogen receptors. The top hits were shortlisted based on binding orientation and docking score and subjected to molecular dynamics simulation to explore the conformational stability followed by binding energy calculation. The ligand [(1S,5R)-8-Methyl-8-Azabicyclo [3.2.1] Octan-3-yl] (2R)-3-Hydroxy-2-Phenylpropanoate depicts highly acceptable MD simulations outcomes and drug-likeness profile. Knowledge-based de novo design and similar ligand screening were executed using the structural information. The designed ligand DL-50 exhibited satisfactory binding, drug-likeness profile, and well-accepted ADMET profile followed by easy synthetic accessibility which further requires experimental validation.

A critical assessment of bioactive compounds databases

Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

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

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

Bioinformatics approach for searching for natural products in vector-borne disease management

Vector-borne disease is an important public health problem. This disease is common in tropical areas and affects millions of people. The control and management of disease is an important consideration. Effective treatment is important in management of patients infected with vector-borne disease. A common problem in therapeutic management of the patient is the lack of an effective drug. Therefore, it is necessary to find a new effective drug for managing vector-borne disease. To search for a new drug, new technologies are applicable. Bioinformatics technologies are useful in new drug search. Application of the bioinformatics technologies in new anti-vector-borne disease drug search is interesting. In this review, the author briefly discusses the use of bioinformatics technology in searching for natural products in vector-borne disease management. Concepts and examples of some important diseases are presented.

Subtractive sequence-mediated therapeutic targets from the conserved gene clusters of Campylobacter hyointestinalis and computational inhibition assessment

Campylobacter hyointestinalis is a causative agent of enteritis, proctitis, human gastroenteritis, and diarrhea. Reported transmission is from pigs to humans. Link with gastrointestinal carcinoma has also been established in non-Helicobacter pylori patients carrying this strain. The genome size of the strain LMG9260 is 1.8 MB with 1785 chromosomal and seven plasmid proteins. No therapeutic targets have been identified and reported in this bacterium. Therefore, subtractive computational screening of its genome was carried out for the purpose. In total, 31 such targets were mined and riboflavin synthase was utilized for screening natural product inhibitors against it. Among more than 30,000 screened natural compounds from the NPASS library, three (NPC472060, NPC33653, and NPC313886) were prioritized to have the potential to be developed into new antimicrobial drugs. Dynamics simulation assay along with other relevant parameters like absorption, toxicity, and distribution of the inhibiting compounds were also predicted and NPC33653 was identified as having the best drug-like properties among the prioritized compounds. Thus, it has potential to be pursued further for the inhibition of riboflavin synthesis in C. hyointestinalis for subsequent obstruction of its growth and survival.Communicated by Ramaswamy H. Sarma.

A comprehensive computational study to explore promising natural bioactive compounds targeting glycosyltransferase MurG in Escherichia coli for potential drug development

Peptidoglycan is a carbohydrate with a cross-linked structure that protects the cytoplasmic membrane of bacterial cells from damage. The mechanism of peptidoglycan biosynthesis involves the main synthesizing enzyme glycosyltransferase MurG, which is known as a potential target for antibiotic therapy. Many MurG inhibitors have been recognized as MurG targets, but high toxicity and drug-resistant Escherichia coli strains remain the most important problems for further development. In addition, the discovery of selective MurG inhibitors has been limited to the synthesis of peptidoglycan-mimicking compounds. The present study employed drug discovery, such as virtual screening using molecular docking, drug likeness ADMET proprieties predictions, and molecular dynamics (MD) simulation, to identify potential natural products (NPs) for Escherichia coli. We conducted a screening of 30,926 NPs from the NPASS database. Subsequently, 20 of these compounds successfully passed the potency, pharmacokinetic, ADMET screening assays, and their validation was further confirmed through molecular docking. The best three hits and the standard were chosen for further MD simulations up to 400 ns and energy calculations to investigate the stability of the NPs-MurG complexes. The analyses of MD simulations and total binding energies suggested the higher stability of NPC272174. The potential compounds can be further explored in vivo and in vitro for promising novel antibacterial drug discovery.

Screening inhibitors against the Ef-Tu of Fusobacterium nucleatum: a docking, ADMET and PBPK assessment study

The oral pathogen Fusobacterium nucleatum has recently been associated with an elevated risk of colorectal cancer (CRC), endometrial metastasis, chemoresistance, inflammation, metastasis, and DNA damage, along with several other diseases. This study aimed to explore the disruption of protein machinery of F. nucleatum via inhibition of elongation factor thermo unstable (Ef-Tu) protein, through natural products. No study on Ef-Tu inhibition by natural products or in Fusobacterium spp. exists till todate. Ef-Tu is an abundant specialized drug target in bacteria that varies from human Ef-Tu. Elfamycins target Ef-Tu and hence, Enacyloxin IIa was used to generate pharmacophore for virtual screening of three natural product libraries, Natural Product Activity and Species Source (NPASS) (n = 30000 molecules), Tibetan medicinal plant database (n = 54 molecules) and African medicinal plant database (n > 6000 molecules). Peptaibol Septocylindrin B (NPC141050), Hirtusneanoside, and ZINC95486259 were prioritized from these libraries as potential therapeutic candidates. ADMET profiling was done for safety assessment, physiological-based pharmacokinetic modeling in human and mouse for getting insight into drug interaction with body tissues and molecular dynamics was used to assess stability of the best hit NPC141050 (Septocylindrin B). Based on the promising results, we propose further in vitro, in vivo and pharmacokinetic testing on the lead Septocylindrin B, for possible translation into therapeutic interventions.

Hepa-ToxMOA: a pathway-screening method for evaluating cellular stress and hepatic metabolic-dependent toxicity of natural products

In the field of drug discovery, natural products have emerged as therapeutic agents for diseases such as cancer. However, their potential toxicity poses significant obstacles in the developing effective drug candidates. To overcome this limitation, we propose a pathway-screening method based on imaging analysis to evaluate cellular stress caused by natural products. We have established a cellular stress sensing system, named Hepa-ToxMOA, which utilizes HepG2 cells expressing green fluorescent protein (GFP) fluorescence under the control of transcription factor response elements (TREs) for transcription factors (AP1, P53, Nrf2, and NF-κB). Additionally, to augment the drug metabolic activity of the HepG2 cell line, we evaluated the cytotoxicity of 40 natural products with and without S9 fraction-based metabolic activity. Our finding revealed different activities of Hepa-ToxMOA depending on metabolic or non-metabolic activity, highlighting the involvement of specific cellular stress pathways. Our results suggest that developing a Hepa-ToxMOA system based on activity of drug metabolizing enzyme provides crucial insights into the molecular mechanisms initiating cellular stress during liver toxicity screening for natural products. The pathway-screening method addresses challenges related to the potential toxicity of natural products, advancing their translation into viable therapeutic agents.

High-throughput virtual screening, pharmacophore modelling and antagonist effects of small molecule inhibitors against cytotoxin-induced cytotoxicity

Cobra venom cytotoxins (CTX) cause dermonecrosis in envenomed patients who suffered from limb amputations due to the limitation of serotherapy-based antivenoms. This study aimed to identify small molecule inhibitors against CTX. A structure-based high-throughput virtual screening (HTVS) was conducted based on a conserved CTX, using the Natural Product Activity and Species Source (NPASS) screening library. The hits were valerenic acid, 1-oxo-2H-isoquinoline-4-carboxylic acid, acenaphthene, and 5-bromopyrrole-2-carboxamide, which interacted with contemporary antivenom binding site A and functional loops I-III of CTX, respectively, in molecular docking studies. Furthermore, molecular dynamic simulations were performed along with analysis of ligand fitness through their pharmacophore and pharmacokinetics properties. The antagonist effects of these hits on CTX-induced cytotoxicity were examined in human keratinocytes (HaCaT). Despite having a low binding affinity (KD = 14.45 × 10-4 M), acenaphthene demonstrated a significant increase of cell viability at 6 h and 24 h in experimental envenomed HaCaT. It also demonstrated the highest neutralization potency against CTX with a median effective concentration (EC50) of 0.05 mL/mg. Acenaphthene interacted with the functional loop II, which is the crucial cytotoxic site of CTX. It has an aromatic ring as its primary pharmacophoric feature, commonly used for rational drug design. In conclusion, acenaphthene could be a promising lead compound as a small molecule inhibitor.Communicated by Ramaswamy H. Sarma.

Mechanisms of Actinidia chinensis Planch in treating colon cancer based on the integration of network pharmacology, molecular docking, and experimental verification

BACKGROUND

As an anticancer Chinese herbal medicine, the effective components and mechanism of Actinidia chinensis Planch (ACP, Tengligen) in the treatment of colon cancer are still unclear. In the present study, the integration of network pharmacology, molecular docking, and cell experiments was employed to study the effective mechanism of ACP against colon cancer.

METHODS

The Venn diagram and STRING database were used to construct the protein-protein interaction network (PPI) of ACP-colon cancer, and further topological analysis was used to obtain the key target genes of ACP in colon cancer. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to visualize the related functions and pathways. Molecular docking between key targets and compounds was determined using software such as AutoDockTools. Finally, the effect of ACP on CT26 cells was observed in vitro.

RESULTS

The study identified 40 ACP-colon key targets, including CASP3, CDK2, GSK3B, and PIK3R1. GO and KEGG enrichment analyses found that these genes were involved in 211 biological processes and 92 pathways, among which pathways in cancer, PI3K-Akt, p53, and cell cycle might be the main pathways of ACP against colon cancer. Molecular docking verified that the key components of ACP could stably bind to the corresponding targets. The experimental results showed that ACP could inhibit proliferation, induce apoptosis, and downregulate the phosphorylation of PIK3R1, Akt, and GSK3B in CT26 cells.

CONCLUSION

ACP is an anti-colon cancer herb with multiple components, and involvement of multiple target genes and signaling pathways. ACP can significantly inhibit proliferation and induce apoptosis of colon cancer cells, which may be closely related to the regulation of PI3K/AKT/GSK3B signal transduction.

PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science, and its application on natural product (NP) occurrence database processing

BACKGROUND

The standardization of biological data using unique identifiers is vital for seamless data integration, comprehensive interpretation, and reproducibility of research findings, contributing to advancements in bioinformatics and systems biology. Despite being widely accepted as a universal identifier, scientific names for biological species have inherent limitations, including lack of stability, uniqueness, and convertibility, hindering their effective use as identifiers in databases, particularly in natural product (NP) occurrence databases, posing a substantial obstacle to utilizing this valuable data for large-scale research applications.

RESULT

To address these challenges and facilitate high-throughput analysis of biological data involving scientific names, we developed PhyloSophos, a Python package that considers the properties of scientific names and taxonomic systems to accurately map name inputs to entries within a chosen reference database. We illustrate the importance of assessing multiple taxonomic databases and considering taxonomic syntax-based pre-processing using NP occurrence databases as an example, with the ultimate goal of integrating heterogeneous information into a single, unified dataset.

CONCLUSIONS

We anticipate PhyloSophos to significantly aid in the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, enabling full-scale bioinformatics analysis using these valuable data resources.

The OREGANO knowledge graph for computational drug repurposing

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).

Identification of novel natural product inhibitors of BRD4 using high throughput virtual screening and MD simulation

Bromodomains are evolutionarily conserved structural motifs that recognize acetylated lysine residues on histone tails. They play a crucial role in shaping chromatin architecture and regulating gene expression in various biological processes. Mutations in bromodomains containing proteins lead to multiple human diseases, which makes them attractive target for therapeutic intervention. Extensive studies have been done on BRD4 as a target for several cancers, such as Acute Myeloid Leukemia (AML) and Burkitt Lymphoma. Several potential inhibitors have been identified against the BRD4 bromodomain. However, most of these inhibitors have drawbacks such as non-specificity and toxicity, decreasing their appeal and necessitating the search for novel non-toxic inhibitors. This study aims to address this need by virtually screening natural compounds from the NPASS database against the Kac binding site of BRD4-BD1 using high throughput molecular docking followed by similarity clustering, pharmacokinetic screening, MD simulation and MM-PBSA binding free energy calculations. Using this approach, we identified five natural product inhibitors having a similar or better binding affinity to the BRD4 bromodomain compared to JQ1 (previously reported inhibitor of BRD4). Further systematic analysis of these inhibitors resulted in the top three hits: NPC268484 (Palodesangren-B), NPC295021 (Candidine) and NPC313112 (Buxifoliadine-D). Collectively, our in silico results identified some promising natural products that have the potential to act as potent BRD4-BD1 inhibitors and can be considered for further validation through future in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

Exploring natural products library to identify potential inhibitors targeting isoniazid-resistant tuberculosis

Mycobacterium tuberculosis (MTB) causing tuberculosis (TB) infection is a leading source of illness and death in developing nations, and the emergence of drug-resistant TB remains a significant global threat and a challenge in treating the disease. Mutations in the inhA and katG genes are connected to the principal molecular mechanism of isoniazid (INH) resistance, and continuous treatment of INH for more than a decade led to the evolution of INH resistant-TB (inhR-TB). Structure-based drug discovery approaches on traditional natural compounds are the contemporary source to identify significant lead molecules. This work focuses on discovering effective small compounds from natural compound libraries and applying pharmacophore-based virtual screening to filter out the molecules. The best-identified hit complexes were used for molecular dynamics simulations (MDS) to observe their stability and compactness. A three-dimensional e-pharmacophore hypothesis and screening generated 62 hits based on phase fitness scores from the pharmacophore-based virtual screening. Molecular docking experiments in Maestro's GLIDE module indicated that ZINC000002383126 and ASN22022 may be potential inhibitors of inhA and katG (native, inhA mutants S94A, Y158A, Y158F and Y158S and D137S, Y229F, S315T, W321F, and R418L mutants of katG). In addition, MDS analysis indicated that the native and mutant docked complexes of inhA and katG had good stability and remained compact in the binding pocket of the targets. In vitro studies can further validate the compounds that can act as INH competitive inhibitors.Communicated by Ramaswamy H. Sarma.

ccTCM: A quantitative component and compound platform for promoting the research of traditional Chinese medicine

Traditional Chinese medicine (TCM) databases play a vital role in bridging the gap between TCM and modern medicine, as well as in promoting the popularity of TCM. Elucidating the bioactive ingredients of Chinese medicinal materials is key to TCM modernization and new drug discovery. However, one drawback of current TCM databases is the lack of quantitative data on the constituents of Chinese medicinal materials. Herein, we present ccTCM, a web-based platform designed to provide a component and compound-content-based resource on TCM and analysis services for medical experts. In terms of design features, ccTCM combines resource distribution, similarity analysis, and molecular-mechanism analysis to accelerate the discovery of bioactive ingredients in TCM. ccTCM contains 273 Chinese medicinal materials commonly used in clinical settings, covering 29 functional classifications. By searching and comparing, we finally adopted 2043 studies, from which we collected the compounds contained in each TCM with content greater than 0.001 %, and a total of 1449 were extracted. Subsequently, we collected 40,767 compound-target pairs by integrating multiple databases. Taken together, ccTCM is a versatile platform that can be used by TCM scientists to perform scientific and clinical TCM studies based on quantified ingredients of Chinese medicinal materials. ccTCM is freely accessible at http://www.cctcm.org.cn.

Pharmacophore mapping approach to find anti-cancer phytochemicals with metformin-like activities against transforming growth factor (TGF)-beta receptor I kinase: An in silico study

The most frequently prescribed first-line treatment for type II diabetes mellitus is metformin. Recent reports asserted that this diabetes medication can also shield users from cancer. Metformin induces cell cycle arrest in cancer cells. However, the exact mechanism by which this occurs in the cancer system is yet to be elucidated. Here, we investigated the impact of metformin on cell cycle arrest in cancer cells utilizing transforming growth factor (TGF)-beta pathway. TGF-ß pathway has significant effect on cell progression and growth. In order to gain an insight on the underlying molecular mechanism of metformin's effect on TGF beta receptor 1 kinase, molecular docking was performed. Metformin was predicted to interact with transforming growth factor (TGF)-beta receptor I kinase based on molecular docking and molecular dynamics simulations. Furthermore, pharmacophore was generated for metformin-TGF-ßR1 complex to hunt for novel compounds having similar pharmacophore as metformin with enhanced anti-cancer potentials. Virtual screening with 29,000 natural compounds from NPASS database was conducted separately for the generated pharmacophores in Ligandscout® software. Pharmacophore mapping showed 60 lead compounds for metformin-TGF-ßR1 complex. Molecular docking, molecular dynamics simulation for 100 ns and ADMET analysis were performed on these compounds. Compounds with CID 72473, 10316977 and 45140078 showed promising binding affinities and formed stable complexes during dynamics simulation with aforementioned protein and thus have potentiality to be developed into anti-cancer medicaments.

MCF2Chem: A manually curated knowledge base of biosynthetic compound production

BACKGROUND

Microbes have been used as cell factories to synthesize various chemical compounds. Recent advances in synthetic biological technologies have accelerated the increase in the number and capacity of microbial cell factories; the variety and number of synthetic compounds produced via these cell factories have also grown substantially. However, no database is available that provides detailed information on the microbial cell factories and the synthesized compounds.

RESULTS

In this study, we established MCF2Chem, a manually curated knowledge base on the production of biosynthetic compounds using microbial cell factories. It contains 8888 items of production records related to 1231 compounds that were synthesizable by 590 microbial cell factories, including the production data of compounds (titer, yield, productivity, and content), strain culture information (culture medium, carbon source/precursor/substrate), fermentation information (mode, vessel, scale, and condition), and other information (e.g., strain modification method). The database contains statistical analyses data of compounds and microbial species. The data statistics of MCF2Chem showed that bacteria accounted for 60% of the species and that "fatty acids", "terpenoids", and "shikimates and phenylpropanoids" accounted for the top three chemical products. Escherichia coli, Saccharomyces cerevisiae, Yarrowia lipolytica, and Corynebacterium glutamicum synthesized 78% of these chemical compounds. Furthermore, we constructed a system to recommend microbial cell factories suitable for synthesizing target compounds and vice versa by combining MCF2Chem data, additional strain- and compound-related data, the phylogenetic relationships between strains, and compound similarities.

CONCLUSIONS

MCF2Chem provides a user-friendly interface for querying, browsing, and visualizing detailed statistical information on microbial cell factories and their synthesizable compounds. It is publicly available at https://mcf.lifesynther.com . This database may serve as a useful resource for synthetic biologists.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Thiadiazine-thiones as inhibitors of leishmania pteridine reductase (PTR1) target: investigations and in silico approach

Leishmaniasis is a widespread parasitic disease and is one of the major public health concerns in developing countries. Many drugs have been identified for leishmania as targets, but the potential toxicity and long-term treatment remain the most significant problems in terms of further development. The present study employed physicochemical investigations, structure-based virtual screening, ADMET analysis, molecular dynamics simulation, and MM-PBSA, to identify potential compounds for Leishmania. We evaluated 30,926 natural products from the NPASS database, and four potentials passed the pharmacokinetic ADMET studies and were verified using the molecular docking approach. Molecular docking results showed good binding interaction of the compounds with the active site of leishmania pteridine reductase enzyme PTR1, with compound TTC1 showing FRED and Autodock binding energies of -10.33 and -10.94, respectively, which were comparable with the antileishmania drugs of Allopurinol, Miltefosine and the original ligand, methotrexate. TTC1 compound was found to be favorable for hydrophobic interaction with PTR1. In addition, the physicochemical properties of the compounds were studied using the SwissADME web server. All compounds followed Lipinski's rule of five and can be considered as good oral candidates. The analysis of the 100 ns molecular dynamics simulation results based on the best-docked TTC1 with PTR1 receptor demonstrates stable interactions, and the complex undergoes low conformational fluctuations. The average of the calculated binding free energy of the TTC1-1e7w complex is (-68.67 kJ/mol), and the result demonstrated that the TTC1 promoted stability to the Leishmania-PTR1 complex. The potential compounds can be further explored for their antileishmanial activity.Communicated by Ramaswamy H. Sarma.

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

Exploring the Traditional Chinese Medicine (TCM) database chemical space to target I7L protease from monkeypox virus using molecular screening and simulation approaches

In the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. Moreover, molecular simulation revealed stable dynamics, tighter structural packing and less flexible behaviour for all the complexes. We further reported that the average hydrogen bonds in TCM27763, TCM33057, TCM34450 and TCM31564I7L complexes remained higher than the control drug. Finally, the BF energy results revealed -62.60 ± 0.65 for the controlI7L complex, for the TCM27763I7L complex -71.92 ± 0.70 kcal/mol, for the TCM33057I7L complex the BF energy was -70.94 ± 0.70 kcal/mol, for the TCM34450I7L the BF energy was -69.94 ± 0.85 kcal/mol while for the TCM31564I7L complex the BF energy was calculated to be -69.16 ± 0.80 kcal/mol. Although, we used stateoftheart computational methods, these are theoretical insights that need further experimental validation.

Global analysis of the biosynthetic chemical space of marine prokaryotes

BACKGROUND

Marine prokaryotes are a rich source of novel bioactive secondary metabolites for drug discovery. Recent genome mining studies have revealed their great potential to bio-synthesize novel secondary metabolites. However, the exact biosynthetic chemical space encoded by the marine prokaryotes has yet to be systematically evaluated.

RESULTS

We first investigated the secondary metabolic potential of marine prokaryotes by analyzing the diversity and novelty of the biosynthetic gene clusters (BGCs) in 7541 prokaryotic genomes from cultivated and single cells, along with 26,363 newly assembled medium-to-high-quality genomes from marine environmental samples. To quantitatively evaluate the unexplored biosynthetic chemical space of marine prokaryotes, the clustering thresholds for constructing the biosynthetic gene cluster and molecular networks were optimized to reach a similar level of the chemical similarity between the gene cluster family (GCF)-encoded metabolites and molecular family (MF) scaffolds using the MIBiG database. The global genome mining analysis demonstrated that the predicted 70,011 BGCs were organized into 24,536 mostly new (99.5%) GCFs, while the reported marine prokaryotic natural products were only classified into 778 MFs at the optimized clustering thresholds. The number of MF scaffolds is only 3.2% of the number of GCF-encoded scaffolds, suggesting that at least 96.8% of the secondary metabolic potential in marine prokaryotes is untapped. The unexplored biosynthetic chemical space of marine prokaryotes was illustrated by the 88 potential novel antimicrobial peptides encoded by ribosomally synthesized and post-translationally modified peptide BGCs. Furthermore, a sea-water-derived Aquimarina strain was selected to illustrate the diverse biosynthetic chemical space through untargeted metabolomics and genomics approaches, which identified the potential biosynthetic pathways of a group of novel polyketides and two known compounds (didemnilactone B and macrolactin A 15-ketone).

CONCLUSIONS

The present bioinformatics and cheminformatics analyses highlight the promising potential to explore the biosynthetic chemical diversity of marine prokaryotes and provide valuable knowledge for the targeted discovery and biosynthesis of novel marine prokaryotic natural products. Video Abstract.

Accessing hidden microbial biosynthetic potential from underexplored sources for novel drug discovery

Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently needed to be developed to combat the growing antimicrobial resistance and other public health problems. The advances in next-generation sequencing technologies and powerful computational tools open up new opportunities to explore microbial biosynthetic potential from underexplored sources, with millions of secondary metabolites awaiting discovery. The review highlights challenges associated with discovery of new chemical entities, rich reservoirs provided by untapped taxa, ecological niches or host microbiomes, emerging synthetic biotechnologies to unearth the hidden microbial biosynthetic potential for novel drug discovery at scale and speed.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

The convergent application of metabolites from Avena sativa and gut microbiota to ameliorate non-alcoholic fatty liver disease: a network pharmacology study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a serious public health issue globally, currently, the treatment of NAFLD lies still in the labyrinth. In the inchoate stage, the combinatorial application of food regimen and favorable gut microbiota (GM) are considered as an alternative therapeutic. Accordingly, we integrated secondary metabolites (SMs) from GM and Avena sativa (AS) known as potent dietary grain to identify the combinatorial efficacy through network pharmacology.

METHODS

We browsed the SMs of AS via Natural Product Activity & Species Source (NPASS) database and SMs of GM were retrieved by gutMGene database. Then, specific intersecting targets were identified from targets related to SMs of AS and GM. The final targets were selected on NAFLD-related targets, which was considered as crucial targets. The protein-protein interaction (PPI) networks and bubble chart analysis to identify a hub target and a key signaling pathway were conducted, respectively. In parallel, we analyzed the relationship of GM or AS─a key signaling pathway─targets─SMs (GASTM) by merging the five components via RPackage. We identified key SMs on a key signaling pathway via molecular docking assay (MDA). Finally, the identified key SMs were verified the physicochemical properties and toxicity in silico platform.

RESULTS

The final 16 targets were regarded as critical proteins against NAFLD, and Vascular Endothelial Growth Factor A (VEGFA) was a key target in PPI network analysis. The PI3K-Akt signaling pathway was the uppermost mechanism associated with VEGFA as an antagonistic mode. GASTM networks represented 122 nodes (60 GM, AS, PI3K-Akt signaling pathway, 4 targets, and 56 SMs) and 154 edges. The VEGFA-myricetin, or quercetin, GSK3B-myricetin, IL2-diosgenin complexes formed the most stable conformation, the three ligands were derived from GM. Conversely, NR4A1-vestitol formed stable conformation with the highest affinity, and the vestitol was obtained from AS. The given four SMs were no hurdles to develop into drugs devoid of its toxicity.

CONCLUSION

In conclusion, we show that combinatorial application of AS and GM might be exerted to the potent synergistic effects against NAFLD, dampening PI3K-Akt signaling pathway. This work provides the importance of dietary strategy and beneficial GM on NAFLD, a data mining basis for further explicating the SMs and pharmacological mechanisms of combinatorial application (AS and GM) against NAFLD.

Molecular Docking and Dynamics Simulation Studies Predict Potential Anti-ADAR2 Inhibitors: Implications for the Treatment of Cancer, Neurological, Immunological and Infectious Diseases

Altered RNA editing has been linked to several neurodevelopmental disorders, including autism spectrum disorder (ASD) and intellectual disability, in addition to depression, schizophrenia, some cancers, viral infections and autoimmune disorders. The human ADAR2 is a potential therapeutic target for managing these various disorders due to its crucial role in adenosine to inosine editing. This study applied consensus scoring to rank potential ADAR2 inhibitors after performing molecular docking with AutoDock Vina and Glide (Maestro), using a library of 35,161 compounds obtained from traditional Chinese medicine. A total of 47 compounds were predicted to be good binders of the human ADAR2 and had insignificant toxicity concerns. Molecular dynamics (MD) simulations, including the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) procedure, also emphasized the binding of the shortlisted compounds. The potential compounds had plausible binding free energies ranging from −81.304 to −1068.26 kJ/mol from the MM/PBSA calculations. ZINC000085511995, a naphthoquinone had more negative binding free energy (−1068.26 kJ/mol) than inositol hexakisphosphate (IHP) [−873.873 kJ/mol], an agonist and a strong binder of ADAR2. The potential displacement of IHP by ZINC000085511995 in the IHP binding site of ADAR2 could be explored for possible deactivation of ADAR2. Bayesian-based biological activity prediction corroborates the neuropharmacological, antineoplastic and antiviral activity of the potential lead compounds. All the potential lead compounds, except ZINC000014612330 and ZINC000013462928, were predicted to be inhibitors of various deaminases. The potential lead compounds also had probability of activity (Pa) > 0.442 and probability of inactivity (Pi) < 0.116 values for treating acute neurologic disorders, except for ZINC000085996580 and ZINC000013462928. Pursuing these compounds for their anti-ADAR2 activities holds a promising future, especially against neurological disorders, some cancers and viral infections caused by RNA viruses. Molecular interaction, hydrogen bond and per-residue decomposition analyses predicted Arg400, Arg401, Lys519, Trp687, Glu689, and Lys690 as hot-spot residues in the ADAR2 IHP binding site. Most of the top compounds were observed to have naphthoquinone, indole, furanocoumarin or benzofuran moieties. Serotonin and tryptophan, which are beneficial in digestive regulation, improving sleep cycle and mood, are indole derivatives. These chemical series may have the potential to treat neurological disorders, prion diseases, some cancers, specific viral infections, metabolic disorders and eating disorders through the disruption of ADAR2 pathways. A total of nine potential lead compounds were shortlisted as plausible modulators of ADAR2.

Structure-based design of promising natural products to inhibit thymidylate kinase from Monkeypox virus and validation using free energy calculations

Monkeypox (MPXV) is a globally growing public health concern with 80,328 active cases and 53 deaths have been reported. No specific vaccine or drug is available for the treatment of MPXV. Hence, the current study also employed structure-based drug designing, molecular simulation, and free energy calculation methods to identify potential hit molecules against the TMPK of MPXV, which is a replicatory protein that helps the virus to replicate its DNA and increase the number of DNAs in the host cell. The 3D structure of TMPK was modeled with AlphaFold and screening of multiple natural products libraries (4,71,470 compounds) identified TCM26463, TCM2079, and TCM29893 from traditional Chinese medicines database (TCM), SANC00240, SANC00984, and SANC00986 South African natural compounds database (SANCDB), NPC474409, NPC278434 and NPC158847 from NPASS (natural product activity and species source database) while CNP0404204, CNP0262936, and CNP0289137 were shortlisted from coconut database (collection of open natural products) as the best hits. These compounds interact with the key active site residues through hydrogen bonds, salt bridges, and pie-pie interactions. The structural dynamics and binding free energy results further revealed that these compounds possess stable dynamics with excellent binding free energy scores. Moreover, the dissociation constant (K_D) and bioactivity analysis revealed stronger activity of these compounds exhibit stronger biological activity against MPXV and may inhibit it in in vitro conditions. All the results demonstrated that the designed novel compounds possess stronger inhibitory activity than the control complex (TPD-TMPK) from the vaccinia virus. The current study is the first to design small molecule inhibitors for the replication protein of MPXV which may help in controlling the current epidemic and also overcome the challenge of vaccine evasion.

Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

Systematic Description of the Content Variation of Natural Products (NPs): To Prompt the Yield of High-Value NPs and the Discovery of New Therapeutics

Natural products (NPs) have long been associated with human production and play a key role in the survival of species. Significant variations in NP content may severely affect the "return on investment" of NP-based industries and render ecological systems vulnerable. Thus, it is crucial to construct a platform that relates variations in NP content to their corresponding mechanisms. In this study, a publicly accessible online platform, NPcVar (http://npcvar.idrblab.net/), was developed, which systematically described the variations of NP contents and their corresponding mechanisms. The platform comprises 2201 NPs and 694 biological resources, including plants, bacteria, and fungi, curated using 126 diverse factors with 26,425 records. Each record contains information about the species, NP, and factors involved, as well as NP content data, parts of the plant that produce NPs, the location of the experiment, and reference information. All factors were manually curated and categorized into 42 classes which belong to four mechanisms (molecular regulation, species factor, environmental condition, and combined factor). Additionally, the cross-links of species and NP to well-established databases and the visualization of NP content under various experimental conditions were provided. In conclusion, NPcVar is a valuable resource for understanding the relationship between species, factors, and NP contents and is anticipated to serve as a promising tool for improving the yield of high-value NPs and facilitating the development of new therapeutics.

Virtual screening and multilevel precision-based prioritisation of natural inhibitors targeting the ATPase domain of human DNA topoisomerase II alpha

Human DNA topoisomerase II alpha (hTopIIα) is a classic chemotherapeutic drug target. The existing hTopIIα poisons cause numerous side effects such as the development of cardiotoxicity, secondary malignancies, and multidrug resistance. The use of catalytic inhibitors targeting the ATP-binding cavity of the enzyme is considered a safer alternative due to the less deleterious mechanism of action. Hence, in this study, we carried out high throughput structure-based virtual screening of the NPASS natural product database against the ATPase domain of hTopIIα and identified the five best ligand hits. This was followed by comprehensive validation through molecular dynamics simulations, binding free energy calculation and ADMET analysis. On stringent multilevel prioritization, we identified promising natural product catalytic inhibitors that showed high binding affinity and stability within the ligand-binding cavity and may serve as ideal hits for anticancer drug development.Communicated by Ramaswamy H. Sarma.

Exploring the components and mechanism of Solanum nigrum L. for colon cancer treatment based on network pharmacology and molecular docking

Background

Solanum nigrum L. (SNL) (Longkui) is a Chinese herb that can be used to treat colon cancer. The present study explored the components and mechanisms of SNL in treating colon cancer by using network pharmacology and molecular docking.

Methods

The components of SNL were collected from the TCMSP, ETCM, HERB, and NPASS databases. Meanwhile, the target proteins of these ingredients were collected/predicted by the TCMSP, SEA, SwissTargetPrediction, and the STITCH databases colon cancer-related target genes were identified from TCGA and GTEx databases. The interaction networks were established via Cytoscape 3.7.2. Gene Ontology and KEGG pathways were enriched by using the David 6.8 online tool. Finally, the binding of key components and targets was verified by molecular docking, and the cellular thermal shift assay (CETSA) was used to detect the efficiency of apigenin and kaempferol binding to the AURKB protein in CT26 cells.

Results

A total of 37 SNL components, 796 SNL targets, 5,356 colon cancer genes, and 241 shared targets of SNL and colon cancer were identified. A total of 43 key targets were obtained through topology analysis. These key targets are involved in multiple biological processes, such as signal transduction and response to drug and protein phosphorylation. At the same time, 104 signaling pathways, such as pathways in cancer, human cytomegalovirus infection, and PI3K-Akt signaling pathway, are also involved. The binding of the four key components (i.e., quercetin, apigenin, kaempferol, and luteolin) and the key targets was verified by molecular docking. The CETSA results showed that apigenin and kaempferol were able to bind to the AURKB protein to exert anti-CRC effects.

Conclusions

Quercetin, apigenin, kaempferol, and luteolin are the main components of SNL in treating colon cancer. SNL regulates multiple bioprocesses via signaling pathways, such as pathways in cancer, PI3K-Akt, and cell cycle signaling pathways.

A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

As a type of stress hormone, glucocorticoids (GCs) affect numerous physiological pathways by binding to the glucocorticoid receptor (GR) and regulating the transcription of various genes. However, when GCs are dysregulated, the resulting hypercortisolism may contribute to various metabolic disorders, including obesity. Thus, attempts have been made to discover potent GR antagonists that can reverse excess-GC-related metabolic diseases. Phytochemicals are a collection of valuable bioactive compounds that are known for their wide variety of chemotypes. Recently, various computational methods have been developed to obtain active phytochemicals that can modulate desired target proteins. In this study, we developed a workflow comprising two consecutive quantitative structure-activity relationship-based machine learning models to discover novel GR-antagonizing phytochemicals. These two models collectively identified 65 phytochemicals that bind to and antagonize GR. Of these, nine commercially available phytochemicals were validated for GR-antagonist and anti-obesity activities. In particular, we confirmed that demethylzeylasteral, a phytochemical of the Tripterygium wilfordii Radix, exhibits potent anti-obesity activity in vitro through GR antagonism.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Flavonoids and saponins: What have we got or missed?

BACKGROUND

Flavonoids and saponins are important bioactive compounds that have attracted wide research interests. This review aims to summarise the state of the art of the pharmacology, toxicology and clinical efficacy of these compounds.

METHODS

Data were retrieved from PubMed, Cochrane Library, Web of Science, Proquest, CNKI, Chongqing VIP, Wanfang, NPASS and HIT 2.0 databases. Meta-analysis and systematic reviews were evaluated following the PRISMA guideline. Statistical analyses were conducted using SPSS23.0.

RESULTS

Rising research trends on flavonoids and saponins were observed since the 1990s and the 2000s, respectively. Studies on pharmacological targets and activities of flavonoids and saponins represent an important area of research advances over the past decade, and these important resources have been documented in open-access specialised databases and can be retrieved with ease. The rising research on flavonoids and saponins can be attributed, at least in part, to their links with some highly investigated fields of research, e.g., oxidative stress, inflammation and cancer; i.e., 6.88% and 3.03% of publications on oxidative stress cited by PubMed in 1990 - 2021 involved flavonoids and saponins, respectively, significantly higher than the percentage involving alkaloids (1.88%). The effects of flavonoids concern chronic venous insufficiency, cervical lesions, diabetes, rhinitis, dermatopathy, prostatitis, menopausal symptoms, angina pectoris, male pattern hair loss, lymphocytic leukaemia, gastrointestinal diseases and traumatic cerebral infarction, etc, while those of saponins may have impact on venous oedema in chronic deep vein incompetence, erectile dysfunction, acute impact injuries and systemic lupus erythematosus, etc. The volume of in vitro research appears way higher than in vivo and clinical studies, with only 10 meta-analyses and systematic reviews (involving 290 interventional and observational studies), and 36 clinical studies on flavonoids and saponins. Data are sorely needed on pharmacokinetics, in vitro pan-assay interferences, purity of tested compounds, interactions in complex herbal extracts, real impact of anti-oxidative strategies, and mid- and long-term toxicities. To fill these important gaps, further investigations are warranted. On the other hand, drug interactions may cause adverse effects but might also be useful for synergism, with the goals of enhancing effects or of detoxifying. Furthermore, the interactions between phytochemicals and the intestinal microbiota are worth investigating as the field may present a promising potential for novel drug development.

Antimicrobial resistance in Klebsiella pneumoniae: identification of bacterial DNA adenine methyltransferase as a novel drug target from hypothetical proteins using subtractive genomics

Klebsiella pneumoniae is a gram-negative bacterium that is known for causing infection innosocomial settings. As reported by the World Health Organization, carbapenem-resistantEnterobacteriaceae, a category that includes K. pneumoniae, are classified as an urgentthreat, and the greatest concern is that these bacterial pathogens may acquire genetictraits that make them resistant towards antibiotics. The last class of antibiotics, carbapenems, are not able to combat these bacterial pathogens, allowing them to clonally expandantibiotic-resistant strains. Most antibiotics target essential pathways of bacterial cells;however, these targets are no longer susceptible to antibiotics. Hence, in our study, we focused on a hypothetical protein in K. pneumoniae that contains a DNA methylation proteindomain, suggesting a new potential site as a drug target. DNA methylation regulates theattenuation of bacterial virulence. We integrated computational-aided drug design by using a bioinformatics approach to perform subtractive genomics, virtual screening, and fingerprint similarity search. We identified a new potential drug, koenimbine, which could bea novel antibiotic.

Virtual screening of natural compounds as selective inhibitors of polo-like kinase-1 at C-terminal polo box and N-terminal catalytic domain

The over-expression of Polo-like kinase-1 (PLK1) is associated with cancer prognosis due to its pivotal role in cell proliferation. The N-terminal catalytic domain (NCD) and C-terminal polo box domain (PBD) of PLK1 are critical for the activity of the protein. Drugs that inhibit PLK1 by targeting these domains are on clinical trials, but so far, none has been approved by FDA. Thus, this study targets the two domains of PLK1 to identify compounds with inhibitory potential. Four validated e-pharmacophore models from NCD (PDB ID: 2OU7 and 4J52) and PBD (PDB ID: 5NEI and 5NN2) were used to screen over 26,000 natural compounds from NPASS database. Hits were identified after the well-fitted compounds were subjected to molecular docking study and ADME prediction. The pIC50 and electronic behaviour of the identified hits selectively targeting NCD and PBD of PLK1 were predicted via an externally validated QSAR model and quantum mechanics. The results showed that CAA180504, CAA197326, CAA74619, CAA328856 modulating PLK1 at NCD, and CBB130581, CBB230713, CBB206123, CBB12656 and CBB267117 modulating PLK1 at PBD had better molecular docking scores, pharmacokinetics and drug-like properties than NCD (volasertib) and PBD (purpurogallin) reference inhibitors. The compounds all had satisfactory inhibitory (pIC50) values which range from 6.187 to 7.157. The electronic behaviours of understudied compounds using HOMO/LUMO and global descriptive parameters revealed the atomic portion of the compounds prone to donating and accepting electrons. In conclusion, the hit compounds identified from the library of natural compounds are worthy of further experimental validation.Communicated by Ramaswamy H. Sarma.

NPASS database update 2023: quantitative natural product activity and species source database for biomedical research

Quantitative activity and species source data of natural products (NPs) are important for drug discovery, medicinal plant research, and microbial investigations. Activity values of NPs against specific targets are useful for discovering targeted therapeutic agents and investigating the mechanism of medicinal plants. Composition/concentration values of NPs in individual species facilitate the assessments and investigations of the therapeutic quality of herbs and phenotypes of microbes. Here, we describe an update of the NPASS natural product activity and species source database previously featured in NAR. This update includes: (i) new data of ∼95 000 records of the composition/concentration values of ∼1 490 NPs/NP clusters in ∼390 species, (ii) extended data of activity values of ∼43 200 NPs against ∼7 700 targets (∼40% and ∼32% increase, respectively), (iii) extended data of ∼31 600 species sources of ∼94 400 NPs (∼26% and ∼32% increase, respectively), (iv) new species types of ∼440 co-cultured microbes and ∼420 engineered microbes, (v) new data of ∼66 600 NPs without experimental activity values but with estimated activity profiles from the established chemical similarity tool Chemical Checker, (vi) new data of the computed drug-likeness properties and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for all NPs. NPASS update version is freely accessible at http://bidd.group/NPASS.

Deciphering the impact and mechanism of Trikatu, a spices-based formulation on alcoholic liver disease employing network pharmacology analysis and in vivo validation

Trikatu Churna (TC) comprising Zingiber officinale rhizome, Piper longum, and Piper nigrum fruit, is effective in treating liver diseases and has high nutraceutical values. However, the efficacy of TC in treating alcoholic liver disease (ALD) and its mechanism remain largely unknown. This study evaluated the hepatoprotective effects of different doses of TC as well as to identify the bioactive components and determine their mechanism of action against ethanol-induced ALD. A compound-target network analysis model of TC was established to identify its potential bioactive compounds and pathways that might regulate its hepatoprotective effects. Further, in-vivo studies were performed to validate the potential of TC (200 mg/kg and 400 mg/kg b.w.) in the treatment and management of ALD. The study revealed that both the dosages of TC demonstrate significant (p > 0.0001) hepatoprotective effects by improving body weight, total bilirubin, serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT), serum alkaline phosphate (ALP), total cholesterol, total protein, globulin, albumin, and liver morphology. The High-performance thin-layer chromatography (HPTLC) fingerprinting of TC showed the presence of piperine. Network pharmacology identifies the role of TC in regulating various signaling processes including Advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE), Hypoxia-inducible factors (HIF-1), Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-Kappa B), and Phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling to exert its anti-inflammatory, antioxidant and anti-apoptotic role in managing ALD. Based on the bioinformatics analysis, some of the key targets of TC were found to be Prostaglandin-Endoperoxide Synthase 2 (PTGS2) or Cyclooxygenase-2 (COX-2), Sirtuin 1 (SRT1), and caspase-3. These effects may serve as a novel therapeutic option for the treatment of ALD. These preclinical validation studies for the ethnopharmacological potential of TC in ALD treatment further paved the way for researchers to perform next-level translational and clinical studies. Further, in-depth experimental studies for the validation of these bioinformatics-based results will give a clearer picture of mechanisms.

Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review

Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.

Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro

Differentially expressed genes can serve as drug targets and are used to predict drug response and disease progression. In silico drug analysis based on the expression of these genetic biomarkers allows the detection of putative therapeutic agents, which could be used to reverse a pathological gene expression signature. Indeed, a set of bioinformatics tools can increase the accuracy of drug discovery, helping in biomarker identification. Once a drug target is identified, in vitro cell line models of disease are used to evaluate and validate the therapeutic potential of putative drugs and novel natural molecules. This study describes the development of efficacious PCR primers that can be used to identify gene expression of specific genetic pathways, which can lead to the identification of natural molecules as therapeutic agents in specific molecular pathways. For this study, genes involved in health conditions and processes were considered. In particular, the expression of genes involved in obesity, xenobiotics metabolism, endocannabinoid pathway, leukotriene B4 metabolism and signaling, inflammation, endocytosis, hypoxia, lifespan, and neurotrophins were evaluated. Exploiting the expression of specific genes in different cell lines can be useful in in vitro to evaluate the therapeutic effects of small natural molecules.

Targeting and ultrabroad insight into molecular basis of Resistance-nodulation-cell division efflux pumps

Resistance-nodulation-cell devision (RND) efflux pump variants have attracted a great deal of attention for efflux of many antibiotic classes, which leads to multidrug-resistant bacteria. The present study aimed to discover the interaction between the RND efflux pumps and antibiotics, find the conserved and hot spot residues, and use this information to target the most frequent RND efflux pumps. Protein sequence and 3D conformational alignments, pharmacophore modeling, molecular docking, and molecular dynamics simulation were used in the first level for discovering the function of the residues in interaction with antibiotics. In the second level, pharmacophore-based screening, structural-based screening, multistep docking, GRID MIF, pharmacokinetic modeling, fragment molecular orbital, and MD simulation were utilized alongside the former level information to find the most proper inhibitors. Five conserved residues, containing Ala209, Tyr404, Leu415, Asp416, and Ala417, as well as their counterparts in other OMPs were evaluated as the crucial conserved residues. MD simulation confirmed that a number of these residues had a key role in the performance of the efflux antibiotics; therefore, some of them were hot spot residues. Fourteen ligands were selected, four of which interacted with all the crucial conserved residues. NPC100251 was the fittest OMP inhibitor after pharmacokinetic computations. The second-level MD simulation and FMO supported the efficacy of the NPC100251. It was exhibited that perhaps OMPs worked as the intelligent and programable protein. NPC100251 was the strongest OMPs inhibitor, and may be a potential therapeutic candidate for MDR infections.

Large-scale comparison of machine learning algorithms for target prediction of natural products

Natural products (NPs) and their derivatives are important resources for drug discovery. There are many in silico target prediction methods that have been reported, however, very few of them distinguish NPs from synthetic molecules. Considering the fact that NPs and synthetic molecules are very different in many characteristics, it is necessary to build specific target prediction models of NPs. Therefore, we collected the activity data of NPs and their derivatives from the public databases and constructed four datasets, including the NP dataset, the NPs and its first-class derivatives dataset, the NPs and all its derivatives and the ChEMBL26 compounds dataset. Conditions, including activity thresholds and input features, were explored to access the performance of eight machine learning methods of target prediction of NPs, including support vector machines (SVM), extreme gradient boosting, random forests, K-nearest neighbor, naive Bayes, feedforward neural networks (FNN), convolutional neural networks and recurrent neural networks. As a result, the NPs and all their derivatives datasets were selected to build the best NP-specific models. Furthermore, the consensus models, as well as the voting models, were additionally applied to improve the prediction performance. More evaluations were made on the external validation set and the results demonstrated that (1) the NP-specific model performed better on the target prediction of NPs than the traditional models training on the whole compounds of ChEMBL26. (2) The consensus model of FNN + SVM possessed the best overall performance, and the voting model can significantly improve recall and specificity.