HIM-herbal ingredients in-vivo metabolism database

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

Molecular exploration of natural and synthetic compounds databases for promising hypoxia inducible factor (HIF) Prolyl-4- hydroxylase domain (PHD) inhibitors using molecular simulation and free energy calculations

Hypoxia-inducible factors (HIFs) are transcription factors that regulate erythropoietin (EPO) synthesis and red blood cell (RBC) production. Prolyl-4-hydroxylase domain (PHD) enzymes are key regulators of HIF's stability and activity. Inhibiting PHD enzymes can enhance HIF-mediated responses and have therapeutic potential for diseases such as anemia, cancer, stroke, ischemia, neurodegeneration, and inflammation. In this study, we searched for novel PHD inhibitors from four databases of natural products and synthetic compounds: AfroDb Natural Products, AnalytiCon Discovery Natural Product (NP), HIM-Herbal Ingredients In-Vivo Metabolism, and Herbal Ingredients' Targets, with a total number of 13,597 compounds. We screened the candidate compounds by molecular docking and validated them by molecular dynamics simulations and free energy calculations. We identified four target hits (ZINC36378940, ZINC2005305, ZINC31164438, and ZINC67910437) that showed stronger binding affinity to PHD2 compared to the positive control, Vadadustat (AKB-6548), with docking scores of - 13.34 kcal/mol, - 12.76 kcal/mol, - 11.96 kcal/mol, - 11.41 kcal/mol, and - 9.04 kcal/mol, respectively. The target ligands chelated the active site iron and interacted with key residues (Arg 383, Tyr329, Tyr303) of PHD2, in a similar manner as Vadadustat. Moreover, the dynamic stability-based assessment revealed that they also exhibited stable dynamics and compact trajectories. Then the total binding free energy was calculated for each complex which revealed that the control has a TBE of - 31.26 ± 0.30 kcal/mol, ZINC36378940 reported a TBE of - 38.65 ± 0.51 kcal/mol, for the ZINC31164438 the TBE was - 26.16 ± 0.30 kcal/mol while the ZINC2005305 complex reported electrostatic energy of - 32.75 ± 0.58 kcal/mol. This shows that ZINC36378940 is the best hit than the other and therefore further investigation should be performed for the clinical usage. Our results suggest that these target hits are promising candidates that reserve further in vitro and in vivo validations as potential PHD inhibitors for the treatment of renal anemia, cancer, stroke, ischemia, neurodegeneration, and inflammation.

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.

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Unraveling the potential mechanisms of the anti-osteoporotic effects of the Achyranthes bidentata-Dipsacus asper herb pair: a network pharmacology and experimental study

Background: Osteoporosis is a prevalent bone metabolism disease characterized by a reduction in bone density, leading to several complications that significantly affect patients' quality of life. The Achyranthes bidentata-Dipsacus asper (AB-DA) herb pair is commonly used in Traditional Chinese Medicine (TCM) to treat osteoporosis. This study aimed to investigate the therapeutic compounds and potential mechanisms of AB-DA using network pharmacology, molecular docking, molecular dynamics simulation, and experimental verification. Methods: Identified compounds of AB-DA were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicine Information Database (TCM-ID), TCM@Taiwan Database, BATMAN-TCM, and relevant literature. The main bioactive ingredients were screened based on the criteria of "OB (oral bioavailability) ≥ 30, DL (drug-likeness) ≥ 0.18." Potential targets were predicted using the PharmMapper and SwissTargetPrediction websites, while disease (osteoporosis)-related targets were obtained from the GeneCards, DisGeNET, and OMIM databases. The PPI network and KEGG/GO enrichment analysis were utilized for core targets and pathway screening in the STRING and Metascape databases, respectively. A drug-compound-target-pathway-disease network was constructed using Cytoscape software to display core regulatory mechanisms. Molecular docking and dynamics simulation techniques explored the binding reliability and stability between core compounds and targets. In vitro and in vivo validation experiments were utilized to explore the anti-osteoporosis efficiency and mechanism of sitogluside. Results: A total of 31 compounds with 83 potential targets for AB-DA against osteoporosis were obtained. The PPI analysis revealed several hub targets, including AKT1, CASP3, EGFR, IGF1, MAPK1, MAPK8, and MAPK14. GO/KEGG analysis indicated that the MAPK cascade (ERK/JNK/p38) is the main pathway involved in treating osteoporosis. The D-C-T-P-T network demonstrated therapeutic compounds that mainly consisted of iridoids, steroids, and flavonoids, such as sitogluside, loganic acid, and β-ecdysterone. Molecular docking and dynamics simulation analyses confirmed strong binding affinity and stability between core compounds and targets. Additionally, the validation experiments showed preliminary evidence of antiosteoporosis effects. Conclusion: This study identified iridoids, steroids, and flavonoids as the main therapeutic compounds of AB-DA in treating osteoporosis. The underlying mechanisms may involve targeting core MAPK cascade (ERK/JNK/p38) targets, such as MAPK1, MAPK8, and MAPK14. In vivo experiments preliminarily validated the anti-osteoporosis effect of sitogluside. Further in-depth experimental studies are required to validate the therapeutic value of AB-DA for treating osteoporosis in clinical practice.

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries.

DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.

Exploring the mechanism of Alisma orientale for the treatment of pregnancy induced hypertension and potential hepato-nephrotoxicity by using network pharmacology, network toxicology, molecular docking and molecular dynamics simulation

Background: Pregnancy-induced Hypertension (PIH) is a disease that causes serious maternal and fetal morbidity and mortality. Alisma Orientale (AO) has a long history of use as traditional Chinese medicine therapy for PIH. This study explores its potential mechanism and biosafety based on network pharmacology, network toxicology, molecular docking and molecular dynamics simulation. Methods: Compounds of AO were screened in TCMSP, TCM-ID, TCM@Taiwan, BATMAN, TOXNET and CTD database; PharmMapper and SwissTargetPrediction, GeneCards, DisGeNET and OMIM databases were used to predict the targets of AO anti-PIH. The protein-protein interaction analysis and the KEGG/GO enrichment analysis were applied by STRING and Metascape databases, respectively. Then, we constructed the "herb-compound-target-pathway-disease" map in Cytoscape software to show the core regulatory network. Finally, molecular docking and molecular dynamics simulation were applied to analyze binding affinity and reliability. The same procedure was conducted for network toxicology to illustrate the mechanisms of AO hepatotoxicity and nephrotoxicity. Results: 29 compounds with 78 potential targets associated with the therapeutic effect of AO on PIH, 10 compounds with 117 and 111 targets associated with AO induced hepatotoxicity and nephrotoxicity were obtained, respectively. The PPI network analysis showed that core therapeutic targets were IGF, MAPK1, AKT1 and EGFR, while PPARG and TNF were toxicity-related targets. Besides, GO/KEGG enrichment analysis showed that AO might modulate the PI3K-AKT and MAPK pathways in treating PIH and mainly interfere with the lipid and atherosclerosis pathways to induce liver and kidney injury. The "herb-compound-target-pathway-disease" network showed that triterpenoids were the main therapeutic compounds, such as Alisol B 23-Acetate and Alisol C, while emodin was the main toxic compounds. The results of molecular docking and molecular dynamics simulation also showed good binding affinity between core compounds and targets. Conclusion: This research illustrated the mechanism underlying the therapeutic effects of AO against PIH and AO induced hepato-nephrotoxicity. However, further experimental verification is warranted for optimal use of AO during clinical practice.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

Inhibition of Hepatitis E Virus Replication by Novel Inhibitor Targeting Methyltransferase

Hepatitis E Virus (HEV) is a quasi-enveloped virus having a single-stranded, positive-sense RNA genome (~7.2 kb), flanked with a 5′ methylated cap and a 3′ polyadenylated tail. The HEV open reading frame 1 (ORF1) encodes a 186-kDa polyprotein speculated to get processed and produce Methyltransferase (MTase), one of the four essential replication enzymes. In this study, we report the identification of the MTase inhibitor, which may potentially deplete its enzymatic activity, thus causing the cessation of viral replication. Using in silico screening through docking, we identified ten putative compounds, which were tested for their anti-MTase activity. This resulted in the identification of 3-(4-Hydroxyphenyl)propionic acid (HPPA), with an IC₅₀ value of 0.932 ± 0.15 μM, which could be perceived as an effective HEV inhibitor. Furthermore, the compound was tested for inhibition of HEV replication in the HEV culture system. The viral RNA copies were markedly decreased from ~3.2 × 10⁶ in untreated cells to ~4.3 × 10^2.8 copies in 800 μM HPPA treated cells. Therefore, we propose HPPA as a potential drug-like inhibitor against HEV-MTase, which would need further validation through in vivo analysis using animal models and the administration of Pharmacokinetic and Pharmacodynamic (PK/PD) studies.

MetNC: Predicting Metabolites in vivo for Natural Compounds

Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.

HIT 2.0: an enhanced platform for Herbal Ingredients' Targets

Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients’ Targets covering PubMed literatures 2000–2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into ‘My-target’ system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.

A review on network pharmacology based phytotherapy in treating diabetes- An environmental perspective

Diabetes has become common lifestyle disorder associated with obesity and cardiovascular diseases. Environmental factors like physical inactivity, polluted surroundings and unhealthy dieting also plays a vital role in diabetes pathogenesis. As the current anti-diabetic drugs possess unprecedented side effects, traditional herbal medicine can be used an alternative therapy. The paramount challenge with the herbal formulation usage is the lack of standardized procedure, entangled with little knowledge on drug safety and mechanism of drug action. Heavy metal contamination is a major environmental hazard where plants tend to accumulate toxic metals like nickel, chromium and lead through industrial and agricultural activities. It becomes inappropriate to use these plants for phytotherapy as it may affect the human health on long term consumption. This review discuss about the environmental risk factors related to diabetes and better implication of medicinal plants in anti-diabetic therapy using network pharmacology. It is an in silico analytical tool that helps to unravel the multi-targeted action of herbal formulations rich in secondary metabolites. Also, a special focus is attempted to pool the databases regarding the medicinal plants for diabetes and associated diseases, their bioactive compounds, possible diabetic targets, drug-target interaction and toxicology reports that may open an aisle in safer, effective and toxicity-free drug discovery.

Latin American databases of natural products: biodiversity and drug discovery against SARS-CoV-2

In this study, we evaluated 3444 Latin American natural products using cheminformatic tools. We also characterized 196 compounds for the first time from the flora of El Salvador that were compared with the databases of secondary metabolites from Brazil, Mexico, and Panama, and 42 969 compounds (natural, semi-synthetic, synthetic) from different regions of the world. The overall analysis was performed using drug-likeness properties, molecular fingerprints of different designs, two parameters similarity, molecular scaffolds, and molecular complexity metrics. It was found that, in general, Salvadoran natural products have a large diversity based on fingerprints. Simultaneously, those belonging to Mexico and Panama present the greatest diversity of scaffolds compared to the other databases. This study provided evidence of the high structural complexity that Latin America's natural products have as a benchmark. The COVID-19 pandemic has had a negative effect on a global level. Thus, in the search for substances that may influence the coronavirus life cycle, the secondary metabolites from El Salvador and Panama were evaluated by docking against the endoribonuclease NSP-15, an enzyme involved in the SARS CoV-2 viral replication. We propose in this study three natural products as potential inhibitors of NSP-15.

COCONUT online: Collection of Open Natural Products database

Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other in silico applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at https://coconut.naturalproducts.net .

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Study on the Mechanism of Ginseng in the Treatment of Lung Adenocarcinoma Based on Network Pharmacology

Background

Ginseng, a traditional Chinese medicine, was used to prevent and treat many diseases such as diabetes, inflammation, and cancer. In recent years, there are some reports about the treatment of lung adenocarcinoma with ginseng monomer compounds, but there is no systematic study on the related core targets and mechanism of ginseng in the treatment of lung adenocarcinoma up to now. Therefore, this study systematically and comprehensively studied the molecular mechanism of ginseng in the treatment of lung adenocarcinoma based on network pharmacology and further proved the potential targets by A549 cell experiments for the first time.

Methods

The targets of disease and drug were obtained from Gene database. Subsequently, the compound-target network was constructed, and the core potential targets were screened out by plug-in into Cytoscape. Furthermore, the core targets and mechanism of ginseng in the treatment of lung adenocarcinoma were verified by MTT test, cell scratch test, immunohistochemistry, and qRT-PCR.

Results

1791 disease targets and 144 drug targets were obtained by searching the Gene database. Meanwhile, 15 core targets were screened out: JUN, MAPK8, PTGS2, CASP3, VEGFA, MMP9, AKT1, TNF, FN1, FOS, MMP782, IL-1β, IL-2, ICAM1, and HMOX1. The results of cell experiments indicate that ginseng could treat lung adenocarcinoma by cell proliferation, migration, and apoptosis. In addition, according to the results of the 15 core targets by qRT-PCR, JUN, IL-1β, IL-2, ICAM1, HMOX1, MMP9, and MMP2 are upregulated core targets, while PTGS2 and TNF are downregulated core targets.

Conclusion

This study systematically and comprehensively studied 15 core targets by network pharmacology for the first time. Subsequently, it is verified that 9 core targets for ginseng treatment of lung adenocarcinoma, namely, JUN, IL-1β, IL-2, ICAM1, HMOX1, MMP9, MMP2, PTGS2, and TNF, are closely related to the proliferation, migration, and apoptosis of lung adenocarcinoma cells. This study has reference value for the clinical application of ginseng in the treatment of lung adenocarcinoma.

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

Fragment-based drug design of nature-inspired compounds

Natural product (NP)-derived drugs can be extracts, biological macromolecules, or purified small molecule substances. Small molecule drugs can be originally purified from NPs, can represent semisynthetic molecules, natural fragments containing small molecules, or are fully synthetic molecules that mimic natural compounds. New semisynthetic NP-like drugs are entering the pharmaceutical market almost every year and reveal growing interests in the application of fragment-based approaches for NPs. Thus, several NP databases were constructed to be implemented in the fragment-based drug design (FBDD) workflows. FBDD has been established previously as an approach for hit identification and lead generation. Several biophysical and computational methods are used for fragment screening to identify potential hits. Once the fragments within the binding pocket of the protein are identified, they can be grown, linked, or merged to design more active compounds. This work discusses applications of NPs and NP scaffolds to FBDD. Moreover, it briefly reviews NP databases containing fragments and reports on case studies where the approach has been successfully applied for the design of antimalarial and anticancer drug candidates.

Structure based virtual screening of natural products to disrupt the structural integrity of TRAF6 C-terminal domain homotrimer

Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an E3 ligase which takes part in different cellular pathways. TRAF6 is seen to be highly expressed in various cancers and most importantly is known to drive cancer metastasis. This makes TRAF6 a potential therapeutic target. In our previous studies, we observed that the C-terminal domain of TRAF6 forms a mushroom shaped trimer structure. Lys340 and Glu345 were identified to be the most critical residues in the trimer interface. In this current work, we screened for more than 14000 small molecules derived from various natural sources and they were screened against TRAF6 C-terminal trimer interaction interface to prevent the formation of the interface. All the obtained molecules were tested for their drug-likeliness properties. The ligands which qualified the filter were considered for protein-ligand docking or structure based virtual screening in GOLD 5.2. Pose selection was carried out on the basis of GoldScore and ChemScore function of GOLD 5.2. Top 20 molecules binding the TRAF6 trimeric interface were tested for their ADME properties. From the top 20 molecules, top 3 ligands were chosen based on their abilities to pass the maximum numbers of ADME filters.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Structure-based identification of potent VEGFR-2 inhibitors from in vivo metabolites of a herbal ingredient

Vascular endothelial growth factor receptor-2 (VEGFR-2) is one of the regulatory elements of angiogenesis that is expressed highly in various diseases and is also essential for solid tumor growth. The present study was aimed at identifying potent inhibitors of VEGFR-2 by considering herbal secondary metabolites; as natural molecules are less toxic than synthetic derivatives. A structure-based virtual screening protocol consisting of molecular docking, MM-GBSA and ADME/T analysis was initially used to screen a library of in vivo metabolites of the herbal ingredient. Using a fixed cutoff value, four potent virtual hits were identified from molecular docking, ADME/T and binding affinity calculations, which were considered further for molecular dynamics (MD) simulation to broadly describe the binding mechanisms to VEGFR-2. The results suggested that these molecules have high affinity for the catalytic region of VEGFR-2, and form strong hydrophobic and polar interactions with the amino acids involved in the binding site of ATP and linker regions of the catalytic site. Subsequently, the stability of the docked complexes and binding mechanisms were evaluated by MD simulations, and the energy of binding was calculated through MM-PBSA analysis. The results uncovered two virtual hits, designated ZINC14762520 and ZINC36470466, as VEGFR-2 inhibitors, and suggested that they bind to kinase domain in an ATP-competitive manner. These virtual hits will offer a suitable starting point for the further design of their various analogs, allowing a rational search for more effective inhibitors in the future. Graphical abstract.

NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules

Natural products (NPs) remain the most prolific resource for the development of small-molecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates similarity maps, which highlight atoms that contribute significantly to the classification of small molecules as a natural product or synthetic molecule. The method can hence be utilized to (i) identify natural products in large molecular libraries, (ii) quantify the natural product-likeness of small molecules, and (iii) visualize atoms in small molecules that are characteristic of natural products or synthetic molecules. The models are based on random forest classifiers trained on data sets consisting of more than 265,000 to 322,000 natural products and synthetic molecules. Two-dimensional molecular descriptors, MACCS keys and Morgan2 fingerprints were explored. On an independent test set the models reached areas under the receiver operating characteristic curve (AUC) of 0.997 and Matthews correlation coefficients (MCCs) of 0.954 and higher. The method was further tested on data from the Dictionary of Natural Products, ChEMBL and other resources. The best-performing models are accessible as a free web service at http://npscout.zbh.uni-hamburg.de/npscout.

Resources for Chemical, Biological, and Structural Data on Natural Products

Natural products from plants, marine life, animals, fungi, bacteria, and other organisms remain the most productive source of inspiration for small-molecule drug discovery. Today, a wealth of information on natural products that is particularly valuable to applications in cheminformatics is at our disposal. In this contribution, we provide a timely overview of relevant resources for measured chemical, biological, and structural data on natural products. In particular, we comment on the accessibility, scope, chemical space, and limitations of the individual data sources. The bottleneck of natural products remains the limited availability of material for testing. In this context, we analyze the number of natural products readily obtainable from commercial and other sources.

Novel natural non-nucleoside inhibitors of HIV-1 reverse transcriptase identified by shape- and structure-based virtual screening techniques

In this work we report a parallel application of both docking- and shape-based virtual screening (VS) methods, followed by Molecular Dynamics simulations (MDs), for discovering new compounds able to inhibit the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) RNA-dependent DNA polymerase activity. Specifically, we screened more than 143000 natural compounds commercially available in the ZINC database against the best five RT crystallographic models, taking into account the five approved NNRTIs as query compounds. As a result, 20 hit molecules were selected and tested on biochemical assays for the inhibition of the RNA dependent DNA polymerase RT function and, among them, an indoline pyrrolidine (hit1), an indonyl piperazine (hit2) and an indolyl indolinone (hit3) derivatives were identified as novel non-nucleoside RT inhibitors in the low micromolar range.

Identification and analyses of natural compounds as potential inhibitors of TRAF6-Basigin interactions in melanoma using structure-based virtual screening and molecular dynamics simulations

The interaction of the proteins, tumor necrosis factor receptor-associated factor6 (TRAF6) and Basigin (CD147), is known to be associated with the over-expression of matrix metalloproteinases (MMPs) in melanoma cells. MMPs are known to be responsible for melanoma metastasis. Hence, the TRAF6-Basigin complex can act as a potential therapeutic target. In previous studies, amino acid residues Lys340, Lys 384, Glu417 and Glu511 of TRAF6 were identified as the most vital residues on the basis of their contributions to interaction energy, relative solvent accessibility and electrostatic interactions in the TRAF6-Basigin protein-protein interaction (PPI) scheme. In our current work, we performed structure-based virtual screenings of some natural compounds obtained from ZINC database (n = 14509) to search for molecules which can act as inhibitors against the formation of TRAF6-Basigin complex. Three potential inhibitors were identified which were observed to make intermolecular interactions with Lys384 and Glu511 of TRAF6. Molecular dynamics simulation results suggested the substantial pharmacological importance of the ligand molecules as it was observed that there was total destabilization of TRAF6-Basigin complex upon binding of the molecule ZINC02578057. From our studies, we could conclude that the ligands termed as ZINC49048033, ZINC02578057 and ZINC72320240 could have great potentials to act as inhibitors to prevent melanoma metastasis.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

Xuefu zhuyu decoction improves cognitive impairment in experimental traumatic brain injury via synaptic regulation

An overarching consequence of traumatic brain injury (TBI) is the cognitive impairment. It may hinder individual performance of daily tasks and determine people's subjective well-being. The damage to synaptic plasticity, one of the key mechanisms of cognitive dysfunction, becomes the potential therapeutic strategy of TBI. In this study, we aimed to investigate whether Xuefu Zhuyu Decoction (XFZYD), a traditional Chinese medicine, provided a synaptic regulation to improve cognitive disorder following TBI. Morris water maze and modified neurological severity scores were performed to assess the neurological and cognitive abilities. The PubChem Compound IDs of the major compounds of XFZYD were submitted into BATMAN-TCM, an online bioinformatics analysis tool, to predict the druggable targets related to synaptic function. Furthermore, we validated the prediction through immunohistochemical, RT-PCR and western blot analyses. We found that XFZYD enhanced neuroprotection, simultaneously improved learning and memory performances in controlled cortical impact rats. Bioinformatics analysis revealed that the improvements of XFZYD implied the Long-term potentiation relative proteins including NMDAR1, CaMKII and GAP-43. The further confirmation of molecular biological studies confirmed that XFZYD upregulated the mRNA and protein levels of NMDAR1, CaMKII and GAP-43. Pharmacological synaptic regulation of XFZYD could provide a novel therapeutic strategy for cognitive impairment following TBI.

Bitter or not? BitterPredict, a tool for predicting taste from chemical structure

Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70–90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension

CONTEXT

Pulmonary hypertension (PH) is a devastating disease characterized by progressive elevation of pulmonary arterial pressure and vascular resistance due to pulmonary vasoconstriction and vessel remodeling. The activation of RhoA/Rho-kinase (ROCK) pathway plays a central role in the pathologic progression of PH and thus the Rho kinase, an essential effector of the ROCK pathway, is considered as a potential therapeutic target to attenuate PH.

OBJECTIVE

In the current study, a synthetic pipeline is used to discover new potent Rho inhibitors from various natural products.

MATERIALS AND METHODS

In the pipeline, the stepwise high-throughput virtual screening, quantitative structure-activity relationship (QSAR)-based rescoring, and kinase assay were integrated. The screening was performed against a structurally diverse, drug-like natural product library, from which six identified compounds were tested to determine their inhibitory potencies agonist Rho by using a standard kinase assay protocol.

RESULTS

With this scheme, we successfully identified two potent Rho inhibitors, namely phloretin and baicalein, with activity values of IC50 = 0.22 and 0.95 μM, respectively.

DISCUSSION AND CONCLUSION

Structural examination suggested that complicated networks of non-bonded interactions such as hydrogen bonding, hydrophobic forces, and van der Waals contacts across the complex interfaces of Rho kinase are formed with the screened compounds.

BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts

Background

Tuberculosis (TB) is the second leading cause of death from a single infectious organism, demanding attention towards discovery of novel anti-tubercular compounds. Natural products or their derivatives have provided more than 50% of all existing drugs, offering a chemically diverse space for discovery of novel drugs.

Description

BioPhytMol has been designed to systematically curate and analyze the anti-mycobacterial natural product chemical space. BioPhytMol is developed as a drug-discovery community resource with anti-mycobacterial phytomolecules and plant extracts. Currently, it holds 2582 entries including 188 plant families (692 genera and 808 species) from global flora, manually curated from literature. In total, there are 633 phytomolecules (with structures) curated against 25 target mycobacteria. Multiple analysis approaches have been used to prioritize the library for drug-like compounds, for both whole cell screening and target-based approaches. In order to represent the multidimensional data on chemical diversity, physiochemical properties and biological activity data of the compound library, novel approaches such as the use of circular graphs have been employed.

Conclusion

BioPhytMol has been designed to systematically represent and search for anti-mycobacterial phytochemical information. Extensive compound analyses can also be performed through web-application for prioritizing drug-like compounds. The resource is freely available online at http://ab-openlab.csir.res.in/biophytmol/.

Graphical Abstract