TIPdb: a database of anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan

The discovery of subunit-selective GluN1/GluN2B NMDAR antagonist via pharmacophere-based virtual screening

The incidence and mortality rates of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are gradually increasing worldwide. Numerous studies have demonstrated that N-methyl-D-aspartic acid receptor (NMDAR)-mediated excitotoxicity contributes to neurodegenerative diseases. Ifenprodil, a subtype-selective NMDAR antagonist, showed strong therapeutic potential. However, it suffers from low oral bioavailability and off-target side effects. In this study, natural compounds were identified for selective inhibition of GluN1/GluN2B NMDAR of human. We obtained a set of natural compounds (n = 81,366) from COCONUT, TIPdb, PAMDB, CMNPD, YMDB, and NPAtlas databases, and then virtually screened by an ifenprodil-structure-based pharmacophore model and molecular docking. The top 100 compounds were selected for binding affinity prediction via batch drug-target affinity (BatchDTA). Then, the top 50 compounds were analyzed by absorption, distribution, metabolism, excretion, toxicity (ADMET). Molecular dynamics involving molecular mechanics/position-Boltzmann surface area (MM-PBSA) calculation were performed to further screening. The top 15 compounds with strong binding affinity and ifenprodil, a proven GluN2B-selective NMDAR antagonist, were subjected to molecular dynamic simulations (100 ns), root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), H-bonds, solvent accessible surface area (SASA), principal component analysis (PCA), and binding free energy calculations. Based on these analyses, one possible lead compound carrying positive charges (CNP0099440) was identified, with great binding affinity and less off-target activity by contrast to ifenprodil. CNP0099440 has great potential to be a GluN1/GluN2B NMDAR antagonist candidate and can be further detected via in vitro and in vivo experiments.

AVPCD: a plant-derived medicine database of antiviral phytochemicals for cancer, Covid-19, malaria and HIV

Serious illnesses caused by viruses are becoming the world's most critical public health issues and lead millions of deaths each year in the world. Thousands of studies confirmed that the plant-derived medicines could play positive therapeutic effects on the patients with viral diseases. Since thousands of antiviral phytochemicals have been identified as lifesaving drugs in medical research, a comprehensive database is highly desirable to integrate the medicinal plants with their different medicinal properties. Therefore, we provided a friendly antiviral phytochemical database AVPCD covering 2537 antiviral phytochemicals from 383 medicinal compounds and 319 different families with annotation of their scientific, family and common names, along with the parts used, disease information, active compounds, links of relevant articles for COVID-19, cancer, HIV and malaria. Furthermore, each compound in AVPCD was annotated with its 2D and 3D structure, molecular formula, molecular weight, isomeric SMILES, InChI, InChI Key and IUPAC name and 21 other properties. Each compound was annotated with more than 20 properties. Specifically, a scoring method was designed to measure the confidence of each phytochemical for the viral diseases. In addition, we constructed a user-friendly platform with several powerful modules for searching and browsing the details of all phytochemicals. We believe this database will facilitate global researchers, drug developers and health practitioners in obtaining useful information against viral diseases.

Computational Biology of BRCA2 in Male Breast Cancer, through Prediction of Probable nsSNPs, and Hit Identification

Male breast cancer (MBC) is a relatively rare disease, but emerging data recommend the development of novel therapeutics considering its alarming threats. Compared to female breast cancer (FBC), MBC is reportedly associated with inferior outcomes (poor survival) owing to their late diagnosis and lack of adequate treatment. Treatment typically correlates with FBC, involving surgical removal of the breast tissue along with chemo/hormonal/radiation therapy, the tamoxifen being a standard adjuvant. Considering the distinct immunophenotypic (implying different pathogenesis and progression) differences from FBC, the identification of diagnostics, prognostics, and therapeutics for MBC is highly desirable. In this context, we have analyzed the most deleterious nsSNPs of BRCA2, a human tumor suppressor gene constituting the potential biomarker for tumors including MBC, to predict the structural changes associated with the mutants hampering the normal protein-protein and protein-ligand interactions, resulting in MBC progression. Among 27 nsSNPs confined to 21 rsIDs pertaining to MBC, the 19 nsSNPs constituting 14 rsIDs have been predicted as highly deleterious. We believe that these nsSNPs could serve as potential biomarkers for diagnostic and prognostic purposes and could be the pivotal target for MBC drug discovery. Subsequently, the study highlights the exploration of the key nsSNPs (of BRCA2 associated with the MBC) and its applications toward the identification of therapeutic hit TIP006136 following the homology modeling, virtual screening of 5284 phytochemicals retrieved from the TIPdb (a database of phytochemicals from indigenous plants in Taiwan) database, molecular docking (against native and mutant BRCA2), dynamic simulations (against native and mutant BRCA2), density functional theory (DFT), and molecular electrostatic potential. To the best of our knowledge, this is the first report to use diverse computational modules to investigate the important nsSNPs of BRCA2 related to MBC, implying that TIP006136 could be a potential hit and must be studied further (in vitro and in vivo) to establish its anticancer property and efficacy against MBC.

In silico screening, molecular dynamic simulations, and in vitro activity of selected natural compounds as an inhibitor of Leishmania donovani 3-mercaptopyruvate sulfurtransferase

In Leishmania sp., the enzymes of de novo cysteine biosynthesis pathway require sulfide. Other organisms utilize sulfide through the sulfide reduction pathway, but Leishmania lacks the gene that encodes these enzymes. Hence, the major source of sulfide for Leishmania is believed to be from the action of 3-mercaptopyruvate sulfurtransferase (3MST) on 3-mercapto-pyruvate (3MP). There has been no effort reported in the past to screen inhibitors against L. donovani 3MST (Ld3MST). As a result, this study examines natural compounds that are potent against Ld3MST and validates it by in vitro activity and cytotoxicity tests. Initially, a library of ~ 5000 natural compounds was subjected to molecular docking approach for screening, and the best hit was validated using a long-term molecular dynamic simulation (MD). Among the docking results, quercetine-3-rutinoside (Rutin) was deemed the best hit. The results of the MD indicated that Rutin was highly capable of interacting with the varied active site segments, possibly blocking substrate access. Additionally, promastigotes and amastigotes were tested for Rutin activity and the IC50 was found to be 40.95 and 90.09 μM, respectively. Similarly, the cytotoxicity assay revealed that Rutin was not toxic even at a concentration of 819.00 μM to THP-1 cell lines. Additionally, the Ld3MST was cloned, purified, and evaluated for enzyme activity in the presence of Rutin. Reduction in the enzyme activity (~ 85%) was observed in the presence of ~ 40 μM Rutin. Thus, this study suggests that Rutin may act as a potent inhibitor of Ld3MST. With further in vivo investigations, Rutin could be a small molecule of choice for combating leishmaniasis.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

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.

NPBS database: a chemical data resource with relational data between natural products and biological sources

NPBS (Natural Products & Biological Sources) database is a chemical data resource with relational data between natural products and biological sources, manually curated from literatures of natural product researches. The relational data link a specific species and all the natural products derived from it and contrarily link a specific natural product and all the biological sources. The biological sources cover diverse species of plant, bacterial, fungal and marine organisms; the natural molecules have proper chemical structure data and computable molecular properties and all the relational data have corresponding references. NPBS database provides a wider choice of biological sources and can be used for dereplication to prevent re-isolation and re-characterization of already known natural products.

Database URL: http://www.organchem.csdb.cn/scdb/NPBS

An enumeration of natural products from microbial, marine and terrestrial sources

The discovery of a new drug is a multidisciplinary and very costly task. One of the major steps is the identification of a lead compound, i.e. a compound with a certain degree of potency and that can be chemically modified to improve its activity, metabolic properties, and pharmacokinetics profiles. Terrestrial sources (plants and fungi), microbes and marine organisms are abundant resources for the discovery of new structurally diverse and biologically active compounds. In this chapter, an attempt has been made to quantify the numbers of known published chemical structures (available in chemical databases) from natural sources. Emphasis has been laid on the number of unique compounds, the most abundant compound classes and the distribution of compounds in terrestrial and marine habitats. It was observed, from the recent investigations, that ~500,000 known natural products (NPs) exist in the literature. About 70 % of all NPs come from plants, terpenoids being the most represented compound class (except in bacteria, where amino acids, peptides, and polyketides are the most abundant compound classes). About 2,000 NPs have been co-crystallized in PDB structures.

In-silico approaches to detect inhibitors of the human severe acute respiratory syndrome coronavirus envelope protein ion channel

Recent outbreak of Coronavirus disease (COVID-19) pandemic around the world is associated with ‘severe acute respiratory syndrome’ (SARS-CoV2) in humans. SARS-CoV2 is an enveloped virus and E proteins present in them are reported to form ion channels, which is mainly associated with pathogenesis. Thus, there is always a quest to inhibit these ion channels, which in turn may help in controlling diseases caused by SARS-CoV2 in humans. Considering this, in the present study, authors employed computational approaches for studying the structure as well as function of the human ‘SARS-CoV2 E’ protein as well as its interaction with various phytochemicals. Result obtained revealed that α-helix and loops present in this protein experience random movement under optimal condition, which in turn modulate ion channel activity; thereby aiding the pathogenesis caused via SARS-CoV2 in human and other vertebrates. However, after binding with Belachinal, Macaflavanone E, and Vibsanol B, the random motion of the human ‘SARS-CoV2 E’ protein gets reduced, this, in turn, inhibits the function of the ‘SARS-CoV2 E’ protein. It is pertinent to note that two amino acids, namely VAL25 and PHE26, play a key role while interacting with these three phytochemicals. As these three phytochemicals, namely, Belachinal, Macaflavanone E & Vibsanol B, have passed the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) property as well as ‘Lipinski’s Rule of 5s’, they may be utilized as drugs in controlling disease caused via SARS-COV2, after further investigation.

Communicated by Ramaswamy H. Sarma

A computational structural biology study to understand the impact of mutation on structure-function relationship of inward-rectifier potassium ion channel Kir6.2 in human

Type 2 diabetes (T2D) is clinically characterized via hyperglycemia. Polymorphism rs5219 in the KCNJ11 gene is a risk factor for developing T2D in humans. KCNJ11 encodes the 'inward-rectifier potassium ion channel (Kir6.2)'. However, because of the absence of the complete crystal/NMR structures of Kir6.2 proteins, insight into its structure and function and its interaction with diverse ligands remain elusive to date. Therefore, a computational approach was employed for predicting the best plausible 'three-dimensional' structure of Kir6.2 as well as for studying the influence of mutation (p. GLU23LYS) on both architectures as well as the function of Kir6.2 employing simulation studies. Results obtained revealed that though, with increased time, 'Gibbs free energy' becomes positive, residues in wild type Kir6.2 experiences less random movement as compared to mutant Kir6.2. The less random movement of residues in wild type Kir6.2 represents the standard coupling between open and closing of 'K_ATP channel' and thus the normal secretion of insulin. The more dispersed motion of mutant Kir6.2 residues represents 'overactivity' of the 'K_ATP channel' and thus insulin 'under-secretion'. Further, molecular docking and simulation studies identified two phytochemicals/drugs, namely, A-348441 and chushizisin I, which retains the wild type property of Kir6.2 after binding with mutant protein. Unlike A-348441, this is for the first time, the present study is reporting about the plausible anti-diabetic property of chushizisin I. As these two phytochemicals/drugs, namely, A-348441 and chushizisin I, have passed ADMET test, in the near future, they may be utilized as anti-diabetic drugs after further investigation.Communicated by Ramaswamy H. Sarma.

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.

STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products

Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.

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.

Discovery of Potential Plant-Derived Peptide Deformylase (PDF) Inhibitors for Multidrug-Resistant Bacteria Using Computational Studies

Bacterial peptide deformylase (PDF) is an attractive target for developing novel inhibitors against several types of multidrug-resistant bacteria. The objective of the current study is to retrieve potential phytochemicals as prospective drugs against Staphylococcus aureus peptide deformylase (SaPDF). The current study focuses on applying ligand-based pharmacophore model (PharmL) and receptor-based pharmacophore (PharmR) approaches. Utilizing 20 known active compounds, pharmL was built and validated using Fischer's randomization, test set method and the decoy set method. PharmR was generated from the knowledge imparted by the Interaction Generation protocol implemented on the Discovery Studio (DS) v4.5 and was validated using the decoy set that was employed for pharmL. The selection of pharmR was performed based upon the selectivity score and further utilizing the Pharmacophore Comparison module available on the DS. Subsequently, the validated pharmacophore models were escalated for Taiwan Indigenous Plants (TIP) database screening and furthermore, a drug-like evaluation was performed. Molecular docking was initiated for the resultant compounds, employing CDOCKER (available on the DS) and GOLD. Eventually, the stability of the final PDF⁻hit complexes was affirmed using molecular dynamics (MD) simulation conducted by GROMACS v5.0.6. The redeemed hits demonstrated a similar binding mode and stable intermolecular interactions with the key residues, as determined by no aberrant behaviour for 50 ns. Taken together, it can be stated that the hits can act as putative scaffolds against SaPDF, with a higher therapeutic value. Furthermore, they can act as fundamental structures for designing new drug candidates.

Computational evaluation of phytocompounds for combating drug resistant tuberculosis by multi-targeted therapy

The cell wall of Mycobacterium tuberculosis interacts with the host counterpart during the pathogenesis of tuberculosis. L-rhamnosyl (L-Rha) residue, a linker connects the arabinogalactan and peptidoglycan moieties in the bacterial cell wall. The biosynthesis of L-rhamnose utilizes four successive enzymes RmlA, RmlB, RmlC and RmlD. Neither rhamnose nor the genes responsible for its synthesis are observed in humans. Thus, drugs inhibiting enzymes of this pathway are unlikely to interfere with metabolic pathways in humans. The adverse drug effects of first and second line drugs along with the development of multi-drug resistance tuberculosis have stimulated the research in search of new therapeutic drugs. Thus, it is attractive to hypothesize that inhibition of the biosynthesis of L-Rha would be lethal to the mycobacteria. Nature provides innumerable secondary metabolites with novel structural architectures with reported activity against M. tuberculosis. Combination of structure based virtual screening with physicochemical and pharmacokinetic studies against rhamnose pathway enzymes identified potential leads. The crucial screening studies recognized four phytocompounds butein, diospyrin, indicanine, and rumexneposide A with good binding affinity towards the rhamnose pathway proteins. Furthermore, the high throughput screening methods recognized butein, a secondary metabolite from Butea monosperma with strong anti-tubercular bioactive spectrum. Butein displayed promising anti-mycobacterial activity which is validated by Microplate alamar blue assay (MABA). The focus on novel agents like these phytocompounds which exhibit preference toward the successive enzymes of a single pathway can prevent the development of bacterial resistance.

In vitro anti-proliferative activity of Argemone gracilenta and identification of some active components

Background

Cancer is one of the leading causes of death worldwide. Natural products have been regarded as important sources of potential chemotherapeutic agents. In this study, we evaluated the anti-proliferative activity of Argemone gracilenta’s methanol extract and its fractions. We identified those compounds of the most active fractions that displayed anti-proliferative activity.

Methods

The anti-proliferative activity on different cancerous cell lines (M12.C3F6, RAW 264.7, HeLa) was evaluated in vitro using the MTT colorimetric method. Identification of the active compounds present in the fractions with the highest activity was achieved by nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS) analyses.

Results

Both argemonine and berberine alkaloids, isolated from the ethyl acetate fraction, displayed high anti-proliferative activity with IC₅₀ values of 2.8, 2.5, 12.1, and 2.7, 2.4, 79.5 μg/mL on M12.C3F6, RAW 264.7, and HeLa cancerous cell lines, respectively. No activity was shown on the normal L-929 cell line. From the hexane fraction, a mixture of fatty acids and fatty acid esters of 16 or more carbon atoms with anti-proliferative activity was identified, showing a range of IC₅₀ values of 16.8-24.9, 34.1-35.4, and 67.6-91.8 μg/mL on M12.C3F6, RAW 264.7, and HeLa cancerous cell lines, respectively. On the normal L-929 cell line, this mixture showed a range of IC₅₀ values of 85.1 to 100 μg/mL.

Conclusion

This is the first study that relates argemonine, berberine, and a mixture of fatty acids and fatty acid esters with the anti-proliferative activity displayed by Argemone gracilenta.

Natural products as leads in schistosome drug discovery

Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ), the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs) as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS) strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.

TIPdb-3D: the three-dimensional structure database of phytochemicals from Taiwan indigenous plants

The rich indigenous and endemic plants in Taiwan serve as a resourceful bank for biologically active phytochemicals. Based on our TIPdb database curating bioactive phytochemicals from Taiwan indigenous plants, this study presents a three-dimensional (3D) chemical structure database named TIPdb-3D to support the discovery of novel pharmacologically active compounds. The Merck Molecular Force Field (MMFF94) was used to generate 3D structures of phytochemicals in TIPdb. The 3D structures could facilitate the analysis of 3D quantitative structure–activity relationship, the exploration of chemical space and the identification of potential pharmacologically active compounds using protein–ligand docking.

Database URL: http://cwtung.kmu.edu.tw/tipdb.