NuBBEDB: an updated database to uncover chemical and biological information from Brazilian biodiversity

Predicting biochemical and physiological effects of natural products from molecular structures using machine learning

Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.

Determination of the Phytochemical Composition and Antioxidant Potential of Eugenia copacabanensis and Myrciaria tenella Leaves (Myrtaceae) Using a Saccharomyces cerevisiae Model

Eugenia copacabanensis and Myrciaria tenella are present in restingas of the Atlantic Forest, but little information is available about their chemical and biological potential. In this context, the hexane, dichloromethane, ethyl acetate and butanol fractions from the leaves of methanolic extract were analyzed by GC/MS and HPLC-DAD and the antioxidant potential was determined by DPPH and ABTS assays and using a Saccharomyces cerevisiae model. Dereplication allowed the identification of 68 compounds, 42 and 41 of which, respectively, are first reported here for E. copacabanensis and M. tenella. In vivo results revealed that the ethyl acetate and butanol fractions showed expressive antioxidant protection in the BY4741 and Δgsh1 strains, with greater impact on glutathione-deficient cells. With a high diversity of phenolic compounds, these polar fractions of E. copacabanensis and M. tenella leaves are potential protectors against intracellular oxidative stress.

Myrcia eriopus DC. (Myrtaceae) fruits, a new endemic Brazilian source of bioactive anthocyanins

Myrcia eriopus DC. (Myrtaceae) is a native and endemic Brazilian species, and there is no information about its chemical composition. In our study, five different anthocyanins and two other phenolic compounds were described for the first time in M. eriopus fruits. Delphinidin 3-O-glucoside (1), cyanidin 3-O-glucoside (2), petunidin 3-O-glucoside (3), peonidin 3-O-glucoside (4), malvidin 3-O-glucoside (5), gallic acid (6) and myricetin (7) were identified by offline system RP-HPLC-DAD/ESI-QTOF-MS and compared to the scientific literature. Furthermore, the lyophilized powder of M. eriopus fruits showed a high anthocyanin content (1878.14 mg cy-3-glu eqv/100 g f.w.), with greater levels of compounds 1 and 3, and an antioxidant potential in DPPH and ABTS assays (EC₅₀ 2419 μg/mL and 339 μmol Trolox/g f.w.), results superior to other non-conventional Brazilian fruits. This first report about the chemical composition of M. eriopus fruit reveals the potential of this fruit as a new source of bioactive anthocyanins.

SANCDB: an update on South African natural compounds and their readily available analogs

BACKGROUND

South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/ ) is the sole and a fully referenced database of natural chemical compounds of South African biodiversity. It is freely available, and since its inception in 2015, the database has become an important resource to several studies. Its content has been: used as training data for machine learning models; incorporated to larger databases; and utilized in drug discovery studies for hit identifications.

DESCRIPTION

Here, we report the updated version of SANCDB. The new version includes 412 additional compounds that have been reported since 2015, giving a total of 1012 compounds in the database. Further, although natural products (NPs) are an important source of unique scaffolds, they have a major drawback due to their complex structure resulting in low synthetic feasibility in the laboratory. With this in mind, SANCDB is, now, updated to provide direct links to commercially available analogs from two major chemical databases namely Mcule and MolPort. To our knowledge, this feature is not available in other NP databases. Additionally, for easier access to information by users, the database and website interface were updated. The compounds are now downloadable in many different chemical formats.

CONCLUSIONS

The drug discovery process relies heavily on NPs due to their unique chemical organization. This has inspired the establishment of numerous NP chemical databases. With the emergence of newer chemoinformatic technologies, existing chemical databases require constant updates to facilitate information accessibility and integration by users. Besides increasing the NPs compound content, the updated SANCDB allows users to access the individual compounds (if available) or their analogs from commercial databases seamlessly.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Target Prediction Model for Natural Products Using Transfer Learning

A large proportion of lead compounds are derived from natural products. However, most natural products have not been fully tested for their targets. To help resolve this problem, a model using transfer learning was built to predict targets for natural products. The model was pre-trained on a processed ChEMBL dataset and then fine-tuned on a natural product dataset. Benefitting from transfer learning and the data balancing technique, the model achieved a highly promising area under the receiver operating characteristic curve (AUROC) score of 0.910, with limited task-related training samples. Since the embedding distribution difference is reduced, embedding space analysis demonstrates that the model's outputs of natural products are reliable. Case studies have proved our model's performance in drug datasets. The fine-tuned model can successfully output all the targets of 62 drugs. Compared with a previous study, our model achieved better results in terms of both AUROC validation and its success rate for obtaining active targets among the top ones. The target prediction model using transfer learning can be applied in the field of natural product-based drug discovery and has the potential to find more lead compounds or to assist researchers in drug repurposing.

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.

Identification of a Prenyl Chalcone as a Competitive Lipoxygenase Inhibitor: Screening, Biochemical Evaluation and Molecular Modeling Studies

Cyclooxygenase (COX) and lipoxygenase (LOX) are key targets for the development of new anti-inflammatory agents. LOX, which is involved in the biosynthesis of mediators in inflammation and allergic reactions, was selected for a biochemical screening campaign to identify LOX inhibitors by employing the main natural product library of Brazilian biodiversity. Two prenyl chalcones were identified as potent inhibitors of LOX-1 in the screening. The most active compound, (E)-2-O-farnesyl chalcone, decreased the rate of oxygen consumption to an extent similar to that of the positive control, nordihydroguaiaretic acid. Additionally, studies on the mechanism of the action indicated that (E)-2-O-farnesyl chalcone is a competitive LOX-1 inhibitor. Molecular modeling studies indicated the importance of the prenyl moieties for the binding of the inhibitors to the LOX binding site, which is related to their pharmacological properties.

Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy.

The Potential of Biologically Active Brazilian Plant Species as a Strategy to Search for Molecular Models for Mosquito Control

Natural products are a valuable source of biologically active compounds and continue to play an important role in modern drug discovery due to their great structural diversity and unique biological properties. Brazilian biodiversity is one of the most extensive in the world and could be an effective source of new chemical entities for drug discovery. Mosquitoes are vectors for the transmission of dengue, Zika, chikungunya, yellow fever, and many other diseases of public health importance. These diseases have a major impact on tropical and subtropical countries, and their incidence has increased dramatically in recent decades, reaching billions of people at risk worldwide. The prevention of these diseases is mainly through vector control, which is becoming more difficult because of the emergence of resistant mosquito populations to the chemical insecticides. Strategies to provide efficient and safe vector control are needed, and secondary metabolites from plant species from the Brazilian biodiversity, especially Cerrado, that are biologically active for mosquito control are herein highlighted. Also, this is a literature revision of targets as insights to promote advances in the task of developing active compounds for vector control. In view of the expansion and occurrence of arboviruses diseases worldwide, scientific reviews on bioactive natural products are important to provide molecular models for vector control and contribute with effective measures to reduce their incidence.

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 .

StreptomeDB 3.0: an updated compendium of streptomycetes natural products

Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.

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.

Identification of Persuasive Antiviral Natural Compounds for COVID-19 by Targeting Endoribonuclease NSP15: A Structural-Bioinformatics Approach

SARS-CoV-2 is a positive-stranded RNA virus that bundles its genomic material as messenger-sense RNA in infectious virions and replicates these genomes through RNA intermediates. Several virus-encoded nonstructural proteins play a key role during the viral life cycle. Endoribonuclease NSP15 is vital for the replication and life cycle of the virus, and is thus considered a compelling druggable target. Here, we performed a combination of multiscoring virtual screening and molecular docking of a library of 1624 natural compounds (Nuclei of Bioassays, Ecophysiology and Biosynthesis of Natural Products (NuBBE) database) on the active sites of NSP15 (PDB:6VWW). After sequential high-throughput screening by LibDock and GOLD, docking optimization by CDOCKER, and final scoring by calculating binding energies, top-ranked compounds NuBBE-1970 and NuBBE-242 were further investigated via an indepth molecular-docking and molecular-dynamics simulation of 60 ns, which revealed that the binding of these two compounds with active site residues of NSP15 was sufficiently strong and stable. The findings strongly suggest that further optimization and clinical investigations of these potent compounds may lead to effective SARS-CoV-2 treatment.

Pharmacoinformatic Investigation of Medicinal Plants from East Africa

Medicinal plants have widely been used in the traditional treatment of ailments and have been proven effective. Their contribution still holds an important place in modern drug discovery due to their chemical, and biological diversities. However, the poor documentation of traditional medicine, in developing African countries for instance, can lead to the loss of knowledge related to such practices. In this study, we present the Eastern Africa Natural Products Database (EANPDB) containing the structural and bioactivity information of 1870 unique molecules isolated from about 300 source species from the Eastern African region. This represents the largest collection of natural products (NPs) from this geographical region, covering literature data of the period from 1962 to 2019. The computed physicochemical properties and toxicity profiles of each compound have been included. A comparative analysis of some physico-chemical properties like molecular weight, H-bond donor/acceptor, logPo/w , etc. as well scaffold diversity analysis has been carried out with other published NP databases. EANPDB was combined with the previously published Northern African Natural Products Database (NANPDB), to form a merger African Natural Products Database (ANPDB), containing ∼6500 unique molecules isolated from about 1000 source species (freely available at http://african-compounds.org). As a case study, latrunculins A and B isolated from the sponge Negombata magnifica (Podospongiidae) with previously reported antitumour activities, were identified via substructure searching as molecules to be explored as putative binders of histone deacetylases (HDACs).

In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

Towards a unified Latin American Natural Products Database: LANaPD

Around the world, the number of compound databases of natural products in the public domain is rising. This is in line with the increasing synergistic combination of natural product research and chemoinformatics. Toward this global endeavor, countries in Latin America are assembling, curating, and analyzing the contents and diversity of natural products available in their geographical regions. In this manuscript we collect and analyze the efforts that countries in Latin America have made so far to build natural product databases. We further encourage the scientific community in particular in Latin America, to continue their efforts to building quality natural product databases and, whenever possible, to make them publicly accessible. It is proposed that all compound collections could be assembled into a unified resource called LANaPD: Latin American Natural Products Database. Opportunities and challenges to build, distribute and maintain LANaPD are also discussed.

Molecular docking and dynamics simulations studies of OmpATb identifies four potential novel natural product-derived anti-Mycobacterium tuberculosis compounds

The outer membrane protein A (OmpATb) of Mycobacterium tuberculosis is a virulence factor that neutralizes the host pH to impede the uptake of hydrophilic antitubercular drugs. Identifying natural compounds with the potential to inhibit OmpATb could allow circumvention of the porin-like activities of OmpATb. Four potential leads comprising ZINC000003958185, ZINC000000157405, ZINC000000001392 and ZINC000034268676 were obtained by virtual screening of 6394 diverse natural products. Characterization of the binding interactions of the potential leads with OmpATb revealed nine critical residues comprising ARG86, LEU110, LEU113, LEU114, ALA115, PHE142, SER145, VAL146, and PHE151. Molecular dynamics simulations also revealed very stable protein-lead complexes. Most residues contributed lower binding energies to the overall molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energies of the interactions between the molecules and OmpATb protein. Induced Fit Docking (IFD) of the compounds regenerated poses of the molecular docking using AutoDock Vina. These molecules could be starting templates for designing inhibitors to bypass the pore mediating activities of OmpATb. Based on structural similarity, ZINC000034268676 was suggested as a potential scaffold for designing efflux pump inhibitors of the gate mediating activities of OmpATb and may enhance the uptake of hydrophilic drugs to reduce the duration time of tuberculosis treatment. Furthermore, structurally similar compounds available in the DrugBank database with a similarity threshold of 0.7 have been reported to exhibit antitubercular and anti-mycobacterial activities. These biomolecules can be further characterized experimentally to corroborate their antitubercular activity. Also, the skeletons of the molecules can be adopted as sub-structures for the design of future anti-mycobacterial drugs.

Privileged Scaffold Analysis of Natural Products with Deep Learning-based Indication Prediction Model

Natural products play a vital role in the drug discovery and development process as an important source of reliable and novel lead structures. But the existing criteria for drug leads were usually developed for synthetic compounds and cannot be directly applied to identify lead scaffolds from natural products. To solve this problem, we propose a method to predict indications and identify privileged scaffolds of natural products for drug design. A deep learning model was built to predict indications for natural products. Entropy-based information metrics were used to identify the privileged scaffolds for each indication and a Privileged Scaffold Dataset (PSD) of natural products was constructed. The PSD could serve as a novel source of lead compounds and circumvent existing drug patents. This method could be generalized by replacing the training set, the prediction algorithm, and the compound set, to obtain more personalized-PSDs.

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.

Cheminformatics Analysis and Modeling with MacrolactoneDB

Macrolactones, macrocyclic lactones with at least twelve atoms within the core ring, include diverse natural products such as macrolides with potent bioactivities (e.g. antibiotics) and useful drug-like characteristics. We have developed MacrolactoneDB, which integrates nearly 14,000 existing macrolactones and their bioactivity information from different public databases, and new molecular descriptors to better characterize macrolide structures. The chemical distribution of MacrolactoneDB was analyzed in terms of important molecular properties and we have utilized three targets of interest (Plasmodium falciparum, Hepatitis C virus and T-cells) to demonstrate the value of compiling this data. Regression machine learning models were generated to predict biological endpoints using seven molecular descriptor sets and eight machine learning algorithms. Our results show that merging descriptors yields the best predictive power with Random Forest models, often boosted by consensus or hybrid modeling approaches. Our study provides cheminformatics insights into this privileged, underexplored structural class of compounds with high therapeutic potential.

Plant Metabolomics Using NMR Spectroscopy

The major goal in plant metabolomics is to study complex extracts for the purposes of metabolic exploration and natural products discovery. To achieve this goal, plant metabolomics relies on accurate and selective acquisition of all possible chemical information, which includes maximization of the number of detected metabolites and their correct molecular assignment. Nuclear magnetic resonance (NMR) spectroscopy has been recognized as a powerful platform for obtaining the metabolite profiles of plant extracts. In this chapter, we provide a workflow for targeted and untargeted metabolite profiling of plant extracts using both 1D and 2D NMR methods. The protocol includes sample preparation, instrument operation, data processing, multivariate analysis, biomarker elucidation, and metabolite quantitation. It also addresses the annotation of plant metabolite peaks considering NMR's capabilities to cover a broad range of metabolites and elucidate structures for unknown compounds.

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.

Advanced Glycation End Product Inhibition by Alkaloids from Ocotea paranapiacabensis for the Prevention of Skin Aging

A bioassay-guided study aiming at identifying inhibitors of the glycation process on the leaves of Ocotea paranapiacabensis afforded four benzylisoquinoline alkaloids (1-4), with 1 and 2 identified as new naturals products, while 3 and 4 were previously described in the literature, with 3 being identified as magnocurarine. Purification was performed by column chromatography and high-performance liquid chromatography. The structures of the isolated compounds were elucidated by spectroscopic methods including UV, NMR, and HRMS. The process of skin aging has been recently associated with advanced glycation end products (AGEs), and strategies inhibiting their formation have been addressed by pharmaceutical companies for the development of novel antiaging compounds. Alkaloids 1-4 were evaluated for their potential to inhibit AGE formation and showed inhibition of 62.9%, 83.3%, 26.1%, and 98.2% (150 μM), respectively. The antiaging potential of compounds 1 and 4 were evaluated with a reconstructed human skin model in vitro, and results showed a decrease in dermis contraction (8.7% and 4.2% respectively for 1 and 4) when compared to the glycated control (57.4%). Additionally, absorption, distribution, metabolism, and excretion (ADME) and toxicity properties were predicted using in silico methods, and the results were considered significantly promising for alkaloids 1 and 4 to continue the development of these alkaloids with skincare properties.

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.

ADMET profiling of geographically diverse phytochemical using chemoinformatic tools

Aim: Phytocompounds are important due to their uniqueness, however, only few reach the development phase due to their poor pharmacokinetics. Therefore, preassessing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties is essential in drug discovery. Methodology: Biologically diverse databases (Phytochemica, SerpentinaDB, SANCDB and NuBBE_DB) covering the region of India, Brazil and South Africa were considered to predict the ADMET using chemoinformatic tools (Qikprop, pkCSM and DataWarrior). Results: Screening through each of pharmacokinetic criteria resulted in identification of 24 compounds that adhere to all the ADMET properties. Furthermore, assessment revealed that five have potent anticancer biological activity against cancer cell lines. Conclusion: We have established an open-access database (ADMET-BIS) to enable identification of promising molecules that follow ADMET properties and can be considered for drug development.

The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery

Despite rapid evolution in the area of microbial natural products chemistry, there is currently no open access database containing all microbially produced natural product structures. Lack of availability of these data is preventing the implementation of new technologies in natural products science. Specifically, development of new computational strategies for compound characterization and identification are being hampered by the lack of a comprehensive database of known compounds against which to compare experimental data. The creation of an open access, community-maintained database of microbial natural product structures would enable the development of new technologies in natural products discovery and improve the interoperability of existing natural products data resources. However, these data are spread unevenly throughout the historical scientific literature, including both journal articles and international patents. These documents have no standard format, are often not digitized as machine readable text, and are not publicly available. Further, none of these documents have associated structure files (e.g., MOL, InChI, or SMILES), instead containing images of structures. This makes extraction and formatting of relevant natural products data a formidable challenge. Using a combination of manual curation and automated data mining approaches we have created a database of microbial natural products (The Natural Products Atlas, www.npatlas.org) that includes 24 594 compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. This database is accompanied by an interactive web portal that permits searching by structure, substructure, and physical properties. The Web site also provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. These interactive tools offer a powerful knowledge base for natural products discovery with a central interface for structure and property-based searching and presents new viewpoints on structural diversity in natural products. The Natural Products Atlas has been developed under FAIR principles (Findable, Accessible, Interoperable, and Reusable) and is integrated with other emerging natural product databases, including the Minimum Information About a Biosynthetic Gene Cluster (MIBiG) repository, and the Global Natural Products Social Molecular Networking (GNPS) platform. It is designed as a community-supported resource to provide a central repository for known natural product structures from microorganisms and is the first comprehensive, open access resource of this type. It is expected that the Natural Products Atlas will enable the development of new natural products discovery modalities and accelerate the process of structural characterization for complex natural products libraries.

Computational methods for NMR and MS for structure elucidation II: database resources and advanced methods

Technological advances have contributed to the evolution of the natural product chemistry and drug discovery programs. Recently, computational methods for nuclear magnetic resonance (NMR) and mass spectrometry (MS) have speeded up and facilitated the process of structural elucidation even in high complex biological samples. In this chapter, the current computational tools related to NMR and MS databases and spectral similarity networks, as well as their applications on dereplication and determination of biological biomarkers, are addressed.

Virtual screening of antibacterial compounds by similarity search of Enoyl-ACP reductase (FabI) inhibitors

Aim: Antibiotic resistance is an alarming issue, as multidrug-resistant bacteria are growing worldwide, hence the decrease of therapeutic potential of available antibiotic arsenal. Among these bacteria, Staphylococcus aureus was pointed by the WHO in the pathogens list to be prioritized in drug development. Methods: We report the use of chemical similarity models for the virtual screening of new antibacterial with structural similarity to known inhibitors of FabI. The potential inhibitors were experimentally evaluated for antibacterial activity and membrane disrupting capabilities. Results & conclusion: These models led to the finding of four new compounds with antibacterial activity, one of which having antimicrobial activity already reported in the literature.

Cheminformatics Explorations of Natural Products

The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.

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.

Analysis of the Acid/Base Profile of Natural Products from Different Sources

For many years drug discovery and other areas in chemistry have successfully relied on natural products. Recent advances in computational methods have made possible to study the chemical space of natural products from different sources. Ionizable acidic and basic functional groups heavily influence physicochemical properties and thus a molecule's absorption, distribution, metabolism, excretion, and toxicity characteristics as well as their affinity for biological targets. This work reports the generation and critical comparison of the acid/base profiles of ten chemical databases including seven natural products sets from different origins, a set of semisynthetic compounds, a collection of approved drugs, and a compendium of food chemicals. Similarities were found in the proportion of the main charge state categories among the natural products databases with few differences in their pK_a distributions. Clear differences were observed between natural products and the approved drugs and semi-synthetic natural products databases, whereas natural products share some trends with the food chemical database. We noted that the natural products collections comprise around 45 % of neutral compounds. The proportion of single acids was approximately twice that found for FDA drugs, and they demonstrated a similar distribution of pK_a values. In contrast to drugs, only 5 % of compounds among the natural products sets had a single basic group. Likewise, simple ampholytes were less prevalent in the natural products databases relative to drugs.

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.

A general approach for retrosynthetic molecular core analysis

Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied “single molecule–single scaffold” correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure–property relationships, and identification of analog series and ASBS. The molecule–core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure–property relationships analyses.

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.

Medicinal Plants from Brazilian Cerrado: Antioxidant and Anticancer Potential and Protection against Chemotherapy Toxicity

The use of natural antioxidants in cancer therapy has increased: first, due to the potential of natural antioxidants to kill tumour cells and second, because of their capacity to protect healthy cells from the damage caused by chemotherapy. This review article discusses the antioxidant properties of extracts obtained from medicinal plants from the Brazilian Cerrado and the cell death profile induced by each of these extracts in malignant cells. Next, we describe the capacity of other medicinal plants from the Cerrado to protect against chemotherapy-induced cell toxicity. Finally, we focus on recent insights into the cell death profile induced by extracts from Cerrado plants and perspectives for future therapeutic approaches.

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE_DB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.

Flavonoids from Brazilian Cerrado: Biosynthesis, Chemical and Biological Profile

Flavonoids are highly bioactive compounds with very low toxicity, which makes them attractive starting points in drug discovery. This study aims to provide information on plant species containing flavonoids, which are found in the Brazilian Cerrado. First, we present the characterization and plant diversity with emphasis on the families of flavonoid-producing plants, and then we describe the phenylpropanoid pathway which represents the flavonoids’ main route biosynthesis—generally conserved in all species. Chemical structures and biological activities of flavonoids isolated from the Cerrado’s plant species are also described based on examples from the relevant literature studies. Finally, research on the biodiversity of the Cerrado biome should be encouraged, due to the discovery of new sources of flavonoids which can provide several benefits to human health and the possibility of developing new drugs by the pharmaceutical industry.

NaPLeS: a natural products likeness scorer-web application and database

Natural products (NPs), often also referred to as secondary metabolites, are small molecules synthesised by living organisms. Natural products are of interest due to their bioactivity and in this context as starting points for the development of drugs and other bioactive synthetic products. In order to select compounds from virtual libraries, Ertl et al. developed a natural product likeness score which was later published as an open data, open source implementation. Here we present NaPLeS, an easily portable, containerised, open source web application based on open data to compute natural product likeness scores for chemical libraries.

Natural Products: Perspectives and Challenges for use of Brazilian Plant Species in the Bioeconomy

The development of our society has been based on the use of biodiversity, especially for nutrition, medicines and beauty. Brazil is the nation with the largest biodiversity in the world, with a rich chemical diversity, which is a potential source for bioeconomy. Considering the chemical and biological diversity of the Brazilian territory, we would like to highlight the value of secondary metabolites from Brazilian biodiversity with potential application for new products and technologies and the importance of scientific programs to support the sustainable use of biodiversity.

Mass Spectral Similarity Networking and Gas-Phase Fragmentation Reactions in the Structural Analysis of Flavonoid Glycoconjugates

Flavonoids represent an important class of natural products with a central role in plant physiology and human health. Their accurate annotation using untargeted mass spectrometry analysis still relies on differentiating similar chemical scaffolds through spectral matching to reference library spectra. In this work, we combined molecular network analysis with rules for fragment reactions and chemotaxonomy to enhance the annotation of similar flavonoid glyconjugates. Molecular network topology progressively propagated the flavonoid chemical functionalization according to collision-induced dissociation (CID) reactions, as the following chemical attributes: aglycone nature, saccharide type and number, and presence of methoxy substituents. This structure-based distribution across the spectral networks revealed the chemical composition of flavonoids across intra- and interspecies and guided the putatively assignment of 64 isomers and isobars in the Chrysobalanaceae plant species, most of which are not accurately annotated by automated untargeted MS² matching. These proof of concept results demonstrate how molecular networking progressively grouped structurally related molecules according to their product ion scans, abundances, and ratios. The approach can be extrapolated to other classes of metabolites sharing similar structures and diagnostic fragments from tandem mass spectrometry.

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.

Chemical space of naturally occurring compounds

The chemical space of naturally occurring compounds is vast and diverse. Other than biologics, naturally occurring small molecules include a large variety of compounds covering natural products from different sources such as plant, marine, and fungi, to name a few, and several food chemicals. The systematic exploration of the chemical space of naturally occurring compounds have significant implications in many areas of research including but not limited to drug discovery, nutrition, bio- and chemical diversity analysis. The exploration of the coverage and diversity of the chemical space of compound databases can be carried out in different ways. The approach will largely depend on the criteria to define the chemical space that is commonly selected based on the goals of the study. This chapter discusses major compound databases of natural products and cheminformatics strategies that have been used to characterize the chemical space of natural products. Recent exemplary studies of the chemical space of natural products from different sources and their relationships with other compounds are also discussed. We also present novel chemical descriptors and data mining approaches that are emerging to characterize the chemical space of naturally occurring compounds.

The impact of decreasing biodiversity on novel drug discovery: is there a serious cause for concern?

INTRODUCTION

The aim of this perspective is to discuss the current and potential situation concerning the loss of biodiversity and its current and potential effects upon the search for novel bioactive agents from natural sources, be they from marine, microbial or terrestrial environments. Areas covered: Herein, the author covers terrestrial plants, marine organisms (but not vertebrates), and unicellular microbes from both terrestrial and marine sources. The emphasis is on the unknown effects of biodiversity perturbation and/or loss of microbes that are now realized to underlie the production of a significant number of natural products, whether they were first found in plants or marine invertebrates. Expert opinion: From the discussion of the areas above comes the realization that we do not know what we still have. Furthermore, we cannot measure, other than in very gross terms, what we have lost. Thus, deciding how, and where geographically, one should now search for novel bioactive agents is a major and continuing problem.

Ecological Insights to Track Cytotoxic Compounds among Maytenus ilicifolia Living Individuals and Clones of an Ex Situ Collection

Biodiversity is key for maintenance of life and source of richness. Nevertheless, concepts such as phenotype expression are also pivotal to understand how chemical diversity varies in a living organism. Sesquiterpene pyridine alkaloids (SPAs) and quinonemethide triterpenes (QMTs) accumulate in root bark of Celastraceae plants. However, despite their known bioactive traits, there is still a lack of evidence regarding their ecological functions. Our present contribution combines analytical tools to study clones and individuals of Maytenus ilicifolia (Celastraceae) kept alive in an ex situ collection and determine whether or not these two major biosynthetic pathways could be switched on simultaneously. The relative concentration of the QMTs maytenin (1) and pristimerin (2), and the SPA aquifoliunin E1 (3) were tracked in raw extracts by HPLC-DAD and ¹H-NMR. Hierarchical Clustering Analysis (HCA) was used to group individuals according their ability to accumulate these metabolites. Semi-quantitative analysis showed an extensive occurrence of QMT in most individuals, whereas SPA was only detected in minor abundance in five samples. Contrary to QMTs, SPAs did not accumulate extensively, contradicting the hypothesis of two different biosynthetic pathways operating simultaneously. Moreover, the production of QMT varied significantly among samples of the same ex situ collection, suggesting that the terpene contents in root bark extracts were not dependent on abiotic effects. HCA results showed that QMT occurrence was high regardless of the plant age. This data disproves the hypothesis that QMT biosynthesis was age-dependent. Furthermore, clustering analysis did not group clones nor same-age samples together, which might reinforce the hypothesis over gene regulation of the biosynthesis pathways. Indeed, plants from the ex situ collection produced bioactive compounds in a singular manner, which postulates that rhizosphere environment could offer ecological triggers for phenotypical plasticity.

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.